The rRNA m(6)A methyltransferase METTL5 is involved in pluripotency and developmental programs

Covalent chemical modifications of cellular RNAs directly impact all biological processes. However, our mechanistic understanding of the enzymes catalyzing these modifications, their substrates and biological functions, remains vague. Amongst RNA modifications N-6-methyladenosine (m(6)A) is widespread and found in messenger (mRNA), ribosomal (rRNA), and noncoding RNAs. Here, we undertook a systematic screen to uncover new RNA methyltransferases. We demonstrate that the methyltransferase-like 5 (METTL5) protein catalyzes m(6)A in 18S rRNA at position A(1832). We report that absence of Mett15 in mouse embryonic stem cells (mESCs) results in a decrease in global translation rate, spontaneous loss of pluripotency, and compromised differentiation potential. METTL5-deficient mice are born at non-Mendelian rates and develop morphological and behavioral abnormalities. Importantly, mice lacking METTL5 recapitulate symptoms of patients with DNA variants in METTL5, thereby providing a new mouse disease model. Overall, our biochemical, molecular, and in vivo characterization highlights the importance of m(6)A in rRNA in stemness, differentiation, development, and diseases

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

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