Decoding the Muscle Epitranscriptome: Deep Learning Detection of RNA Modifications in Aging Muscle

RNA modifications represent a critical but poorly understood mechanism regulating protein production in aging muscle tissue. This proposal develops innovative computational and experimental approaches to map and understand how chemical modifications of RNA molecules change during muscle aging. Using a combination of cutting-edge nanopore sequencing technology and artificial intelligence, we will create new methods to precisely detect RNA modifications, particularly 5-methylcytosine (m5C), in muscle tissue. We will then apply these methods to examine how modification patterns differ between fast- and slow-twitch muscle fibers across age and sex, while determining their impact on protein synthesis. The project introduces novel synthetic RNA training strategies for improved modification detection while integrating chemical biology, machine learning, and muscle physiology. This work will establish new paradigms for studying RNA modifications in aging tissues while revealing fundamental mechanisms of muscle aging. The findings will identify potential RNA-based therapeutic targets for age-related muscle loss while providing valuable resources for the broader aging research community. Project Number: 1R21AG098189-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute on Aging (NIA) | Principal Investigator: YUAN WEN | Institution: UNIVERSITY OF KENTUCKY, LEXINGTON, KY | Award Amount: $616,000 | Activity Code: R21 | Study Section: Skeletal Muscle and Exercise Physiology Study Section[SMEP] View on NIH RePORTER: https://reporter.nih.gov/project-details/11282866