Changes in the distribution of fitness effects and adaptive mutational spectra following a single first step towards adaptation

Abstract

The fitness effects of random mutations are contingent upon the genetic and environmental contexts in which they occur, and this contributes to the unpredictability of evolutionary outcomes at the molecular level. Despite this unpredictability, the rate of adaptation in homogeneous environments tends to decrease over evolutionary time, due to diminishing returns epistasis, causing relative fitness gains to be predictable over the long term. Here, we studied the extent of diminishing returns epistasis and the changes in the adaptive mutational spectra after yeast populations have already taken their first adaptive mutational step. We used three distinct adaptive clones that arose under identical conditions from a common ancestor, from which they diverge by a single point mutation, to found populations that we further evolved. We followed the evolutionary dynamics of these populations by lineage tracking and determined adaptive outcomes using fitness assays and whole genome sequencing. We found compelling evidence for diminishing returns: fitness gains during the 2 nd step of adaptation are smaller than those of the 1 st step, due to a compressed distribution of fitness effects in the 2 nd step. We also found strong evidence for historical contingency at the genic level: the beneficial mutational spectra of the 2 nd -step adapted genotypes differ with respect to their ancestor and to each other, despite the fact that the three founders' 1 st -step mutations provided their fitness gains due to similar phenotypic improvements. While some targets of selection in the second step are shared with those seen in the common ancestor, other targets appear to be contingent on the specific first step mutation, with more phenotypically similar founding clones having more similar adaptive mutational spectra. Finally, we found that disruptive mutations, such as nonsense and frameshift, were much more common in the first step of adaptation, contributing an additional way that both diminishing returns and historical contingency are evident during 2 nd step adaptation. Stephen Jay Gould argued that historical contingency makes evolutionary outcomes largely unpredictable, and that were we to replay the "tape of life", we would likely end up with a different world each time 1 . However, frequently observed instances of both parallel 2-4 and convergent 5-7 evolution suggest that, at least under some circumstances, adapting populations may simply take different paths to the same peak on a fitness landscape. Environmental similarities, genotypic relatedness and proximity to an optimum in the fitness landscape constitute some of the constraints contributing to convergent or parallel adaptive responses 4,8-21 .Closely related genotypes are often employed to study the effects of evolutionary history on adaptation in various experimental systems [22][23][24][25][26][27][28][29][30] . A frequent observation is that fitness gains decrease over time during adaptive evolution-termed diminishing returns-most convincingly demonstrated in cases whe...

A genotype-phenotype-fitness map reveals local modularity and global pleiotropy of adaptation

“…Such studies find that multiple mutations in the same functional unit occur less than expected by chance presumably because those mutations would have redundant effects on fitness (Tenaillon et al, 2012). Similarly, studies also find that second-step adaptive mutations tend to be in different pathways or functional modules than the first adaptive step (Aggeli et al, 2020;Fumasoni and Murray, 2020). The novel observation from our paper is that mutations with redundant effects on fitness in the evolution condition are not necessarily identical because they may influence different latent phenotypes.…”

Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation

“…Such studies find that multiple mutations in the same functional unit occur less than expected by chance presumably because those mutations would have redundant effects on fitness ( Tenaillon et al, 2012 ). Similarly, studies also find that second-step adaptive mutations tend to be in different pathways or functional modules than the first adaptive step ( Aggeli et al, 2020 ; Fumasoni and Murray, 2020 ). The novel observation from our paper is that mutations with redundant effects on fitness in the evolution condition are not necessarily identical because they may influence different latent phenotypes.…”

The Population Genetics of Collateral Resistance and Sensitivity

“…In reality, JDFEs probably vary from one genotype to another, but how they vary is not yet known. Recently, researchers began to systematically probe how the effects of new individual mutations on fitness in one environment and their distribution (i.e., the DFE) vary among genotypes (Khan et al, 2011;Chou et al, 2011;Kryazhimskiy et al, 2014;Johnson et al, 2019;Wang et al, 2016;Aggeli et al, 2020). These studies suggest that the fitness effects of mutations available to a genotype and its overall DFE in a given environment depend primarily on the fitness of that genotype in that environment, a phenomenon referred to as "global epistasis" (Wiser et al, 2013;Kryazhimskiy et al, 2014;Reddy and Desai, 2020;Husain and Murugan, 2020).…”

“…Next, we model global diminishing returns epistasis by assuming that the mean of the DFE in the home environment µ 1 declines with the fitness of the genotype in the home environment x (Kryazhimskiy et al, 2014;Aggeli et al, 2020), i.e.,…”

“…Like the DFE, the JDFE is a local property of the fitness landscape which means that it can be at least in principle estimated, for example using a variety of modern high-throughput techniques (e.g., Qian et al, 2012;Hietpas et al, 2013;Van Opijnen et al, 2009;Stiffler et al, 2015;Chevereau et al, 2015;Levy et al, 2015;Blundell et al, 2019;Bakerlee et al, 2021). The downside of this approach is that the JDFE can change over time as the population traverses the fitness landscape (Good et al, 2017;Venkataram et al, 2020;Aggeli et al, 2020). However, in the context of collateral drug resistance and sensitivity, we are primarily interested in short time scales over which JDFE can be reasonably expected to stay approximately constant.…”