Daniel P. Rice

Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations

The dynamics of adaptation determine which mutations fix in a population, and hence how reproducible evolution will be. This is central to understanding the spectra of mutations recovered in the evolution of antibiotic resistance, the response of pathogens to immune selection, and the dynamics of cancer progression. In laboratory evolution experiments, demonstrably beneficial mutations are found repeatedly, but are often accompanied by other mutations with no obvious benefit. Here we use whole-genome whole-population sequencing to examine the dynamics of genome sequence evolution at high temporal resolution in 40 replicate Saccharomyces cerevisiae populations growing in rich medium for 1,000 generations. We find pervasive genetic hitchhiking: multiple mutations arise and move synchronously through the population as mutational ‘cohorts’. Multiple clonal cohorts are often present simultaneously, competing with each other in the same population. Our results show that patterns of sequence evolution are driven by a balance between these chance effects of hitchhiking and interference, which increase stochastic variation in evolutionary outcomes, and the deterministic action of selection on individual mutations, which favours parallel evolutionary solutions in replicate populations.

Sex speeds adaptation by altering the dynamics of molecular evolution

Sex and recombination are pervasive throughout nature despite their substantial costs. Understanding the evolutionary forces that maintain these phenomena is a central challenge in biology. One longstanding hypothesis argues that sex is beneficial because recombination speeds adaptation. Theory has proposed several distinct population genetic mechanisms that could underlie this advantage. For example, sex can promote the fixation of beneficial mutations either by alleviating interference competition (the Fisher–Muller effect) or by separating them from deleterious load (the ruby in the rubbish effect). Previous experiments confirm that sex can increase the rate of adaptation, but these studies did not observe the evolutionary dynamics that drive this effect at the genomic level. Here we present the first, to our knowledge, comparison between the sequence-level dynamics of adaptation in experimental sexual and asexual Saccharomyces cerevisiae populations, which allows us to identify the specific mechanisms by which sex speeds adaptation. We find that sex alters the molecular signatures of evolution by changing the spectrum of mutations that fix, and confirm theoretical predictions that it does so by alleviating clonal interference. We also show that substantially deleterious mutations hitchhike to fixation in adapting asexual populations. In contrast, recombination prevents such mutations from fixing. Our results demonstrate that sex both speeds adaptation and alters its molecular signature by allowing natural selection to more efficiently sort beneficial from deleterious mutations.

POPULATION SUBDIVISION AND ADAPTATION IN ASEXUAL POPULATIONS OF SACCHAROMYCES CEREVISIAE

Population subdivision limits competition between individuals, which can have a profound effect on adaptation. Subdivided populations maintain more genetic diversity at any given time compared to well‐mixed populations, and thus “explore” larger parts of the genotype space. At the same time, beneficial mutations take longer to spread in such populations, and thus subdivided populations do not “exploit” discovered mutations as efficiently as well‐mixed populations. Whether subdivision inhibits or promotes adaptation in a given environment depends on the relative importance of exploration versus exploitation, which in turn depends on the structure of epistasis among beneficial mutations. Here we investigate the relative importance of exploration versus exploitation for adaptation by evolving 976 independent asexual populations of budding yeast with several degrees of geographic subdivision. We find that subdivision systematically inhibits adaptation: even the luckiest demes in subdivided populations on average fail to discover genotypes that are fitter than those discovered by well‐mixed populations. Thus, exploitation of discovered mutations is more important for adaptation in our system than a thorough exploration of the mutational neighborhood, and increasing subdivision slows adaptation.

Gene Expression Evolves under a House-of-Cards Model of Stabilizing Selection

Divergence in gene regulation is hypothesized to underlie much of phenotypic evolution, but the role of natural selection in shaping the molecular phenotype of gene expression continues to be debated. To resolve the mode of gene expression, evolution requires accessible theoretical predictions for the effect of selection over long timescales. Evolutionary quantitative genetic models of phenotypic evolution can provide such predictions, yet those predictions depend on the underlying hypotheses about the distributions of mutational and selective effects that are notoriously difficult to disentangle. Here, we draw on diverse genomic data sets including expression profiles of natural genetic variation and mutation accumulation lines, empirical estimates of genomic mutation rates, and inferences of genetic architecture to differentiate contrasting hypotheses for the roles of stabilizing selection and mutation in shaping natural expression variation. Our analysis suggests that gene expression evolves in a domain of phenotype space well fit by the House-of-Cards (HC) model. Although the strength of selection inferred is sensitive to the number of loci controlling gene expression, the model is not. The consistency of these results across evolutionary time from budding yeast through fruit fly implies that this model is general and that mutational effects on gene expression are relatively large. Empirical estimates of the genetic architecture of gene expression traits imply that selection provides modest constraints on gene expression levels for most genes, but that the potential for regulatory evolution is high. Our prediction using data from laboratory environments should encourage the collection of additional data sets allowing for more nuanced parameterizations of HC models for gene expression.

Multidrug Therapy and Evolution of Antibiotic Resistance: When Order Matters

ABSTRACT The evolution of drug resistance among pathogenic bacteria has led public health workers to rely increasingly on multidrug therapy to treat infections. Here, we compare the efficacy of combination therapy (i.e., using two antibiotics simultaneously) and sequential therapy (i.e., switching two antibiotics) in minimizing the evolution of multidrug resistance. Using in vitro experiments, we show that the sequential use of two antibiotics against Pseudomonas aeruginosa can slow down the evolution of multiple-drug resistance when the two antibiotics are used in a specific order. A simple population dynamics model reveals that using an antibiotic associated with high costs of resistance first minimizes the chance of multidrug resistance evolution during sequential therapy under limited mutation supply rate. As well as presenting a novel approach to multidrug therapy, this work shows that costs of resistance not only influences the persistence of antibiotic-resistant bacteria but also plays an important role in the emergence of resistance.

A genomic and evolutionary approach reveals non-genetic drug resistance in malaria.

BackgroundDrug resistance remains a major public health challenge for malaria treatment and eradication. Individual loci associated with drug resistance to many antimalarials have been identified, but their epistasis with other resistance mechanisms has not yet been elucidated.ResultsWe previously described two mutations in the cytoplasmic prolyl-tRNA synthetase (cPRS) gene that confer resistance to halofuginone. We describe here the evolutionary trajectory of halofuginone resistance of two independent drug resistance selections in Plasmodium falciparum. Using this novel methodology, we discover an unexpected non-genetic drug resistance mechanism that P. falciparum utilizes before genetic modification of the cPRS. P. falciparum first upregulates its proline amino acid homeostasis in response to halofuginone pressure. We show that this non-genetic adaptation to halofuginone is not likely mediated by differential RNA expression and precedes mutation or amplification of the cPRS gene. By tracking the evolution of the two drug resistance selections with whole genome sequencing, we further demonstrate that the cPRS locus accounts for the majority of genetic adaptation to halofuginone in P. falciparum. We further validate that copy-number variations at the cPRS locus also contribute to halofuginone resistance.ConclusionsWe provide a three-step model for multi-locus evolution of halofuginone drug resistance in P. falciparum. Informed by genomic approaches, our results provide the first comprehensive view of the evolutionary trajectory malaria parasites take to achieve drug resistance. Our understanding of the multiple genetic and non-genetic mechanisms of drug resistance informs how we will design and pair future anti-malarials for clinical use.

A Test for Selection Employing Quantitative Trait Locus and Mutation Accumulation Data

Evolutionary biologists attribute much of the phenotypic diversity observed in nature to the action of natural selection. However, for many phenotypic traits, especially quantitative phenotypic traits, it has been challenging to test for the historical action of selection. An important challenge for biologists studying quantitative traits, therefore, is to distinguish between traits that have evolved under the influence of strong selection and those that have evolved neutrally. Most existing tests for selection employ molecular data, but selection also leaves a mark on the genetic architecture underlying a trait. In particular, the distribution of quantitative trait locus (QTL) effect sizes and the distribution of mutational effects together provide information regarding the history of selection. Despite the increasing availability of QTL and mutation accumulation data, such data have not yet been effectively exploited for this purpose. We present a model of the evolution of QTL and employ it to formulate a test for historical selection. To provide a baseline for neutral evolution of the trait, we estimate the distribution of mutational effects from mutation accumulation experiments. We then apply a maximum-likelihood-based method of inference to estimate the range of selection strengths under which such a distribution of mutations could generate the observed QTL. Our test thus represents the first integration of population genetic theory and QTL data to measure the historical influence of selection.

The Evolutionarily Stable Distribution of Fitness Effects

The distribution of fitness effects (DFE) of new mutations is a key parameter in determining the course of evolution. This fact has motivated extensive efforts to measure the DFE or to predict it from first principles. However, just as the DFE determines the course of evolution, the evolutionary process itself constrains the DFE. Here, we analyze a simple model of genome evolution in a constant environment in which natural selection drives the population toward a dynamic steady state where beneficial and deleterious substitutions balance. The distribution of fitness effects at this steady state is stable under further evolution and provides a natural null expectation for the DFE in a population that has evolved in a constant environment for a long time. We calculate how the shape of the evolutionarily stable DFE depends on the underlying population genetic parameters. We show that, in the absence of epistasis, the ratio of beneficial to deleterious mutations of a given fitness effect obeys a simple relationship independent of population genetic details. Finally, we analyze how the stable DFE changes in the presence of a simple form of diminishing-returns epistasis.

Adaptive evolution of genomically recoded Escherichia coli

Significance The construction of an organism with an altered genetic code negatively impacted its fitness. We evolved this organism for ∼1,100 generations in the laboratory to recover fitness and learn what changes would accumulate during evolutionary trajectories toward faster growth rates. We observed several selective mutations that helped alleviate insufficient translation termination or that corrected for unintended mutations that accumulated when we originally altered the genetic code. Further observed mutations were generally adaptive in a nonrecoded background. This work bolsters our understanding of the pliability of the genetic code and will help guide future efforts seeking to recode genomes. Finally, it results in a useful strain for nonstandard amino acid incorporation in numerous contexts relevant for research and industry. Efforts are underway to construct several recoded genomes anticipated to exhibit multivirus resistance, enhanced nonstandard amino acid (nsAA) incorporation, and capability for synthetic biocontainment. Although our laboratory pioneered the first genomically recoded organism (Escherichia coli strain C321.∆A), its fitness is far lower than that of its nonrecoded ancestor, particularly in defined media. This fitness deficit severely limits its utility for nsAA-linked applications requiring defined media, such as live cell imaging, metabolic engineering, and industrial-scale protein production. Here, we report adaptive evolution of C321.∆A for more than 1,000 generations in independent replicate populations grown in glucose minimal media. Evolved recoded populations significantly exceeded the growth rates of both the ancestral C321.∆A and nonrecoded strains. We used next-generation sequencing to identify genes mutated in multiple independent populations, and we reconstructed individual alleles in ancestral strains via multiplex automatable genome engineering (MAGE) to quantify their effects on fitness. Several selective mutations occurred only in recoded evolved populations, some of which are associated with altering the translation apparatus in response to recoding, whereas others are not apparently associated with recoding, but instead correct for off-target mutations that occurred during initial genome engineering. This report demonstrates that laboratory evolution can be applied after engineering of recoded genomes to streamline fitness recovery compared with application of additional targeted engineering strategies that may introduce further unintended mutations. In doing so, we provide the most comprehensive insight to date into the physiology of the commonly used C321.∆A strain.

Resampling QTL effects in the QTL sign test leads to incongruous sensitivity to variance in effect size.

Allelic effects at quantitative trait loci (QTL) between lineages are potentially informative for indicating the action of natural selection. The QTL Sign Test uses the number of + and − alleles observed in a QTL study to infer a history of selection. This test has been constructed to condition on the phenotypic difference between the two lines in question. By applying the test to QTL data simulated under selection, we demonstrate that conditioning on the phenotypic difference results in a loss of power to reject the neutral hypothesis and marked sensitivity to variation in locus effect magnitude.