A programmable fate decision landscape underlies single-cell aging in yeast

Abstract

Chromatin instability and mitochondrial decline are conserved processes that contribute to cellular aging. Although both processes have been explored individually in the context of their distinct signaling pathways, the mechanism that determines which process dominates during aging of individual cells is unknown. We show that interactions between the chromatin silencing and mitochondrial pathways lead to an epigenetic landscape of yeast replicative aging with multiple equilibrium states that represent different types of terminal states of aging. The structure of the landscape drives single-cell differentiation toward one of these states during aging, whereby the fate is determined quite early and is insensitive to intracellular noise. Guided by a quantitative model of the aging landscape, we genetically engineered a long-lived equilibrium state characterized by an extended life span.

Progress of clinical evaluation for vascular aging in humans

“…[54] Nucleolus Scientists have found that about half of the cells age through a gradual decline in the stability of the nucleolus, which is a region of nuclear DNA where key components of protein are synthesized. [55] Maintenance of nucleolar homeostasis is suggested to contribute to counteracting JOURNAL OF TRANSLATIONAL INTERNAL MEDICINE / AOP cells aging. [56] Activation of the NF-κB pathway by nucleolar stress and induction of apoptosis by nucleolar sequestration of NF-κB/RelA may emerge as a mechanism of vascular aging.…”

Protein interaction potential landscapes for yeast replicative aging

“…Other factors that affect lifespan, such as caloric restriction (CR), were also found dependent on the genotypes 9 . A recent report found overexpression of both SIR2 and HAP4 genes leads to a cell state with considerably long RLS 10 .…”

Single-Cell Technologies to Understand the Mechanisms of Cellular Adaptation in Chemostats

“…Microfluidic single-cell cultivation systems enable timeresolved analysis of individual cells in accurately controlled environments by application of, e.g., online fluorescent readouts or phase contrast images. These systems are typically used to study cell division, morphology, aging, or gene expression (Elowitz and Leibler, 2000;Wang et al, 2010;Ullman et al, 2013;Grünberger et al, 2015;Li Y. et al, 2020). Contrary to studies in bioreactors, it is possible to follow phenotypic development and regulation of isolated cells with spatiotemporal resolution and to distinguish contributions from intrinsic stochastic processes and environmental factors (Weibel et al, 2007;Dusny and Schmid, 2015).…”

“…Alternative mechanisms causing phenotypic heterogeneity can be related to aging and the asymmetrical division of exponentially growing cells. Recently, Li Y. et al (2020) showed that genetically identical yeast cells age at different rates and toward different phenotypes in a constant glucose-limited environment, for example.…”