High-resolution mapping reveals a conserved, widespread, dynamic meiotically regulated mRNA methylation program [Hs]

N6-methyladenosine (m6A) is a common modification of mRNA, with potential roles in fine-tuning the RNA life cycle, but little is known about the pathways regulating this process and its physiological role. Here, we used mass-spectrometry to identify a dense network of proteins physically interacting with METTL3, a core component of the methyltransferase complex, and show that two of them, WTAP and KIAA1429, are required for methylation. Combining high resolution m6A-Seq with knockdown of WTAP allowed us to define accurate maps, at near single-nucleotide resolution, of sites of mRNA methylation across four dynamic programs in human and mouse, including development, differentiation, reprogramming and immune response. Internal WTAP-dependent methylation sites were largely static across the different surveyed conditions and present in the majority of mRNAs. However, methylations were found at much lower levels within highly expressed mRNAs, and methylation is inversely correlated with mRNA stability, consistent with a role in establishing an overall basal, cell-type invariant, distribution of degradation rates. In addition, we identify thousands of WTAP-independent methylation sites at transcription initiation sites, forming part of the mRNA cap structure. We show that the methylations occur at the first transcribed nucleotide, and find that thousands of transcripts are present in different isoforms differing in the methylation state of the first transcribed nucleotide, a previously unappreciated complexity of the transcriptome. Together, our data sheds new light on the proteomic and transcriptional underpinnings of this epitranscriptomic modification in mammals. Overall design: Examination of m6A methylation in human Hek293 and A549 cell lines, in human embryonic stem cells (ESCs) undergoing differentiation to neural progenitor cells (NPCs), in OKMS inducible fibroblasts reprogrammed into iPSC, and upon knockdown of factors using siRNAs or shRNAs.

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Schwartz S, Mumbach MR, Jovanovic M, Wang T, Maciag K, Bushkin GG, Mertins P, Ter-Ovanesyan D, Habib N, Cacchiarelli D, Sanjana NE, Freinkman E, Pacold ME, Satija R, Mikkelsen TS, Hacohen N, Zhang F, Carr SA, Lander ES, Regev A