Date of Award
Fall 2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Experimental Pathology
First Advisor
Levine, Morgan
Abstract
Aging is associated with dramatic changes to DNA methylation (DNAm), although the causes and consequences of such alterations are unknown. Our ability to experimentally uncover mechanisms of epigenetic aging would be greatly enhanced by our ability to study and manipulate these changes using in vitro models. However, it remains unclear whether the changes elicited by cells in culture can serve as a model of what is observed in aging tissues in vivo. To test this, I developed a computational platform utilizing machine learning (elastic net regression modeling) and consensus clustering to identify modules of CpGs that covary across cumulative doublings in cell culture and throughout aging and disease. I created serially passaged murine (mouse embryonic fibroblasts) and human (hTERT immortalized astrocytes) cell models and established physiologically relevant perturbations in culture are driven by cellular replication, not senescence. Such multi-tissue aging signals we’re modulable by caloric restriction and re-programming and could be used to detect risk of tumorigenesis. I show a pure epigenetic signature of cellular replication – termed CellDRIFT – distinguishes tumor from normal tissue and is escalated in normal breast tissue from cancer patients. Additionally, within-person tissue differences are correlated with both predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Enrichment analysis implicates Polycomb (PcG) group factors as the important regulators and link, suggesting repeated mitotic turnover and re-methylation events over the life course may establish disease prone DNAm states via chromatin re-organization. Overall, this dissertation supports the concept that physiologically relevant aging changes can be induced in vitro and used to uncover mechanistic insights into epigenetic aging and disease.
Recommended Citation
Minteer, Christopher, "Deciphering Fact from Fiction: Utilizing Machine Learning to Extract Drivers of Epigenetic Aging in Cell Culture" (2022). Yale Graduate School of Arts and Sciences Dissertations. 720.
https://elischolar.library.yale.edu/gsas_dissertations/720
