Obesity caused by an OVOL2 mutation reveals dual roles of OVOL2 in promoting thermogenesis and limiting white adipogenesis.

Using random germline mutagenesis in mice, we identified a viable hypomorphic allele (boh) of the transcription-factor-encoding gene Ovol2 that resulted in obesity, which initially developed with normal food intake and physical activity but decreased energy expenditure. Fat weight was dramatically increased, while lean weight was reduced in 12-week-old boh homozygous mice, culminating by 24 weeks in massive obesity, hepatosteatosis, insulin resistance, and diabetes. The Ovol2boh/boh genotype augmented obesity in Lepob/ob mice, and pair-feeding failed to normalize obesity in Ovol2boh/boh mice. OVOL2-deficient mice were extremely cold intolerant. OVOL2 is essential for brown/beige adipose tissue-mediated thermogenesis. In white adipose tissues, OVOL2 limited adipogenesis by blocking C/EBPα engagement of its transcriptional targets. Overexpression of OVOL2 in adipocytes of mice fed with a high-fat diet reduced total body and liver fat and improved insulin sensitivity. Our data reveal that OVOL2 plays dual functions in thermogenesis and adipogenesis to maintain energy balance.

Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the identification of mutations causing immune phenotypes in mice by creating Candidate Explorer (CE), a machine-learning software program that integrates 67 features of genetic mapping data into a single numeric score, mathematically convertible to the probability of verification of any putative mutation-phenotype association. At this time, CE has evaluated putative mutation-phenotype associations arising from screening damaging mutations in ∼55% of mouse genes for effects on flow cytometry measurements of immune cells in the blood. CE has therefore identified more than half of genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.