Adaptation to extreme stress under the growth-survival fitness trade-off

TL;DR

This study models how yeast populations adapt to extreme stress through physiological trade-offs, revealing that growth and survival can be optimized simultaneously under certain environmental conditions.

Contribution

The paper introduces a quantitative model linking key life-history traits to a single phenotype, elucidating how populations navigate growth-survival trade-offs in fluctuating environments.

Findings

Growth-survival trade-off strength depends on environmental parameters.

Populations optimized for stress can coexist with growth-optimized populations.

Physiological trade-offs do not always lead to fitness trade-offs at the population level.

Abstract

Microbial adaptation to extreme stress, such as starvation, antimicrobial exposure, or freezing often reveals fundamental trade-offs between survival and proliferation. Understanding how populations navigate these trade-offs in fluctuating environments remains a central challenge. We develop a quantitative model to investigate the adaptation of populations of yeast (Saccharomyces cerevisiae) subjected to cycles of growth and extreme freeze-thaw stress, focusing on the role of quiescence as a mediator of survival. Our model links key life-history traits: growth rate, lag time, quiescence probability, and stress survival, to a single underlying phenotype, motivated by the role of intracellular trehalose in the adaptation of yeast to freeze-thaw stress. Through stochastic population simulations and analytical calculation of the long-term growth rate, we identify the evolutionary attractors of the system. We find that the strength of the growth-survival trade-off depends critically on environmental parameters, such as the duration of the growth phase. Crucially, our analysis reveals that populations optimized for growth-stress cycles can often maintain viability alongside growth-optimized populations even in the absence of stress. This demonstrates that underlying physiological trade-offs do not necessarily translate into fitness trade-offs at the population level, providing general insights into the complex interplay between environmental fluctuations, physiological constraints, and evolutionary dynamics.