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

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Item Type:Article
Title:Identification of genetic elements in metabolism by high-throughput mouse phenotyping
Creators Name:Rozman, J., Rathkolb, B., Oestereicher, M.A., Schütt, C., Ravindranath, A.C., Leuchtenberger, S., Sharma, S., Kistler, M., Willershäuser, M., Brommage, R., Meehan, T.F., Mason, J., Haselimashhadi, H., Hough, T., Mallon, A.M., Wells, S., Santos, L., Lelliott, C.J., White, J.K., Sorg, T., Champy, M.F., Bower, L.R., Reynolds, C. L., Flenniken, A.M., Murray, St.A., Nutter, L.M.J., Svenson, K.L., West, D., Tocchini-Valentini, G.P., Beaudet, A.L., Bosch, F., Braun, R.B., Dobbie, M.S., Gao, X., Herault, Y., Moshiri, A., Moore, B.A., Kent Lloyd, K.C., McKerlie, C., Masuya, H., Tanaka, N., Flicek, P., Parkinson, H.E., Sedlacek, R., Seong, J.K., Wang, C.K.L., Moore, M., Brown, S.D., Tschöp, M.H., Wurst, W., Klingenspor, M., Wolf, E., Beckers, J., Machicao, F., Peter, A., Staiger, H., Häring, H.U., Grallert, H., Campillos, M., Maier, H., Fuchs, H., Gailus-Durner, V., Werner, T. and Hrabe de Angelis, M.
Abstract: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.
Keywords:Area Under Curve, Basal Metabolism, Blood Glucose, Body Weight, Type 2, Diabetes Mellitus, Gene Regulatory Networks, Genome-Wide Association Study, High-Throughput Screening Assays, Knockout, Mice, Metabolic Diseases, Obesity, Oxygen Consumption, Triglycerides, Phenotype, Animals, Mice
Source:Nature Communications
Publisher:Nature Publishing Group
Page Range:288
Date:18 January 2018
Additional Information:Ralf Kühn is a member of the IMPC Consortium.
Official Publication:https://doi.org/10.1038/s41467-017-01995-2
PubMed:View item in PubMed

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