Identification of microRNA-regulated gene networks by expression analysis of target genes

  1. Sandro Banfi1,7,9
  1. 1Telethon Institute of Genetics and Medicine (TIGEM), Naples 80131, Italy;
  2. 2Institute of Genetics and Biophysics “A. Buzzati Traverso,” CNR, Naples 80131, Italy;
  3. 3Conservatoire National des Arts et Métiers (CNAM) - Laboratoire Cédric & Chaire de Statistique Appliquée, F-75141 Paris, France;
  4. 4Bioinformatics, Animal Physiology and Evolution, Stazione Zoologica Anton Dohrn, Villa Comunale, Naples 80121, Italy;
  5. 5Department of Molecular and Human Genetics, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, Texas 77030, USA;
  6. 6Medical Genetics, Department of Pediatrics, “Federico II” University, Naples 80131, Italy;
  7. 7Medical Genetics, Department of General Pathology, Second University of Naples, Naples 80138, Italy
    • 8 Present address: Molecular and Human Genetics Department, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA.

    Abstract

    MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGFβ pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGFβ signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.

    Footnotes

    • 9 Corresponding authors.

      E-mail banfi{at}tigem.it.

      E-mail sardiell{at}bcm.edu.

    • [Supplemental material is available for this article.]

    • Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.130435.111.

    • Received August 14, 2011.
    • Accepted February 14, 2012.

    This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/.

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