Nature communications · 2018

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен

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Rozman Jan, Rathkolb Birgit, Oestereicher Manuela A, Schütt Christine, Ravindranath Aakash Chavan, Leuchtenberger Stefanie, Sharma Sapna, Kistler Martin, Willershäuser Monja, Brommage Robert, Meehan Terrence F, Mason Jeremy, Haselimashhadi Hamed, IMPC Consortium, Hough Tertius, Mallon Ann-Marie, Wells Sara, Santos Luis, Lelliott Christopher J, White Jacqueline K, Sorg Tania, Champy Marie-France, Bower Lynette R, Reynolds Corey L, Flenniken Ann M, Murray Stephen A, Nutter Lauryl M J, Svenson Karen L, West David, Tocchini-Valentini Glauco P, Beaudet Arthur L, Bosch Fatima, Braun Robert B, Dobbie Michael S, Gao Xiang, Herault Yann, Moshiri Ala, Moore Bret A, Kent Lloyd K C, McKerlie Colin, Masuya Hiroshi, Tanaka Nobuhiko, Flicek Paul, Parkinson Helen E, Sedlacek Radislav, Seong Je Kyung, Wang Chi-Kuang Leo, Moore Mark, Brown Steve D, Tschöp Matthias H, Wurst Wolfgang, Klingenspor Martin, Wolf Eckhard, Beckers Johannes, Machicao Fausto, Peter Andreas, Staiger Harald, Häring Hans-Ulrich, Grallert Harald, Campillos Monica, Maier Holger, Fuchs Helmut, Gailus-Durner Valerie, Werner Thomas, Hrabe de Angelis Martin

Аннотация

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.

Предметные рубрики MeSH

Animals Area Under Curve Basal Metabolism Blood Glucose Body Weight Diabetes Mellitus, Type 2 Gene Regulatory Networks Genome-Wide Association Study High-Throughput Screening Assays Humans Metabolic Diseases Mice Mice, Knockout Obesity Oxygen Consumption Phenotype Triglycerides

Identification of genetic elements in metabolism by high-throughput mouse phenotyping.

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Аннотация

Предметные рубрики MeSH