scholarly journals Shifts in mutation spectra enhance access to beneficial mutations

2020 ◽  
Author(s):  
Mrudula Sane ◽  
Gaurav D Diwan ◽  
Bhoomika A Bhat ◽  
Lindi M Wahl ◽  
Deepa Agashe
Keyword(s):  
Adaptive Evolution ◽  
Mutation Spectrum ◽  
Mutational Bias ◽  
Beneficial Mutation ◽  
Beneficial Mutations ◽  
Bacterial Phylogeny ◽  
Mutation Spectra

Biased mutation spectra are pervasive, with widely varying direction and magnitude of mutational bias. Why are unbiased spectra rare, and how do such diverse biases evolve? We find that experimentally changing the mutation spectrum increases the beneficial mutation supply, because populations sample mutational classes that were poorly explored by the ancestor. Simulations show that selection does not oppose the evolution of a mutational bias in an unbiased ancestor; but it favours changing the direction of a long-term bias. Indeed, spectrum changes in the bacterial phylogeny are frequent, typically involving reversals of ancestral bias. Thus, shifts in mutation spectra evolve under selection, and may directly alter outcomes of adaptive evolution by facilitating access to beneficial mutations.

scholarly journals The Probability of Fixation in Populations of Changing Size

Genetics ◽  
1997 ◽  
Vol 146 (2) ◽  
pp. 723-733 ◽  
Author(s):  
Sarah P Otto ◽  
Michael C Whitlock
Keyword(s):  
Diffusion Equation ◽  
Adaptive Evolution ◽  
Process Model ◽  
Branching Process ◽  
Approximate Solutions ◽  
Logistic Growth ◽  
Beneficial Mutations ◽  
Demographic Models ◽  
Deleterious Alleles ◽  
Probability Of Fixation

The rate of adaptive evolution of a population ultimately depends on the rate of incorporation of beneficial mutations. Even beneficial mutations may, however, be lost from a population since mutant individuals may, by chance, fail to reproduce. In this paper, we calculate the probability of fixation of beneficial mutations that occur in populations of changing size. We examine a number of demographic models, including a population whose size changes once, a population experiencing exponential growth or decline, one that is experiencing logistic growth or decline, and a population that fluctuates in size. The results are based on a branching process model but are shown to be approximate solutions to the diffusion equation describing changes in the probability of fixation over time. Using the diffusion equation, the probability of fixation of deleterious alleles can also be determined for populations that are changing in size. The results developed in this paper can be used to estimate the fixation flux, defined as the rate at which beneficial alleles fix within a population. The fixation flux measures the rate of adaptive evolution of a population and, as we shall see, depends strongly on changes that occur in population size.

Prevalence and Adaptive Impact of Introgression

2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Nathaniel B. Edelman ◽  
James Mallet
Keyword(s):  
Genetic Variation ◽  
Adaptive Evolution ◽  
Genetic Variants ◽  
Genomic Data ◽  
Annual Review ◽  
Publication Date ◽  
Adaptive Introgression ◽  
Changing Environments ◽  
Potential Benefits

Alleles that introgressed between species can influence the evolutionary and ecological fate of species exposed to novel environments. Hybrid offspring of different species are often unfit, and yet it has long been argued that introgression can be a potent force in evolution, especially in plants. Over the last two decades, genomic data have increasingly provided evidence that introgression is a critically important source of genetic variation and that this additional variation can be useful in adaptive evolution of both animals and plants. Here, we review factors that influence the probability that foreign genetic variants provide long-term benefits (so-called adaptive introgression) and discuss their potential benefits. We find that introgression plays an important role in adaptive evolution, particularly when a species is far from its fitness optimum, such as when they expand their range or are subject to changing environments. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Evolutionary stalling and a limit on the power of natural selection to improve a cellular module

2020 ◽  
Vol 117 (31) ◽  
pp. 18582-18590 ◽  
Author(s):  
Sandeep Venkataram ◽  
Ross Monasky ◽  
Shohreh H. Sikaroodi ◽  
Sergey Kryazhimskiy ◽  
Betul Kacar
Keyword(s):  
Escherichia Coli ◽  
Natural Selection ◽  
Adaptive Evolution ◽  
Genetic Load ◽  
Performance Theory ◽  
Biological Functions ◽  
Beneficial Mutations ◽  
Translation Machinery ◽  
Adaptive Mutations ◽  
Organismal Fitness

Cells consist of molecular modules which perform vital biological functions. Cellular modules are key units of adaptive evolution because organismal fitness depends on their performance. Theory shows that in rapidly evolving populations, such as those of many microbes, adaptation is driven primarily by common beneficial mutations with large effects, while other mutations behave as if they are effectively neutral. As a consequence, if a module can be improved only by rare and/or weak beneficial mutations, its adaptive evolution would stall. However, such evolutionary stalling has not been empirically demonstrated, and it is unclear to what extent stalling may limit the power of natural selection to improve modules. Here we empirically characterize how natural selection improves the translation machinery (TM), an essential cellular module. We experimentally evolved populations ofEscherichia coliwith genetically perturbed TMs for 1,000 generations. Populations with severe TM defects initially adapted via mutations in the TM, but TM adaptation stalled within about 300 generations. We estimate that the genetic load in our populations incurred by residual TM defects ranges from 0.5 to 19%. Finally, we found evidence that both epistasis and the depletion of the pool of beneficial mutations contributed to evolutionary stalling. Our results suggest that cellular modules may not be fully optimized by natural selection despite the availability of adaptive mutations.

scholarly journals Hitchhiking and epistasis give rise to cohort dynamics in adapting populations

2017 ◽  
Vol 114 (31) ◽  
pp. 8330-8335 ◽  
Author(s):  
Sean W. Buskirk ◽  
Ryan Emily Peace ◽  
Gregory I. Lang
Keyword(s):  
Adaptive Evolution ◽  
Driving Force ◽  
Parallel Evolution ◽  
Low Frequency ◽  
Target Size ◽  
Driver Mutations ◽  
Beneficial Mutations ◽  
Large Target ◽  
Asexual Populations ◽  
Comprehensive Study

Beneficial mutations are the driving force of adaptive evolution. In asexual populations, the identification of beneficial alleles is confounded by the presence of genetically linked hitchhiker mutations. Parallel evolution experiments enable the recognition of common targets of selection; yet these targets are inherently enriched for genes of large target size and mutations of large effect. A comprehensive study of individual mutations is necessary to create a realistic picture of the evolutionarily significant spectrum of beneficial mutations. Here we use a bulk-segregant approach to identify the beneficial mutations across 11 lineages of experimentally evolved yeast populations. We report that nearly 80% of detected mutations have no discernible effects on fitness and less than 1% are deleterious. We determine the distribution of driver and hitchhiker mutations in 31 mutational cohorts, groups of mutations that arise synchronously from low frequency and track tightly with one another. Surprisingly, we find that one-third of cohorts lack identifiable driver mutations. In addition, we identify intracohort synergistic epistasis between alleles of hsl7 and kel1, which arose together in a low-frequency lineage.

scholarly journals Adaptation, Clonal Interference, and Frequency-Dependent Interactions in a Long-Term Evolution Experiment with Escherichia coli

2015 ◽  
Author(s):  
Rohan Maddamsetti ◽  
Richard E. Lenski ◽  
Jeffrey E. Barrick
Keyword(s):  
Escherichia Coli ◽  
Frequency Dependence ◽  
Evolutionary Dynamics ◽  
Selective Sweeps ◽  
Clonal Interference ◽  
Beneficial Mutations ◽  
Frequency Dependent ◽  
Evolution Experiment ◽  
History Of

Twelve replicate populations of Escherichia coli have been evolving in the laboratory for more than 25 years and 60,000 generations. We analyzed bacteria from whole-population samples frozen every 500 generations through 20,000 generations for one well-studied population, called Ara???1. By tracking 42 known mutations in these samples, we reconstructed the history of this population???s genotypic evolution over this period. The evolutionary dynamics of Ara???1 show strong evidence of selective sweeps as well as clonal interference between competing lineages bearing different beneficial mutations. In some cases, sets of several mutations approached fixation simultaneously, often conveying no information about their order of origination; we present several possible explanations for the existence of these mutational cohorts. Against a backdrop of rapid selective sweeps both earlier and later, we found that two clades coexisted for over 6000 generations before one drove the other extinct. In that time, at least nine mutations arose in the clade that prevailed. We found evidence that the clades evolved a frequency-dependent interaction, which prevented the competitive exclusion of either clade, but which eventually collapsed as beneficial mutations accumulated in the clade that prevailed. Clonal interference and frequency dependence can occur even in the simplest microbial populations. Furthermore, frequency dependence may generate dynamics that extend the period of coexistence that would otherwise be sustained by clonal interference alone.

scholarly journals Variation of Burkholderia cenocepacia cell wall morphology and mechanical properties during cystic fibrosis lung infection, assessed by atomic force microscopy

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
A. Amir Hassan ◽  
Miguel V. Vitorino ◽  
Tiago Robalo ◽  
Mário S. Rodrigues ◽  
Isabel Sá-Correia
Keyword(s):  
Mechanical Properties ◽  
Cystic Fibrosis ◽  
Atomic Force Microscopy ◽  
Cell Wall ◽  
Adaptive Evolution ◽  
Burkholderia Cenocepacia ◽  
Force Microscopy ◽  
Atomic Force ◽  
Morphology And Mechanical Properties

Abstract The influence that Burkholderia cenocepacia adaptive evolution during long-term infection in cystic fibrosis (CF) patients has on cell wall morphology and mechanical properties is poorly understood despite their crucial role in cell physiology, persistent infection and pathogenesis. Cell wall morphology and physical properties of three B. cenocepacia isolates collected from a CF patient over a period of 3.5 years were compared using atomic force microscopy (AFM). These serial clonal variants include the first isolate retrieved from the patient and two late isolates obtained after three years of infection and before the patient’s death with cepacia syndrome. A consistent and progressive decrease of cell height and a cell shape evolution during infection, from the typical rods to morphology closer to cocci, were observed. The images of cells grown in biofilms showed an identical cell size reduction pattern. Additionally, the apparent elasticity modulus significantly decreases from the early isolate to the last clonal variant retrieved from the patient but the intermediary highly antibiotic resistant clonal isolate showed the highest elasticity values. Concerning the adhesion of bacteria surface to the AFM tip, the first isolate was found to adhere better than the late isolates whose lipopolysaccharide (LPS) structure loss the O-antigen (OAg) during CF infection. The OAg is known to influence Gram-negative bacteria adhesion and be an important factor in B. cenocepacia adaptation to chronic infection. Results reinforce the concept of the occurrence of phenotypic heterogeneity and adaptive evolution, also at the level of cell size, form, envelope topography and physical properties during long-term infection.

scholarly journals Phenotypic plasticity and adaptive evolution contribute to advancing flowering phenology in response to climate change

2012 ◽  
Vol 279 (1743) ◽  
pp. 3843-3852 ◽  
Author(s):  
Jill T. Anderson ◽  
David W. Inouye ◽  
Amy M. McKinney ◽  
Robert I. Colautti ◽  
Tom Mitchell-Olds
Keyword(s):  
Climate Change ◽  
Phenotypic Plasticity ◽  
Adaptive Evolution ◽  
Field Experiments ◽  
Flowering Phenology ◽  
Directional Selection ◽  
Major Life ◽  
Quantitative Genetic ◽  
Evolutionary Response

Anthropogenic climate change has already altered the timing of major life-history transitions, such as the initiation of reproduction. Both phenotypic plasticity and adaptive evolution can underlie rapid phenological shifts in response to climate change, but their relative contributions are poorly understood. Here, we combine a continuous 38 year field survey with quantitative genetic field experiments to assess adaptation in the context of climate change. We focused on Boechera stricta (Brassicaeae), a mustard native to the US Rocky Mountains. Flowering phenology advanced significantly from 1973 to 2011, and was strongly associated with warmer temperatures and earlier snowmelt dates. Strong directional selection favoured earlier flowering in contemporary environments (2010–2011). Climate change could drive this directional selection, and promote even earlier flowering as temperatures continue to increase. Our quantitative genetic analyses predict a response to selection of 0.2 to 0.5 days acceleration in flowering per generation, which could account for more than 20 per cent of the phenological change observed in the long-term dataset. However, the strength of directional selection and the predicted evolutionary response are likely much greater now than even 30 years ago because of rapidly changing climatic conditions. We predict that adaptation will likely be necessary for long-term in situ persistence in the context of climate change.

scholarly journals The effect of weak clonal interference on average fitness trajectories in the presence of macroscopic epistasis

2021 ◽  
Author(s):  
Yipei Guo ◽  
Ariel Amir
Keyword(s):  
Mutation Rate ◽  
Fitness Landscape ◽  
Large Population ◽  
Beneficial Mutation ◽  
Clonal Interference ◽  
Beneficial Mutations ◽  
Speed Up ◽  
Fixation Probabilities ◽  
Population Competition ◽  
Open Question

Adaptation dynamics on fitness landscapes is often studied theoretically in the strong-selection, weak-mutation (SSWM) regime. However, in a large population, multiple beneficial mutants can emerge before any of them fixes in the population. Competition between mutants is known as clonal interference, and how it affects the form of long-term fitness trajectories in the presence of epistasis is an open question. Here, by considering how changes in fixation probabilities arising from weak clonal interference affect the dynamics of adaptation on fitness-parameterized landscapes, we find that the change in the form of fitness trajectory arises only through changes in the supply of beneficial mutations (or equivalently, the beneficial mutation rate). Furthermore, a depletion of beneficial mutations as a population climbs up the fitness landscape can speed up the functional form of the fitness trajectory, while an enhancement of the beneficial mutation rate does the opposite of slowing down the form of the dynamics. Our findings suggest that by carrying out evolution experiments in both regimes (with and without clonal interference), one could potentially distinguish the different sources of macroscopic epistasis (fitness effect of mutations vs. change in fraction of beneficial mutations).

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.

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