Identification of genetic elements in metabolism by high-throughput mouse phenotyping.
Fiche publication
Date publication
janvier 2018
Journal
Nature communications
Auteurs
Membres identifiés du Cancéropôle Est :
Dr HERAULT Yann, Dr SORG Tania
Tous les auteurs :
Rozman J, Rathkolb B, Oestereicher MA, Schütt C, Ravindranath AC, Leuchtenberger S, Sharma S, Kistler M, Willershäuser M, Brommage R, Meehan TF, Mason J, Haselimashhadi H, , Hough T, Mallon AM, Wells S, Santos L, Lelliott CJ, White JK, Sorg T, Champy MF, Bower LR, Reynolds CL, Flenniken AM, Murray SA, Nutter LMJ, Svenson KL, West D, Tocchini-Valentini GP, Beaudet AL, Bosch F, Braun RB, Dobbie MS, Gao X, Herault Y, Moshiri A, Moore BA, Kent Lloyd KC, McKerlie C, Masuya H, Tanaka N, Flicek P, Parkinson HE, Sedlacek R, Seong JK, Wang CL, Moore M, Brown SD, Tschöp MH, Wurst W, Klingenspor M, Wolf E, Beckers J, Machicao F, Peter A, Staiger H, Häring HU, Grallert H, Campillos M, Maier H, Fuchs H, Gailus-Durner V, Werner T, Hrabe de Angelis M
Lien Pubmed
Résumé
Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co-regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome.
Mots clés
Animals, Area Under Curve, Basal Metabolism, genetics, Blood Glucose, metabolism, Body Weight, genetics, Diabetes Mellitus, Type 2, genetics, Gene Regulatory Networks, Genome-Wide Association Study, High-Throughput Screening Assays, Humans, Metabolic Diseases, genetics, Mice, Mice, Knockout, Obesity, genetics, Oxygen Consumption, genetics, Phenotype, Triglycerides, metabolism
Référence
Nat Commun. 2018 Jan 18;9(1):288