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      Gene Expression Profiling in Fibromyalgia Indicates an Autoimmune Origin of the Disease and Opens New Avenues for Targeted Therapy

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          Abstract

          Fibromyalgia is a chronic disorder characterized by widespread pain and by several non-pain symptoms. Autoimmunity, small fiber neuropathy and neuroinflammation have been suggested to be involved in the pathogenesis of the disease. We have investigated the gene expression profile in peripheral blood mononuclear cells obtained from ten patients and ten healthy subjects. Of the 545,500 transcripts analyzed, 1673 resulted modulated in fibromyalgic patients. The majority of these genes are involved in biological processes and pathways linked to the clinical manifestations of the disease. Moreover, genes involved in immunological pathways connected to interleukin-17 and to Type I interferon signatures were also modulated, suggesting that autoimmunity plays a role in the disease. We then aimed at identifying differentially expressed Long non-coding RNAs (LncRNAs) functionally connected to modulated genes both directly and via microRNA targeting. Only two LncRNAs of the 298 found modulated in patients, were able to target the most highly connected genes in the fibromyalgia interactome, suggesting their involvement in crucial gene regulation. Our gene expression data were confirmed by real time PCR, by autoantibody testing, detection of soluble mediators and Th-17 polarization in a validation cohort of 50 patients. Our results indicate that genetic and epigenetic mechanisms as well as autoimmunity play a pivotal role in the pathogenesis of fibromyalgia.

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          Most cited references24

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Integration of biological networks and gene expression data using Cytoscape.

            Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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              FunRich: An open access standalone functional enrichment and interaction network analysis tool.

              As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user-friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                10 June 2020
                June 2020
                : 9
                : 6
                : 1814
                Affiliations
                [1 ]Department of Medicine, University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, Italy; marziadolcino@ 123456gmail.com (M.D.); elisa.tinazzi@ 123456univr.it (E.T.)
                [2 ]Department of Experimental Medicine, Section of Histology, University of Genova, Via G.B. Marsano 10, 16132 Genova, Italy; antonio.puccetti@ 123456unige.it
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-6446-2990
                Article
                jcm-09-01814
                10.3390/jcm9061814
                7356177
                32532082
                453d5fdf-425e-4da7-9acb-12ad167ad399
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 April 2020
                : 07 June 2020
                Categories
                Article

                fibromyalgia,long non-coding rna,signaling pathway,protein-protein (ppi) network,gene module

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