Mapping the Peaks: Fitness Landscapes of the Fittest and the Flattest

Abstract

Populations exposed to a high mutation rate harbor abundant deleterious genetic variation, leading to depressed mean fitness. This reduction in mean fitness presents an opportunity for selection to restore fitness through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to flat genotypes (with low fitness but high robustness) outcompeting fit genotypes (with high fitness but low robustness)—a phenomenon known as survival of the flattest. While this effect was previously explored using the digital evolution system Avida, a complete analysis of the local fitness landscapes of fit and flat genotypes has been lacking, leading to uncertainty about the genetic basis of the survival-of-the-flattest effect. Here, we repeated the survival-of-the-flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that the flat genotypes, compared to the fit genotypes, had a reduced likelihood of deleterious mutations as well as an increased likelihood of neutral and, surprisingly, of lethal mutations. This trend holds for mutants one to four substitutions away from the wild-type sequence. We also found that flat genotypes have, on average, no epistasis between mutations, while fit genotypes have, on average, positive epistasis. Our results demonstrate that the genetic causes of mutational robustness on complex fitness landscapes are multifaceted. While the traditional idea of the survival of the flattest emphasized the evolution of increased neutrality, others have argued for increased mutational sensitivity in response to strong mutational loads. Our results show that both increased neutrality and increased lethality can lead to the evolution of mutational robustness. Furthermore, strong negative epistasis is not required for mutational sensitivity to lead to mutational robustness. Overall, these results suggest that mutational robustness is achieved by minimizing heritable deleterious variation.

Moderate amounts of epistasis are not evolutionarily stable in small populations

“…The mean directional epistasis between mutations plays a role in both effects. While fitness peaks for mutationally robust populations tend to be flatter with little epistasis between mutations, we also observe something akin to truncation selection [8,9]. In drift robustness, we observe both an increase in neutral mutations as well as an increase in strongly deleterious and lethal mutations, mediated by strong negative epistasis (q > 1).…”

“…It turns out that populations can adapt to these threats, for example by evolving mutational robustness [4], or by evolving resistance to the effects of drift [5,6,7]. Mutational robustness is achieved by a genetic architecture that implies a flat fitness peak, so that a large fraction of mutations are either neutral or have a small fitness effect [8,9], while giving up wild-type fitness in return. Robustness to drift, on the other hand, appears to involve favoring fitness peaks that have steep flanks, enabled by mutations that are synergistic in their deleterious effect [5,6], while reducing (rather than increasing) the likelihood of mutations with small effect, and increasing the fraction of mutations that are lethal.…”

“…Relationship between critical epistasis q* and selection (s) for different population sizes (N ). The lines represent analytically derived q*, as given by Eq (9)…”

Embodied Computational Evolution: Feedback Between Development and Evolution in Simulated Biorobots

“…But one could extend this model to let the rate of transcriptional error itself evolve. When rates of mutation are high and mean fitness of the population is depressed, selection may restore fitness by evolving mutational robustness ( Franklin et al, 2019 ); thus in the case of this ECE model, we would predict that the rate of transcriptional error would increase in response to high, fixed mutation rates. This would be a test of the “survival of the flattest” hypothesis, where so-called “flat” genotypes, with low fitness and high robustness, may out-compete “fit” genotypes, which possess high fitness and low robustness ( Wilke et al, 2001 ; Franklin et al, 2019 ).…”

“…But it is important to recognize that in populations of fewer than 100 haploid individuals, reductions in population size drastically alter the critical mutation rate, the threshold above which individuals with greater mutational robustness are favored over individuals with greater fitness ( Aston et al, 2013 ). In our system of 60 haploid, asexual individuals, we did not measure critical mutation rate and so cannot address whether our intentional changes in mutation rate shifted the population from the evolution of the fittest to the evolution of the “flattest” ( Wilke et al, 2001 ; Franklin et al, 2019 ). Both fluctuating population size and changes in critical mutation rate offer intriguing opportunities for the next set of experiments using this ECE model.…”