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      Transcriptomic Profiling of Collagenous Colitis Identifies Hallmarks of Nondestructive Inflammatory Bowel Disease

      research-article
      1 , , 2 , 3 , 2 , 4 , 2 , 3 , 4 , 5 , 1 , 6 , 2 , 4 , 5 , 1 , 7 , 8
      Cellular and Molecular Gastroenterology and Hepatology
      Elsevier
      Epithelial Cells, Microscopic Colitis, RNA Sequencing, Ulcerative Colitis, auCC, active/untreated collagenous colitis, aRCC, active/refractory (nonresponding) collagenous colitis, CC, collagenous colitis, DEG, differentially expressed gene, DN, double negative, FDR, false discovery rate, GSEA, gene set enrichment analysis, GSVA, gene set variation analysis, IBD, inflammatory bowel disease, IEC, intestinal epithelial cell, IFN, interferon, IHC, immunohistochemistry, itCC, inactive/treated (responding) collagenous colitis, MMP, matrix metalloproteinase, PBS, phosphate-buffered saline, RNA-seq, RNA-sequencing, RT-qPCR, reverse-transcription quantitative polymerase chain reaction, TIMP, tissue inhibitor of metalloproteinase, UC, ulcerative colitis

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          Abstract

          Background and Aims

          The pathophysiology of the inflammatory bowel disease collagenous colitis (CC) is poorly described. Our aim was to use RNA sequencing of mucosal samples from patients with active CC, CC in remission, refractory CC, ulcerative colitis (UC), and control subjects to gain insight into CC pathophysiology, identify genetic signatures linked to CC, and uncover potentially druggable disease pathways.

          Methods

          We performed whole transcriptome sequencing of CC samples from patients before and during treatment with the corticosteroid drug budesonide, CC steroid-refractory patients, UC patients, and healthy control subjects (n = 9–13). Bulk mucosa and laser-captured microdissected intestinal epithelial cell (IEC) gene expression were analyzed by gene set enrichment and gene set variation analyses to identify significant pathways and cells, respectively, altered in CC. Leading genes and cells were validated using reverse-transcription quantitative polymerase chain reaction or immunohistochemistry.

          Results

          We identified an activation of the adaptive immune response to bacteria and viruses in active CC that could be mediated by dendritic cells. Moreover, IECs display hyperproliferation and increased antigen presentation in active CC. Further analysis revealed that genes related to the immune response ( DUOX2, PLA2G2A, CXCL9), DNA transcription ( CTR9), protein processing ( JOSD1, URI1), and ion transport ( SLC9A3) remained dysregulated even after budesonide-induced remission. Budesonide-refractory CC patients fail to restore normal gene expression, and displayed a transcriptomic profile close to UC.

          Conclusions

          Our study confirmed the implication of innate and adaptive immune responses in CC, governed by IECs and dendritic cells, respectively, and identified ongoing epithelial damage. Refractory CC could share pathomechanisms with UC.

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          Most cited references70

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Contributors
                Journal
                Cell Mol Gastroenterol Hepatol
                Cell Mol Gastroenterol Hepatol
                Cellular and Molecular Gastroenterology and Hepatology
                Elsevier
                2352-345X
                2021
                27 April 2021
                : 12
                : 2
                : 665-687
                Affiliations
                [1 ]Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
                [2 ]Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
                [3 ]Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
                [4 ]Clinic of Medicine, St Olav’s University Hospital, Trondheim, Norway
                [5 ]Department of Gastroenterology and Hepatology, St Olav’s University Hospital, Trondheim, Norway
                [6 ]Wallenberg Centre for Molecular Medicine, Linköping University, Linköping, Sweden
                [7 ]Department of Gastroenterology and Hepatology, Linköping University, Linköping, Sweden
                [8 ]Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
                Author notes
                [] Correspondence Address correspondence to: Celia Escudero Hernández, PhD, Institute of Clinical Molecular Biology (IKMB), Christian-Albrechts-University-Kiel, and University Hospital Schleswig-Holstein, Rosalind-Franklin-Straße 12, 24105, Kiel, Germany. celia.escher@ 123456gmail.com
                Article
                S2352-345X(21)00082-5
                10.1016/j.jcmgh.2021.04.011
                8267496
                33930606
                fe9d8ac0-0cc3-46c0-ac54-a85f4b19cf18
                © 2021 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 November 2020
                : 19 April 2021
                Categories
                Original Research

                epithelial cells,microscopic colitis,rna sequencing,ulcerative colitis,aucc, active/untreated collagenous colitis,arcc, active/refractory (nonresponding) collagenous colitis,cc, collagenous colitis,deg, differentially expressed gene,dn, double negative,fdr, false discovery rate,gsea, gene set enrichment analysis,gsva, gene set variation analysis,ibd, inflammatory bowel disease,iec, intestinal epithelial cell,ifn, interferon,ihc, immunohistochemistry,itcc, inactive/treated (responding) collagenous colitis,mmp, matrix metalloproteinase,pbs, phosphate-buffered saline,rna-seq, rna-sequencing,rt-qpcr, reverse-transcription quantitative polymerase chain reaction,timp, tissue inhibitor of metalloproteinase,uc, ulcerative colitis

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