m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

摘要

As the most prevalent mammalian mRNA epigenetic modification, N6-methyladenosine(m6A) has been shown to possess important post-transcriptional regulatory functions. However, the regulatory mechanisms and functional circuits of m6A are still largely elusive. Tohelp unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop herem6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whosefunctional interactions are likely to be actively modulated by m6A methylation under a specific condition. Specifically, m6A-Driver integrates the PPI network and the predicted differential m6A methylation sites from methylated RNA immunoprecipitation sequencing(MeRIP-Seq) data using a Random Walk with Restart (RWR) algorithm and then builds aconsensus m6A-driven network of m6A-driven genes. To evaluate the performance, weapplied m6A-Driver to build the context-specific m6A-driven networks for 4 known m6A (de)methylases, i.e., FTO, METTL3, METTL14 and WTAP. Our results suggest that m6A-Drivercan robustly and efficiently identify m6A-driven genes that are functionally more enrichedand associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networksfurther revealed the regulatory circuitry underlying the dynamic interplays between themethyltransferases and demethylase at the epitranscriptomic layer of gene regulation.