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MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model

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      Abstract

      BackgroundTo understand biological processes and diseases, it is crucial to unravel the concerted interplay of transcription factors (TFs), microRNAs (miRNAs) and their targets within regulatory networks and fundamental sub-networks. An integrative computational resource generating a comprehensive view of these regulatory molecular interactions at a genome-wide scale would be of great interest to biologists, but is not available to date.ResultsTo identify and analyze molecular interaction networks, we developed MIR@NT@N, an integrative approach based on a meta-regulation network model and a large-scale database. MIR@NT@N uses a graph-based approach to predict novel molecular actors across multiple regulatory processes (i.e. TFs acting on protein-coding or miRNA genes, or miRNAs acting on messenger RNAs). Exploiting these predictions, the user can generate networks and further analyze them to identify sub-networks, including motifs such as feedback and feedforward loops (FBL and FFL). In addition, networks can be built from lists of molecular actors with an a priori role in a given biological process to predict novel and unanticipated interactions. Analyses can be contextualized and filtered by integrating additional information such as microarray expression data. All results, including generated graphs, can be visualized, saved and exported into various formats. MIR@NT@N performances have been evaluated using published data and then applied to the regulatory program underlying epithelium to mesenchyme transition (EMT), an evolutionary-conserved process which is implicated in embryonic development and disease.ConclusionsMIR@NT@N is an effective computational approach to identify novel molecular regulations and to predict gene regulatory networks and sub-networks including conserved motifs within a given biological context. Taking advantage of the M@IA environment, MIR@NT@N is a user-friendly web resource freely available at http://mironton.uni.lu which will be updated on a regular basis.

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      Most cited references 44

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      Epithelial-mesenchymal transitions in development and disease.

      The epithelial to mesenchymal transition (EMT) plays crucial roles in the formation of the body plan and in the differentiation of multiple tissues and organs. EMT also contributes to tissue repair, but it can adversely cause organ fibrosis and promote carcinoma progression through a variety of mechanisms. EMT endows cells with migratory and invasive properties, induces stem cell properties, prevents apoptosis and senescence, and contributes to immunosuppression. Thus, the mesenchymal state is associated with the capacity of cells to migrate to distant organs and maintain stemness, allowing their subsequent differentiation into multiple cell types during development and the initiation of metastasis.
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        The basics of epithelial-mesenchymal transition.

        The origins of the mesenchymal cells participating in tissue repair and pathological processes, notably tissue fibrosis, tumor invasiveness, and metastasis, are poorly understood. However, emerging evidence suggests that epithelial-mesenchymal transitions (EMTs) represent one important source of these cells. As we discuss here, processes similar to the EMTs associated with embryo implantation, embryogenesis, and organ development are appropriated and subverted by chronically inflamed tissues and neoplasias. The identification of the signaling pathways that lead to activation of EMT programs during these disease processes is providing new insights into the plasticity of cellular phenotypes and possible therapeutic interventions.
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          Combinatorial microRNA target predictions.

          MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.
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            Author and article information

            Affiliations
            [1 ]Cytoskeleton and Cell Plasticity lab, Life Sciences Research Unit-FSCT, University of Luxembourg, L-1511 Luxembourg, Luxembourg
            [2 ]Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, University of British Columbia, 950 West 28th Avenue, Vancouver, BC V5Z 4H4, Canada
            [3 ]Structure and Function of the Cell Nucleus, Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal (Québec), Canada
            [4 ]Institut de Recherche en Cancérologie de Montpellier INSERM U896, Université Montpellier1, CRLC Val d'Aurelle Paul Lamarque, Montpellier, F-34298, France
            [5 ]Institut de Génétique Moléculaire de Montpellier UMR 5535 CNRS, 1919 route de Mende, F-34293 Montpellier cedex 5, France
            Contributors
            Journal
            BMC Bioinformatics
            BMC Bioinformatics
            BioMed Central
            1471-2105
            2011
            4 March 2011
            : 12
            : 67
            3061897
            1471-2105-12-67
            21375730
            10.1186/1471-2105-12-67
            Copyright ©2011 Le Béchec et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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            Software

            Bioinformatics & Computational biology

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