33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A functional genomics predictive network model identifies regulators of inflammatory bowel disease

      research-article
      1 , 2 , 3 , 4 , 5 , 6 , 7 , 1 , 2 , 4 , 1 , 2 , 8 , 9 , 1 , 2 , 1 , 2 , 4 , 1 , 2 , 4 , 4 , 1 , 2 , 1 , 2 , 10 , 1 , 2 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 9 , 1 , 2 , 4 , 4 , 4 , 4 , 11 , 12 , 13 , 14 , 15 , 16 , 1 , 2 , 1 , 2 , 1 , 2 , 1 , 2 , 3 , 1 , 2 , 4 , 17 , 9 , 18 , 1 , 2 , 3
      Nature genetics

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A major challenge in inflammatory bowel disease (IBD) is the integration of diverse IBD data sets to construct predictive models of IBD. We present a predictive model of the immune component of IBD that informs causal relationships among loci previously linked to IBD through genome-wide association studies (GWAS) using functional and regulatory annotations that relate to the cells, tissues, and pathophysiology of IBD. Our model consists of individual networks constructed using molecular data generated from intestinal samples isolated from three populations of patients with IBD at different stages of disease. We performed key driver analysis to identify genes predicted to modulate network regulatory states associated with IBD, prioritizing and prospectively validating 12 of the top key drivers experimentally. This validated key driver set not only introduces new regulators of processes central to IBD but also provides the integrated circuits of genetic, molecular, and clinical traits that can be directly queried to interrogate and refine the regulatory framework defining IBD.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

          , , (2013)
          Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Chemically induced mouse models of intestinal inflammation.

            Animal models of intestinal inflammation are indispensable for our understanding of the pathogenesis of Crohn disease and ulcerative colitis, the two major forms of inflammatory bowel disease in humans. Here, we provide protocols for establishing murine 2,4,6-trinitro benzene sulfonic acid (TNBS)-, oxazolone- and both acute and chronic dextran sodium sulfate (DSS) colitis, the most widely used chemically induced models of intestinal inflammation. In the former two models, colitis is induced by intrarectal administration of the covalently reactive reagents TNBS/oxazolone, which are believed to induce a T-cell-mediated response against hapten-modified autologous proteins/luminal antigens. In the DSS model, mice are subjected several days to drinking water supplemented with DSS, which seems to be directly toxic to colonic epithelial cells of the basal crypts. The procedures for the hapten models of colitis and acute DSS colitis can be accomplished in about 2 weeks but the protocol for chronic DSS colitis takes about 2 months.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Genetics of gene expression and its effect on disease.

              Common human diseases result from the interplay of many genes and environmental factors. Therefore, a more integrative biology approach is needed to unravel the complexity and causes of such diseases. To elucidate the complexity of common human diseases such as obesity, we have analysed the expression of 23,720 transcripts in large population-based blood and adipose tissue cohorts comprehensively assessed for various phenotypes, including traits related to clinical obesity. In contrast to the blood expression profiles, we observed a marked correlation between gene expression in adipose tissue and obesity-related traits. Genome-wide linkage and association mapping revealed a highly significant genetic component to gene expression traits, including a strong genetic effect of proximal (cis) signals, with 50% of the cis signals overlapping between the two tissues profiled. Here we demonstrate an extensive transcriptional network constructed from the human adipose data that exhibits significant overlap with similar network modules constructed from mouse adipose data. A core network module in humans and mice was identified that is enriched for genes involved in the inflammatory and immune response and has been found to be causally associated to obesity-related traits.
                Bookmark

                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                25 September 2017
                11 September 2017
                October 2017
                01 October 2018
                : 49
                : 10
                : 1437-1449
                Affiliations
                [1 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [2 ]Icahn Institute of Genomics and Multi-scale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [3 ]Sema4, a Mount Sinai venture, Stamford, Connecticut, USA
                [4 ]Janssen Research and Development, LLC., Spring House, Pennsylvania, USA
                [5 ]Department of Immunology, University of Toronto, Toronto, Ontario, Canada
                [6 ]Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [7 ]Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York, USA
                [8 ]Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [9 ]The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [10 ]Institute for Next-Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                [11 ]Division of Immunogenetics, Department of Immunobiology and Neuroscience, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
                [12 ]University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
                [13 ]Department of Comparative Medicine, University of Washington, Seattle, Washington, USA
                [14 ]Department of Medicine (Cardiovascular Division), Albert Einstein College of Medicine, Bronx, New York, USA
                [15 ]Thurston Arthritis Research Center and Department of Medicine, Division of Rheumatology, Allergy, and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [16 ]Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [17 ]Bristol-Myers Squibb, Research & Development, Pennington, New Jersey, USA
                [18 ]Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
                Author notes
                Correspondence should be addressed to E.E.S. ( eric.schadt@ 123456sema4genomics.com )
                Article
                NIHMS907859
                10.1038/ng.3947
                5660607
                28892060
                7591f5c9-4018-4f66-aad0-2d4cc82de4f8

                Reprints and permissions information is available online at http://www.nature.com/reprints/index.html

                History
                Categories
                Article

                Genetics
                Genetics

                Comments

                Comment on this article