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

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.

A resource of targeted mutant mouse lines for 5,061 genes

“…To date, more than 17,500 individual production attempts (microinjection or aggregation) have resulted in the germline transmission of KO alleles for 5,061 unique genes ( Figure 1A; Supplemental Table ST1). These lines have been expanded for phenotyping, providing key insights into mammalian biology and disease [10][11][12][13][14][15] ; www.mousephenotype.org). The IMPC contribution extends the total number of genes with targeted KO alleles produced by the scientific community from the 8,391 reported and curated by Mouse Genome Informatics (MGI; 16 )( Figure 1B; Supplemental Table ST2), to 11,241, or more than half of the genome.…”

A big-data approach to understanding metabolic rate and response to obesity in laboratory mice

“…Application to large-scale dataset. A role for location, temperature, mass and sex To understand the relevance of the initial analysis, we applied this approach to the IMPC dataset (Rozman et al, 2018). We similarly plotted the change in energy balance vs weight change between the start and end of the IMPC calorimetry experiments.…”

“…One of the biggest advantages conferred by a repository of metabolic data will be the opportunity for researchers to investigate phenotypes of specific alleles or interventions. In the publication describing the metabolic data from the IMPC, the authors chose a ratio-based reporting system based on a residuals from multiple linear regression (Rozman et al, 2018). We have reported the residual as well as standard deviation from the mean.…”

“…IMPC data were collected as described (Rozman et al, 2018). In version 10.0 of this dataset, the 24 hr averaged metabolic data from more than 30,000 mice is publicly available.…”

A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium

“…The consortium has also generated mouse models for 360 diseases by creating 3,328 mouse knockouts [10]. The IMPC uses high throughput assays to measure phenotypes throughout the life of a knockout mouse and have successfully associated 974 genes with metabolic phenotypes and diseases [11]. Biomedical researchers can avail IMPC services to receive specific knockout biospecimens and search associated phenotype data using the mousephenotype.org website.…”