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Identification of Genetic Elements in Metabolism by High-Throughput

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ARTICLE DOI: 10.1038/s41467-017-01995-2 OPEN Identification of genetic elements in metabolism by high-throughput mouse phenotyping Jan Rozman et al.# 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 1234567890 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. Correspondence and requests for materials should be addressed to M.H.d.A. (email: [email protected]) #A full list of authors and their affliations appears at the end of the paper NATURE COMMUNICATIONS | (2018) 9:288 | DOI: 10.1038/s41467-017-01995-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-01995-2 etabolic disorders, including obesity and type 2 diabetes results obtained from genotype–phenotype associations in disease Mmellitus, are major challenges for public health. High model organisms14. initial treatment costs are compounded by complica- At the International Mouse Phenotyping Consortium (IMPC), tions that arise after diagnosis, including a number of con- we are generating a comprehensive catalog of mammalian gene sequential diseases, which together generate a significant burden functions to gain functional insights for every protein-coding – for health care systems1 5. Genetic variations play established gene, by producing and phenotyping more than 20,000 knockout – roles in the susceptibility and pathogenesis of these diseases6 11. mouse strains15,16. Knockout strains are analyzed in a compre- However, identification of the underlying gene variants and their hensive, standardized phenotyping screen that covers multiple pathogenic roles is difficult because (1) functional annotation is areas of biology and disease with 14 compulsory test procedures still not available for many genes, especially genes that may be and several additional optional tests (wwww.mousephenotype. involved in disease but currently lack biological characteriza- org/impress). Phenotyping is conducted in 10 research centers in tion12. (2) It also became clear that gene variants do not work in Europe, North America, and Asia. Each site follows the IMPC’s isolation but as parts of networks13. (3) There are concerns standardized operating procedures (IMPReSS), which were regarding the reproducibility, predictability, and relevance of developed during the pilot programs EUMORPHIA and IMPC phenotypic data (2016 knockout mouse strains) Subset of seven metabolic parameters (2016 genes) T0 AUC TG BM MR VO2 RER Down Down Down Down Down Down Down Down Down Down Down Down Down Down Up Up Up Up Up Up Up Up Up Up Up Up Up Up 28 gene lists phenotype/sex/phenotype orientation Ranking/Determination Strong phenotype of 28 outlier lists (974 genes) 0–5%, 95–100% Promoter analysis (225 MORE sets 428 genes) Known link to metabolism 51 OMIM VO2 database No known link to MR BM metabolism 378 545 Genes of unknown AUC Independent function analysis 460 new genes Zranb2 MORE-network Strong phenotype and literature links Slcs2a2 Cpe 5 4 (264 genes) Epha56 Dpm2 Rabl2 5 1 Snap25 4 13 + 5 Dtnbp1 Literature Drd2 Zranb2 Zranb1 3 3 2 2 2 Non-strong phenotype Ggnbp2 6 Bbs5 Prkab2 2 2 (251 genes) Ap4e1 Chmp3 Metabolic pathway Integration mapping of candidate genes (Zranb2) 429 VO2 Pbx3 Shh Gh Grm3 402 human MR Col4A5 II15 Atg3 New orthologs BM Ap4e1 Chmp3 Orc1 Foxo3 phenotypes AUC mouse Anapc4 Prkab2 Iqsec3 SNP/GWAS Overlap with pathways Assignment of 23 genes (268 genes) (AUC 15 genes) phenotype (with human SNPs) (150 genes ) 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 2 NATURE COMMUNICATIONS | (2018) 9:288 | DOI: 10.1038/s41467-017-01995-2 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | DOI: 10.1038/s41467-017-01995-2 ARTICLE Table 1 Number of genes analyzed and candidate hits identified by phenotyping mutant and wild-type mice from both sexes Parameter Number of genes Females and males (in brackets: expected number if sex equally affected) Females Males Both Outlier <5% Outlier >95% Outlier <5% and >95% T0 1843 1840 1839 162 (96) 166 (96) 324 (192) AUC 0–120 1846 1840 1839 172 (96) 163 (96) 334 (192) Triglycerides 1384 1383 1380 124 (73) 129 (72) 249 (145) Body mass 1649 1645 1645 121 (79) 143 (86) 264 (165) Metabolic rate 335 910 329 55 (48) 53 (48) 108 (96) VO2 335 910 329 58 (48) 52 (46) 110 (94) RER 319 901 313 55 (47) 54 (46) 109 (93) Total 1995 2012 2016 575 587 974 EUMODIC17,18. Standardization, data quality control, an auto- percentile of the ratio distributions (shown as the filled areas in mated statistical analysis pipeline, and the phenotyping of refer- Fig. 2). Based on these thresholds, we generated 28 gene lists, one ence strains to assess inter-center variation all help to ensure for every sex parameter combination. Our 28 lists included a total – robust and reproducible data19 22. Adherence to each of these of 974 “strong metabolic phenotype” genes (Fig. 1, second part standards enables derivation of a high-quality, powerful from top). We used these genes as a data mining resource for hypothesis-generating resource that includes genes from a sub- further investigation into potential links to human metabolic stantial proportion of the entire mouse genome. Here we analyze disorders (see Supplementary Data 1—Mutant wildtype ratios for phenotyping data of 2016 knockout strains for strong metabolic complete gene lists and Supplementary Data 2—Strong metabolic phenotypes and identify 974 known and previously unannotated phenotype genes for strong phenotype genes). genes with relevance for metabolic diseases. Twenty-three genes for which we find new strong metabolic phenotypes also have Evaluation of false discovery rates. The reproducibility and links to human metabolic disorders. We introduce a focus on robustness of large-scale biology projects depends on minimizing network/context analysis to demonstrate how systemic phenotype the risks of false-positive or false-negative results. Using a post information can be linked to known metabolic pathways. This hoc approach to evaluate the risk of false-negative findings, we pathway mapping revealed an unexpected degree of metabolic compiled a list of 666 mouse and human genes with published – dimorphism between sexes. In addition, phenotype-associated links to obesity and type 2 diabetes25 29. We then used these regulatory networks allow the prediction of previously unknown candidate genes to estimate the rate of false-negative discoveries gene functions. Therefore, our results underline the value of the in our project (see Supplementary Data 3—Candidate genes for IMPC resource for gene function discovery and augment the false negative discovery). Hundred and one knockout mutants of translational potential of metabolic phenotypes in mice. these genes had been phenotyped by the IMPC at the time when we conducted our analysis. Hundred of 101 had a detectable “ Results metabolic phenotype: 58 (57.4%) scored as strong metabolic phenotype genes” (p value 0.04, null hypothesis was defined as no Strong metabolic phenotypes in IMPC mutants. We analyzed a “ ” “ ” total of 2016 IMPC mouse strains that were homozygous for a enrichment of strong metabolic phenotype genes, see Methods single-gene knockout on a C57BL/6N background or hetero- section for description of the permutation simulation). With only zygous when homozygotes were lethal or sub-viable (see Fig. 1 for one exception (Serpinf1, 210 MGI: 108080), knockout mice of all other 43 remaining genes even though not scoring as “strong a study overview). We chose seven metabolic parameters with ” diagnostic relevance in human clinical research for our study: metabolic phenotype genes had phenotypic deviations in meta- fasting basal blood glucose level before glucose tolerance test (T0), bolic parameters in the range of low 20th or high 80th percentiles area under the curve of blood glucose level after intraperitoneal of their respective frequency distributions (p value 0.047). glucose administration relative to basal blood glucose level (AUC), plasma triglyceride levels (TG), body mass (BM), metabolic rate New knockout mouse models for metabolic diseases. For 58 of (MR), oxygen consumption rate (VO2), and respiratory exchange our 666 candidate genes with known links to metabolic functions, ratio (RER), which is a measure of whole-body metabolic fuel the link was based only on GWAS data with no further evidence utilization. To identify the universal cross-project metabolic available. Based on a systematic search of the mouse genome characteristics of knockouts, we calculated mean mutant/wild-type informatics (MGI) curated database, knockout mouse models had ratios separately for all contributing centers23. Males and females not yet been described for 4 of these 58 candidate genes before our 28 26 29 were analyzed separately because sexual dimorphism is common study: Lypla1 , Rfx6 , Slc6a14, and Slc6a3 .
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