12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis

      research-article

      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

          Target tissues of four autoimmune diseases show similar gene expression signatures emphasizing a dialog immune cell-target tissue.

          Abstract

          Autoimmune diseases are typically studied with a focus on the immune system, and less attention is paid to responses of target tissues exposed to the immune assault. We presently evaluated, based on available RNA sequencing data, whether inflammation induces similar molecular signatures at the target tissues in type 1 diabetes, systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis. We identified confluent signatures, many related to interferon signaling, indicating pathways that may be targeted for therapy, and observed a high (>80%) expression of candidate genes for the different diseases at the target tissue level. These observations suggest that future research on autoimmune diseases should focus on both the immune system and the target tissues, and on their dialog. Discovering similar disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

          Related collections

          Most cited references81

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

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
                Bookmark

                Author and article information

                Journal
                Sci Adv
                Sci Adv
                SciAdv
                advances
                Science Advances
                American Association for the Advancement of Science
                2375-2548
                January 2021
                06 January 2021
                : 7
                : 2
                : eabd7600
                Affiliations
                [1 ]ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles (ULB), Brussels, Belgium.
                [2 ]Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles-Vrije Universiteit Brussel, Brussels, Belgium.
                [3 ]Section of Rheumatology, Yale University School of Medicine, New Haven, CT, USA.
                [4 ]Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA.
                [5 ]Indiana Biosciences Research Institute (IBRI), Indianapolis, IN, USA.
                Author notes
                [*]

                These authors contributed equally to this work.

                []Corresponding author. Email: mcolli@ 123456ulb.ac.be (M.L.C.); deizirik@ 123456ulb.ac.be (D.L.E.)
                Author information
                http://orcid.org/0000-0002-5431-270X
                http://orcid.org/0000-0002-2200-4761
                http://orcid.org/0000-0003-4822-2908
                http://orcid.org/0000-0001-7764-8663
                http://orcid.org/0000-0003-2453-5889
                Article
                abd7600
                10.1126/sciadv.abd7600
                7787485
                33523973
                e1c2af39-8206-4c84-946a-b409ae614287
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

                This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

                History
                : 10 July 2020
                : 16 November 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01 DK093954
                Funded by: doi http://dx.doi.org/10.13039/100008871, JDRF;
                Award ID: 2-SRA-2018-493-A-B and JDRF 2-SRA-2019-834-S-B
                Funded by: doi http://dx.doi.org/10.13039/100008871, JDRF;
                Funded by: doi http://dx.doi.org/10.13039/100008871, JDRF;
                Award ID: 2-SRA-2018-493-A-B and JDRF 2-SRA-2019-834-S-B
                Funded by: Fonds National de la Recherche Scientifique;
                Award ID: CR-2015A-06 and CR-2019C-04
                Funded by: VA Merit Award;
                Award ID: I01BX001733
                Funded by: Dutch Diabetes Fonds (DDFR);
                Funded by: Innovative Medicines Initiative 2;
                Award ID: 115797 (INNODIA) and 945268 (INNODIA HARVEST)
                Categories
                Research Article
                Research Articles
                SciAdv r-articles
                Diseases and Disorders
                Immunology
                Diseases and Disorders
                Custom metadata
                Sef Rio

                Comments

                Comment on this article