QUANTITATIVE ASSESSMENT OF BIOLOGICAL AGE AND ITS APPLICATIONS

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ABSTRACT Quantifying aging is a major goal in Geroscience research as the availability of a reliable marker of aging can facilitate understanding of the fundamental biology of aging, enable tracking of the aging process in different tissues and cell systems, and support identification and validation of interventions that extend lifespan and healthspan. Traditionally, aging has been monitored by following chronological age, mortality, age-related changes in gene expression, and/or other molecular features, however, there is currently no consensus on the best practices for quantitatively tracking progression through aging. The recent advent of biomarkers based on advanced omics approaches, such as DNA methylation, have provided some hope to support development of precise estimates of age, both in humans and mice. Nevertheless, the majority of such measures are trained as chronological age predictors, with little to no integration of biological, functional, or phenotypic data. Further, the modifiability of aging measures based on DNA methylation in response to lifespan and healthspan extending interventions is almost entirely unknown. We propose to address these challenges by developing a series of novel DNA methylation clocks by integrating information on phenotypic and functional aging, investigating links between DNA methylation and aging hallmarks, and evaluating DNA methylation responses to longevity interventions. We suggest that these clocks will offer a much-needed resource for the Geroscience community. We will develop these clocks using three general approaches. First, we will use cultured cells (MEFs) to induce or establish models of three well-known hallmarks of aging—cellular senescence, DNA damage, and mitochondrial dysregulation. We will then train epigenetic predictors of these hallmarks and validate them in vivo. We will also establish epigenetic alterations in response to novel and established longevity interventions. In doing so, we will develop biomarkers of intervention response that can be used to test mimetics, and/or optimize aging biomarkers. Finally, building on the highly characterized SLAM colony of C57Bl/6 and UM-HET3 animals, we will produce longitudinal methylation data across the lifespan that can be used to develop an epigenetic clock that can serve as a robust predictor of healthspan. We hypothesize that these new clocks will better capture biological age than chronological age trained clocks. Given that they were developed to capture different facets associated with the aging process, they can be combined to create a single aging measure that is more biologically informed and characterized compared to existing epigenetic clocks.