Epistatic interactions determine the mutational pathways and coexistence of lineages in clonal Escherichia coli populations

Experimental evolution of hybrid populations to identify Dobzhansky-Muller incompatibility loci

Epistatic interactions between loci that reduce fitness in interspecies hybrids, Dobzhansky-Muller incompatibilities (DMIs), contribute genetically to the inviability and infertility within hybrid populations. It remains a challenge, however, to identify the loci that contribute to DMIs as causes of reproductive isolation between species. Here, we assess through forward simulation the power of evolve-and-resequence (E&R) experimental evolution of hybrid populations to map DMI loci. We document conditions under which such a mapping strategy may be most feasible and demonstrate how mapping power is sensitive to biologically relevant parameters such as one-way versus two-way incompatibility type, selection strength, recombination rate, and dominance interactions. We also assess the influence of parameters under direct control of an experimenter, including duration of experimental evolution and number of replicate populations. We conclude that an E&R strategy for mapping DMI loci, and other cases of epistasis, can be a viable option under some circumstances for study systems with short generation times like Caenorhabditis nematodes.

Fitness of evolving bacterial populations is contingent on deep and shallow history but only shallow history creates predictable patterns

Long-term evolution experiments have tested the importance of genetic and environmental factors in influencing evolutionary outcomes. Differences in phylogenetic history, recent adaptation to distinct environments and chance events, all influence the fitness of a population. However, the interplay of these factors on a population's evolutionary potential remains relatively unexplored. We tracked the outcome of 2000 generations of evolution of four natural isolates of Escherichia coli bacteria that were engineered to also create differences in shallow history by adding previously identified mutations selected in a separate long-term experiment. Replicate populations started from each progenitor evolved in four environments. We found that deep and shallow phylogenetic histories both contributed significantly to differences in evolved fitness, though by different amounts in different selection environments. With one exception, chance effects were not significant. Whereas the effect of deep history did not follow any detectable pattern, effects of shallow history followed a pattern of diminishing returns whereby fitter ancestors had smaller fitness increases. These results are consistent with adaptive evolution being contingent on the interaction of several evolutionary forces but demonstrate that the nature of these interactions is not fixed and may not be predictable even when the role of chance is small.

Evolutionary dynamics and structural consequences of de novo beneficial mutations and mutant lineages arising in a constant environment

BACKGROUND

Microbial evolution experiments can be used to study the tempo and dynamics of evolutionary change in asexual populations, founded from single clones and growing into large populations with multiple clonal lineages. High-throughput sequencing can be used to catalog de novo mutations as potential targets of selection, determine in which lineages they arise, and track the fates of those lineages. Here, we describe a long-term experimental evolution study to identify targets of selection and to determine when, where, and how often those targets are hit.

RESULTS

We experimentally evolved replicate Escherichia coli populations that originated from a mutator/nonsense suppressor ancestor under glucose limitation for between 300 and 500 generations. Whole-genome, whole-population sequencing enabled us to catalog 3346 de novo mutations that reached > 1% frequency. We sequenced the genomes of 96 clones from each population when allelic diversity was greatest in order to establish whether mutations were in the same or different lineages and to depict lineage dynamics. Operon-specific mutations that enhance glucose uptake were the first to rise to high frequency, followed by global regulatory mutations. Mutations related to energy conservation, membrane biogenesis, and mitigating the impact of nonsense mutations, both ancestral and derived, arose later. New alleles were confined to relatively few loci, with many instances of identical mutations arising independently in multiple lineages, among and within replicate populations. However, most never exceeded 10% in frequency and were at a lower frequency at the end of the experiment than at their maxima, indicating clonal interference. Many alleles mapped to key structures within the proteins that they mutated, providing insight into their functional consequences.

CONCLUSIONS

Overall, we find that when mutational input is increased by an ancestral defect in DNA repair, the spectrum of high-frequency beneficial mutations in a simple, constant resource-limited environment is narrow, resulting in extreme parallelism where many adaptive mutations arise but few ever go to fixation.

Learning the pattern of epistasis linking genotype and phenotype in a protein

Understanding the pattern of epistasis-the non-independence of mutations-is critical for relating genotype and phenotype. However, the combinatorial complexity of potential epistatic interactions has severely limited the analysis of this problem. Using new mutational approaches, we report a comprehensive experimental study of all 2¹³ mutants that link two phenotypically distinct variants of the Entacmaea quadricolor fluorescent protein-an opportunity to examine epistasis up to the 13^th order. The data show the existence of many high-order epistatic interactions between mutations, but also reveal extraordinary sparsity, enabling novel experimental and computational strategies for learning the relevant epistasis. We demonstrate that such information, in turn, can be used to accurately predict phenotypes in practical situations where the number of measurements is limited. Finally, we show how the observed epistasis shapes the solution space of single-mutation trajectories between the parental fluorescent proteins, informative about the protein's evolutionary potential. This work provides conceptual and experimental strategies to profoundly characterize epistasis in a protein, relevant to both natural and laboratory evolution.

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.

Bypass of genetic constraints during mutator evolution to antibiotic resistance

Genetic constraints can block many mutational pathways to optimal genotypes in real fitness landscapes, yet the extent to which this can limit evolution remains to be determined. Interestingly, mutator bacteria elevate only specific types of mutations, and therefore could be very sensitive to genetic constraints. Testing this possibility is not only clinically relevant, but can also inform about the general impact of genetic constraints in adaptation. Here, we evolved 576 populations of two mutator and one wild-type Escherichia coli to doubling concentrations of the antibiotic cefotaxime. All strains carried TEM-1, a β-lactamase enzyme well known by its low availability of mutational pathways. Crucially, one of the mutators does not elevate any of the relevant first-step mutations known to improve cefatoximase activity. Despite this, both mutators displayed a similar ability to evolve more than 1000-fold resistance. Initial adaptation proceeded in parallel through general multi-drug resistance mechanisms. High-level resistance, in contrast, was achieved through divergent paths; with the a priori inferior mutator exploiting alternative mutational pathways in PBP3, the target of the antibiotic. These results have implications for mutator management in clinical infections and, more generally, illustrate that limits to natural selection in real organisms are alleviated by the existence of multiple loci contributing to fitness.

Elucidating the molecular architecture of adaptation via evolve and resequence experiments

Evolve and resequence (E&R) experiments use experimental evolution to adapt populations to a novel environment, then next-generation sequencing to analyse genetic changes. They enable molecular evolution to be monitored in real time on a genome-wide scale. Here, we review the field of E&R experiments across diverse systems, ranging from simple non-living RNA to bacteria, yeast and the complex multicellular organism Drosophila melanogaster. We explore how different evolutionary outcomes in these systems are largely consistent with common population genetics principles. Differences in outcomes across systems are largely explained by different starting population sizes, levels of pre-existing genetic variation, recombination rates and adaptive landscapes. We highlight emerging themes and inconsistencies that future experiments must address.

Simple phenotypic sweeps hide complex genetic changes in populations

Changes in allele frequencies and the fixation of beneficial mutations are central to evolution. The precise relationship between mutational and phenotypic sweeps is poorly described however, especially when multiple alleles are involved. Here, we investigate these relationships in a bacterial population over 60 days in a glucose-limited chemostat in a large population. High coverage metagenomic analysis revealed a disconnection between smooth phenotypic sweeps and the complexity of genetic changes in the population. Phenotypic adaptation was due to convergent evolution and involved soft sweeps by 7–26 highly represented alleles of several genes in different combinations. Allele combinations spread from undetectably low baselines, indicating that minor subpopulations provide the basis of most innovations. A hard sweep was also observed, involving a single combination of rpoS, mglD, malE, sdhC, and malT mutations sweeping to greater than 95% of the population. Other mutant genes persisted but at lower abundance, including hfq, consistent with its demonstrated frequency-dependent fitness under glucose limitation. Other persistent, newly identified low-frequency mutations were in the aceF, galF, ribD and asm genes, in noncoding regulatory regions, three large indels and a tandem duplication; these were less affected by fluctuations involving more dominant mutations indicating separate evolutionary paths. Our results indicate a dynamic subpopulation structure with a minimum of 42 detectable mutations maintained over 60 days. We also conclude that the massive population-level mutation supply in combination with clonal interference leads to the soft sweeps observed, but not to the exclusion of an occasional hard sweep.

The functional basis of adaptive evolution in chemostats

Two of the central problems in biology are determining the molecular basis of adaptive evolution and understanding how cells regulate their growth. The chemostat is a device for culturing cells that provides great utility in tackling both of these problems: it enables precise control of the selective pressure under which organisms evolve and it facilitates experimental control of cell growth rate. The aim of this review is to synthesize results from studies of the functional basis of adaptive evolution in long-term chemostat selections using Escherichia coli and Saccharomyces cerevisiae. We describe the principle of the chemostat, provide a summary of studies of experimental evolution in chemostats, and use these studies to assess our current understanding of selection in the chemostat. Functional studies of adaptive evolution in chemostats provide a unique means of interrogating the genetic networks that control cell growth, which complements functional genomic approaches and quantitative trait loci (QTL) mapping in natural populations. An integrated approach to the study of adaptive evolution that accounts for both molecular function and evolutionary processes is critical to advancing our understanding of evolution. By renewing efforts to integrate these two research programs, experimental evolution in chemostats is ideally suited to extending the functional synthesis to the study of genetic networks.

Experimental microbial evolution: history and conceptual underpinnings

We chronicle and dissect the history of the field of Experimental Microbial Evolution, beginning with work by Monod. Early research was largely carried out by microbiologists and biochemists, who used experimental evolutionary change as a tool to understand structure-function relationships. These studies attracted the interest of evolutionary biologists who recognized the power of the approach to address issues such as the tempo of adaptive change, the costs and benefits of sex, parallelism, and the role which contingency plays in the evolutionary process. In the 1980s and 1990s, an ever-expanding body of microbial, physiological and biochemical data, together with new technologies for manipulating microbial genomes, allowed such questions to be addressed in ever-increasing detail. Since then, technological advances leading to low-cost, high-throughput DNA sequencing have made it possible for these and other fundamental questions in evolutionary biology to be addressed at the molecular level.

Contribution of silent mutations to thermal adaptation of RNA bacteriophage Qβ

Changes in protein function and other biological properties, such as RNA structure, are crucial for adaptation of organisms to novel or inhibitory environments. To investigate how mutations that do not alter amino acid sequence may be positively selected, we performed a thermal adaptation experiment using the single-stranded RNA bacteriophage Qβ in which the culture temperature was increased from 37.2°C to 41.2°C and finally to an inhibitory temperature of 43.6°C in a stepwise manner in three independent lines. Whole-genome analysis revealed 31 mutations, including 14 mutations that did not result in amino acid sequence alterations, in this thermal adaptation. Eight of the 31 mutations were observed in all three lines. Reconstruction and fitness analyses of Qβ strains containing only mutations observed in all three lines indicated that five mutations that did not result in amino acid sequence changes but increased the amplification ratio appeared in the course of adaptation to growth at 41.2°C. Moreover, these mutations provided a suitable genetic background for subsequent mutations, altering the fitness contribution from deleterious to beneficial. These results clearly showed that mutations that do not alter the amino acid sequence play important roles in adaptation of this single-stranded RNA virus to elevated temperature.

IMPORTANCE

Recent studies using whole-genome analysis technology suggested the importance of mutations that do not alter the amino acid sequence for adaptation of organisms to novel environmental conditions. It is necessary to investigate how these mutations may be positively selected and to determine to what degree such mutations that do not alter amino acid sequences contribute to adaptive evolution. Here, we report the roles of these silent mutations in thermal adaptation of RNA bacteriophage Qβ based on experimental evolution during which Qβ showed adaptation to growth at an inhibitory temperature. Intriguingly, four synonymous mutations and one mutation in the untranslated region that spread widely in the Qβ population during the adaptation process at moderately high temperature provided a suitable genetic background to alter the fitness contribution of subsequent mutations from deleterious to beneficial at a higher temperature.

A shift from magnitude to sign epistasis during adaptive evolution of a bacterial social trait

Although the importance of epistasis in evolution has long been recognized, remarkably little is known about the processes by which epistatic interactions evolve in real time in specific biological systems. Here, we have characterized how the epistatic fitness relationship between a social gene and an adapting genome changes radically over a short evolutionary time frame in the social bacterium Myxococcus xanthus. We show that a highly beneficial effect of this social gene in the ancestral genome is gradually reduced--and ultimately reversed into a deleterious effect--over the course of an experimental adaptive trajectory in which a primitive form of novel cooperation evolved. This reduction and reversal of a positive social allelic effect is driven solely by changes in the genetic context in which the gene is expressed as new mutations are sequentially fixed during adaptive evolution, and explicitly demonstrates a significant evolutionary change in the genetic architecture of an ecologically important social trait.

Ex uno plures: clonal reinforcement drives evolution of a simple microbial community

A major goal of genetics is to define the relationship between phenotype and genotype, while a major goal of ecology is to identify the rules that govern community assembly. Achieving these goals by analyzing natural systems can be difficult, as selective pressures create dynamic fitness landscapes that vary in both space and time. Laboratory experimental evolution offers the benefit of controlling variables that shape fitness landscapes, helping to achieve both goals. We previously showed that a clonal population of E. coli experimentally evolved under continuous glucose limitation gives rise to a genetically diverse community consisting of one clone, CV103, that best scavenges but incompletely utilizes the limiting resource, and others, CV101 and CV116, that consume its overflow metabolites. Because this community can be disassembled and reassembled, and involves cooperative interactions that are stable over time, its genetic diversity is sustained by clonal reinforcement rather than by clonal interference. To understand the genetic factors that produce this outcome, and to illuminate the community's underlying physiology, we sequenced the genomes of ancestral and evolved clones. We identified ancestral mutations in intermediary metabolism that may have predisposed the evolution of metabolic interdependence. Phylogenetic reconstruction indicates that the lineages that gave rise to this community diverged early, as CV103 shares only one Single Nucleotide Polymorphism with the other evolved clones. Underlying CV103's phenotype we identified a set of mutations that likely enhance glucose scavenging and maintain redox balance, but may do so at the expense of carbon excreted in overflow metabolites. Because these overflow metabolites serve as growth substrates that are differentially accessible to the other community members, and because the scavenging lineage shares only one SNP with these other clones, we conclude that this lineage likely served as an “engine” generating diversity by creating new metabolic niches, but not the occupants themselves.

The variability of natural systems makes it difficult to deduce how organisms' genotypes manifest as phenotypes, and how communities of interacting organisms arise. Using laboratory experimental evolution we can control this variation. We previously showed that a population of E. coli that originated from a single clone and was cultured in the presence of a single limiting resource, evolves into a stable, three-membered community, wherein one clone excretes metabolites that the others utilize as carbon sources. To discern the genetic factors at work in producing this outcome and to illuminate the community's physiology, we sequenced the genomes of the ancestral and evolved clones. We identified in the ancestor mutations that may have predisposed evolution of cross-feeding. We found that the lineages which gave rise to the community diverged early on, and that the numerically dominant lineage that best scavenges limiting glucose does so as a result of adaptive mutations that enhance glucose uptake but favor fermentative over respiratory pathways, resulting in overflow metabolites. Because this clone produces secondary resources that sustain other community members, and because it shares with them only one mutation, we conclude that it is an “engine” generating diversity by creating new niches, but not the occupants themselves.

Inferring fitness landscapes by regression produces biased estimates of epistasis

The genotype-fitness map plays a fundamental role in shaping the dynamics of evolution. However, it is difficult to directly measure a fitness landscape in practice, because the number of possible genotypes is astronomical. One approach is to sample as many genotypes as possible, measure their fitnesses, and fit a statistical model of the landscape that includes additive and pairwise interactive effects between loci. Here, we elucidate the pitfalls of using such regressions by studying artificial but mathematically convenient fitness landscapes. We identify two sources of bias inherent in these regression procedures, each of which tends to underestimate high fitnesses and overestimate low fitnesses. We characterize these biases for random sampling of genotypes as well as samples drawn from a population under selection in the Wright-Fisher model of evolutionary dynamics. We show that common measures of epistasis, such as the number of monotonically increasing paths between ancestral and derived genotypes, the prevalence of sign epistasis, and the number of local fitness maxima, are distorted in the inferred landscape. As a result, the inferred landscape will provide systematically biased predictions for the dynamics of adaptation. We identify the same biases in a computational RNA-folding landscape as well as regulatory sequence binding data treated with the same fitting procedure. Finally, we present a method to ameliorate these biases in some cases.

Mapping the fitness landscape of gene expression uncovers the cause of antagonism and sign epistasis between adaptive mutations

How do adapting populations navigate the tensions between the costs of gene expression and the benefits of gene products to optimize the levels of many genes at once? Here we combined independently-arising beneficial mutations that altered enzyme levels in the central metabolism of Methylobacterium extorquens to uncover the fitness landscape defined by gene expression levels. We found strong antagonism and sign epistasis between these beneficial mutations. Mutations with the largest individual benefit interacted the most antagonistically with other mutations, a trend we also uncovered through analyses of datasets from other model systems. However, these beneficial mutations interacted multiplicatively (i.e., no epistasis) at the level of enzyme expression. By generating a model that predicts fitness from enzyme levels we could explain the observed sign epistasis as a result of overshooting the optimum defined by a balance between enzyme catalysis benefits and fitness costs. Knowledge of the phenotypic landscape also illuminated that, although the fitness peak was phenotypically far from the ancestral state, it was not genetically distant. Single beneficial mutations jumped straight toward the global optimum rather than being constrained to change the expression phenotypes in the correlated fashion expected by the genetic architecture. Given that adaptation in nature often results from optimizing gene expression, these conclusions can be widely applicable to other organisms and selective conditions. Poor interactions between individually beneficial alleles affecting gene expression may thus compromise the benefit of sex during adaptation and promote genetic differentiation.

The pace and outcome of a series of adaptive steps in an evolving lineage depends upon how well different beneficial mutations stack on top of each other. We found that independent beneficial mutations that affected gene expression for a metabolic pathway did not work well together, and were often jointly deleterious. The most beneficial mutations interacted the most poorly with others, which was a trend we found common in other biological systems. Through generating a model that accounted for enzymatic benefits and expression costs, we uncovered that this antagonism was caused by a phenotype to fitness mapping that had an intermediate peak. This allowed us to predict the fitness effect of double mutants and to uncover that the single winning mutations tended to move straight to the peak in a single step. These findings demonstrate the importance of considering the phenotypic changes that cause nonlinear interactions between mutations upon fitness, and thus influence how populations evolve.