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Background The development of Crohn's disease [CD] involves immune cell signalling pathways regulated by epigenetic modifications. Aberrant DNA methylation has been identified in peripheral blood and bulk intestinal tissue from CD patients. However, the DNA methylome of disease-associated intestinal CD4+ lymphocytes has not been evaluated. Materials and Methods Genome-wide DNA methylation sequencing was performed from terminal ileum CD4+ cells from 21 CD patients and 12 age- and sex-matched controls. Data were analysed for differentially methylated CpGs [DMCs] and methylated regions [DMRs]. Integration was performed with RNA-sequencing data to evaluate the functional impact of DNA methylation changes on gene expression. DMRs were overlapped with regions of differentially open chromatin [by ATAC-seq] and CCCTC-binding factor [CTCF] binding sites [by ChIP-seq] between peripherally derived Th17 and Treg cells. Results CD4+ cells in CD patients had significantly increased DNA methylation compared to those from the controls. A total of 119 051 DMCs and 8113 DMRs were detected. While hypermethylated genes were mostly related to cell metabolism and homeostasis, hypomethylated genes were significantly enriched within the Th17 signalling pathway. The differentially enriched ATAC regions in Th17 cells [compared to Tregs] were hypomethylated in CD patients, suggesting heightened Th17 activity. There was significant overlap between hypomethylated DNA regions and CTCF-associated binding sites. Conclusions The methylome of CD patients shows an overall dominant hypermethylation yet hypomethylation is more concentrated in proinflammatory pathways, including Th17 differentiation. Hypomethylation of Th17-related genes associated with areas of open chromatin and CTCF binding sites constitutes a hallmark of CD-associated intestinal CD4+ cells. [ABSTRACT FROM AUTHOR]

MCAM (CD146) is a cell surface adhesion molecule that has been reported to promote cancer development, progression and metastasis and is considered as a potential tumor biomarker and therapeutic target. However, inconsistent reports exist, and its clinical value is yet to be confirmed. Here we took advantage of several large genomic data collections (Genotype-Tissue Expression, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia) and comprehensively analyzed MCAM expression in thousands of normal and cancer samples and cell lines along with their clinical phenotypes and drug response information. Our results show that MCAM is very highly expressed in large vessel tissues while majority of tissues have low or minimal expression. Its expression is dramatically increased in a few tumors but significantly decreased in most other tumors relative to their pairing normal tissues. Increased MCAM expression is associated with a higher tumor stage and worse patient survival for some less common tumors but not for major ones. Higher MCAM expression in primary tumors may be complicated by tumor-associated or normal stromal blood vessels yet its significance may differ from the one from cancer cells. MCAM expression is weakly associated with the response to a few small molecular drugs and the association with targeted anti-BRAF agents suggests its involvement in that pathway which warrants further investigation. [ABSTRACT FROM AUTHOR]

Background The aetiology of Crohn's disease [CD] involves immune dysregulation in a genetically susceptible individual. Genome-wide association studies [GWAS] have identified 200 loci associated with CD, ulcerative colitis, or both, most of which fall within non-coding DNA regions. Long non-coding RNAs [lncRNAs] regulate gene expression by diverse mechanisms and have been associated with disease activity in inflammatory bowel disease. However, disease-associated lncRNAs have not been characterised in pathogenic immune cell populations. Methods Terminal ileal samples were obtained from 22 CD patients and 13 controls. RNA from lamina propria CD4+ T cells was sequenced and long intergenic non-coding RNAs [lincRNAs] were detected. Overall expression patterns, differential expression [DE], and pathway and gene enrichment analyses were performed. Knockdown of novel lincRNAs XLOC_000261 and XLOC_000014 was performed. Expression of Th1 or Th17-associated transcription factors, T-bet and RORγt, respectively, was assessed by flow cytometry. Results A total of 6402 lincRNAs were expressed, 960 of which were novel. Unsupervised clustering and principal component analysis showed that the lincRNA expression discriminated patients from controls. A total of 1792 lincRNAs were DE, and 295 [79 novel; 216 known] mapped to 267 of 5727 DE protein-coding genes. The novel lincRNAs were enriched in inflammatory and Notch signalling pathways [ p <0.05]. Furthermore, DE lincRNAs in CD patients were more frequently found in DNA regions with known inflammatory bowel disease [IBD]-associated loci. The novel lincRNA XLOC_000261 negatively regulated RORγt expression in Th17 cells. Conclusions We describe a novel set of DE lincRNAs in CD-associated CD4+ cells and demonstrate that novel lincRNA XLOC_000261 appears to negatively regulate RORγt protein expression in Th17 cells. [ABSTRACT FROM AUTHOR]

Motivation: Reference genome assemblies are subject to change and refinement from time to time. Generally, researchers need to convert the results that have been analyzed according to old assemblies to newer versions, or vice versa, to facilitate meta-analysis, direct comparison, data integration and visualization. Several useful conversion tools can convert genome interval files in browser extensible data or general feature format, but none have the functionality to convert files in sequence alignment map or BigWig format. This is a significant gap in computational genomics tools, as these formats are the ones most widely used for representing high-throughput sequencing data, such as RNA-seq, chromatin immunoprecipitation sequencing, DNA-seq, etc.Results: Here we developed CrossMap, a versatile and efficient tool for converting genome coordinates between assemblies. CrossMap supports most of the commonly used file formats, including BAM, sequence alignment map, Wiggle, BigWig, browser extensible data, general feature format, gene transfer format and variant call format.Availability and implementation: CrossMap is written in Python and C. Source code and a comprehensive user’s manual are freely available at: http://crossmap.sourceforge.net/.Contact: Kocher.JeanPierre@mayo.edu or wang.liguo@mayo.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that integrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation.Availability and implementation: SAAP-RRBS is freely available to non-commercial users at the web site http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm.Contact: baheti.saurabh@mayo.edu or sun.zhifu@mayo.eduSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways.Availability and implementation: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfmContact: Hossain.Asif@mayo.edu; Kocher.JeanPierre@mayo.eduSupplementary information: Supplementary data are provided at Bioinformatics online. [ABSTRACT FROM PUBLISHER]