Morgan Levine, Yale University
Aubrey’s contribution at 47:44, Keith Comito (and others throughout), 5:05 is the definition of methylation.
transcription: https://otter.ai/u/AIIhn4i_p4DIXHAJx0ZaG0HUnAU
Morgan Levine from Yale University will join us to discuss her recent article, \”Underlying features of epigenetic ageing clocks\”, which she co-authored.
The presentation will compare and contrast epigenetic clocks, and describe how these can be deconstructed in order to improve our understanding of the causes and consequences associated with epigenetic aging.
Article Abstract
The development of epigenetic clocks based on DNA methylation has been used widely to quantify biological ageing in various tissues/cells. Many epigenetic clocks exist, but they are not strongly correlated, which suggests that they might capture different biological processes. Multi-omics data collected from different human tissues/cells is used to identify common features among eleven epigenetic clocks. Multi-omics analysis revealed that five clocks (Horvath1, Horvath2, Levine Hannum and Lin) shared transcriptional associations across purified CD14+ monoocytes and the dorsolateral cortex, despite the striking lack in CpGs. The pathways that were enriched by the shared transcriptional associations suggested links between epigenetic ageing and metabolism, immune system, and autophagy. In vitro results showed that Levine and Lin clocks were accelerated according to two hallmarks for aging: mitochondrial dysfunction and cellular senescence. We developed a meta clock using data from multiple tissues to deconstruct epigenetic clock signals. This clock showed better prediction of mortality and was more closely related to hallmarks in vitro.
Morgan’s Bio:
Morgan Levine, Assistant Professor at Yale School of Medicine in the Department of Pathology and a member of the Yale Combined Program for Computational Biology and Bioinformatics and the Yale Center for Research on Aging, is a ladder rank. Her work is based on an interdisciplinary method, which integrates theories and methods of statistical genetics and computational biology with mathematical demography in order to develop biomarkers for aging in humans and animal models by using high-dimensional data from omics. She has extensive experience as a PI, co-Investigator, or principal investigator on multiple NIH, Foundation, and University funded projects. These projects used systems-level, machine learning, to track epigenetic changes, transcriptomic and proteomic change with aging, and incorporate this data to develop measures for risk stratification of major chronic diseases such as Alzheimer’s and cancer. Her work involves the development of system-level outcomes measures of aging to facilitate evaluation of geroprotective intervention. She has used a number of biological aging measurements in basic research and observational studies.
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