A Bayesian MCMC Approach to Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments

Abstract

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.

Contingency and entrenchment in protein evolution under purifying selection

“…Here the selection coefficient, s, denotes the difference in log fitness. This choice of fitness function is thus consistent with experimental data on the distribution of fitness effects of mutants (70)(71)(72)(73).…”

On the unfounded enthusiasm for soft selective sweeps

“…But perhaps the most specific information currently available regarding beneficial mutation rates comes from experiments in which all mutations may be generated individually (as opposed to mutation-accumulation studies) and directly evaluated across different environmental conditions (see refs 19,40). Within this framework, Bank et al 41 recently evaluated all possible 560 individual mutations in a subregion of a yeast heat shock protein across six different environmental conditions (standard, as well as temperature and salinity variants), identifying few beneficials in the standard environment, and multiple beneficials associated with high salinity. To quantify this shift, the authors fit a Generalized Pareto Distribution, using the shape parameter (K) to summarize the changing DFE-with the Weibull domain fitting the lesschallenging environments (that is, demonstrating that the DFE is right-bounded, suggesting that the populations are near optimum), and the Frechet domain fitting the challenging environment (that is, a heavy-tailed distribution owing to the presence of strongly beneficial mutations, potentially suggesting that the population is more distant from optimum).…”

Viewing Protein Fitness Landscapes Through a Next-Gen Lens

“…In addition to environmental conditions, experimental reproducibility of bulk competitions depends on many factors, including counting robustness (e.g., due to sequencing depth) and population management (e.g., bottlenecks where population size approaches mutational diversity will lead to stochastic frequency changes from random sampling). With careful attention to these issues (Hietpas et al 2013a), full experimental repeats of bulk competitions monitored by sequencing exhibit strong reproducibility and are capable of distinguishing functional effects on the order of 0.1% (Hietpas et al 2013b;Bank et al 2014).…”

“…The ability to control environmental conditions in laboratory selections is valuable for exploring how distinct conditions impact the effects of mutations (McLaughlin et al 2012;Hietpas et al 2013b;Bank et al 2014), but sampling all possible conditions that could be experienced in nature is impractical. For this reason, there is great promise in combining ecological studies that identify relevant conditions in nature with laboratory investigations that sample how these conditions impact mutational landscapes.…”