Zhang Song-Yao, Zhang Shao-Wu, Liu Lian, Meng Jia, Huang Yufei
School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
Department of Biological Sciences, HRINU, SUERI, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China.
PLoS Comput Biol. 2016 Dec 27;12(12):e1005287. doi: 10.1371/journal.pcbi.1005287. eCollection 2016 Dec.
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.
作为最普遍的哺乳动物mRNA表观遗传修饰，N6-甲基腺苷（m6A）已被证明具有重要的转录后调控功能。然而，m6A的调控机制和功能回路仍 largely难以捉摸。为了帮助揭示由mRNA m6A甲基化介导的调控回路，我们在此开发了m6A-Driver，一种用于预测m6A驱动基因和相关网络的算法，其功能相互作用可能在特定条件下被m6A甲基化积极调节。具体而言，m6A-Driver使用带重启的随机游走（RWR）算法整合蛋白质-蛋白质相互作用（PPI）网络和来自甲基化RNA免疫沉淀测序（MeRIP-Seq）数据的预测差异m6A甲基化位点，然后构建一个由m6A驱动基因组成的共识m6A驱动网络。为了评估性能，我们应用m6A-Driver为4种已知的m6A（去）甲基化酶，即FTO、METTL3、METTL14和WTAP构建特定背景的m6A驱动网络。我们的结果表明，m6A-Driver能够稳健且高效地识别出功能上更富集且与差异表达程度更高相关的m6A驱动基因，相比于差异m6A甲基化基因。对构建的特定背景的m6A驱动基因网络进行通路分析，进一步揭示了在基因调控的表观转录组层面上甲基转移酶和去甲基酶之间动态相互作用的潜在调控回路。
