Adaptive damage retention mechanism enables healthier yeast population
Artikel i vetenskaplig tidskrift, 2019

During cytokinesis in budding yeast (Saccharomyces cerevisiae) damaged proteins are distributed asymmetrically between the daughter and the mother cell. Retention of damaged proteins is a crucial mechanism ensuring a healthy daughter cell with full replicative potential and an ageing mother cell. However, the protein quality control (PQC)system is tuned for optimal reproduction success which suggests optimal health and size of the population, rather than long-term survival of the mother cell. Modelling retention of damage as an adaptable mechanism, we propose two damage retention strategies to find an optimal way of decreasing damage retention efficiency to maximize population size and minimize the damage in the individual yeast cell. A pedigree model is used to investigate the impact of small variations in the strategies over the whole population. These impacts are based on the altruistic effects of damage retention mechanism and are measured by a cost function whose minimum value provides the optimal health and size of the population. We showed that fluctuations in the cost function allow yeast cell to continuously vary its strategy, suggesting that optimal reproduction success is a local minimum of the cost function. Our results suggest that a rapid decrease in the efficiency of damage retention, at the time when the mother cell is almost exhausted, produces fewer daughters with high levels of damaged proteins. In addition, retaining more damage during the early divisions increases the number of healthy daughters in the population.

Yeast

Damage retention

Asymmetrical division

Pedigree-tree model

Dynamical modelling

Författare

Qasim Ali

North Carolina State University

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Riccardo Dainese

Ecole Polytechnique Federale De Lausanne

Marija Cvijovic

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Journal of Theoretical Biology

0022-5193 (ISSN) 1095-8541 (eISSN)

Vol. 473 52-66

Ämneskategorier

Neurovetenskaper

Annan biologi

Biomedicinsk laboratorievetenskap/teknologi

DOI

10.1016/j.jtbi.2019.04.005

PubMed

30980870

Mer information

Senast uppdaterat

2019-06-10