Department of Pathology, Yale University School of Medicine, New Haven, United States; Program in Computational Biology and Bioinformatics, Yale University, New Haven, United States
Translational Gerontology Branch, Biomedical Research Center, National Institute on Aging, National Institutes of Health, Baltimore, United States; Calico Life Sciences, South San Francisco, United States
Theresa Meade
Comparative Medicine Section, Biomedical Research Center, National Institute on Aging, National Institutes of Health, Baltimore, United States
Laboratory of Molecular Biology and Immunology, Flow Core Unit, Biomedical Research Center, National Institute on Aging, National Institutes of Health, Baltimore, United States
Molecular Biology Institute and Departments of Energy Laboratory of Structural Biology and Molecular Medicine, and Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, United States
Robust biomarkers of aging have been developed from DNA methylation in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages—by incorporating behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relates to phenotypic aging. Reduced representation bisulfite sequencing (RRBS) data was used to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. This measure correlated with age at (r = 0.93) in an independent sample, and related to physical functioning (p=5.9e-3), after adjusting for age and cell counts. DNAmAge was also found to correlate with age in male C57BL/6 mice (r = 0.79), and was decreased in response to caloric restriction. Our signatures driven by CpGs in intergenic regions that showed substantial overlap with H3K9me3, H3K27me3, and E2F1 transcriptional factor binding.
