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Year: 2014 Volume: 10

Decomposition of RNA methylome reveals co-methylation patterns induced by latent enzymatic regulators of the epitranscriptome.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Liu, Lian Zhang, Shao-Wu Zhang, Yu-Chen Liu, Hui Zhang, Lin Chen, Runsheng Huang, Yufei Meng, Jia
Description	Country affiliation: China Author Affiliation: Liu L ( Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710027, China. zhangsw@nwpu.edu.cn.)
Abstract	Biochemical modifications to mRNA, especially N6-methyladenosine (m6A) and 5-methylcytosine (m5C), have been recently shown to be associated with crucial biological functions. Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets collected from 10 different experimental conditions (cell type/tissue or treatment) is performed, and the combinatorial epitranscriptome, which consists of 42 758 m6A sites, is extracted and divided into 3 clusters, in which the methylation sites are likely to be hyper- or hypo-methylated simultaneously (or co-methylated), indicating the sharing of a common methylation regulator. Four different clustering approaches are used, including K-means, hierarchical clustering (HC), Bayesian factor regression model (BFRM) and nonnegative matrix factorization (NMF) to unveil the co-methylation patterns. To validate whether the patterns are corresponding to enzymatic regulators, i.e., RNA methyltransferases or demethylases, the target sites of a known m6A regulator, fat mass and obesity-associated protein (FTO), are identified from an independent mouse MeRIP-Seq dataset and lifted to human. Our study shows that 3 out of the 4 clustering approaches used can successfully identify a group of methylation sites overlapping with FTO target sites at a significance level of 0.05 (after multiple hypothesis adjustment), among which, the result of NMF is the most significant (p-value 2.81×10(-06)). We defined a new approach evaluating the consistency between two clustering results which shows that clustering results of different methods are highly correlated strongly indicating the existence of co-methylation patterns. Consistent with recent studies, a number of cancer and neuronal disease-related bimolecular functions are enriched in the identified clusters, which are biological functions that can be regulated at the epitranscriptional level, indicating the pharmaceutical prospect of RNA N6-methyladenosine-related studies. This result successfully reveals the linkage between the global RNA co-methylation patterns embedded in the epitranscriptomic data under multiple experimental conditions and the latent enzymatic regulators, suggesting a promising direction towards a more comprehensive understanding of the epitranscriptome.
File Format	HTM / HTML
ISSN	1742206X
e-ISSN	17422051
DOI	10.1039/c4mb00604f
Journal	Molecular BioSystems
Issue Number	1
Volume Number	11
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2015-01-01
Publisher Place	Great Britain (UK)
Access Restriction	Open
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Epigenesis, Genetic Genetics Metabolism Transcriptome Algorithms Animals Cell Line Cluster Analysis Computational Biology Gene Expression Profiling Gene Expression Regulation Methylation Methyltransferases Mice Nucleotide Motifs Position-specific Scoring Matrices Workflow Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't Research Support, U.s. Gov't, Non-p.h.s.
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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