Fitness effects of beneficial mutations: the mutational landscape model in experimental evolution

2006 ◽  
Vol 16 (6) ◽  
pp. 618-623 ◽  
Author(s):  
Andrea J Betancourt ◽  
Jonathan P Bollback
Keyword(s):  
Experimental Evolution ◽  
Landscape Model ◽  
Beneficial Mutations ◽  
Mutational Landscape ◽  
Fitness Effects

An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus

2005 ◽  
Vol 37 (4) ◽  
pp. 441-444 ◽  
Author(s):  
Darin R Rokyta ◽  
Paul Joyce ◽  
S Brian Caudle ◽  
Holly A Wichman
Keyword(s):  
Empirical Test ◽  
Landscape Model ◽  
Dna Virus ◽  
Single Stranded Dna ◽  
Mutational Landscape

scholarly journals Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data

2016 ◽  
Author(s):  
Paula Tataru ◽  
Maéva Mollion ◽  
Sylvain Glemin ◽  
Thomas Bataillon
Keyword(s):  
Molecular Evolution ◽  
Evolutionary Genetics ◽  
Deleterious Mutations ◽  
Probabilistic Framework ◽  
Evolutionary Trajectory ◽  
Beneficial Mutations ◽  
Fitness Effects ◽  
Polymorphism Data ◽  
Inference Methods ◽  
Biased Estimates

ABSTRACTThe distribution of fitness effects (DFE) encompasses deleterious, neutral and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (α). Inference of DFE and α from patterns of polymorphism (SFS) and divergence data has been a longstanding goal of evolutionary genetics. A widespread assumption shared by numerous methods developed so far to infer DFE and α from such data is that beneficial mutations contribute only negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and α is only predicted by contrasting the SFS with divergence data from an outgroup. Here, we develop a hierarchical probabilistic framework that extends on previous methods and also can infer DFE and α from polymorphism data alone. We use extensive simulations to examine the performance of our method. We show that both a full DFE, comprising both deleterious and beneficial mutations, and α can be inferred without resorting to divergence data. We demonstrate that inference of DFE from polymorphism data alone can in fact provide more reliable estimates, as it does not rely on strong assumptions about a shared DFE between the outgroup and ingroup species used to obtain the SFS and divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and α. We illustrate these points using our newly developed framework, while also comparing to one of the most widely used inference methods available.

scholarly journals Predicting mutational routes to new adaptive phenotypes

2018 ◽  
Author(s):  
Peter A. Lind ◽  
Eric Libby ◽  
Jenny Herzog ◽  
Paul B. Rainey
Keyword(s):  
Mathematical Modelling ◽  
Adaptive Evolution ◽  
Experimental Evolution ◽  
Additional Data ◽  
Mutation Rates ◽  
Mechanistic Models ◽  
Short Term ◽  
Regulatory Pathways ◽  
Fitness Effects ◽  
Mutational Hotspots

AbstractPredicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium Pseudomonas fluorescens in which the genotype-to-phenotype map determining evolution of the adaptive “wrinkly spreader” (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.Impact statementA combination of genetics, experimental evolution and mathematical modelling defines information necessary to predict the outcome of short-term adaptive evolution.

scholarly journals The distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii inferred using frequency changes under experimental evolution

2021 ◽  
Author(s):  
Katharina B. Böndel ◽  
Toby Samuels ◽  
Rory J. Craig ◽  
Rob W. Ness ◽  
Nick Colegrave ◽  
...  
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Experimental Evolution ◽  
Spontaneous Mutations ◽  
Negative Effects ◽  
Serial Transfer ◽  
Fitness Effects ◽  
Markov Chain Method ◽  
Bayesian Monte Carlo ◽  
High Fraction ◽  
Frequency Changes

The distribution of fitness effects (DFE) for new mutations is fundamental for many aspects of population and quantitative genetics. In this study, we have inferred the DFE in the single-celled alga Chlamydomonas reinhardtii by estimating changes in the frequencies of 254 spontaneous mutations under experimental evolution and equating the frequency changes of linked mutations with their selection coefficients. We generated seven populations of recombinant haplotypes by crossing seven independently derived mutation accumulation lines carrying an average of 36 mutations in the homozygous state to a mutation-free strain of the same genotype. We then allowed the populations to evolve under natural selection in the laboratory by serial transfer in liquid culture. We observed substantial and repeatable changes in the frequencies of many groups of linked mutations, and, surprisingly, as many mutations were observed to increase as decrease in frequency. We developed a Bayesian Monte Carlo Markov Chain method to infer the DFE. This computes the likelihood of the observed distribution of changes of frequency, and obtains the posterior distribution of the selective effects of individual mutations, while assuming a two-sided gamma distribution of effects. We infer that the DFE is a highly leptokurtic distribution, and that approximately equal proportions of mutations have positive and negative effects on fitness. This result is consistent with what we have observed in previous work on a different C. reinhardtii strain, and suggests that a high fraction of new spontaneously arisen mutations are advantageous in a simple laboratory environment.

scholarly journals The Dynamics of Adaptive Genetic Diversity During the Early Stages of Clonal Evolution

2017 ◽  
Author(s):  
Jamie R. Blundell ◽  
Katja Schwartz ◽  
Danielle Francois ◽  
Daniel S. Fisher ◽  
Gavin Sherlock ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Mathematical Modelling ◽  
Experimental Evolution ◽  
General Feature ◽  
Clonal Evolution ◽  
Cell Populations ◽  
Fitness Effects ◽  
Microbial Infections ◽  
Adaptive Genetic Diversity ◽  
Lineage Tracking

The dynamics of genetic diversity in large clonally-evolving cell populations are poorly understood, despite having implications for the treatment of cancer and microbial infections. Here, we combine barcode lineage tracking, sequencing of adaptive clones, and mathematical modelling of mutational dynamics to understand diversity changes during experimental evolution. We find that, despite differences in beneficial mutational mechanisms and fitness effects between two environments, early adaptive genetic diversity increases predictably, driven by the expansion of many single-mutant lineages. However, a crash in diversity follows, caused by highly-fit double-mutants fed from exponentially growing single-mutants, a process closely related to the classic Luria-Delbruck experiment. The diversity crash is likely to be a general feature of clonal evolution, however its timing and magnitude is stochastic and depends on the population size, the distribution of beneficial fitness effects, and patterns of epistasis.

scholarly journals Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks

2021 ◽  
Author(s):  
Grace Avecilla ◽  
Julie Chuong ◽  
Fangfei Li ◽  
Gavin J Sherlock ◽  
David Gresham ◽  
...  
Keyword(s):  
Adaptive Evolution ◽  
Sequential Monte Carlo ◽  
De Novo ◽  
Formation Rate ◽  
De Novo Mutation ◽  
Heritable Variation ◽  
Beneficial Mutations ◽  
Fitness Effects ◽  
Simulation Based ◽  
Inference Methods

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these two parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use the observed CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based Bayesian likelihood-free inference approaches. We tested the suitability of two evolutionary models: a standard Wright-Fisher model and a chemostat growth model. We evaluated two likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models we show that NPE has several advantages over ABC-SMC and that a Wright-Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in yeast as 10-4.7 -10-4 per cell division, and a selection coefficient of 0.04 - 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our estimates using barcode lineage tracking and pairwise fitness assays. Our results are consistent with a high beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining their outsized importance in rapid adaptive evolution. More generally, our study demonstrates the utility of novel simulation-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data.

scholarly journals Reversion is most likely under high mutation supply, when compensatory mutations don’t fully restore fitness costs

2020 ◽  
Author(s):  
Pleuni S. Pennings ◽  
C. Brandon Ogbunugafor ◽  
Ruth Hershberg
Keyword(s):  
Mutation Rate ◽  
Experimental Evolution ◽  
Evolutionary Dynamics ◽  
Microbial Evolution ◽  
Compensatory Mutation ◽  
Adaptive Mutations ◽  
Fitness Effects ◽  
Theoretical Approaches ◽  
Evolution Study ◽  
Compensatory Mutations

AbstractAdaptive mutations are often associated with a fitness cost. These costs can be compensated for through the acquisition of additional mutations, or the adaptations can be lost through reversion, in settings where they are no longer favored. While the dynamics of adaptation, reversion and compensation have been central features in several studies of microbial evolution, few studies have attempted to resolve the population genetics underlying how and when either compensation or reversion occur. Specifically, questions remain regarding how certain actors—the evolution of mutators and whether compensatory mutations alleviate costs fully or partially—may influence evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from an experimental evolution study by utilizing computational and theoretical approaches towards a more refined understanding of how mutation rate and the fitness effects of compensatory mutation influence evolutionary dynamics. We find that high mutation rates increase the probability of reversion of deleterious adaptations when compensation is only partial. The existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion. Experimental results suggest that, in some contexts, compensatory mutations are not able to fully alleviate costs associated with adaption. Our findings emphasize the role of both mutation rate and the fitness effects of compensatory mutation in crafting evolutionary dynamics, and highlight the importance of population genetic theory for explaining findings from experimental evolution.

scholarly journals The Adaptive Potential of the Middle Domain of Yeast Hsp90

2020 ◽  
Author(s):  
Pamela A Cote-Hammarlof ◽  
Inês Fragata ◽  
Julia Flynn ◽  
David Mavor ◽  
Konstantin B Zeldovich ◽  
...  
Keyword(s):  
Protein Sequence ◽  
Structural Information ◽  
Chaperone Activity ◽  
Adaptive Potential ◽  
Beneficial Mutations ◽  
Fitness Effects ◽  
Middle Domain ◽  
Large Stretch ◽  
The Cost ◽  
Binding Interfaces

Abstract The distribution of fitness effects (DFEs) of new mutations across different environments quantifies the potential for adaptation in a given environment and its cost in others. So far, results regarding the cost of adaptation across environments have been mixed, and most studies have sampled random mutations across different genes. Here, we quantify systematically how costs of adaptation vary along a large stretch of protein sequence by studying the distribution of fitness effects of the same ≈2,300 amino-acid changing mutations obtained from deep mutational scanning of 119 amino acids in the middle domain of the heat shock protein Hsp90 in five environments. This region is known to be important for client binding, stabilization of the Hsp90 dimer, stabilization of the N-terminal-Middle and Middle-C-terminal interdomains, and regulation of ATPase–chaperone activity. Interestingly, we find that fitness correlates well across diverse stressful environments, with the exception of one environment, diamide. Consistent with this result, we find little cost of adaptation; on average only one in seven beneficial mutations is deleterious in another environment. We identify a hotspot of beneficial mutations in a region of the protein that is located within an allosteric center. The identified protein regions that are enriched in beneficial, deleterious, and costly mutations coincide with residues that are involved in the stabilization of Hsp90 interdomains and stabilization of client-binding interfaces, or residues that are involved in ATPase–chaperone activity of Hsp90. Thus, our study yields information regarding the role and adaptive potential of a protein sequence that complements and extends known structural information.

scholarly journals Inferring Parameters of the Distribution of Fitness Effects of New Mutations When Beneficial Mutations Are Strongly Advantageous and Rare

2020 ◽  
Vol 10 (7) ◽  
pp. 2317-2326 ◽  
Author(s):  
Tom R. Booker
Keyword(s):  
Evolutionary Genetics ◽  
Molecular Data ◽  
Substantial Contribution ◽  
Beneficial Mutations ◽  
Adaptive Mutations ◽  
Fitness Effects ◽  
Relative Contribution ◽  
Standing Variation ◽  
Accuracy And Precision ◽  
New Mutations

Characterizing the distribution of fitness effects (DFE) for new mutations is central in evolutionary genetics. Analysis of molecular data under the McDonald-Kreitman test has suggested that adaptive substitutions make a substantial contribution to between-species divergence. Methods have been proposed to estimate the parameters of the distribution of fitness effects for positively selected mutations from the unfolded site frequency spectrum (uSFS). Such methods perform well when beneficial mutations are mildly selected and frequent. However, when beneficial mutations are strongly selected and rare, they may make little contribution to standing variation and will thus be difficult to detect from the uSFS. In this study, I analyze uSFS data from simulated populations subject to advantageous mutations with effects on fitness ranging from mildly to strongly beneficial. As expected, frequent, mildly beneficial mutations contribute substantially to standing genetic variation and parameters are accurately recovered from the uSFS. However, when advantageous mutations are strongly selected and rare, there are very few segregating in populations at any one time. Fitting the uSFS in such cases leads to underestimates of the strength of positive selection and may lead researchers to false conclusions regarding the relative contribution adaptive mutations make to molecular evolution. Fortunately, the parameters for the distribution of fitness effects for harmful mutations are estimated with high accuracy and precision. The results from this study suggest that the parameters of positively selected mutations obtained by analysis of the uSFS should be treated with caution and that variability at linked sites should be used in conjunction with standing variability to estimate parameters of the distribution of fitness effects in the future.

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