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.

Keywords: Saccharomyces cerevisiae; budding yeast; longevity; replicative lifespan; single cells.

Figure 1:. Population mortality shows similar patterns across a broad evolutionary distance.

A) Survival curves for four of the most commonly used model organisms for aging research: budding yeast, C. elegans, fruit flies and mice. For most organisms, aging is measured as a function of time, but for budding yeast it is measured as a function of the number of replications a cell has undergone. B) The mortality rate for organisms begins low, but increases in a log-linear fashion that can be modeled by the Gompertz–Makeham law of mortality.

Figure2:

Variation in traits and population aging trajectories. A) Even isogenic cells have variations in phenotypes based on protein fluctuations or random noise. Variation in traits can be assumed to be normally distributed with a mean μ and standard deviation σ. B) Isogenic cells could follow a single, unified trajectory of aging where the variation in lifespan or traits is a function of the normally distributed population. C) Alternatively, cells could follow divergent trajectories as they age where each trajectory could be characterized by a dominant failure mechanism that would result in different phenotypes. Each trajectory would have a unique μ and σ, and thus still have variation within the population following a specific trajectory.

Figure 3:. Population level data can hide divergent paths at the single cell level.

A) Data for a hypothetical stress response phenotype that increases with age on the population level. B) Although the stress response increases uniformly at the population level, it is possible that individual cells follow different paths where some cells activate the stress response with age and others don’t. C, D) Kymographs showing individual cells, and the activation of the stress response. C) The population data from A could be explained by complete penetrance where all cells only moderately activate the stress response. D) The population data from A could also be explained by divergent trajectories where only a sub-population of cells activates the stress response, but they activate it very strongly.

Figure 4:. Interventions (environmental or genetic) that affect population aging can act similarly on all cells or only affect the age-related phenotype in a subset of cells.

A) Schematic showing an increasing stress response with age that only affects a sub-population of wild-type cells. B) An intervention that lowers the population mean response (top) by a reduction in the penetrance of the specific trajectory (middle). Cells that proceed down the trajectory activate the stress response just as strongly as wild-type cells C) An intervention that lowers the population mean response (top) by a reduction in the activation within a single trajectory (middle). The fraction of cells that proceed down a specific trajectory is unchanged, but cells that proceed down the trajectory benefit by a reduced stress response.