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      • Record: found
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      A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

      1 , 1 , 1 , 2 , 3 , 4 , 5 , 2 , 6 , 7 , 8 , 2 , 9 , 10 , 1 , 11 , 16 , 1 , 6 , 10 , 12 , 13 , 14 , 16 , 1 , 15 , 16 , 10 , 2 , 10 , 10 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 17 , 23 , , 1 , 10 , , 1

      Genome Medicine

      BioMed Central

      Network tools, scRNA-seq, Biomarker and drug discovery

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          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

          Background

          Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.

          Methods

          The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.

          Results

          We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.

          Conclusions

          Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

          Electronic supplementary material

          The online version of this article (10.1186/s13073-019-0657-3) contains supplementary material, which is available to authorized users.

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          Most cited references 65

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          An Integrated Encyclopedia of DNA Elements in the Human Genome

          Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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            The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

            A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.
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              Inflammation, atherosclerosis, and coronary artery disease.

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                Author and article information

                Contributors
                huan.zhang@liu.se
                mikael.benson@liu.se
                Journal
                Genome Med
                Genome Med
                Genome Medicine
                BioMed Central (London )
                1756-994X
                30 July 2019
                30 July 2019
                2019
                : 11
                Affiliations
                [1 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Centre for Personalized Medicine, , Linköping University, ; Linköping, Sweden
                [2 ]Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden
                [3 ]ISNI 0000 0001 2284 9230, GRID grid.410367.7, Departament d’Enginyeria Informàtica i Matemàtiques, , Universitat Rovira i Virgili, ; Tarragona, Spain
                [4 ]Department of Surgery, Region Jönköping County, Jönköping, Sweden
                [5 ]Office for Control of Communicable Diseases, Region Jönköping County, Jönköping, Sweden
                [6 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Division of Rheumatology, Autoimmunity, and Immune Regulation, Department of Clinical and Experimental Medicine, , Linköping University, ; Linköping, Sweden
                [7 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Department of Clinical Immunology and Transfusion Medicine, , Linköping University, ; Linköping, Sweden
                [8 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Department of Gastroenterology and Department of Clinical and Experimental Medicine, , Linköping University, ; Linköping, Sweden
                [9 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Department of Medical and Health Sciences, , Linköping University, ; Linköping, Sweden
                [10 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Bioinformatics, Department of Physics, Chemistry and Biology, , Linköping University, ; Linköping, Sweden
                [11 ]ISNI 0000 0004 0470 5454, GRID grid.15444.30, Department of Otorhinolaryngology, , Yonsei University College of Medicine, ; Seoul, Korea
                [12 ]Clinical Microbiology, Region Jönköping County, Jönköping, Sweden
                [13 ]Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institute, Karolinska University Hospital Huddinge, Stockholm, Sweden
                [14 ]ISNI 0000 0000 9241 5705, GRID grid.24381.3c, Karolinska University Laboratory, , Karolinska University Hospital, ; Solna, Sweden
                [15 ]Department of Dermatology and Venereology, Region Jönköping County, Jönköping, Sweden
                [16 ]ISNI 0000 0001 2162 9922, GRID grid.5640.7, Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, , Linköping University, ; Linköping, Sweden
                [17 ]Futurum – Academy for Health and Care, Department of Pediatrics, Region Jönköping County, Jönköping, Sweden
                [18 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Institute for Medical Engineering and Science, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [19 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Department of Chemistry, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [20 ]ISNI 0000 0001 2341 2786, GRID grid.116068.8, Koch Institute for Integrative Cancer Research, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [21 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [22 ]ISNI 0000 0004 0489 3491, GRID grid.461656.6, Ragon Institute of MGH, MIT and Harvard, ; Cambridge, MA USA
                [23 ]Department of Pediatrics, Institution for Clinical Sciences, Göteborg, Sweden
                Article
                657
                10.1186/s13073-019-0657-3
                6664760
                31358043
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                © The Author(s) 2019

                Molecular medicine

                network tools, scrna-seq, biomarker and drug discovery

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