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

2019 ◽  
Vol 25 (3) ◽  
pp. 250-262 ◽  
Author(s):  
Joshua Franklin ◽  
Thomas LaBar ◽  
Christoph Adami
Keyword(s):  
Fitness Landscapes ◽  
Complete Analysis ◽  
Evolution System ◽  
Digital Evolution ◽  
Mutational Robustness ◽  
Mean Fitness ◽  
Selection For ◽  
Negative Epistasis ◽  
Opportunity For Selection ◽  
Type Sequence

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.

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

2018 ◽  
Author(s):  
Joshua Franklin ◽  
Thomas LaBar ◽  
Christoph Adami
Keyword(s):  
Fitness Landscapes ◽  
Complete Analysis ◽  
Evolution System ◽  
Digital Evolution ◽  
Mutational Robustness ◽  
Mean Fitness ◽  
Selection For ◽  
Negative Epistasis ◽  
Opportunity For Selection ◽  
Type Sequence

AbstractBackgroundPopulations 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 adaptation through the evolution of mutational robustness. In extreme cases, selection for mutational robustness can lead to “flat” genotypes (with low fitness but high robustness) out-competing “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.ResultsHere, we repeated the survival of the flattest study and analyzed the mutational neighborhoods of fit and flat genotypes. We found that 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.ConclusionsOur 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 effect 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.

scholarly journals An estimator of the Opportunity for Selection that is independent of mean fitness

2020 ◽  
Author(s):  
Robin S. Waples
Keyword(s):  
Null Model ◽  
Relative Fitness ◽  
Offspring Number ◽  
Evolutionary Response ◽  
Fisher Model ◽  
Wide Range ◽  
Mean Fitness ◽  
Selection For ◽  
Opportunity For Selection ◽  
Straightforward Interpretation

AbstractVariation among individuals in number of offspring (fitness, k) sets an upper limit to the evolutionary response to selection. This constraint is quantified by Crow’s Opportunity for Selection (I), which is the variance in relative fitness . Crow’s I has been widely used but remains controversial because it depends on mean offspring number in a sample . Here I used a generalized Wright-Fisher model that allows for unequal probabilities of producing offspring to evaluate behavior of Crow’s I and related indices under a wide range of sampling scenarios. Analytical and numerical results are congruent and show that rescaling the sample variance to its expected value at a fixed removes dependence of I on mean offspring number, but the result still depends on choice of . A new index is introduced, , which makes Î independent of sample without the need for variance rescaling. ΔI has a straightforward interpretation as the component of variance in relative fitness that exceeds that expected under a null model of random reproductive success. ΔI can be used to directly compare estimates of the Opportunity for Selection for samples from different studies, different sexes, and different life stages.

scholarly journals On the (un-)predictability of a large intragenic fitness landscape

2016 ◽  
Author(s):  
Claudia Bank ◽  
Sebastian Matuszewski ◽  
Ryan T. Hietpas ◽  
Jeffrey D. Jensen
Keyword(s):  
Amino Acid ◽  
Fitness Landscape ◽  
Fitness Landscapes ◽  
Specific Information ◽  
Theoretical Approaches ◽  
Elevated Salinity ◽  
Theoretical Predictions ◽  
Experimental Approaches ◽  
Deterministic Processes ◽  
Negative Epistasis

AbstractThe study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with NGS methods enable accurate and extensive studies of the fitness effects of mutations – allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape, and its implications for the predictability and repeatability of evolution.Here, we present a uniquely large multi-allelic fitness landscape comprised of 640 engineered mutants that represent all possible combinations of 13 amino-acid changing mutations at six sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multi-allelic fitness landscape. Using subsets of the data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino-acid specific epistatic hotspots, and that inference is additionally confounded by the non-random choice of mutations for experimental fitness landscapes.Author SummaryThe study of fitness landscapes is fundamentally concerned with understanding the relative roles of stochastic and deterministic processes in adaptive evolution. Here, the authors present a uniquely large and complete multi-allelic intragenic fitness landscape of 640 systematically engineered mutations in yeast Hsp90. Using a combination of traditional and recently proposed theoretical approaches, they study the accessibility of the global fitness peak, and the potential for predictability of the fitness landscape topography. They report local ruggedness of the landscape and the existence of epistatic hotspot mutations, which together make extrapolation and hence predictability inherently difficult, if mutation-specific information is not considered.

scholarly journals Selection against expression noise explains the origin of the hourglass pattern of Evo-Devo

2019 ◽  
Author(s):  
Jialin Liu ◽  
Michael Frochaux ◽  
Vincent Gardeux ◽  
Bart Deplancke ◽  
Marc Robinson-Rechavi
Keyword(s):  
Regulatory Elements ◽  
Mutational Robustness ◽  
Expression Variability ◽  
Expression Noise ◽  
Environmental Perturbations ◽  
Phylotypic Stage ◽  
Robustness To Noise ◽  
Selection For ◽  
Hourglass Model ◽  
Evo Devo

The evolution of embryological development has long been characterized by deep conservation. Both morphological and transcriptomic surveys have proposed a “hourglass” model of Evo-Devo1,2. A stage in mid-embryonic development, the phylotypic stage, is highly conserved among species within the same phylum3–7. However, the reason for this phylotypic stage is still elusive. Here we hypothesize that the phylotypic stage might be characterized by selection for robustness to noise and environmental perturbations. This could lead to mutational robustness, thus evolutionary conservation of expression and the hourglass pattern. To test this, we quantified expression variability of single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that indeed expression variability is lower at extended germband, the phylotypic stage. We explain this pattern by stronger histone modification mediated transcriptional noise control at this stage. In addition, we find evidence that histone modifications can also contribute to mutational robustness in regulatory elements. Thus, the robustness to noise does indeed contributes to robustness of gene expression to genetic variations, and to the conserved phylotypic stage.

scholarly journals Higher-fitness yeast genotypes are less robust to deleterious mutations

Science ◽  
2019 ◽  
Vol 366 (6464) ◽  
pp. 490-493 ◽  
Author(s):  
Milo S. Johnson ◽  
Alena Martsul ◽  
Sergey Kryazhimskiy ◽  
Michael M. Desai
Keyword(s):  
Second Order ◽  
Order Selection ◽  
Deleterious Mutations ◽  
Mutational Robustness ◽  
First Order ◽  
Fitness Effects ◽  
Yeast Strains ◽  
Selection For ◽  
Microbial Systems ◽  
Insertion Mutations

Natural selection drives populations toward higher fitness, but second-order selection for adaptability and mutational robustness can also influence evolution. In many microbial systems, diminishing-returns epistasis contributes to a tendency for more-fit genotypes to be less adaptable, but no analogous patterns for robustness are known. To understand how robustness varies across genotypes, we measure the fitness effects of hundreds of individual insertion mutations in a panel of yeast strains. We find that more-fit strains are less robust: They have distributions of fitness effects with lower mean and higher variance. These differences arise because many mutations have more strongly deleterious effects in faster-growing strains. This negative correlation between fitness and robustness implies that second-order selection for robustness will tend to conflict with first-order selection for fitness.

scholarly journals Bet-hedging across generations can affect the evolution of variance-sensitive strategies within generations

2019 ◽  
Vol 286 (1916) ◽  
pp. 20192070 ◽  
Author(s):  
Thomas R. Haaland ◽  
Jonathan Wright ◽  
Irja I. Ratikainen
Keyword(s):  
Life Histories ◽  
Environmental Variability ◽  
Geometric Mean ◽  
Simulation Models ◽  
Arithmetic Mean ◽  
Bet Hedging ◽  
Individual Fitness ◽  
Mean Fitness ◽  
Selection For ◽  
Independent Decision

In order to understand how organisms cope with ongoing changes in environmental variability, it is necessary to consider multiple adaptations to environmental uncertainty on different time scales. Conservative bet-hedging (CBH) represents a long-term genotype-level strategy maximizing lineage geometric mean fitness in stochastic environments by decreasing individual fitness variance, despite also lowering arithmetic mean fitness. Meanwhile, variance-prone (aka risk-prone) strategies produce greater variance in short-term payoffs, because this increases expected arithmetic mean fitness if the relationship between payoffs and fitness is accelerating. Using evolutionary simulation models, we investigate whether selection for such variance-prone strategies is counteracted by selection for bet-hedging that works to adaptively reduce fitness variance. In our model, variance proneness evolves in fine-grained environments (lower correlations among individuals in energetic state and/or payoffs), and with larger numbers of independent decision events over which resources accumulate prior to selection. Conversely, multiplicative fitness accumulation, caused by coarser environmental grain and fewer decision events selection, favours CBH via greater variance aversion. We discuss examples of variance-sensitive strategies in optimal foraging, migration, life histories and cooperative breeding using this bet-hedging perspective. By linking disparate fields of research studying adaptations to variable environments, we should be better able to understand effects of human-induced rapid environmental change.

scholarly journals Selection for thermostability can lead to the emergence of mutational robustness in an RNA virus

2010 ◽  
Vol 23 (11) ◽  
pp. 2453-2460 ◽  
Author(s):  
P. DOMINGO-CALAP ◽  
M. PEREIRA-GÓMEZ ◽  
R. SANJUÁN
Keyword(s):  
Rna Virus ◽  
Mutational Robustness ◽  
Selection For

Biophysical determinants of mutational robustness in a viral molecular fitness landscape

2017 ◽  
Author(s):  
Manasi A. Pethe ◽  
Aliza B. Rubenstein ◽  
Dmitri Zorine ◽  
Sagar D. Khare
Keyword(s):  
Fitness Landscape ◽  
Rna Virus ◽  
Fitness Landscapes ◽  
Viral Fitness ◽  
Mutational Robustness ◽  
Hcv Protease ◽  
Viral Polyprotein ◽  
Biophysical Interactions ◽  
Proteins And Peptides

Biophysical interactions between proteins and peptides are key determinants of genotype-fitness landscapes, but an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape functional landscapes remains elusive. Combining information from yeast-based library screening, next-generation sequencing and structure-based modeling, we report comprehensive sequence-energetics-function mapping of the specificity landscape of the Hepatitis C Virus (HCV) NS3/4A protease, whose function — site-specific cleavages of the viral polyprotein — is a key determinant of viral fitness. We elucidate the cleavability of 3.2 million substrate variants by the HCV protease and find extensive clustering of cleavable and uncleavable motifs in sequence space indicating mutational robustness, and thereby providing a plausible molecular mechanism to buffer the effects of low replicative fidelity of this RNA virus. Specificity landscapes of known drug-resistant variants are similarly clustered. Our results highlight the key and constraining role of molecular-level energetics in shaping plateau-like fitness landscapes from quasispecies theory.

Negative Epistasis in Experimental RNA Fitness Landscapes

2017 ◽  
Vol 85 (5-6) ◽  
pp. 159-168 ◽  
Author(s):  
Devin P. Bendixsen ◽  
Bjørn Østman ◽  
Eric J. Hayden
Keyword(s):  
Fitness Landscapes ◽  
Negative Epistasis
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