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Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G×E), gene-by-gene (G×G or epistatic), or gene- by-gene-by-environment (G×G×E) interactions. We quantified G×G×E effects inEscherichia coliacross environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single nucleotide ABR mutations previously identified to vary in the extent of G×E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G×G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments.

Simulation is a powerful tool to study polygenic trait adaptation under the influence of complex evolutionary forces. Here, we implement a forward-in-time population-genetic simulation package based on Wright-Fisher dynamics. In this package, we assume that haploid recombining populations use standing genetic variation to adapt to a sudden environmental change, exposing the population to a new fitness landscape. The fitness landscapes can contain any level of epistasis. We offer out-of-the-box implementations for five fitness landscape models. In addition, our package allows for the specification of any custom fitness landscapes and recombination maps.Availability and implementationSTUN is implemented in Rust. Its source code is available athttps://github.com/banklab/STUN, which also includes a link to the software’s manual and binary files for Linux, macOS and Windows.

How does standing genetic variation affect polygenic adaptation in recombining populations? Despite a large body of work in quantitative genetics, epistatic and weak additive fitness effects among simultaneously segregating genetic variants are difficult to capture experimentally or to predict theoretically. In this study, we simulated adaptation on fitness landscapes with tunable ruggedness driven by standing genetic variation in recombining populations. We confirmed that recombination hinders the movement of a population through a rugged fitness landscape. When surveying the effect of epistasis on the fixation of alleles, we found that the combined effects of high ruggedness and high recombination probabilities lead to preferential fixation of alleles that had a high initial frequency. This indicates that positively epistatic alleles escape from being broken down by recombination when they start at high frequency. We further extract direct selection coefficients and pairwise epistasis along the adaptive path. When taking the final fixed genotype as the reference genetic background, we observe that, along the adaptive path, beneficial direct selection appears stronger and pairwise epistasis weaker than in the underlying fitness landscape. Quantitatively, the ratio of epistasis and direct selection is smaller along the adaptive path (≈1) than expected. Thus, adaptation on a rugged fitness landscape may lead to spurious signals of direct selection generated through epistasis. Our study highlights how the interplay of epistasis and recombination constrains the adaptation of a diverse population to a new environment.

Hybridization opens a unique window for observing speciation mechanisms and is a potential engine of speciation. One controversially discussed outcome of hybridization is homoploid hybrid speciation by reciprocal sorting, where a hybrid population maintains a mixed combination of the parental genetic incompatibilities, preventing further gene exchange between the newly formed population and the two parental sources. Previous work showed that, for specific linkage architectures (i.e., the genomic location and order of hybrid incompatibilities), reciprocal sorting could reliably result in hybrid speciation. Yet, the sorting of incompatibilities creates a risk of population extinction. To understand how demographic consequences of the purging of incompatibilities interact with the formation of a hybrid species, we model an isolated hybrid population resulting from a single admixture event. We study how population size, linkage architecture and the strength of the incompatibility affect survival of the hybrid population, resolution/purging of the genetic incompatibilities and the probability of observing hybrid speciation. We demonstrate that the extinction risk is highest for intermediately strong hybrid incompatibilities. In addition, the linkage architecture displaying the highest hybrid speciation probabilities changes drastically with population size. Overall, this indicates that population dynamics can strongly affect the outcome of hybridization and the hybrid speciation probability.

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. We here investigate evolutionary rescue in an environment that fluctuates between a favorable state, in which the population grows, and a harsh state, in which the population declines. We develop a stochastic model that includes both population dynamics and genetics. We derive analytical predictions for the mean extinction time of a non-adapted population given that it is not rescued, the probability of rescue by a generalist mutation, and the mean appearance time of a rescue mutant, which we validate using numerical simulations. We find that evolutionary rescue is less (respectively more) likely if the environmental fluctuations are stochastic rather than deterministic and if the mean time between each environmental change is less (respectively more) than the mean survival time of the population in the harsh environment. We demonstrate that high equilibrium population sizes andper capitagrowth rates maximize the chances of evolutionary rescue. We show that an imperfectly harsh environment, which does not fully prevent births but makes the death rate to birth rate ratio much greater than unity, has almost the same rescue probability as a perfectly harsh environment, which fully prevents births. Finally, we put our results in the context of antimicrobial resistance and conservation biology.

After more than 100 years of generating monoculture batch culture growth curves, microbial ecologists and evolutionary biologists still lack a reference method for inferring growth rates. Our work highlights the challenges of estimating the growth rate from growth curve data and shows that inaccurate estimates of growth rates significantly impact the estimated relative fitness, a principal quantity in evolution and ecology. First, we conducted a literature review and found which different types of methods are currently used to estimate growth rates. These methods differ in the meaning of the estimated growth rate parameter. Kinetic models estimate the intrinsic growth rate µ whereas statistical methods – both model-based and model-free – estimate the maximum per capita growth rate µmax. Using math and simulations, we show the conditions in which µmax is not a good estimator of µ. Then, we demonstrate that inaccurate absolute estimates of µ is not overcome by calculating relative values. Importantly, we find that poor approximations for µ sometimes lead to wrongly classifying a beneficial mutant as deleterious. Finally, we re-analyzed four published data-sets using most of the methods found by our literature review. We detected no single best-fitting model across all experiments within a data-set and found that the Gompertz models, which were among the most commonly used, were often among the worst fitting. Our study provides suggestions for how experimenters can improve their growth rate and associated relative fitness estimates and highlights a neglected but fundamental problem for nearly everyone who studies microbial populations in the lab.

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.

Epigenetic regulation of gene expression allows for the emergence of distinct phenotypic states within the clonal population. Due to the instability of epigenetic inheritance, these phenotypes can intergenerationally switch between states in a stochastic manner. Theoretical studies of evolutionary dynamics predict that the phenotypic heterogeneity enabled by this rapid epigenetic switching between gene expression states would be favored under fluctuating environmental conditions, whereas genetic mutations, as a form of stable inheritance system, would be favored under a stable environment. To test this prediction, we engineered switcher and non-switcher yeast strains, in which the uracil biosynthesis gene URA3 is either continually expressed or switched on and off at two different rates (slow and fast switchers). Competitions between clones with an epigenetically controlled URA3 and clones without switching ability (SIR3 knockout) show that the switchers are favored in fluctuating environments. This occurs in conditions where the environments fluctuate at similar rates to the rate of switching. However, in stable environments, but also in environments with fluctuation frequency higher than the rate of switching, we observed that genetic changes dominated. Remarkably, epigenetic clones with a high, but not with a low, rate of switching can coexist with non-switchers even in a constant environment. Our study offers an experimental proof of concept that helps defining conditions of environmental fluctuation under which epigenetic switching provides an advantage.

Mutational meltdown describes an eco-evolutionary process in which the accumulation of deleterious mutations causes a fitness decline that eventually leads to the extinction of a population. Possible applications of this concept include medical treatment of RNA virus infections based on mutagenic drugs that increase the mutation rate of the pathogen. To determine the usefulness and expected success of such an antiviral treatment, estimates of the expected time to mutational meltdown are necessary. Here, we compute the extinction time of a population under high mutation rates, using both analytical approaches and stochastic simulations. Extinction is the result of three consecutive processes: (1) initial accumulation of deleterious mutations due to the increased mutation pressure; (2) consecutive loss of the fittest haplotype due to Muller’s ratchet; (3) rapid population decline towards extinction. We find accurate analytical results for the mean extinction time, which show that the deleterious mutation rate has the strongest effect on the extinction time. We confirm that intermediatesized deleterious selection coefficients minimize the extinction time. Finally, our simulations show that the variation in extinction time, given a set of parameters, is surprisingly small.

Dobzhansky-Muller incompatibilities (DMIs) are a major component of reproductive isolation between species. DMIs imply negative epistasis, and are exposed when two diverged populations hybridize. Mapping the locations of DMIs has largely relied on classical genetic mapping. Approaches to date are hampered by low power and the challenge of identifying DMI loci on the same chromosome, because strong initial linkage of parental haplotypes weakens statistical tests. Here, we propose new statistics to infer negative epistasis from haplotype frequencies in hybrid populations. When two divergent populations hybridize, the variance in heterozygosity at two loci decreases faster with time at DMI loci than at random pairs of loci. When two populations hybridize at near-even admixture proportions, the deviation of the observed variance from its expectation becomes negative for the DMI pair. This negative deviation enables us to detect intermediate to strong negative epistasis both within and between chromosomes. In practice, the detection window in hybrid populations depends on the demographic scenario, the recombination rate, and the strength of epistasis. When the initial proportion of the two parental populations is uneven, only strong DMIs can be detected with our method unless migration prevents parental haplotypes from being lost. We use the new statistics to infer candidate DMIs from three hybrid populations of swordtail fish. We identify numerous new DMI candidates, some of which are inferred to interact with several loci within and between chromosomes. Moreover, we discuss our results in the context of an expected enrichment in intrachromosomal over interchromosomal DMIs.Author SummaryGenetic incompatibility in the form of (Bateson-)Dobzhansky-Muller incompatibilities (DMIs) is an important component of reproductive isolation between species. However, the evolutionary role of DMIs during the process of speciation is contentious. DMIs occur when two or more genetic variants interact to reduce their carrier’s fitness. Once recombination combines incompatible variants in hybrids, selection acts to remove these variants from the population. One step towards addressing the evolutionary role of DMIs is to quantify the prevalence of DMIs in incipient and hybridizing species. Here, we present statistics that are sensitive to the resulting recombinant imbalance and that can indicate the location of DMIs in hybrid genomes in various demographic scenarios. We use simulations to show that the time window during which a DMI is detectable depends on its genomic location, the severity of the DMI, and the population’s demography. Importantly, our statistic distinguishes genetic associations arising due to physical linkage from those arising due to gene interactions, which allows for the inference of both inter- and intrachromosomal DMIs. Applying our statistics to three hybrid populations of swordtail fish, we confirm previously known DMIs and identify new candidate incompatibilities.

Population growth is a fundamental process in ecology, evolution, and epidemiology. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed-moment dynamics. In other words, the deterministic approach does not take into account the variability of birth times, which is particularly important at small population sizes. We evaluate different moment-closure approximations and show that they do not satisfactorily reduce the error between analytical predictions and simulated data. We present two novel solutions to the stochastic growth dynamics, one of which applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community dynamics. It provides tools for a more accurate analysis of experimental results, including the inference of important growth parameters.

Significance Malaria, the disease caused by Plasmodium spp. infection, remains a major global cause of morbidity and mortality, claiming the lives of over ∼4.5 × 105 individuals per year. Paradoxically, however, up to 98% of infected individuals survive the infection, establishing disease tolerance to malaria. We found that this host defense strategy, which does not target Plasmodium directly, relies on the capacity of renal proximal tubule epithelial cells to detoxify labile heme, a pathologic by-product of hemolysis that accumulates in plasma and urine during the blood stage of infection. This defense strategy prevents the onset of acute kidney injury, a clinical hallmark of severe malaria., Malaria, the disease caused by Plasmodium spp. infection, remains a major global cause of morbidity and mortality. Host protection from malaria relies on immune-driven resistance mechanisms that kill Plasmodium. However, these mechanisms are not sufficient per se to avoid the development of severe forms of disease. This is accomplished instead via the establishment of disease tolerance to malaria, a defense strategy that does not target Plasmodium directly. Here we demonstrate that the establishment of disease tolerance to malaria relies on a tissue damage-control mechanism that operates specifically in renal proximal tubule epithelial cells (RPTEC). This protective response relies on the induction of heme oxygenase-1 (HMOX1; HO-1) and ferritin H chain (FTH) via a mechanism that involves the transcription-factor nuclear-factor E2-related factor-2 (NRF2). As it accumulates in plasma and urine during the blood stage of Plasmodium infection, labile heme is detoxified in RPTEC by HO-1 and FTH, preventing the development of acute kidney injury, a clinical hallmark of severe malaria.

The presence of extra centrioles, termed centrosome amplification, is a hallmark of cancer. The distribution of centriole numbers within a cancer cell population appears to be at an equilibrium maintained by centriole overproduction and selection, reminiscent of mutation-selection balance. It is unknown to date if the interaction between centriole overproduction and selection can quantitatively explain the intra- and inter-population heterogeneity in centriole numbers. Here, we define mutation-selection-like models and employ a model selection approach to infer patterns of centriole overproduction and selection in a diverse panel of human cell lines. Surprisingly, we infer strong and uniform selection against any number of extra centrioles in most cell lines. Finally we assess the accuracy and precision of our inference method and find that it increases non-linearly as a function of the number of sampled cells. We discuss the biological implications of our results and how our methodology can inform future experiments., Author summary Human cells possess small structures called centrioles, which need to be duplicated and properly segregated to ensure cell viability. Paradoxically, cells with a variable number of excess centrioles are commonly found in cancer. It is thought that these cells arise from centriole overproduction and are subsequently eliminated by selection, such that their frequency is stable in the population. However, it is not known if this overproduction-selection balance is sufficient to explain the observed intra- and inter-population variation in centriole numbers. In this study, we model the cell population dynamics of abnormal centriole numbers inspired by classical evolutionary theory, and infer overproduction and selection parameters from a panel of 67 human cell lines. Surprisingly, our results indicate that the observed variability in most cell lines can be best explained by models assuming a single penalty against extra centrioles, regardless of their number, and complex overproduction “rules”, where multiple centrioles can be gained in a single event. Furthermore, we estimate that selection against extra centrioles is generally very strong. Our work presents a novel quantitative approach to analyse centriole number variation and to further our understanding of the role of centriole number abnormalities in cancer development.

Recirculating aquaculture systems (RAS), often used in fish farming, rely on microorganisms to maintain healthy water quality, nutrient cycling, animal welfare, and disease control. However, many daily operations in fish farms (e.g., stocking) may negatively affect the microorganisms' community composition and create a favorable environment for opportunistic pathogens. Currently, understanding microorganisms' communities within RAS is scarce, which presents an obstacle for pro-active system management. To better understand microorganism communities' spatial and temporal structure within fish farms using a RAS, we collected samples of filtered water and biofilm swabs from two different Swiss fish farms and two different locations within each farm. Water was collected from within one tank and the biofilter, while biofilm swabs were collected from the same tank's wall where the water sample was collected. DNA was extracted using the Purelink Microbial DNA Purification kit, and then each sample was prepared with three different primer pairs, 341F/805R (V3V4 region), 515F/806R (V4), 27F/534R (V1-3), and ran on the MiSeq platform (v3 600 cycles). The pilot study aimed to understand 1) how the microbiota composition changes regarding spatial and temporal scales within and between farms, 2) the primer effect on detected taxa, and 3) the difference between commonly-used 16s pipelines.

Epigenetic inheritance allows for the emergence of phenotypic plasticity in clonal populations and enables the rapid stochastic switching between distinct phenotypes. In natural environments, where stress conditions can recurrently fluctuate, clones with an epigenetic control of genes targeted by selection should be fitter than clones that rely solely on genetic mutation. To test this prediction, we engineered switcher and non-switcher yeast strains, where the uracil biosynthesis gene URA3 is under fluctuating selection. Competitions of clones with an epigenetically controlled URA3 with clones without switching ability (SIR3 knock-out), show that epigenetic switching dominates under rapidly changing stresses. We further show that this advantage depends both on the switching rate and the period of environmental fluctuations. Remarkably, epigenetic clones with a high, but not with a low, rate of switching can co-exist with non-switchers even under a constant selective pressure, consistent with different constraints on the evolution of the rate of epigenetic switching.

By formalizing the relationship between genotype or phenotype and fitness, fitness landscapes harbor information on molecular and evolutionary constraints. The shape of the fitness landscape determines the potential for adaptation and speciation, as well as our ability to predict evolution. Consequently, fitness landscape theory has been invoked across the natural sciences and across multiple levels of biological organization. We review here the existing literature on fitness landscape theory by describing the main types of fitness landscape models, and highlight how these are increasingly integrated into an applicable statistical framework for the study of evolution. Specifically, we demonstrate how the interpretation of experimental studies with respect to fitness landscape models enables a direct link between evolution, molecular biology, and systems biology.

Gene-environment interactions have long been theorized to influence molecular evolution. However, the environmental dependence of most mutations remains unknown. Using deep mutational scanning, we engineered yeast with all 44,604 single codon changes encoding 14,160 amino acid variants in Hsp90 and quantified growth effects under standard conditions and under five stress conditions. To our knowledge these are the largest determined comprehensive fitness maps of point mutants. The growth of many variants differed between conditions, indicating that environment can have a large impact on Hsp90 evolution. Multiple variants provided growth advantages under individual conditions, however these variants tended to exhibit growth defects in other environments. The diversity of Hsp90 sequences observed in extant eukaryotes preferentially contains variants that supported robust growth under all tested conditions. Rather than favoring substitutions in individual conditions, the long-term selective pressure on Hsp90 may have been that of fluctuating environments, leading to robustness under a variety of conditions.

This deposit is composed by the main article plus the supplementary materials of the publication. Influenza virus inflicts a heavy death toll annually and resistance to existing antiviral drugs has generated interest in the development of agents with novel mechanisms of action. Favipiravir is an antiviral drug that acts by increasing the genome-wide mutation rate of influenza A virus (IAV). Potential synergistic benefits of combining oseltamivir and favipiravir have been demonstrated in animal models of influenza, but the population-level effects of combining the drugs are unknown. In order to elucidate the underlying evolutionary processes at play, we performed genome-wide sequencing of IAV experimental populations subjected to serial passaging in vitro under a combined protocol of oseltamivir and favipiravir. We describe the interplay between mutation, selection, and genetic drift that ultimately culminates in population extinction. In particular, selective sweeps around oseltamivir resistance mutations reduce genome-wide variation while deleterious mutations hitchhike to fixation given the increased mutational load generated by favipiravir. This latter effect reduces viral fitness and accelerates extinction compared with IAV populations treated with favipiravir alone, but risks spreading both established and newly emerging mutations, including possible drug resistance mutations, if transmission occurs before the viral populations are eradicated. Office of the Assistant Secretary of Defense for Health Affairs - Peer Reviewed Medical Research Program grant: (W81XWH-15-1-0317); Defense Advanced Research Projects Agency (DARPA) Prophecy Program; Defense Sciences Office (DSO), Contract No. HR0011-11-C-0095, and D13AP00041; MediVector; European Research Council (ERC); Fuji Films; Department of Defense. info:eu-repo/semantics/publishedVersion

Bacteria generally live in species-rich communities, such as the gut microbiota. Yet little is known about bacterial evolution in natural ecosystems. Here, we followed the long-term evolution of commensal Escherichia coli in the mouse gut. We observe the emergence of mutation rate polymorphism, ranging from wild-type levels to 1,000-fold higher. By combining experiments, whole-genome sequencing, and in silico simulations, we identify the molecular causes and explore the evolutionary conditions allowing these hypermutators to emerge and coexist within the microbiota. The hypermutator phenotype is caused by mutations in DNA polymerase III proofreading and catalytic subunits, which increase mutation rate by approximately 1,000-fold and stabilise hypermutator fitness, respectively. Strong mutation rate variation persists for >1,000 generations, with coexistence between lineages carrying 4 to >600 mutations. The in vivo molecular evolution pattern is consistent with fitness effects of deleterious mutations sd ≤ 10−4/generation, assuming a constant effect or exponentially distributed effects with a constant mean. Such effects are lower than typical in vitro estimates, leading to a low mutational load, an inference that is observed in in vivo and in vitro competitions. Despite large numbers of deleterious mutations, we identify multiple beneficial mutations that do not reach fixation over long periods of time. This indicates that the dynamics of beneficial mutations are not shaped by constant positive Darwinian selection but could be explained by other evolutionary mechanisms that maintain genetic diversity. Thus, microbial evolution in the gut is likely characterised by partial sweeps of beneficial mutations combined with hitchhiking of slightly deleterious mutations, which take a long time to be purged because they impose a low mutational load. The combination of these two processes could allow for the long-term maintenance of intraspecies genetic diversity, including mutation rate polymorphism. These results are consistent with the pattern of genetic polymorphism that is emerging from metagenomics studies of the human gut microbiota, suggesting that we have identified key evolutionary processes shaping the genetic composition of this community., Weak-effect deleterious mutations and negative frequency–dependent selection, acting on beneficial mutations, shape the dynamics of molecular evolution within the mouse gut microbiota.

In this book chapter, we discuss the importance of phenotypic switching with respect to adaptation. Hereby, we focus on epigenetic mechanisms of phenotypic switching that are involved in gene expression regulation. We start by placing the discovery of phenotypic switching in the context of the history of evolutionary biology. We highlight how the controversy about whether phenotypic switching can contribute to adaptation arose. We then present a nonexhaustive list of examples of epigenetic phenotypic switching in nature. Finally, we discuss several evolutionary hypotheses about the role of phenotypic switching in adaptation to new and fluctuating environments, and give examples of how these hypotheses have been addressed experimentally.

Bacteria generally live in species-rich communities, such as the gut microbiota. Yet, little is known about bacterial evolution in natural ecosystems. Here, we followed the long-term evolution of commensalEscherichia coliin the mouse gut. We observe the emergence of polymorphism for mutation rate, ranging from wild-type levels to 1000-fold higher. By combining experiments, whole-genome sequencing andin silicosimulations, we identify the molecular causes and evolutionary conditions that allow these hypermutators to emerge and coexist within a complex microbiota. The hypermutator phenotype is caused by mutations in DNA polymerase III, which increase mutation rate by ~1000-fold (a mutation in the proofreading subunit) and stabilize hypermutator fitness (mutations in the catalytic subunit). The strong mutation rate variation persists for >1000 generations, with coexistence between lineages carrying 4 to >600 mutations. Thisin vivomolecular evolution pattern is consistent with deleterious mutations of ~0.01-0.001% fitness effects, 100 to 1000-fold lower than currentin vitroestimates. Despite large numbers of deleterious mutations, we identify multiple beneficial mutations that do not reach fixation over long periods of time. This indicates that the dynamics of beneficial mutations are not shaped by constant positive Darwinian selection but by processes leading to negative frequency-dependent or temporally fluctuating selection. Thus, microbial evolution in the gut is likely characterized by partial sweeps of beneficial mutations combined with hitchhiking of very slightly deleterious mutations, which take a long time to be purged but impose a very weak mutational load. These results are consistent with the pattern of genetic polymorphism that is emerging from metagenomics studies of the human gut microbiota, suggesting that we identified key evolutionary processes shaping the genetic composition of this community.

The rapid evolution of drug resistance remains a critical public health concern. The treatment of influenza A virus (IAV) has proven particularly challenging, due to the ability of the virus to develop resistance against current antivirals and vaccines. Here, we evaluate a novel antiviral drug therapy, favipiravir, for which the mechanism of action in IAV involves an interaction with the viral RNA-dependent RNA polymerase resulting in an effective increase in the viral mutation rate. We used an experimental evolution framework, combined with novel population genetic method development for inference from time-sampled data, to evaluate the effectiveness of favipiravir against IAV. Evaluating whole genome polymorphism data across 15 time points under multiple drug concentrations and in controls, we present the first evidence for the ability of IAV populations to effectively adapt to low concentrations of favipiravir. In contrast, under high concentrations, we observe population extinction, indicative of mutational meltdown. We discuss the observed dynamics with respect to the evolutionary forces at play and emphasize the utility of evolutionary theory to inform drug development.

A recommendation of: Matteo Tomasini, Stephan Peischl When does gene flow facilitate evolutionary rescue? 10.1101/622142

The fitness effect of a mutation can depend on both its genetic background, known as epistasis, and the prevailing external environment. Many examples of these dependencies are known, but few studies consider both aspects in combination, especially as they affect mutations that have been selected together. We examine interactions between five coevolved mutations in eight diverse environments. We find that mutations are, on average, beneficial across environments, but that there is high variation in their fitness effects, including many examples of mutations conferring a cost in some, but not other, genetic background-environment combinations. Indeed, even when global interaction trends are accounted for, specific local mutation interactions are common and differed across environments. One consequence of this dependence is that the range of trade-offs in genotype fitness across selected and alternative environments are contingent on the particular evolutionary path followed over the mutation landscape. Finally, although specific interactions were common, there was a consistent pattern of diminishing returns epistasis whereby mutation effects were less beneficial when added to genotypes of higher fitness. Our results underline that specific mutation effects are highly dependent on the combination of genetic and external environments, and support a general relationship between a genotype's current fitness and its potential to increase in fitness.

The deposited article version is the Epub Ahead of Print version of the article, posted online 22 August 2018, provided by Biorxiv. It hasn´t been submitted to peer-review. This deposit is composed by the main article. The supplementary materials can be accessed through the following link: https://www.biorxiv.org/content/early/2018/06/27/252395.figures-only Fitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies. This work was supported by Fundação Calouste Gulbenkian and an ERC Starting Grant to JDJ. I. Fragata was supported by a postdoctoral fellowship from FCT (Fundação para a Ciência e a Tecnologia) within the project JPIAMR/0001/2016. info:eu-repo/semantics/acceptedVersion

The deposited article version is a post-print and is the "Accepted Author Manuscript" provided by Wiley and posted online on 12th January 2018. This publication hasn't any creative commons license associated. This deposit is composed by the main article plus the supplementary materials of the publication. In many diploid species the sex chromosomes play a special role in mediating reproductive isolation. In haplodiploids, where females are diploid and males haploid, the whole genome behaves similarly to the X/Z chromosomes of diploids. Therefore, haplodiploid systems can serve as a model for the role of sex chromosomes in speciation and hybridization. A previously described population of Finnish Formica wood ants displays genome-wide signs of ploidally and sexually antagonistic selection resulting from hybridization. Here, hybrid females have increased survivorship but hybrid males are inviable. To understand how the unusual hybrid population may be maintained, we developed a mathematical model with hybrid incompatibility, female heterozygote advantage, recombination, and assortative mating. The rugged fitness landscape resulting from the co-occurrence of heterozygote advantage and hybrid incompatibility results in a sexual conflict in haplodiploids, which is caused by the ploidy difference. Thus, whereas heterozygote advantage always promotes long-term polymorphism in diploids, we find various outcomes in haplodiploids in which the population stabilizes either in favor of males, females, or via maximizing the number of introgressed individuals. We discuss these outcomes with respect to the potential long-term fate of the Finnish wood ant population, and provide approximations for the extension of the model to multiple incompatibilities. Moreover, we highlight the general implications of our results for speciation and hybridization in haplodiploids versus diploids, and how the described fitness relationships could contribute to the outstanding role of sex chromosomes as hotspots of sexual antagonism and genes involved in speciation. This article is protected by copyright. All rights reserved. Fundação Calouste Gulbenkian; National Science Foundation grant: (NSF PHY-1125915); Human Frontier Science Program; Finnish Cultural Foundation; Academy of Finland grant: (252411). info:eu-repo/semantics/acceptedVersion

The extinction of RNA virus populations upon application of a mutagenic drug is frequently referred to as evidence for the existence of an error threshold, above which the population cannot sustain the mutational load. To explain the extinction process after reaching this threshold, models of lethal mutagenesis have been proposed, in which extinction is described as a deterministic (and thus population size-independent) process. As a separate body of literature, the population genetics community has developed models of mutational meltdown, which focus on the stochastic (and thus population-size dependent) processes governing extinction. However, recent extensions of both models have blurred these boundaries. Here, we first clarify definitions in terms of assumptions, expectations, and relevant parameter spaces, and then assess similarities and differences. As concepts from both fields converge, we argue for a unified theoretical framework that is focused on the evolutionary processes at play, rather than dispute over terminology.

Speciation is a fundamental evolutionary process, the knowledge of which is crucial for understanding the origins of biodiversity. Genomic approaches are an increasingly important aspect of this research field. We review current understanding of genome-wide effects of accumulating reproductive isolation and of genomic properties that influence the process of speciation. Building on this work, we identify emergent trends and gaps in our understanding, propose new approaches to more fully integrate genomics into speciation research, translate speciation theory into hypotheses that are testable using genomic tools and provide an integrative definition of the field of speciation genomics.

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.

The role of adaptation in molecular evolution has been contentious for decades. Here, we shed light on the adaptive potential in Saccharomyces cerevisiae by presenting systematic fitness measurements for all possible point mutations in a region of Hsp90 under four environmental conditions. Under elevated salinity, we observe numerous beneficial mutations with growth advantages up to 7% relative to the wild type. All of these beneficial mutations were observed to be associated with high costs of adaptation. We thus demonstrate that an essential protein can harbor adaptive potential upon an environmental challenge, and report a remarkable fit of the data to a version of Fisher's geometric model that focuses on the fitness trade-offs between mutations in different environments.

International audience; A recommendation of:Noémie Harmand, Romain Gallet, Roula Jabbour-Zahab, Guillaume Martin, Thomas Lenormand. Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients Evolution, 2017, 71: 23–37. 10.1111/evo.13111

Many features of virus populations make them excellent candidates for population genetic study, including a very high rate of mutation, high levels of nucleotide diversity, exceptionally large census population sizes, and frequent positive selection. However, these attributes also mean that special care must be taken in population genetic inference. For example, highly skewed offspring distributions, frequent and severe population bottleneck events associated with infection and compartmentalization, and strong purifying selection all affect the distribution of genetic variation but are often not taken into account. Here, we draw particular attention to multiple-merger coalescent events and background selection, discuss potential misinference associated with these processes, and highlight potential avenues for better incorporating them into future population genetic analyses.