Local Fitness Landscapes Predict Yeast Evolutionary Dynamics in Directionally Changing Environments. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Gorter, Florien A; Aarts, Mark G M; Zwaan, Bas J; de Visser, J Arjan G M
• Is Part Of: Genetics. Volume 208:Issue 1(2018)
• Page Start: 307
• Page End: 322
• Publication Date: 2018-01-01
• Subjects: fitness landscapes -- experimental evolution -- Saccharomyces cerevisiae -- genotype-environment interaction -- predicting evolution; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.117.300519
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: The fitness landscape is a concept that is widely used for understanding and predicting evolutionary adaptation. The topography of the fitness landscape depends critically on the environment, with potentially far-reaching consequences for evolution under changing conditions. However, few studies have assessed directly how empirical fitness landscapes change across conditions, or validated the predicted consequences of such change. We previously evolved replicate yeast populations in the presence of either gradually increasing, or constant high, concentrations of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn), and analyzed their phenotypic and genomic changes. Here, we reconstructed the local fitness landscapes underlying adaptation to each metal by deleting all repeatedly mutated genes both by themselves and in combination. Fitness assays revealed that the height, and/or shape, of each local fitness landscape changed considerably across metal concentrations, with distinct qualitative differences between unconditionally (Cd) and conditionally toxic metals (Ni and Zn). This change in topography had particularly crucial consequences in the case of Ni, where a substantial part of the individual mutational fitness effects changed in sign across concentrations. Based on the Ni landscape analyses, we made several predictions about which mutations had been selected when during the evolution experiment. Deep sequencing of population samples from different time points generally confirmed these predictions, demonstrating the power of landscape reconstruction analyses for understanding and ultimately predicting evolutionary dynamics, even under complex scenarios of environmental change.