Trajectories of Aging: How Systems Biology in Yeast Can Illuminate Mechanisms of Personalized Aging

Abstract

All organisms age, but the extent to which all organisms age the same way remains a fundamental unanswered question in biology. Across species, it is now clear that at least some aspects of aging are highly conserved and are perhaps universal, but other mechanisms of aging are private to individual species or sets of closely related species. Within the same species, however, it has generally been assumed that the molecular mechanisms of aging are largely invariant from one individual to the next. With the development of new tools for studying aging at the individual cell level in budding yeast, recent data has called this assumption into question. There is emerging evidence that individual yeast mother cells may undergo fundamentally different trajectories of aging. Individual trajectories of aging are difficult to study by traditional population level assays, but through the application of systems biology approaches combined with novel microfluidic technologies, it is now possible to observe and study these phenomena in real time. Understanding the spectrum of mechanisms that determine how different individuals age is a necessary step toward the goal of personalized geroscience, where healthy longevity is optimized for each individual.

Searching for the Mechanisms of Mammalian Cellular Aging Through Underlying Gene Regulatory Networks

“…The intermediate state is located between the fast-aging and the slow-aging state; genes, such as FOXO, Sestrins, p53, and Ulk1 show relatively low expression levels compared to the slow-aging state, while genes, such as SIRT1 and AMPK show relatively high expression levels compared to the slow-aging state. Some organisms undergo rapid aging and death, while others grow old slowly and live far longer, even within a population of isogenic organisms in identical environments (Crane et al, 2020 ). A previous study on an aging model of yeast cell with an intermediate state was proposed based on categorizing the age-dependent phenotypic conditions and was validated through experiment (Jin et al, 2019 ).…”

Protein interaction probability landscapes for yeast replicative aging

“…Other factors that affect lifespan, such as caloric restriction (CR), were also found dependent on the genotypes (Schleit et al 2013). Besides these presumable genetic cues, the individual aging phenotypes vary significantly, even in yeast single cells (Crane and Kaeberlein 2018;Crane et al 2020). Moreover, the aging phenotypes are dependent on gene expressions.…”