An equivalence principle for the incorporation of favorable mutations in asexual populations

Evolution of evolvability in rapidly adapting populations

Mutations can alter the short-term fitness of an organism, as well as the rates and benefits of future mutations. While numerous examples of these evolvability modifiers have been observed in rapidly adapting microbial populations, existing theory struggles to predict when they will be favoured by natural selection. Here we develop a mathematical framework for predicting the fates of genetic variants that modify the rates and benefits of future mutations in linked genomic regions. We derive analytical expressions showing how the fixation probabilities of these variants depend on the size of the population and the diversity of competing mutations. We find that competition between linked mutations can dramatically enhance selection for modifiers that increase the benefits of future mutations, even when they impose a strong direct cost on fitness. However, we also find that modest direct benefits can be sufficient to drive evolutionary dead ends to fixation. Our results suggest that subtle differences in evolvability could play an important role in shaping the long-term success of genetic variants in rapidly evolving microbial populations.

Effects of periodic bottlenecks on the dynamics of adaptive evolution in microbial populations

Population bottlenecks can impact the rate of adaptation in evolving populations. On the one hand, each bottleneck reduces the genetic variation that fuels adaptation. On the other hand, each founder that survives a bottleneck can undergo more generations and leave more descendants in a resource-limited environment, which allows surviving beneficial mutations to spread more quickly. A theoretical model predicted that the rate of fitness gains should be maximized using ~8-fold dilutions. Here we investigate the impact of repeated bottlenecks on the dynamics of adaptation using numerical simulations and experimental populations of Escherichia coli. Our simulations confirm the model's prediction when populations evolve in a regime where beneficial mutations are rare and waiting times between successful mutations are long. However, more extreme dilutions maximize fitness gains in simulations when beneficial mutations are common and clonal interference prevents most of them from fixing. To examine these predictions, we propagated 48 E. coli populations with 2-, 8-, 100-, and 1000-fold dilutions for 150 days. Adaptation began earlier and fitness gains were greater with 100- and 1000-fold dilutions than with 8-fold dilutions, consistent with the simulations when beneficial mutations are common. However, the selection pressures in the 2-fold treatment were qualitatively different from the other treatments, violating a critical assumption of the model and simulations. Thus, varying the dilution factor during periodic bottlenecks can have multiple effects on the dynamics of adaptation caused by differential losses of diversity, different numbers of generations, and altered selection.

Selection for robust metabolism in domesticated yeasts is driven by adaptation to Hsp90 stress

Protein folding both promotes and constrains adaptive evolution. We uncover this surprising duality in the role of the protein-folding chaperone heat shock protein 90 (Hsp90) in maintaining the integrity of yeast metabolism amid proteotoxic stressors within industrial domestication niches. Ethanol disrupts critical Hsp90-dependent metabolic pathways and exerts strong selective pressure for redundant duplications of key genes within these pathways, yielding the classical genomic signatures of beer and bread domestication. This work demonstrates a mechanism of adaptive canalization in an ecology of major economic importance and highlights Hsp90-dependent variation as an important source of phantom heritability in complex traits.

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population's mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.

Predicting bacterial interaction outcomes from monoculture growth and supernatant assays

How to derive principles of community dynamics and stability is a central question in microbial ecology. Bottom-up experiments, in which a small number of bacterial species are mixed, have become popular to address it. However, experimental setups are typically limited because co-culture experiments are labor-intensive and species are difficult to distinguish. Here, we use a four-species bacterial community to show that information from monoculture growth and inhibitory effects induced by secreted compounds can be combined to predict the competitive rank order in the community. Specifically, integrative monoculture growth parameters allow building a preliminary competitive rank order, which is then adjusted using inhibitory effects from supernatant assays. While our procedure worked for two different media, we observed differences in species rank orders between media. We then parameterized computer simulations with our empirical data to show that higher order species interactions largely follow the dynamics predicted from pairwise interactions with one important exception. The impact of inhibitory compounds was reduced in higher order communities because their negative effects were spread across multiple target species. Altogether, we formulated three simple rules of how monoculture growth and supernatant assay data can be combined to establish a competitive species rank order in an experimental four-species community.

Exploiting Differences in Heme Biosynthesis between Bacterial Species to Screen for Novel Antimicrobials

The final three steps of heme biogenesis exhibit notable differences between di- and mono-derm bacteria. The former employs the protoporphyrin-dependent (PPD) pathway, while the latter utilizes the more recently uncovered coproporphyrin-dependent (CPD) pathway. In order to devise a rapid screen for potential inhibitors that differentiate the two pathways, the genes associated with the protoporphyrin pathway in an Escherichia coli YFP strain were replaced with those for the CPD pathway from Staphylococcus aureus (SA) through a sliding modular gene replacement recombineering strategy to generate the E. coli strain Sa-CPD-YFP. Potential inhibitors that differentially target the pathways were identified by screening compound libraries against the YFP-producing Sa-CPD-YFP strain in comparison to a CFP-producing E. coli strain. Using a mixed strain assay, inhibitors targeting either the CPD or PPD heme pathways were identified through a decrease in one fluorescent signal but not the other. An initial screen identified both azole and prodigiosin-derived compounds that were shown to specifically target the CPD pathway and which led to the accumulation of coproheme, indicating that the main target of inhibition would appear to be the coproheme decarboxylase (ChdC) enzyme. In silico modeling highlighted that these inhibitors are able to bind within the active site of ChdC.

A dynamical limit to evolutionary adaptation

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis, and find that under some circumstances this can drive populations towards the boundary state, which can thus represent a long-term evolutionary attractor.

Extreme Sensitivity of Fitness to Environmental Conditions: Lessons from #1BigBatch

The phrase "survival of the fittest" has become an iconic descriptor of how natural selection works. And yet, precisely measuring fitness, even for single-celled microbial populations growing in controlled laboratory conditions, remains a challenge. While numerous methods exist to perform these measurements, including recently developed methods utilizing DNA barcodes, all methods are limited in their precision to differentiate strains with small fitness differences. In this study, we rule out some major sources of imprecision, but still find that fitness measurements vary substantially from replicate to replicate. Our data suggest that very subtle and difficult to avoid environmental differences between replicates create systematic variation across fitness measurements. We conclude by discussing how fitness measurements should be interpreted given their extreme environment dependence. This work was inspired by the scientific community who followed us and gave us tips as we live tweeted a high-replicate fitness measurement experiment at #1BigBatch.

Identifying Targets of Selection in Laboratory Evolution Experiments

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.

Selection in a growing colony biases results of mutation accumulation experiments

Mutations provide the raw material for natural selection to act. Therefore, understanding the variety and relative frequency of different type of mutations is critical to understanding the nature of genetic diversity in a population. Mutation accumulation (MA) experiments have been used in this context to estimate parameters defining mutation rates, distribution of fitness effects (DFE), and spectrum of mutations. MA experiments can be performed with different effective population sizes. In MA experiments with bacteria, a single founder is grown to a size of a colony (~ 108). It is assumed that natural selection plays a minimal role in dictating the dynamics of colony growth. In this work, we simulate colony growth via a mathematical model, and use our model to mimic an MA experiment. We demonstrate that selection ensures that, in an MA experiment, fraction of all mutations that are beneficial is over-represented by a factor of almost two, and that the distribution of fitness effects of beneficial and deleterious mutations are inaccurately captured in an MA experiment. Given this, the estimate of mutation rates from MA experiments is non-trivial. We then perform an MA experiment with 160 lines of E. coli, and show that due to the effect of selection in a growing colony, the size and sector of a colony from which the experiment is propagated impacts the results. Overall, we demonstrate that the results of MA experiments need to be revisited taking into account the action of selection in a growing colony.

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

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 2 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 CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood-free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright-Fisher model and a chemostat model. We evaluated 2 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 the yeast Saccharomyces cerevisiae to be 10-4.7 to 10-4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods-barcode lineage tracking and pairwise fitness assays-which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network-based likelihood-free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

Population genetics of polymorphism and divergence in rapidly evolving populations

In rapidly evolving populations, numerous beneficial and deleterious mutations can arise and segregate within a population at the same time. In this regime, evolutionary dynamics cannot be analyzed using traditional population genetic approaches that assume that sites evolve independently. Instead, the dynamics of many loci must be analyzed simultaneously. Recent work has made progress by first analyzing the fitness variation within a population, and then studying how individual lineages interact with this traveling fitness wave. However, these "traveling wave" models have previously been restricted to extreme cases where selection on individual mutations is either much faster or much slower than the typical coalescent timescale Tc. In this work, we show how the traveling wave framework can be extended to intermediate regimes in which the scaled fitness effects of mutations (Tcs) are neither large nor small compared to one. This enables us to describe the dynamics of populations subject to a wide range of fitness effects, and in particular, in cases where it is not immediately clear which mutations are most important in shaping the dynamics and statistics of genetic diversity. We use this approach to derive new expressions for the fixation probabilities and site frequency spectra of mutations as a function of their scaled fitness effects, along with related results for the coalescent timescale Tc and the rate of adaptation or Muller's ratchet. We find that competition between linked mutations can have a dramatic impact on the proportions of neutral and selected polymorphisms, which is not simply summarized by the scaled selection coefficient Tcs. We conclude by discussing the implications of these results for population genetic inferences.

Bacterial age distribution in soil - Generational gaps in adjacent hot and cold spots

Resource patchiness and aqueous phase fragmentation in soil may induce large differences local growth conditions at submillimeter scales. These are translated to vast differences in bacterial age from cells dividing every thirty minutes in close proximity to plant roots to very old cells experiencing negligible growth in adjacent nutrient poor patches. In this study, we link bacterial population demographics with localized soil and hydration conditions to predict emerging generation time distributions and estimate mean bacterial cell ages using mechanistic and heuristic models of bacterial life in soil. Results show heavy-tailed distributions of generation times that resemble a power law for certain conditions, suggesting that we may find bacterial cells of vastly different ages living side by side within small soil volumes. Our results imply that individual bacteria may exist concurrently with all of their ancestors, resulting in an archive of bacterial cells with traits that have been gained (and lost) throughout time-a feature unique to microbial life. This reservoir of bacterial strains and the potential for the reemergence of rare strains with specific functions may be critical for ecosystem stability and function.

Diet leaves a genetic signature in a keystone member of the gut microbiota

Switching from a low-fat and high-fiber diet to a Western-style high-fat and high-sugar diet causes microbiota imbalances that underlay many pathological conditions (i.e., dysbiosis). Although the effects of dietary changes on microbiota composition and functions are well documented, their impact in gut bacterial evolution remains unexplored. We followed the emergence of mutations in Bacteroides thetaiotaomicron, a prevalent fiber-degrading microbiota member, upon colonization of the murine gut under different dietary regimens. B. thetaiotaomicron evolved rapidly in the gut and Western-style diet selected for mutations that promote degradation of mucin-derived glycans. Periodic dietary changes caused fluctuations in the frequency of such mutations and were associated with metabolic shifts, resulting in the maintenance of higher intraspecies genetic diversity compared to constant dietary regimens. These results show that dietary changes leave a genetic signature in microbiome members and suggest that B. thetaiotaomicron genetic diversity could be a biomarker for dietary differences among individuals.

A branching process model of evolutionary rescue

Evolutionary rescue is the process whereby a declining population may start growing again, thus avoiding extinction, via an increase in the frequency of fitter genotypes. These genotypes may either already be present in the population in small numbers, or arise by mutation as the population declines. We present a simple two-type discrete-time branching process model and use it to obtain results such as the probability of rescue, the shape of the population growth curve of a rescued population, and the time until the first rescuing mutation occurs. Comparisons are made to existing results in the literature in cases where both the mutation rate and the selective advantage of the beneficial mutations are small.

A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling

Stochastic signaling dynamics expand living cells' information processing capabilities. An increasing number of studies report that regulators encode information in their pulsatile dynamics. The evolutionary mechanisms that lead to complex signaling dynamics remain uncharacterized, perhaps because key interactions of signaling proteins are encoded in intrinsically disordered regions (IDRs), whose evolution is difficult to analyze. Here we focused on the IDR that controls the stochastic pulsing dynamics of Crz1, a transcription factor in fungi downstream of the widely conserved calcium signaling pathway. We find that Crz1 IDRs from anciently diverged fungi can all respond transiently to calcium stress; however, only Crz1 IDRs from the Saccharomyces clade support pulsatility, encode extra information, and rescue fitness in competition assays, while the Crz1 IDRs from distantly related fungi do none of the three. On the other hand, we find that Crz1 pulsing is conserved in the distantly related fungi, consistent with the evolutionary model of stabilizing selection on the signaling phenotype. Further, we show that a calcineurin docking site in a specific part of the IDRs appears to be sufficient for pulsing and show evidence for a beneficial increase in the relative calcineurin affinity of this docking site. We propose that evolutionary flexibility of functionally divergent IDRs underlies the conservation of stochastic signaling by stabilizing selection.

High-throughput characterization of mutations in genes that drive clonal evolution using multiplex adaptome capture sequencing

Understanding how cells are likely to evolve can guide medical interventions and bioengineering efforts that must contend with unwanted mutations. The adaptome of a cell-the neighborhood of genetic changes that are most likely to drive adaptation in a given environment-can be mapped by tracking rare beneficial variants during the early stages of clonal evolution. We used multiplex adaptome capture sequencing (mAdCap-seq), a procedure that combines unique molecular identifiers and hybridization-based enrichment, to characterize mutations in eight Escherichia coli genes known to be under selection in a laboratory environment. We tracked 301 mutations at frequencies as low as 0.01% and inferred the fitness effects of 240 of these mutations. There were distinct molecular signatures of selection on protein structure and function for the three genes with the most beneficial mutations. Our results demonstrate how mAdCap-seq can be used to deeply profile a targeted portion of a cell's adaptome.

MPL resolves genetic linkage in fitness inference from complex evolutionary histories

Genetic linkage causes the fate of new mutations in a population to be contingent on the genetic background on which they appear. This makes it challenging to identify how individual mutations affect fitness. To overcome this challenge, we developed marginal path likelihood (MPL), a method to infer selection from evolutionary histories that resolves genetic linkage. Validation on real and simulated data sets shows that MPL is fast and accurate, outperforming existing inference approaches. We found that resolving linkage is crucial for accurately quantifying selection in complex evolving populations, which we demonstrate through a quantitative analysis of intrahost HIV-1 evolution using multiple patient data sets. Linkage effects generated by variants that sweep rapidly through the population are particularly strong, extending far across the genome. Taken together, our results argue for the importance of resolving linkage in studies of natural selection.

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments

Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.

Sexual Dimorphism through the Lens of Genome Manipulation, Forward Genetics, and Spatiotemporal Sequencing

Sexual reproduction often leads to selection that favors the evolution of sex-limited traits or sex-specific variation for shared traits. These sexual dimorphisms manifest due to sex-specific genetic architectures and sex-biased gene expression across development, yet the molecular mechanisms underlying these patterns are largely unknown. The first step is to understand how sexual dimorphisms arise across the genotype-phenotype-fitness map. The emergence of "4D genome technologies" allows for efficient, high-throughput, and cost-effective manipulation and observations of this process. Studies of sexual dimorphism will benefit from combining these technological advances (e.g., precision genome editing, inducible transgenic systems, and single-cell RNA sequencing) with clever experiments inspired by classic designs (e.g., bulked segregant analysis, experimental evolution, and pedigree tracing). This perspective poses a synthetic view of how manipulative approaches coupled with cutting-edge observational methods and evolutionary theory are poised to uncover the molecular genetic basis of sexual dimorphism with unprecedented resolution. We outline hypothesis-driven experimental paradigms for identifying genetic mechanisms of sexual dimorphism among tissues, across development, and over evolutionary time.

Mutation bias can shape adaptation in large asexual populations experiencing clonal interference

The extended evolutionary synthesis invokes a role for development in shaping adaptive evolution, which in population genetics terms corresponds to mutation-biased adaptation. Critics have claimed that clonal interference makes mutation-biased adaptation rare. We consider the behaviour of two simultaneously adapting traits, one with larger mutation rate U, the other with larger selection coefficient s, using asexual travelling wave models. We find that adaptation is dominated by whichever trait has the faster rate of adaptation v in isolation, with the other trait subject to evolutionary stalling. Reviewing empirical claims for mutation-biased adaptation, we find that not all occur in the 'origin-fixation' regime of population genetics where v is only twice as sensitive to s as to U. In some cases, differences in U are at least ten to twelve times larger than differences in s, as needed to cause mutation-biased adaptation even in the 'multiple mutations' regime. Surprisingly, when U > s in the 'diffusive-mutation' regime, the required sensitivity ratio is also only two, despite pervasive clonal interference. Given two traits with identical v, the benefit of having higher s is surprisingly small, occurring largely when one trait is at the boundary between the origin-fixation and multiple mutations regimes.

High-throughput analysis of adaptation using barcoded strains of Saccharomyces cerevisiae

BACKGROUND

Experimental evolution of microbes can be used to empirically address a wide range of questions about evolution and is increasingly employed to study complex phenomena ranging from genetic evolution to evolutionary rescue. Regardless of experimental aims, fitness assays are a central component of this type of research, and low-throughput often limits the scope and complexity of experimental evolution studies. We created an experimental evolution system in Saccharomyces cerevisiae that utilizes genetic barcoding to overcome this challenge.

RESULTS

We first confirm that barcode insertions do not alter fitness and that barcode sequencing can be used to efficiently detect fitness differences via pooled competition-based fitness assays. Next, we examine the effects of ploidy, chemical stress, and population bottleneck size on the evolutionary dynamics and fitness gains (adaptation) in a total of 76 experimentally evolving, asexual populations by conducting 1,216 fitness assays and analyzing 532 longitudinal-evolutionary samples collected from the evolving populations. In our analysis of these data we describe the strengths of this experimental evolution system and explore sources of error in our measurements of fitness and evolutionary dynamics.

CONCLUSIONS

Our experimental treatments generated distinct fitness effects and evolutionary dynamics, respectively quantified via multiplexed fitness assays and barcode lineage tracking. These findings demonstrate the utility of this new resource for designing and improving high-throughput studies of experimental evolution. The approach described here provides a framework for future studies employing experimental designs that require high-throughput multiplexed fitness measurements.

The Landscape of Adaptive Evolution of a Gut Commensal Bacteria in Aging Mice

Aging is a complex process, with many associated time-dependent phenotypes. The gut microbiota have long been postulated as an important factor in shaping healthy aging [1, 2]. During aging, changes in the microbiota composition occur, with taxa that are rare in adults becoming dominant in the elderly [3, 4]. Increased inflammation associated with aging is also known to modulate and be modulated by the microbiota [5]. Ecological interactions are known to affect the evolution of bacteria both in vitro [6] and in vivo [7], but the extent to which these and the host age-dependent inflammatory environment can alter the pattern of evolutionary change of a gut commensal lineage is still unknown [8]. Here, we provide the first genomic analysis of such evolution in cohorts of old mice, under controlled host genetics and lifestyle conditions. We find that Escherichia coli evolution when colonizing the gut of old mice significantly differs from its evolution in young mice. Evolution toward metabolic adaptation is slower in old than young mice, and mutational targets concerning stress-related functions were found specifically in the inflamed gut of old mice. Taking the genetic basis of E. coli short-term evolution as a reflection of the environment it experiences, the sequencing data indicate that aging imposes a more stressful environment to this important colonizer of the mammalian gut.

Specific Eco-evolutionary Contexts in the Mouse Gut Reveal Escherichia coli Metabolic Versatility

Members of the gut microbiota are thought to experience strong competition for nutrients. However, how such competition shapes their evolutionary dynamics and depends on intra- and interspecies interactions is poorly understood. Here, we test the hypothesis that Escherichia coli evolution in the mouse gut is more predictable across hosts in the absence of interspecies competition than in the presence of other microbial species. In support, we observed that lrp, a gene encoding a global regulator of amino acid metabolism, was repeatedly selected in germ-free mice 2 weeks after mono-colonization by this bacterium. We established that this specific genetic adaptation increased E. coli's ability to compete for amino acids, and analysis of gut metabolites identified serine and threonine as the metabolites preferentially consumed by E. coli in the mono-colonized mouse gut. Preference for serine consumption was further supported by testing a set of mutants that showed loss of advantage of an lrp mutant impaired in serine metabolism in vitro and in vivo. Remarkably, the presence of a single additional member of the microbiota, Blautia coccoides, was sufficient to alter the gut metabolome and, consequently, the evolutionary path of E. coli. In this environment, the fitness advantage of the lrp mutant bacteria is lost, and mutations in genes involved in anaerobic respiration were selected instead, recapitulating the eco-evolutionary context from mice with a complex microbiota. Together, these results highlight the metabolic plasticity and evolutionary versatility of E. coli, tailored to the specific ecology it experiences in the gut.

Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages in bacterial populations have so far prevented a detailed elucidation of evolutionary dynamics. Here, we overcome this hurdle by developing a chromosomal-barcoding technique that allows simultaneous tracking of approximately 450,000 distinct lineages in Escherichia coli, which we use to test the effect of sub-inhibitory concentrations of common antibiotics on the evolutionary dynamics of low-frequency lineages. We find that populations lose lineage diversity at distinct rates that correspond to their antibiotic regimen. We also determine that some lineages have similar fates across independent experiments. By analysing the trajectory dynamics, we attribute the reproducible fates of these lineages to the presence of pre-existing beneficial mutations, and we demonstrate how the relative contribution of pre-existing and de novo mutations varies across drug regimens. Finally, we reproduce the observed lineage dynamics by simulations. Altogether, our results provide a valuable methodology for studying bacterial evolution as well as insights into evolution under sub-inhibitory antibiotic levels.

Predicting microbial growth in a mixed culture from growth curve data

Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current "gold standard" for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.

Distribution of fitness effects of mutations obtained from a simple genetic regulatory network model

Beneficial and deleterious mutations change an organism's fitness but the distribution of these mutational effects on fitness are unknown. Several experimental, theoretical, and computational studies have explored this question but are limited because of experimental restrictions, or disconnect with physiology. Here we attempt to characterize the distribution of fitness effects (DFE) due to mutations in a cellular regulatory motif. We use a simple mathematical model to describe the dynamics of gene expression in the lactose utilization network, and use a cost-benefit framework to link the model output to fitness. We simulate mutations by changing model parameters and computing altered fitness to obtain the DFE. We find beneficial mutations distributed exponentially, but distribution of deleterious mutations seems far more complex. In addition, we find neither the starting fitness, nor the exact location on the fitness landscape, affecting these distributions qualitatively. Lastly, we quantify epistasis in our model and find that the distribution of epistatic effects remains qualitatively conserved across different locations on the fitness landscape. Overall, we present a first attempt at exploring the specific statistical features of the fitness landscape associated with a system, by using the specific mathematical model associated with it.

Evolution of Stress-Induced Mutagenesis in the Presence of Horizontal Gene Transfer

Stress-induced mutagenesis has been observed in multiple species of bacteria and yeast. It has been suggested that in asexual populations, a mutator allele that increases the mutation rate during stress can sweep to fixation with the beneficial mutations it generates. However, even asexual microbes can undergo horizontal gene transfer and rare recombination, which typically interfere with the spread of mutator alleles. Here we examine the effect of horizontal gene transfer on the evolutionary advantage of stress-induced mutator alleles. Our results demonstrate that stress-induced mutator alleles are favored by selection even in the presence of horizontal gene transfer and more so when the mutator alleles also increase the rate of horizontal gene transfer. We suggest that when regulated by stress, mutation and horizontal gene transfer can be complementary rather than competing adaptive strategies and that stress-induced mutagenesis has important implications for evolutionary biology, ecology, and epidemiology, even in the presence of horizontal gene transfer and rare recombination.

Clonal interference can cause wavelet-like oscillations of multilocus linkage disequilibrium

Within-host adaptation of pathogens such as human immunodeficiency virus (HIV) often occurs at more than two loci. Multiple beneficial mutations may arise simultaneously on different genetic backgrounds and interfere, affecting each other's fixation trajectories. Here, we explore how these evolutionary dynamics are mirrored in multilocus linkage disequilibrium (MLD), a measure of multi-way associations between alleles. In the parameter regime corresponding to HIV, we show that deterministic early infection models induce MLD to oscillate over time in a wavelet-like fashion. We find that the frequency of these oscillations is proportional to the rate of adaptation. This signature is robust to drift, but can be eroded by high variation in fitness effects of beneficial mutations. Our findings suggest that MLD oscillations could be used as a signature of interference among multiple equally advantageous mutations and may aid the interpretation of MLD in data.

SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes

Motivation

Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution.

Results

To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution.

Availability and implementation

Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.

Supplementary information

Supplementary data are available at Bioinformatics online.

Competition experiments between Brettanomyces bruxellensis strains reveal specific adaptation to sulfur dioxide and complex interactions at intraspecies level

Recent studies have suggested a strong niche adaptation for Brettanomyces bruxellensis strains according to human-related fermentation environments, including beer, wine and bioethanol. This is further supported by a correlation between B. bruxellensis genetic grouping and tolerance to SO2, the main antimicrobial used in wine. The allotriploid AWRI1499-like cluster, in particular, shows high SO2 tolerance suggesting that the genetic configuration observed for these strains may confer a selective advantage in winemaking conditions. To test this hypothesis, we evaluated the relative selective advantage of representatives of the three main B. bruxellensis genetic groups in presence of SO2. As a proof-of-concept and using recently developed transformation cassettes, we compared strains under different SO2 concentrations using pairwise competitive fitness experiments. Our results showed that AWRI1499 is specifically adapted to environments with high SO2 concentrations compared to other B. bruxellensis wine strains, indicating a potential correlation between allotriploidisation origin and environmental adaptation in this species. Additionally, our findings suggest different types of competition between strains, such as coexistence and exclusion, revealing new insights on B. bruxellensis interactions at intraspecies level.

Effective models and the search for quantitative principles in microbial evolution

Microbes evolve rapidly. Yet they do so in idiosyncratic ways, which depend on the specific mutations that are beneficial or deleterious in a given situation. At the same time, some population-level patterns of adaptation are strikingly similar across different microbial systems, suggesting that there may also be simple, quantitative principles that unite these diverse scenarios. We review the search for simple principles in microbial evolution, ranging from the biophysical level to emergent evolutionary dynamics. A key theme has been the use of effective models, which coarse-grain over molecular and cellular details to obtain a simpler description in terms of a few effective parameters. Collectively, these theoretical approaches provide a set of quantitative principles that facilitate understanding, prediction, and potentially control of evolutionary phenomena, though formidable challenges remain due to the ecological complexity of natural populations.

Emergence of evolutionarily stable communities through eco-evolutionary tunnelling

Ecological and evolutionary dynamics of communities are inexorably intertwined. The ecological state determines the fate of newly arising mutants, and mutations that increase in frequency can reshape the ecological dynamics. Evolutionary game theory and its extensions within adaptive dynamics have been the mathematical frameworks for understanding this interplay, leading to notions such as evolutionarily stable states (ESS) in which no mutations are favoured, and evolutionary branching points near which the population diversifies. A central assumption behind these theoretical treatments has been that mutations are rare so that the ecological dynamics has time to equilibrate after every mutation. A fundamental question is whether qualitatively new phenomena can arise when mutations are frequent. Here, we describe an adaptive diversification process that robustly leads to complex ESS, despite the fact that such communities are unreachable through a step-by-step evolutionary process. Rather, the system as a whole tunnels between collective states over a short timescale. The tunnelling rate is a sharply increasing function of the rate at which mutations arise in the population. This makes the emergence of ESS communities virtually impossible in small populations, but generic in large ones. Moreover, communities emerging through this process can spatially spread as single replication units that outcompete other communities. Overall, this work provides a qualitatively new mechanism for adaptive diversification and shows that complex structures can generically evolve even when no step-by-step evolutionary path exists.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

Experimental Studies of Evolutionary Dynamics in Microbes

Evolutionary dynamics in laboratory microbial evolution experiments can be surprisingly complex. In the past two decades, observations of these dynamics have challenged simple models of adaptation and have shown that clonal interference, hitchhiking, ecological diversification, and contingency are widespread. In recent years, advances in high-throughput strain maintenance and phenotypic assays, the dramatically reduced cost of genome sequencing, and emerging methods for lineage barcoding have made it possible to observe evolutionary dynamics at unprecedented resolution. These new methods can now begin to provide detailed measurements of key aspects of fitness landscapes and of evolutionary outcomes across a range of systems. These measurements can highlight challenges to existing theoretical models and guide new theoretical work towards the complications that are most widely important.

Strong disorder leads to scale invariance in complex biological systems

Despite the innate complexity of the cell, emergent scale-invariant behavior is observed in many biological systems. We investigate one example of this phenomenon: the dynamics of large complexes in the bacterial cytoplasm. The observed dynamics of these complexes is scale invariant in three measures of dynamics: mean-squared displacement (MSD), velocity autocorrelation function, and the step-size distribution. To investigate the physical mechanism for this emergent scale invariance, we explore minimal models in which mobility is modeled as diffusion on a rough free-energy landscape in one dimension. We discover that all three scale-invariant characteristics emerge generically in the strong disorder limit. (Strong disorder is defined by the divergence of the ensemble-averaged hop time between lattice sites.) In particular, we demonstrate how the scale invariance of the relative step-size distribution can be understood from the perspective of extreme-value theory in statistics (EVT). We show that the Gumbel scale parameter is simply related to the MSD scaling parameter. The EVT mechanism of scale invariance is expected to be generic to strongly disordered systems and therefore a powerful tool for the analysis of other systems in biology and beyond.

Chromosomal barcoding as a tool for multiplexed phenotypic characterization of laboratory evolved lineages

Adaptive laboratory evolution is an important tool to evolve organisms to increased tolerance towards different physical and chemical stress. It is applied to study the evolution of antibiotic resistance as well as genetic mechanisms underlying improvements in production strains. Adaptive evolution experiments can be automated in a high-throughput fashion. However, the characterization of the resulting lineages can become a time consuming task, when the performance of each lineage is evaluated individually. Here, we present a novel method for the markerless insertion of randomized genetic barcodes into the genome of Escherichia coli using a novel dual-auxotrophic selection approach. The barcoded E. coli library allows multiplexed phenotyping of evolved strains in pooled competition experiments. We use the barcoded library in an adaptive evolution experiment; evolving resistance towards three common antibiotics. Comparing this multiplexed phenotyping with conventional susceptibility testing and growth-rate measurements we can show a significant positive correlation between the two approaches. Use of barcoded bacterial strain libraries for individual adaptive evolution experiments drastically reduces the workload of characterizing the resulting phenotypes and enables prioritization of lineages for in-depth characterization. In addition, barcoded clones open up new ways to profile community dynamics or to track lineages in vivo or situ.

Within-Host Evolution of Human Influenza Virus

The rapid global evolution of influenza virus begins with mutations that arise de novo in individual infections, but little is known about how evolution occurs within hosts. We review recent progress in understanding how and why influenza viruses evolve within human hosts. Advances in deep sequencing make it possible to measure within-host genetic diversity in both acute and chronic influenza infections. Factors like antigenic selection, antiviral treatment, tissue specificity, spatial structure, and multiplicity of infection may affect how influenza viruses evolve within human hosts. Studies of within-host evolution can contribute to our understanding of the evolutionary and epidemiological factors that shape influenza virus's global evolution.

Intrinsic adaptive value and early fate of gene duplication revealed by a bottom-up approach

The population genetic mechanisms governing the preservation of gene duplicates, especially in the critical very initial phase, have remained largely unknown. Here, we demonstrate that gene duplication confers per se a weak selective advantage in scenarios of fitness trade-offs. Through a precise quantitative description of a model system, we show that a second gene copy serves to reduce gene expression inaccuracies derived from pervasive molecular noise and suboptimal gene regulation. We then reveal that such an accuracy in the phenotype yields a selective advantage in the order of 0.1% on average, which would allow the positive selection of gene duplication in populations with moderate/large sizes. This advantage is greater at higher noise levels and intermediate concentrations of the environmental molecule, when fitness trade-offs become more evident. Moreover, we discuss how the genome rearrangement rates greatly condition the eventual fixation of duplicates. Overall, our theoretical results highlight an original adaptive value for cells carrying new-born duplicates, broadly analyze the selective conditions that determine their early fates in different organisms, and reconcile population genetics with evolution by gene duplication.

Nanowire sensors monitor bacterial growth kinetics and response to antibiotics

Miniaturized and cost-efficient methods aiming at high throughput analysis of microbes are of great importance for the surveillance and control of infectious diseases and the related issue of antimicrobial resistance. Here we demonstrate a miniature nanosensor based on a honeycomb-patterned silicon nanowire field effect transistor (FET) capable of detection of bacterial growth and antibiotic response in microbiologically relevant nutrient media. We determine the growth kinetics and metabolic state of Escherichia coli cells in undiluted media via the quantification of changes in the source-drain current caused by varying pH values. Furthermore, by measuring the time dependent profile of pH change for bacterial cultures treated with antibiotics, we demonstrate for the first time the possibility of electrically distinguishing between bacteriostatic and bactericidal drug effects. We believe that the use of such nanoscopic FET devices enables addressing parameters that are not easily accessible by conventional optical methods in a label-free format, i.e. monitoring of microbial metabolic activity or stress response.

Recurrent Reverse Evolution Maintains Polymorphism after Strong Bottlenecks in Commensal Gut Bacteria

The evolution of new strains within the gut ecosystem is poorly understood. We used a natural but controlled system to follow the emergence of intraspecies diversity of commensal Escherichia coli, during three rounds of adaptation to the mouse gut (∼1,300 generations). We previously showed that, in the first round, a strongly beneficial phenotype (loss-of-function for galactitol consumption; gat-negative) spread to >90% frequency in all colonized mice. Here, we show that this loss-of-function is repeatedly reversed when a gat-negative clone colonizes new mice. The regain of function occurs via compensatory mutation and reversion, the latter leaving no trace of past adaptation. We further show that loss-of-function adaptive mutants reevolve, after colonization with an evolved gat-positive clone. Thus, even under strong bottlenecks a regime of strong-mutation-strong-selection dominates adaptation. Coupling experiments and modeling, we establish that reverse evolution recurrently generates two coexisting phenotypes within the microbiota that can or not consume galactitol (gat-positive and gat-negative, respectively). Although the abundance of the dominant strain, the gat-negative, depends on the microbiota composition, gat-positive abundance is independent of the microbiota composition and can be precisely manipulated by supplementing the diet with galactitol. These results show that a specific diet is able to change the abundance of specific strains. Importantly, we find polymorphism for these phenotypes in indigenous Enterobacteria of mice and man. Our results demonstrate that natural selection can greatly overwhelm genetic drift at structuring the strain diversity of gut commensals and that competition for limiting resources may be a key mechanism for maintaining polymorphism in the gut.

Stress-induced mutagenesis: Stress diversity facilitates the persistence of mutator genes

Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy.

Many organisms display increased mutation or recombination rates when exposed to a stressful environment, which can increase the probability that the population acquires adaptations that allow it to avoid extinction. Because of this, it has been suggested that the increase in production rate of genetic variation is itself an adaptation. Here, we use a mathematical model to test this hypothesis. We find that this hypothesis is plausible when the environment is variable enough such that populations do not experience particular stresses too often. We provide an explicit expression for the critical time interval between exposures and discuss its implication for the evolution of resistance. Our results highlight how and when this form of evolvability can evolve by natural selection.

Commensal-to-pathogen transition: One-single transposon insertion results in two pathoadaptive traits in Escherichia coli -macrophage interaction

Escherichia coli is both a harmless commensal in the intestines of many mammals, as well as a dangerous pathogen. The evolutionary paths taken by strains of this species in the commensal-to-pathogen transition are complex and can involve changes both in the core genome, as well in the pan-genome. One way to understand the likely paths that a commensal strain of E. coli takes when evolving pathogenicity is through experimentally evolving the strain under the selective pressures that it will have to withstand as a pathogen. Here, we report that a commensal strain, under continuous pressure from macrophages, recurrently acquired a transposable element insertion, which resulted in two key phenotypic changes: increased intracellular survival, through the delay of phagosome maturation and increased ability to escape macrophages. We further show that the acquisition of the pathoadaptive traits was accompanied by small but significant changes in the transcriptome of macrophages upon infection. These results show that under constant pressures from a key component of the host immune system, namely macrophage phagocytosis, commensal E. coli rapidly acquires pathoadaptive mutations that cause transcriptome changes associated to the host-microbe duet.

Parallel evolution of influenza across multiple spatiotemporal scales

Viral variants that arise in the global influenza population begin as de novo mutations in single infected hosts, but the evolutionary dynamics that transform within-host variation to global genetic diversity are poorly understood. Here, we demonstrate that influenza evolution within infected humans recapitulates many evolutionary dynamics observed at the global scale. We deep-sequence longitudinal samples from four immunocompromised patients with long-term H3N2 influenza infections. We find parallel evolution across three scales: within individual patients, in different patients in our study, and in the global influenza population. In hemagglutinin, a small set of mutations arises independently in multiple patients. These same mutations emerge repeatedly within single patients and compete with one another, providing a vivid clinical example of clonal interference. Many of these recurrent within-host mutations also reach a high global frequency in the decade following the patient infections. Our results demonstrate surprising concordance in evolutionary dynamics across multiple spatiotemporal scales.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Experimental evolution and the dynamics of adaptation and genome evolution in microbial populations

Evolution is an on-going process, and it can be studied experimentally in organisms with rapid generations. My team has maintained 12 populations of Escherichia coli in a simple laboratory environment for >25 years and 60 000 generations. We have quantified the dynamics of adaptation by natural selection, seen some of the populations diverge into stably coexisting ecotypes, described changes in the bacteria's mutation rate, observed the new ability to exploit a previously untapped carbon source, characterized the dynamics of genome evolution and used parallel evolution to identify the genetic targets of selection. I discuss what the future might hold for this particular experiment, briefly highlight some other microbial evolution experiments and suggest how the fields of experimental evolution and microbial ecology might intersect going forward.

Optimization of lag phase shapes the evolution of a bacterial enzyme

Mutations provide the variation that drives evolution, yet their effects on fitness remain poorly understood. Here we explore how mutations in the essential enzyme adenylate kinase (Adk) of Escherichia coli affect multiple phases of population growth. We introduce a biophysical fitness landscape for these phases, showing how they depend on molecular and cellular properties of Adk. We find that Adk catalytic capacity in the cell (the product of activity and abundance) is the major determinant of mutational fitness effects. We show that bacterial lag times are at a well-defined optimum with respect to Adk's catalytic capacity, while exponential growth rates are only weakly affected by variation in Adk. Direct pairwise competitions between strains show how environmental conditions modulate the outcome of a competition where growth rates and lag times have a tradeoff, shedding light on the multidimensional nature of fitness and its importance in the evolutionary optimization of enzymes.

Clear: Composition of Likelihoods for Evolve and Resequence Experiments

The advent of next generation sequencing technologies has made whole-genome and whole-population sampling possible, even for eukaryotes with large genomes. With this development, experimental evolution studies can be designed to observe molecular evolution "in action" via evolve-and-resequence (E&R) experiments. Among other applications, E&R studies can be used to locate the genes and variants responsible for genetic adaptation. Most existing literature on time-series data analysis often assumes large population size, accurate allele frequency estimates, or wide time spans. These assumptions do not hold in many E&R studies. In this article, we propose a method-composition of likelihoods for evolve-and-resequence experiments (Clear)-to identify signatures of selection in small population E&R experiments. Clear takes whole-genome sequences of pools of individuals as input, and properly addresses heterogeneous ascertainment bias resulting from uneven coverage. Clear also provides unbiased estimates of model parameters, including population size, selection strength, and dominance, while being computationally efficient. Extensive simulations show that Clear achieves higher power in detecting and localizing selection over a wide range of parameters, and is robust to variation of coverage. We applied the Clear statistic to multiple E&R experiments, including data from a study of adaptation of Drosophila melanogaster to alternating temperatures and a study of outcrossing yeast populations, and identified multiple regions under selection with genome-wide significance.