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

Abstract

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. To help unveil the regulatory circuitry mediated by mRNA m6A methylation, we develop here m6A-Driver, an algorithm for predicting m6A-driven genes and associated networks, whose functional 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 a consensus m6A-driven network of m6A-driven genes. To evaluate the performance, we applied 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-Driver can robustly and efficiently identify m6A-driven genes that are functionally more enriched and associated with higher degree of differential expression than differential m6A methylated genes. Pathway analysis of the constructed context-specific m6A-driven gene networks further revealed the regulatory circuitry underlying the dynamic interplays between the methyltransferases and demethylase at the epitranscriptomic layer of gene regulation.

The epitranscriptome m6A writer METTL3 promotes chemo- and radioresistance in pancreatic cancer cells

“…The presence of m 6 A seems to affect mRNA fate and function in various ways such as circadian clock (15), mRNA degradation (16,17), translational alteration (10,18,19), splicing effects (3,7,14,20), microRNA maturation (21,22), cell reprogramming (11,23,24), long non-coding RNA (lncRNA) inactivation, X-inactive The epitranscriptome m 6 A writer METTL3 promotes chemo-and radioresistance in pancreatic cancer cells specific transcript (XIST) (25), and RNA-mediated response to ultraviolet (UV)-induced DNA damage (26). Using previously reported MeRIP-Seq data, computational in silico analysis has built an m 6 A-driven network of m 6 A-driven genes, suggesting various roles of m 6 A (27).…”

<p>Progression of Thyroid Carcinoma Is Promoted by the m6A Methyltransferase METTL3 Through Regulating m6A Methylation on TCF1</p>

“…7 Cancer drugs targeting m 6 A methylation are promising in the clinical application. 8,9 Abnormally expressed enzymes involved in m 6 A methylation may result in pathological conditions. 10,11 Under the assistance of methyltransferases (ie METTL3, METTL14 and WTAP), methylation modification presents on the N 6 -Methyladenosine.…”

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

“…In our case, we also want to identify significant m 6 A-regulated genes that not only have high m 6 A-expression correlations but also cooperate with each other functionally in a functional network. To apply HotNet2, we assigned absolute m 6 A-expression correlation coefficients z * score of candidate m 6 A regulated genes as the input heat vector and choose the reference functional network as the four PPI networks, where BioGRID has been shown to contain the significant interactions between m 6 A methylated genes [43] and the other three PPI networks were used in the HotNet2 paper.…”

“…In addition to a lack of single-base, context-specific site prediction algorithms, computational prediction of m 6 A functions has not been adequately addressed. In our previous work [43], we developed m 6 A-Driver, a network-based approach to identify m 6 A driven genes with significant functions under a specific context. However, m 6 A-Driver has several limitations.…”