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Home page > Bioinformatics and Modelling > microRNA Biogenesis

microRNA Biogenesis

Members of the "MicroRNA Biogenesis" team of the Institute of Computational Biology of Montpellier

ABDALLAHI KIDE Amadou - Master Student - Paris 7 (abdallahi.kide@igmm.cnrs.fr)
BESSIERE Chloé - Master Student- UM2 (chloe.bessiere@igmm.cnrs.fr)
BREHELIN Laurent - Staff Scientist (laurent.brehelin@lirmm.fr)
LEBRE Sophie - Staff Scientist - U. Strasbourg-IBC (slebre@unistra.fr)
LECELLIER Charles - Staff Scientist – IGMM and IBC (charles.lecellier@igmm.cnrs.fr)

The "MicroRNA biogenesis" team is supervised by Charles Lecellier, staff researcher at IGMM affiliated to the Institute of Computational Biology of Montpellier (IBC; http://www.ibc-montpellier.fr) and involves Laurent Bréhélin ("Methods and Algorithms for Bioinformatics" team, LIRMM, http://www.lirmm.fr/recherche/equip...) and Sophie Lèbre ("Theoretical Bioinformatics, Data Mining and Stochastic Optimization", University of Strasbourg, http://icube-bfo.unistra.fr/index.p...). We also collaborate with the laboratory of Pr Wyeth W. Wasserman (Center for Molecular Medecine and Therapeutics, University of British Columbia, Vancouver, Canada; http://www.cmmt.ubc.ca/research/inv...) and the FANTOM international consortium (http://fantom.gsc.riken.jp).

The microRNAs (miRNAs) are 18-25 nucleotides long RNAs involved in the control of translation and the adjustment of protein production in response to various stimuli. Their expression is accurately controlled to ensure plethora of cellular processes. On the other hand, their deregulations are often associated with human diseases with profound clinical impact in diagnosis and therapy. While most studies focus on the consequences of the expression of miRNAs, we seek to understand the complex regulatory mechanisms that orchestrate miRNA biogenesis and to decode the causes of their deregulations. The miRNA biogenesis involves several steps, each step being subjected to specific controls (Figure miRNA_biogenesis.png. Source: Wikicommons, Author: Saumet and Lecellier ). First, a long RNA (thousand nucleotides long) called the primary-miRNA (pri-miRNA) is transcribed from the genome. This pri-miRNA contains one or several local stem-loop structures (called precursor(pre)-miRNA) in which the mature miRNA sequence is embedded. Next, a specific complex, called the Microprocessor, crops the pre-miRNA from the pri-miRNA in the nucleus. The pre-miRNA is exported to the cytosplam where it is maturated by a Dicer-containing complex to release a duplex of miRNAs (only one strand of this duplex will guide a protein complex onto mRNAs to trigger translation repression). Similarly to protein coding genes, the control of pri-miRNA transcription involves DNA-binding proteins (i.e. transcription factors, TFs) that recognize specific cis-regulatory DNA motifs in miRNA promoters. On the other hand, the Microprocessor activity and/or the pre-miRNA export can be controlled by RNA-binding proteins (RBPs) recognizing specific RNA motifs on or at the vicinity of the pre-miRNA. Hence, miRNA biogenesis can be controlled at both the transcriptional and the post-transcriptional levels. We specifically use computational approaches to characterize the molecular determinants (motifs, TFs, RBPs) implicated in the regulation of miRNA biogenesis and to mathematically model miRNA expression in various tissues and/or specific biological contexts including infectious diseases and cancers.

For any further information, please contact : charles.lecellier@igmm.cnrs.fr

Publications of the "MicroRNA biogenesis" IBC team members in relation with the project

§ and * indicate equivalent contributions

Saumet A, Mathelier A, Lecellier CH. The potential of microRNAs in personalized medicine against cancers. Biomed Res Int. 2014;2014:642916. doi: 10.1155/2014/642916. Epub 2014 Aug 28. PubMed PMID: 25243170; PubMed Central PMCID: PMC4163464.

Monleau M, Bonnel S, Gostan T, Blanchard D, Courgnaud V*, Lecellier CH*. Comparison of different extraction techniques to profile microRNAs from human sera and peripheral blood mononuclear cells. BMC Genomics. 2014 May 23;15:395. doi: 10.1186/1471-2164-15-395. PubMed PMID: 24885883; PubMed Central PMCID: PMC4041998.

Sanchez-Martínez D, Krzywinska E, Rathore MG, Saumet A, Cornillon A, Lopez-Royuela N, Martínez-Lostao L, Ramirez-Labrada A, Lu ZY, Rossi JF, Fernández-Orth D, Escorza S, Anel A, Lecellier CH, Pardo J, Villalba M. All-trans retinoic acid (ATRA) induces miR-23a expression, decreases CTSC expression and granzyme B activity leading to impaired NK cell cytotoxicity. Int J Biochem Cell Biol. 2014 Apr;49:42-52. doi:

10.1016/j.biocel.2014.01.003. Epub 2014 Jan 15. PubMed PMID: 24440757. Dondelinger F, Lèbre S, Husmeier D. Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure. Machine Learning, 2013, Vol 90, Iss. 2, 191-230.

Assou S, Al-edani T, Haouzi D, Philippe N, Lecellier CH, Piquemal D, Commes T, Aït-Ahmed O, Dechaud H, Hamamah S. MicroRNAs: new candidates for the regulation of the human cumulus-oocyte complex. Hum Reprod. 2013 Nov;28(11):3038-49. doi: 10.1093/humrep/det321. Epub 2013 Jul 30. PubMed PMID: 23904466.

Lajoie M, Gascuel O, Lefort V, Bréhélin L. Computational discovery of regulatory elements in a continuous expression space. Genome Biol. 2012 Nov 27;13(11):R109. doi: 10.1186/gb-2012-13-11-r109. PubMed PMID: 23186104; PubMed Central PMCID: PMC4053739.

Marbach D et al., Wisdom of crowds for robust gene network inference. Nature Methods, 2012

Saumet A, Vetter G, Bouttier M, Antoine E, Roubert C, Orsetti B, Theillet C, Lecellier CH. Estrogen and retinoic acid antagonistically regulate several microRNA genes to control aerobic glycolysis in breast cancer cells. Mol Biosyst. 2012 Oct 30;8(12):3242-53. doi: 10.1039/c2mb25298h. PubMed PMID: 23064179.

Rathore MG, Saumet A, Rossi JF, de Bettignies C, Tempé D, Lecellier CH, Villalba M. The NF-κB member p65 controls glutamine metabolism through miR-23a. Int J Biochem Cell Biol. 2012 Sep;44(9):1448-56. doi:10.1016/j.biocel.2012.05.011. Epub 2012 May 24. PubMed PMID: 22634383.

Le Béchec A, Portales-Casamar E, Vetter G, Moes M, Zindy PJ, Saumet A, Arenillas D, Theillet C, Wasserman WW, Lecellier CH, Friederich E. MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model. BMC Bioinformatics. 2011 Mar 4;12:67. doi: 10.1186/1471-2105-12-67. PubMed PMID:21375730; PubMed Central PMCID: PMC3061897.

Saumet A, Vetter G, Bouttier M, Portales-Casamar E, Wasserman WW, Maurin T, Mari B, Barbry P, Vallar L, Friederich E, Arar K, Cassinat B, Chomienne C, Lecellier CH. Transcriptional repression of microRNA genes by PML-RARA increases expression of key cancer proteins in acute promyelocytic leukemia. Blood. 2009 Jan 8;113(2):412-21. doi: 10.1182/blood-2008-05-158139. Epub 2008 Oct 21. PubMed PMID: 18941112.


Institut de Génétique Moléculaire de Montpellier
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