RNA methylation is beginning to emerge as an universal epigenetic mark that may play a critical role in gene regulation. However, technologies aimed at identifying and characterizing transcriptome-wide RNA methylation (methyltranscriptome) are still at their early stages. This is largely because unlike DNA methylation, RNA methylation has to take into consideration transcript abundance, variations in gene expression levels, mRNA degradation, and most importantly positional bias caused by transcript isoforms. Furthermore, differences in RNA methylation in two different cellular contexts (e.g. normal vs stress) or different disease states (e.g. benign vs. cancer) pose yet another computational challenge for characterizing methyltranscriptome. The overall goal of this proposal is to develop, for the first time, computational graphical models to enable 1) accurate and reproducible detection of global mRNA methylations, and 2) context-specific differential RNA methylations in normal and disease states. To achieve these goals, we propose three specific aims:
in Aim 1, we will develop graphical models for detecting mRNA methylation that accounts for biological variations and read biases. We will also develop graphical model for detecting splicing-specific methylation sites.
In Aim 2, we will develop graphical models for detecting context-specific differential methylation.
In Aim 3, we will characterize and experimentally validate the transcriptome-wide, cell type-specific m5C and m6A methylation in normal and disease states. Successful completion of these aims will not only create a collection of comprehensive tools that enable the identification of global and context-specific mRNA methylations, but will also shed lights on the role of mRNA methylation in regulating gene expression, splicing, RNA editing, and RNA stability. This project leverages our expertise in epigenetics, computational modeling, high performance computing, bioinformatics and high throughput sequencing to add a new dimension to the emerging field of RNA methylaton and greatly contribute to the advances of computational modeling and learning.

Public Health Relevance

This study adds a new paradigm to epigenetics research by proposing a critical role for 'mRNA methylation' in gene regulation in normal and disease states. By identifying changes in mRNA methylation, our study promises mRNA methylation as potential biomarkers in human diseases. Reversibility of mRNA methylation can be also exploited for therapeutic interventions that involve reactivation or re-silencing of specific genes

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM113245-03
Application #
9102208
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Preusch, Peter
Project Start
2014-09-01
Project End
2017-06-30
Budget Start
2016-07-01
Budget End
2017-06-30
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas Health Science Center San Antonio
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
800189185
City
San Antonio
State
TX
Country
United States
Zip Code
78249
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Cui, Xiaodong; Zhang, Lin; Meng, Jia et al. (2018) MeTDiff: A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data. IEEE/ACM Trans Comput Biol Bioinform 15:526-534
Panneerdoss, Subbarayalu; Eedunuri, Vijay K; Yadav, Pooja et al. (2018) Cross-talk among writers, readers, and erasers of m6A regulates cancer growth and progression. Sci Adv 4:eaar8263
Tan, Brandon; Liu, Hui; Zhang, Songyao et al. (2018) Viral and cellular N6-methyladenosine and N6,2'-O-dimethyladenosine epitranscriptomes in the KSHV life cycle. Nat Microbiol 3:108-120
Salekin, Sirajul; Zhang, Jianqiu Michelle; Huang, Yufei (2018) Base-pair resolution detection of transcription factor binding site by deep deconvolutional network. Bioinformatics 34:3446-3453
Onyeagucha, Benjamin; Subbarayalu, Panneerdoss; Abdelfattah, Nourhan et al. (2017) Novel post-transcriptional and post-translational regulation of pro-apoptotic protein BOK and anti-apoptotic protein Mcl-1 determine the fate of breast cancer cells to survive or die. Oncotarget 8:85984-85996
Lin Zhang; Hui Liu; Yufei Huang et al. (2017) Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net. IEEE/ACM Trans Comput Biol Bioinform 14:145-154
Liu, Lian; Zhang, Shao-Wu; Huang, Yufei et al. (2017) QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model. BMC Bioinformatics 18:387
Liu, Yuanhang; Wilson, Desiree; Leach, Robin J et al. (2016) MBDDiff: an R package designed specifically for processing MBDcap-seq datasets. BMC Genomics 17 Suppl 4:432
Cui, Xiaodong; Wei, Zhen; Zhang, Lin et al. (2016) Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features. Biomed Res Int 2016:8367534

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