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 Direct inference of the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii

2019 ◽  
Author(s):  
Katharina B. Böndel ◽  
Susanne A. Kraemer ◽  
Tobias S. Samuels ◽  
Deirdre McClean ◽  
Josianne Lachapelle ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Chlamydomonas Reinhardtii ◽  
Spontaneous Mutation ◽  
Evolutionary Process ◽  
Cumulative Effect ◽  
Neutral Evolution ◽  
Spontaneous Mutations ◽  
Effective Population ◽  
Fitness Effects ◽  
Bayesian Mixture

AbstractSpontaneous mutations are the source of new genetic variation and are thus central to the evolutionary process. In molecular evolution and quantitative genetics, the nature of genetic variation depends critically on the distribution of fitness effects (DFE) of mutations. Spontaneous mutation accumulation (MA) experiments have been the principal approach for investigating the overall rate of occurrence and cumulative effect of mutations, but have not allowed the effects of individual mutations to be studied directly. Here, we crossed MA lines of the green alga Chlamydomonas reinhardtii with its unmutated ancestral strain to create haploid recombinant lines, each carrying an average of 50% of the accumulated mutations in a variety of combinations. With the aid of the genome sequences of the MA lines, we inferred the genotypes of the mutations, assayed their growth rate as a measure of fitness, and inferred the DFE using a novel Bayesian mixture model that allows the effects of individual mutations to be estimated. We infer that the DFE is highly leptokurtic (L-shaped), and that a high proportion of mutations increase fitness in the laboratory environment. The inferred distribution of effects for deleterious mutations is consistent with a strong role for nearly neutral evolution. Specifically, such a distribution predicts that nucleotide variation and genetic variation for quantitative traits will be insensitive to change in the effective population size.

scholarly journals Inferring the distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii

PLoS Biology ◽  
2019 ◽  
Vol 17 (6) ◽  
pp. e3000192 ◽  
Author(s):  
Katharina B. Böndel ◽  
Susanne A. Kraemer ◽  
Toby Samuels ◽  
Deirdre McClean ◽  
Josianne Lachapelle ◽  
...  
Keyword(s):  
Chlamydomonas Reinhardtii ◽  
Spontaneous Mutations ◽  
Fitness Effects

scholarly journals Experimental evolution of an alternating uni- and multicellular life cycle in Chlamydomonas reinhardtii

2013 ◽  
Vol 4 (1) ◽  
Author(s):  
William C. Ratcliff ◽  
Matthew D. Herron ◽  
Kathryn Howell ◽  
Jennifer T. Pentz ◽  
Frank Rosenzweig ◽  
...  
Keyword(s):  
Life Cycle ◽  
Chlamydomonas Reinhardtii ◽  
Experimental Evolution

scholarly journals Fitness Effects of Spontaneous Mutations in Picoeukaryotic Marine Green Algae

2016 ◽  
Vol 6 (7) ◽  
pp. 2063-2071 ◽  
Author(s):  
Marc Krasovec ◽  
Adam Eyre-Walker ◽  
Nigel Grimsley ◽  
Christophe Salmeron ◽  
David Pecqueur ◽  
...  
Keyword(s):  
Green Algae ◽  
Spontaneous Mutations ◽  
Fitness Effects

scholarly journals Distinct Patterns of Selective Sweep and Polygenic Adaptation in Evolve and Resequence Studies

2020 ◽  
Vol 12 (6) ◽  
pp. 890-904 ◽  
Author(s):  
Neda Barghi ◽  
Christian Schlötterer
Keyword(s):  
Experimental Evolution ◽  
Selective Sweep ◽  
Time Series Data ◽  
Empirical Studies ◽  
Series Data ◽  
Stabilizing Selection ◽  
Selection Signatures ◽  
Polygenic Adaptation ◽  
Frequency Changes ◽  
Optimum Model

Abstract In molecular population genetics, adaptation is typically thought to occur via selective sweeps, where targets of selection have independent effects on the phenotype and rise to fixation, whereas in quantitative genetics, many loci contribute to the phenotype and subtle frequency changes occur at many loci during polygenic adaptation. The sweep model makes specific predictions about frequency changes of beneficial alleles and many test statistics have been developed to detect such selection signatures. Despite polygenic adaptation is probably the prevalent mode of adaptation, because of the traditional focus on the phenotype, we are lacking a solid understanding of the similarities and differences of selection signatures under the two models. Recent theoretical and empirical studies have shown that both selective sweep and polygenic adaptation models could result in a sweep-like genomic signature; therefore, additional criteria are needed to distinguish the two models. With replicated populations and time series data, experimental evolution studies have the potential to identify the underlying model of adaptation. Using the framework of experimental evolution, we performed computer simulations to study the pattern of selected alleles for two models: 1) adaptation of a trait via independent beneficial mutations that are conditioned for fixation, that is, selective sweep model and 2) trait optimum model (polygenic adaptation), that is adaptation of a quantitative trait under stabilizing selection after a sudden shift in trait optimum. We identify several distinct patterns of selective sweep and trait optimum models in populations of different sizes. These features could provide the foundation for development of quantitative approaches to differentiate the two models.

A Bayesian Monte Carlo Markov Chain Method for Loss Models and Risk Measure Assessments

2009 ◽  
Vol 12 (03) ◽  
pp. 529-543
Author(s):  
Ling Hu ◽  
Yating Yang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Normal Distribution ◽  
Risk Measure ◽  
Markov Chain Method ◽  
Flood Loss ◽  
Bayesian Monte Carlo ◽  
Loss Models ◽  
Log Normal ◽  
Log Normal Distribution

Natural disasters are also known as catastrophes with low frequency but high damages. Typhoons and floods are the major catastrophes which lead to gargantuan losses in Asia. Once a disaster occurs, a broad region will be affected and this will result in huge social loss. If issuers or governments use the wrong loss models or risk measure indexes to price the related insurance products, they will get an inaccurate price and thus be insolvent to the claims. Previous researches often use a Log-Normal distribution to model a catastrophic loss. This is not appropriate since the characteristics of a loss distribution have some empirical facts, including the positive skewness and the heavy-tailed properties. Recently, some studies (McNeil and Frey, 2000; Rootzen and Tajvidi, 2000; Thuring et al., 2008) also point out that using Log-Normal distribution to model a characteristic loss is not suitable. Therefore, we build a typhoon and flood loss model with higher order moments and estimate the parameters through a Bayesian Monte Carlo Markov Chain method. According to the Kolmogorov-Smirnov test, we find that the Pareto distribution is more adaptive for modeling the loss of typhoon and flood. Further, we evaluate different kinds of risk measure indexes through simulating and numerical analysis. It gives the beacon to issuers or governments when they want to issue the insurance products about typhoon and flood loss.

scholarly journals Spatial structure increases the benefits of antibiotic production in Streptomyces griseus

2018 ◽  
Author(s):  
Sanne Westhoff ◽  
Simon B. Otto ◽  
Aram Swinkels ◽  
Bo Bode ◽  
Gilles P. van Wezel ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Antibiotic Production ◽  
Streptomyces Griseus ◽  
Resistant Mutant ◽  
Cell Number ◽  
Ecological Interactions ◽  
Serial Transfer ◽  
Fitness Effects ◽  
Low Concentrations

AbstractBacteria in the soil compete for limited resources to survive and proliferate. One of the ways they might do this is by producing antibiotics, but the costs of antibiotic production and their low concentrations in soils have led to uncertainty about the role of these natural products for the bacteria that produce them. Here, we examine the fitness effects of streptomycin production by the filamentous soil bacterium Streptomyces griseus and the conditions that modify its ability to invade competitors. Using pairwise competion assays, we first provide direct evidence that streptomycin production enables S. griseus to kill and invade a population of the susceptible species, S. coelicolor, but not a streptomycin-resistant mutant of this species. Next we show that the fitness benefits of streptomycin production are density-dependent, because production scales positively with cell number, and frequency-dependent, with a threshold of invasion of S. griseus at around 1%. Finally, using serial transfer experiments where spatial structure is either maintained or periodically destroyed, we show that spatial structure reduces the threshold frequency of invasion by more than 100-fold, indicating that antibiotic production can permit invasion from extreme rarity. Our results provide clear evidence that streptomycin is both an offensive and defensive weapon that facilitates invasion into occupied habitats and also protects against invasion by competitors. They also indicate that the benefits of antibiotic production rely on ecological interactions occurring at small local scales, suggesting that low antibiotic concentrations in bulk soil are unlikely to be representative of their effective concentrations in nature.

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 Fitness estimation for viral variants in the context of cellular coinfection

2021 ◽  
Author(s):  
Huisheng Zhu ◽  
Brent E Allman ◽  
Katia Koelle
Keyword(s):  
Evolutionary Dynamics ◽  
Sequence Data ◽  
Population Level ◽  
Viral Fitness ◽  
Fitness Effects ◽  
Fitness Advantage ◽  
Viral Adaptation ◽  
Zoonotic Viruses ◽  
Frequency Changes

AbstractAnimal models are frequently used to characterize the within-host dynamics of emerging zoonotic viruses. More recent studies have also deep-sequenced longitudinal viral samples originating from experimental challenges to gain a better understanding of how these viruses may evolve in vivo and between transmission events. These studies have often identified nucleotide variants that can replicate more efficiently within hosts and also transmit more effectively between hosts. Quantifying the degree to which a mutation impacts viral fitness within a host can improve identification of variants that are of particular epidemiological concern and our ability to anticipate viral adaptation at the population level. While methods have been developed to quantify the fitness effects of mutations using observed changes in allele frequencies over the course of a host’s infection, none of the existing methods account for the possibility of cellular coinfection. Here, we develop mathematical models to project variant allele frequency changes in the context of cellular coinfection and, further, integrate these models with statistical inference approaches to demonstrate how variant fitness can be estimated alongside cellular multiplicity of infection. We apply our approaches to empirical longitudinally-sampled H5N1 sequence data from ferrets. Our results indicate that previous studies may have significantly underestimated the within-host fitness advantage of viral variants. These findings underscore the importance of considering the process of cellular coinfection when studying within-host viral evolutionary dynamics.

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