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. 2018 Jan 18;9(1):288.
doi: 10.1038/s41467-017-01995-2.

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

Jan Rozman  1   2 , Birgit Rathkolb  1   2   3 , Manuela A Oestereicher  1 , Christine Schütt  1 , Aakash Chavan Ravindranath  2   4 , Stefanie Leuchtenberger  1 , Sapna Sharma  2   5 , Martin Kistler  1 , Monja Willershäuser  6   7   8 , Robert Brommage  1 , Terrence F Meehan  9 , Jeremy Mason  9 , Hamed Haselimashhadi  9 ; IMPC Consortium; Tertius Hough  10 , Ann-Marie Mallon  10 , Sara Wells  10 , Luis Santos  10 , Christopher J Lelliott  11 , Jacqueline K White  11   12 , Tania Sorg  13   14   15   16   17 , Marie-France Champy  13   14   15   16   17 , Lynette R Bower  18 , Corey L Reynolds  19 , Ann M Flenniken  20   21   22 , Stephen A Murray  12 , Lauryl M J Nutter  20   21 , Karen L Svenson  12 , David West  23 , Glauco P Tocchini-Valentini  24 , Arthur L Beaudet  20   21 , Fatima Bosch  25 , Robert B Braun  12 , Michael S Dobbie  26 , Xiang Gao  27 , Yann Herault  13   14   15   16   17 , Ala Moshiri  28 , Bret A Moore  29 , K C Kent Lloyd  18 , Colin McKerlie  20   21 , Hiroshi Masuya  30 , Nobuhiko Tanaka  30 , Paul Flicek  9 , Helen E Parkinson  9 , Radislav Sedlacek  31 , Je Kyung Seong  32 , Chi-Kuang Leo Wang  33 , Mark Moore  34 , Steve D Brown  10 , Matthias H Tschöp  2   35   36 , Wolfgang Wurst  37   38   39   40 , Martin Klingenspor  6   7   8 , Eckhard Wolf  2   3 , Johannes Beckers  1   2   41 , Fausto Machicao  42 , Andreas Peter  2   42   43 , Harald Staiger  2   43   44 , Hans-Ulrich Häring  2   42   43 , Harald Grallert  2   5   45 , Monica Campillos  2   4 , Holger Maier  1 , Helmut Fuchs  1 , Valerie Gailus-Durner  1 , Thomas Werner  46 , Martin Hrabe de Angelis  47   48   49
Collaborators, Affiliations

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

Jan Rozman et al. Nat Commun. .

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.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Fig. 1
Fig. 1
Strategical abstract depicting the research strategy to identify new genetic elements in metabolism. The IMPC phenotyping data of 2016 knockout mouse strains was systematically evaluated for new links to human metabolic disorders. Nine hundred seventy-four knockout strains showed a strong metabolic phenotype. This set of genes was used as data mining resource. In a multiple line of evidence approach, we finally identified 23 genes that were linked to human disease-related SNPs
Fig. 2
Fig. 2
Frequency distribution of mutant/wild-type ratios for metabolic parameters, separated for males and females. a T0 females, basal blood glucose after overnight food deprivation, b T0 males, basal blood glucose after overnight food deprivation, c AUC females, area under the curve of blood glucose excursions after glucose injection in a glucose tolerance test, d AUC females, area under the curve of blood glucose excursions after glucose injection in a glucose tolerance test, e TG females, plasma triglyceride concentrations, f TG males, plasma triglyceride concentrations, g body mass females, h body mass males, i MR females, metabolic rate obtained from a 21 h indirect calorimetry trial, j MR females, metabolic rate obtained from a 21 h indirect calorimetry trial, k VO2 females, oxygen consumption obtained from a 21 h indirect calorimetry trial, l VO2 males, oxygen consumption obtained from a 21 h indirect calorimetry trial, m RER females, respiratory exchange ratio, n RER females, respiratory exchange ratio. Filled areas of the distributions cover the <5% and >95% strong metabolic phenotype genes, n provides number of mutant lines
Fig. 3
Fig. 3
Links between unexplored metabolic genes and parameters that contribute to strong metabolic phenotypes in females (upper) and males (lower)
Fig. 4
Fig. 4
Cross phenotype meta-analysis of murine genes without prior link to metabolism. SNPs are on the x-axis ordered as per chromosome and the CPMA values (log transferred) are on the y-axis. The chromosomes are shown in different colors. Each column represents the genes and SNPs stacking vertically. The higher the CPMA measure, the higher the significance of SNPs across different phenotypes. SNPs above CPMA = 3.1 were considered to have a significant link to the disease traits
Fig. 5
Fig. 5
MORE set-derived network of the 20 genes having two or more phenotypic associations. Genes were selected from the strong metabolic phenotype list with two or more phenotypic associations to metabolic traits. Genes were chosen as targets to examine shared links to regulatory networks. a Connection of 12 genes and 2 other genes by MORE sets found in their promoters. b The MORE-derived network from a with gene–phenotype associations superimposed as colored areas. This superposition joins the 14 genes to one network. c Four phenotypes are shown separated from each other along with the links between these strong metabolic phenotype genes. This will facilitate recognition of the individual phenotypes
Fig. 6
Fig. 6
The joint MORE set and KEGG pathway network. Fifteen genes from the gene list AUC female impaired (KEGG) for which MORE set connections were also found. Male impaired did not yield results. a AUC gene network derived from KEGG pathway-mapped genes showing AUC-associated MORE sets only. b AUC gene network of KEGG pathway-mapped strong knockout genes by common pathway associations. c AUC overlay of MORE network and KEGG pathway network. (Legend extension: 1 = Focal adhesion—Mus musculus (mouse), 2 = JAK-STAT signaling pathway—Mus musculus (mouse), 3 = Pathways in cancer—Mus musculus (mouse), 4 = Neuroactive ligand-receptor interaction—Mus musculus (mouse), 5 = Chemokine signaling pathway—Mus musculus (mouse), 6 = PI3K-AKT signaling pathway—Mus musculus (mouse), 7 = FoxO signaling pathway—Mus musculus (mouse), 8 = AMPK signaling pathway—Mus musculus (mouse), 9 = Longevity regulating pathway—Mus musculus (mouse), 10 = Endocytosis—Mus musculus (mouse), 11 = HTLV-I infection—Mus musculus (mouse), 12 = PI3K-AKT signaling pathway—Mus musculus (mouse), 13 = Cell cycle—Mus musculus (mouse), 14 = Basal cell carcinoma—Mus musculus (mouse))
Fig. 7
Fig. 7
Zranb2 MORE network. Validation of predicted gene functions based on six shared MORE sets and functional links from literature (further explanation see text)
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References

    1. Ahmed M. Non-alcoholic fatty liver disease in 2015. World J. Hepatol. 2015;7:1450–1459. doi: 10.4254/wjh.v7.i11.1450. - DOI - PMC - PubMed
    1. Boehme MW, et al. Prevalence, incidence and concomitant co-morbidities of type 2 diabetes mellitus in South Western Germany–a retrospective cohort and case control study in claims data of a large statutory health insurance. BMC Public Health. 2015;15:855. doi: 10.1186/s12889-015-2188-1. - DOI - PMC - PubMed
    1. Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine. 2014;42:698–702. doi: 10.1016/j.mpmed.2014.09.007. - DOI - PMC - PubMed
    1. Kharroubi AT, Darwish HM. Diabetes mellitus: the epidemic of the century. World J. Diabetes. 2015;6:850–867. doi: 10.4239/wjd.v6.i6.850. - DOI - PMC - PubMed
    1. Stevens GA, et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul. Health Metr. 2012;10:22. doi: 10.1186/1478-7954-10-22. - DOI - PMC - PubMed

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