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 #
1R01GM113245-01
Application #
8825712
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Brazhnik, Paul
Project Start
2014-09-01
Project End
2017-06-30
Budget Start
2014-09-01
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
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 #
City
San Antonio
State
TX
Country
United States
Zip Code
78249
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