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      Analysis of microRNA and Gene Expression Profiles in Multiple Sclerosis: Integrating Interaction Data to Uncover Regulatory Mechanisms

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          Abstract

          MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of multiple sclerosis (MS), an inflammatory disorder of the central nervous system. Here, we propose a new consensus-based strategy to analyse and integrate miRNA and gene expression data in MS as well as other publically available data to gain a deeper understanding of the role of miRNAs in MS and to overcome the challenges posed by studies with limited patient sample sizes. We processed and analysed microarray datasets, and compared the expression of genes and miRNAs in the blood of MS patients and controls. We then used our consensus and integration approach to construct two molecular networks dysregulated in MS: a miRNA- and a gene-based network. We identified 18 differentially expressed (DE) miRNAs and 128 DE genes that may contribute to the regulatory alterations behind MS. The miRNAs were linked to immunological and neurological pathways, and we exposed let-7b-5p and miR-345-5p as promising blood-derived disease biomarkers in MS. The results suggest that DE miRNAs are more informative than DE genes in uncovering pathways potentially involved in MS. Our findings provide novel insights into the regulatory mechanisms and networks underlying MS.

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          TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support

          As the relevant literature and the number of experiments increase at a super linear rate, databases that curate and collect experimentally verified microRNA (miRNA) targets have gradually emerged. These databases attempt to provide efficient access to this wealth of experimental data, which is scattered in thousands of manuscripts. Aim of TarBase 6.0 (http://www.microrna.gr/tarbase) is to face this challenge by providing a significant increase of available miRNA targets derived from all contemporary experimental techniques (gene specific and high-throughput), while incorporating a powerful set of tools in a user-friendly interface. TarBase 6.0 hosts detailed information for each miRNA–gene interaction, ranging from miRNA- and gene-related facts to information specific to their interaction, the experimental validation methodologies and their outcomes. All database entries are enriched with function-related data, as well as general information derived from external databases such as UniProt, Ensembl and RefSeq. DIANA microT miRNA target prediction scores and the relevant prediction details are available for each interaction. TarBase 6.0 hosts the largest collection of manually curated experimentally validated miRNA–gene interactions (more than 65 000 targets), presenting a 16.5–175-fold increase over other available manually curated databases.
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            Prediction of both conserved and nonconserved microRNA targets in animals.

            MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target prediction models with high specificity. Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA down-regulated gene targets. All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org.
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              MicroRNA profiling of multiple sclerosis lesions identifies modulators of the regulatory protein CD47.

              We established microRNA profiles from active and inactive multiple sclerosis lesions. Using laser capture microdissection from multiple sclerosis lesions to pool single cells and in vitro cultures, we assigned differentially expressed microRNA to specific cell types. Astrocytes contained all 10 microRNA that were most strongly upregulated in active multiple sclerosis lesions, including microRNA-155, which is known to modulate immune responses in different ways but so far had not been assigned to central nervous system resident cells. MicroRNA-155 was expressed in human astrocytes in situ, and further induced with cytokines in human astrocytes in vitro. This was confirmed with astrocyte cultures from microRNA-155-|-lacZ mice. We matched microRNA upregulated in phagocytically active multiple sclerosis lesions with downregulated protein coding transcripts. This converged on CD47, which functions as a 'don't eat me' signal inhibiting macrophage activity. Three microRNA upregulated in active multiple sclerosis lesions (microRNA-34a, microRNA-155 and microRNA-326) targeted the 3'-untranslated region of CD47 in reporter assays, with microRNA-155 even at two distinct sites. Our findings suggest that microRNA dysregulated in multiple sclerosis lesions reduce CD47 in brain resident cells, releasing macrophages from inhibitory control, thereby promoting phagocytosis of myelin. This mechanism may have broad implications for microRNA-regulated macrophage activation in inflammatory diseases.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                03 October 2016
                2016
                : 6
                : 34512
                Affiliations
                [1 ]Department of Systems Biology and Bioinformatics, University of Rostock , Rostock, Germany
                [2 ]Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock , Rostock, Germany
                [3 ]Department of Neurology, Division of Neuroimmunology, University of Rostock , Rostock, Germany
                [4 ]Interdisciplinary Faculty, University of Rostock , Rostock, Germany
                [5 ]Division of Bioinformatics, Department of Biology, Friedrich-Alexander Universität Erlangen-Nürnberg , Erlangen, Germany
                Author notes
                Article
                srep34512
                10.1038/srep34512
                5046091
                27694855
                293298bf-a116-4516-90ab-431dd480b803
                Copyright © 2016, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 01 June 2016
                : 15 September 2016
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