While N6-methyladenosine (m6A), the most abundant internal modification in eukaryotic mRNA, is linked to cell differentiation and tissue development, the biological significance of m6A modification in mammalian glial development remains unknown. Here, we identify a novel m6A reader, Prrc2a (Proline rich coiled-coil 2 A), which controls oligodendrocyte specification and myelination. Nestin-Cre-mediated knockout of Prrc2a induces significant hypomyelination, decreased lifespan, as well as locomotive and cognitive defects in a mouse model. Further analyses reveal that Prrc2a is involved in oligodendrocyte progenitor cells (OPCs) proliferation and oligodendrocyte fate determination. Accordingly, oligodendroglial-lineage specific deletion of Prrc2a causes a similar phenotype of Nestin-Cre-mediated deletion. Combining transcriptome-wide RNA-seq, m6A-RIP-seq and Prrc2a RIP-seq analysis, we find that Olig2 is a critical downstream target gene of Prrc2a in oligodendrocyte development. Furthermore, Prrc2a stabilizes Olig2 mRNA through binding to a consensus GGACU motif in the Olig2 CDS (coding sequence) in an m6A-dependent manner. Interestingly, we also find that the m6A demethylase, Fto, erases the m6A modification of Olig2 mRNA and promotes its degradation. Together, our results indicate that Prrc2a plays an important role in oligodendrocyte specification through functioning as a novel m6A reader. These findings suggest a new avenue for the development of therapeutic strategies for hypomyelination-related neurological diseases.

Double-stranded RNA (dsRNA) is a virus-encoded signature capable of triggering intracellular Rig-like receptors (RLR) to activate antiviral signaling, but whether intercellular dsRNA structural reshaping mediated by the N6-methyladenosine (m6A) modification modulates this process remains largely unknown. Here, we show that, in response to infection by the RNA virus Vesicular Stomatitis Virus (VSV), the m6A methyltransferase METTL3 translocates into the cytoplasm to increase m6A modification on virus-derived transcripts and decrease viral dsRNA formation, thereby reducing virus-sensing efficacy by RLRs such as RIG-I and MDA5 and dampening antiviral immune signaling. Meanwhile, the genetic ablation of METTL3 in monocyte or hepatocyte causes enhanced type I IFN expression and accelerates VSV clearance. Our findings thus implicate METTL3-mediated m6A RNA modification on viral RNAs as a negative regulator for innate sensing pathways of dsRNA, and also hint METTL3 as a potential therapeutic target for the modulation of anti-viral immunity.

BackgroundOver the past years, tremendous efforts have been made to elucidate the molecular basis of the initiation and progression of ovarian cancer. However, most existing studies have been focused on individual genes or a single type of data, which may lack the power to detect the complex mechanisms of cancer formation by overlooking the interactions of different genetic and epigenetic factors.ResultsWe propose an integrative framework to identify genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a probabilistic graphical model based on the Cancer Genome Atlas (TCGA) data. In the feature selection, we first defined a set of seed genes by including 48 candidate tumor suppressors or oncogenes and an additional 20 ovarian cancer related genes reported in the literature. The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation (at gene TP53). We built a Bayesian network model with a logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2. The directed graph revealed many potential genetic pathways, some of which confirmed the existing results in the literature. Clustering analysis further suggested four gene clusters, three of which correspond to well-defined cellular processes including cell division, tumor invasion and mitochondrial system. In addition, two genes related to glycoprotein synthesis, PSG11 and GALNT10, were found highly predictive for the overall survival time of ovarian cancer patients.ConclusionsThe proposed framework is effective in identifying possible important genetic and epigenetic features that are related to complex cancer diseases. The constructed Bayesian network has identified some new genetic/epigenetic pathways, which may shed new light into the molecular mechanisms of ovarian cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0136-9) contains supplementary material, which is available to authorized users.