Negative epistasis between beneficial mutations in an evolving bacterial population

Rates of fitness decline and rebound suggest pervasive epistasis

Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of −0.01, and an effective number of traits nine in mutS⁻E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models.

Adaptation of Enterococcus faecalis to daptomycin reveals an ordered progression to resistance

With increasing numbers of hospital-acquired antibiotic resistant infections each year and staggering health care costs, there is a clear need for new antimicrobial agents, as well as novel strategies to extend their clinical efficacy. While genomic studies have provided a wealth of information about the alleles associated with adaptation to antibiotics, they do not provide essential information about the relative importance of genomic changes, their order of appearance, or potential epistatic relationships between adaptive changes. Here we used quantitative experimental evolution of a single polymorphic population in continuous culture with whole-genome sequencing and allelic frequency measurements to study daptomycin (DAP) resistance in the vancomycin-resistant clinical pathogen Enterococcus faecalis S613. Importantly, we sustained both planktonic and nonplanktonic (i.e., biofilm) populations in coculture as the concentration of antibiotic was raised, facilitating the development of more ecological complexity than is typically observed in laboratory evolution. Quantitative experimental evolution revealed a clear order and hierarchy of genetic changes leading to resistance, the signaling and metabolic pathways responsible, and the relative importance of these mutations to the evolution of DAP resistance. Despite the relative simplicity of this ex vivo approach compared to the ecological complexity of the human body, we showed that experimental evolution allows for rapid identification of clinically relevant adaptive molecular pathways and new targets for drug design in pathogens.

Experiments on the role of deleterious mutations as stepping stones in adaptive evolution

Many evolutionary studies assume that deleterious mutations necessarily impede adaptive evolution. However, a later mutation that is conditionally beneficial may interact with a deleterious predecessor before it is eliminated, thereby providing access to adaptations that might otherwise be inaccessible. It is unknown whether such sign-epistatic recoveries are inconsequential events or an important factor in evolution, owing to the difficulty of monitoring the effects and fates of all mutations during experiments with biological organisms. Here, we used digital organisms to compare the extent of adaptive evolution in populations when deleterious mutations were disallowed with control populations in which such mutations were allowed. Significantly higher fitness levels were achieved over the long term in the control populations because some of the deleterious mutations served as stepping stones across otherwise impassable fitness valleys. As a consequence, initially deleterious mutations facilitated the evolution of complex, beneficial functions. We also examined the effects of disallowing neutral mutations, of varying the mutation rate, and of sexual recombination. Populations evolving without neutral mutations were able to leverage deleterious and compensatory mutation pairs to overcome, at least partially, the absence of neutral mutations. Substantially raising or lowering the mutation rate reduced or eliminated the long-term benefit of deleterious mutations, but introducing recombination did not. Our work demonstrates that deleterious mutations can play an important role in adaptive evolution under at least some conditions.

Additive genetic architecture underlying a rapidly evolving sexual signaling phenotype in the Hawaiian cricket genus Laupala

Complex, quantitative traits are often the function of the coordinated action of many physically independent genetic factors. Interactive properties of multilocus genotypes, such as epistasis, are thought to be pervasive components of the genetic architecture of complex phenotypes. Here, we utilize a panel of interspecific backcross introgression lines to evaluate the genetic architecture of song variation, a quantitative sexual signaling phenotype, in the Hawaiian swordtail cricket genus Laupala. Allelic effects across five quantitative trait loci are consistent with a purely additive model of gene action, where alleles at multiple loci are found to have fully independent and discrete effects with respect to the sexual signaling phenotype. Whereas a more complex genetic architecture featuring non-additive dominance and epistasis components may constrain potential evolutionary trajectories and reduce the rate of evolutionary change, the polygenic, additive genetic architecture observed for sexual signaling in Laupala should respond rapidly to directional selection pressures and freely move throughout phenotypic space. This classic type I genetic architecture may facilitate the explosive radiation of song variation observed across the Laupala genus.

The cost of antibiotic resistance depends on evolutionary history in Escherichia coli

BACKGROUND

The persistence of antibiotic resistance depends on the fitness effects of resistance elements in the absence of antibiotics. Recent work shows that the fitness effect of a given resistance mutation is influenced by other resistance mutations on the same genome. However, resistant bacteria acquire additional beneficial mutations during evolution in the absence of antibiotics that do not alter resistance directly but may modify the fitness effects of new resistance mutations.

RESULTS

We experimentally evolved rifampicin-resistant and sensitive Escherichia coli in a drug-free environment, before measuring the effects of new resistance elements on fitness in antibiotic-free conditions. Streptomycin-resistance mutations had small fitness effects in rifampicin-resistant genotypes that had adapted to antibiotic-free growth medium, compared to the same genotypes without adaptation. We observed a similar effect when resistance was encoded by a different mechanism and carried on a plasmid. Antibiotic-sensitive bacteria that adapted to the same conditions showed the same pattern for some resistance elements but not others.

CONCLUSIONS

Epistatic variation of costs of resistance can result from evolution in the absence of antibiotics, as well as the presence of other resistance mutations.

The conditional nature of genetic interactions: the consequences of wild-type backgrounds on mutational interactions in a genome-wide modifier screen

The phenotypic outcome of a mutation cannot be simply mapped onto the underlying DNA variant. Instead, the phenotype is a function of the allele, the genetic background in which it occurs and the environment where the mutational effects are expressed. While the influence of genetic background on the expressivity of individual mutations is recognized, its consequences on the interactions between genes, or the genetic network they form, is largely unknown. The description of genetic networks is essential for much of biology; yet if, and how, the topologies of such networks are influenced by background is unknown. Furthermore, a comprehensive examination of the background dependent nature of genetic interactions may lead to identification of novel modifiers of biological processes. Previous work in Drosophila melanogaster demonstrated that wild-type genetic background influences the effects of an allele of scalloped (sd), with respect to both its principal consequence on wing development and its interactions with a mutation in optomotor blind. In this study we address whether the background dependence of mutational interactions is a general property of genetic systems by performing a genome wide dominant modifier screen of the sd(E3) allele in two wild-type genetic backgrounds using molecularly defined deletions. We demonstrate that ~74% of all modifiers of the sd(E3) phenotype are background-dependent due in part to differential sensitivity to genetic perturbation. These background dependent interactions include some with qualitative differences in the phenotypic outcome, as well as instances of sign epistasis. This suggests that genetic interactions are often contingent on genetic background, with flexibility in genetic networks due to segregating variation in populations. Such background dependent effects can substantially alter conclusions about how genes influence biological processes, the potential for genetic screens in alternative wild-type backgrounds identifying new loci that contribute to trait expression, and the inferences of the topology of genetic networks.

The strength of genetic interactions scales weakly with mutational effects

Background

Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study, we analyze a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae.

Results

We discuss a geometric definition of epistasis that reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilized this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis, and discuss the consequences for evolutionary dynamics.

Conclusions

Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques, it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.

Bacterial adaptation through loss of function

The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments.

Selection biases the prevalence and type of epistasis along adaptive trajectories

The contribution to an organism's phenotype from one genetic locus may depend upon the status of other loci. Such epistatic interactions among loci are now recognized as fundamental to shaping the process of adaptation in evolving populations. Although little is known about the structure of epistasis in most organisms, recent experiments with bacterial populations have concluded that antagonistic interactions abound and tend to deaccelerate the pace of adaptation over time. Here, we use the NK model of fitness landscapes to examine how natural selection biases the mutations that substitute during evolution based on their epistatic interactions. We find that, even when beneficial mutations are rare, these biases are strong and change substantially throughout the course of adaptation. In particular, epistasis is less prevalent than the neutral expectation early in adaptation and much more prevalent later, with a concomitant shift from predominantly antagonistic interactions early in adaptation to synergistic and sign epistasis later in adaptation. We observe the same patterns when reanalyzing data from a recent microbial evolution experiment. These results show that when the order of substitutions is not known, standard methods of analysis may suggest that epistasis retards adaptation when in fact it accelerates it.

Evolution in microbes

This review presents a broad survey of experimental microbial evolution, covering diverse topics including trade-offs, epistasis, fluctuating conditions, spatial dynamics, cooperation, aging, and stochastic switching. Emphasis is placed on examples that highlight key conceptual points or address theoretical predictions. Experimental evolution is discussed from two points of view. First, population trajectories are described as adaptive walks on a fitness landscape, whose genetic structure can be probed by experiments. Second, populations are viewed from a physiological perspective, and their nongenetic heterogeneity is examined. Bringing together these two viewpoints remains a major challenge for the future.

Substrate ambiguous enzymes within the Escherichia coli proteome offer different evolutionary solutions to the same problem

Many enzymes exhibit some catalytic promiscuity or substrate ambiguity. These weak activities do not affect the fitness of the organism under ordinary circumstances, but can serve as potential evolutionary precursors of new catalytic functions. We wondered whether different proteins with the same substrate ambiguous activity evolve differently under identical selection conditions. Patrick et al. (Patrick WM, Quandt EM, Swartzlander DB, Matsumura I. 2007. Multicopy suppression underpins metabolic evolvability. Mol Biol Evol. 24:2716-2722.) previously showed that three multicopy suppressors, gph, hisB, and ytjC, rescue ΔserB Escherichia coli cells from starvation on minimal media. We directed the evolution of variants of Gph, histidinol phosphatase (HisB), and YtjC that complemented ΔserB more efficiently, and characterized the effects of the amino acid changes, alone and in combination, upon the evolved phosphoserine phosphatase (PSP) activity. Gph and HisB are members of the HAD superfamily of hydrolases, but they adapted through different, kinetically distinguishable, biochemical mechanisms. All of the selected mutations, except N102T in YtjC, proved to be beneficial in isolation. They exhibited a pattern of antagonistic epistasis, as their effects in combination upon the kinetic parameters of the three proteins in reactions with phosphoserine were nonmultiplicative. The N102T mutation exhibited sign epistasis, as it was deleterious in isolation but beneficial in the context of other mutations. We also showed that the D57N mutation in the chromosomal copy of hisB is sufficient to suppress the ΔserB deletion. These results in combination show that proteomes can offer multiple mechanistic solutions to a molecular recognition problem.

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

Understanding how diversity emerges in a single niche is not fully understood. Rugged fitness landscapes and epistasis between beneficial mutations could explain coexistence among emerging lineages. To provide an experimental test of this notion, we investigated epistasis among four pleiotropic mutations in rpoS, mglD, malT, and hfq present in two coexisting lineages that repeatedly fixed in experimental populations of Escherichia coli. The mutations were transferred into the ancestral background individually or in combination of double or triple alleles. The combined competitive fitness of two or three beneficial mutations from the same lineage was consistently lower than the sum of the competitive fitness of single mutants--a clear indication of negative epistasis within lineages. We also found sign epistasis (i.e., the combined fitness of two beneficial mutations lower than the ancestor), not only from two different lineages (i.e., hfq and rpoS) but also from the same lineage (i.e., mglD and malT). The sign epistasis between loci of different lineages indeed indicated a rugged fitness landscape, providing an epistatic explanation for the coexistence of distinct rpoS and hfq lineages in evolving populations. The negative and sign epistasis between beneficial mutations within the same lineage can further explain the order of mutation acquisition.

Patterns of Epistasis between beneficial mutations in an antibiotic resistance gene

Understanding epistasis is central to biology. For instance, epistatic interactions determine the topography of the fitness landscape and affect the dynamics and determinism of adaptation. However, few empirical data are available, and comparing results is complicated by confounding variation in the system and the type of mutations used. Here, we take a systematic approach by quantifying epistasis in two sets of four beneficial mutations in the antibiotic resistance enzyme TEM-1 β-lactamase. Mutations in these sets have either large or small effects on cefotaxime resistance when present as single mutations. By quantifying the epistasis and ruggedness in both landscapes, we find two general patterns. First, resistance is maximal for combinations of two mutations in both fitness landscapes and declines when more mutations are added due to abundant sign epistasis and a pattern of diminishing returns with genotype resistance. Second, large-effect mutations interact more strongly than small-effect mutations, suggesting that the effect size of mutations may be an organizing principle in understanding patterns of epistasis. By fitting the data to simple phenotype resistance models, we show that this pattern may be explained by the nonlinear dependence of resistance on enzyme stability and an unknown phenotype when mutations have antagonistically pleiotropic effects. The comparison to a previously published set of mutations in the same gene with a joint benefit further shows that the enzyme's fitness landscape is locally rugged but does contain adaptive pathways that lead to high resistance.

Exact results for amplitude spectra of fitness landscapes

Starting from fitness correlation functions, we calculate exact expressions for the amplitude spectra of fitness landscapes as defined by Stadler [1996. Landscapes and their correlation functions. J. Math. Chem. 20, 1] for common landscape models, including Kauffman's NK-model, rough Mount Fuji landscapes and general linear superpositions of such landscapes. We further show that correlations decaying exponentially with the Hamming distance yield exponentially decaying spectra similar to those reported recently for a model of molecular signal transduction. Finally, we compare our results for the model systems to the spectra of various experimentally measured fitness landscapes. We claim that our analytical results should be helpful when trying to interpret empirical data and guide the search for improved fitness landscape models.

Bacterial genome evolution within a clonal population: from in vitro investigations to in vivo observations

Bacteria are faced with a diversity of environmental stresses that include high salt concentrations, heavy metals and pH fluctuations. Adaptation to resist such stresses is a complex phenomenon that involves global pathways and simultaneous acquisition of multiple unrelated properties. During the last 3 years, the development of new technologies in the field of molecular biology has led to numerous fundamental and quantitative in vitro and in vivo evolutionary studies that have improved our understanding of the principles underlying bacterial adaptations, and helped us develop strategies to cope with the health burden of bacterial virulence. In this review, the authors discuss the evolution of bacteria in the laboratory and in human patients.

The evolutionary time machine: using dormant propagules to forecast how populations can adapt to changing environments

Evolutionary changes are determined by a complex assortment of ecological, demographic, and adaptive histories. Predicting how evolution will shape the genetic structures of populations coping with current (and future) environmental challenges has principally relied on investigations through space, in lieu of time, because long-term phenotypic and molecular data are scarce. Yet, dormant propagules in sediments, soils, and permafrost are convenient natural archives of population histories from which to trace adaptive trajectories along extended time periods. DNA sequence data obtained from these natural archives, combined with pioneering methods for analyzing both ecological and population genomic time-series data, are likely to provide predictive models to forecast evolutionary responses of natural populations to environmental changes resulting from natural and anthropogenic stressors, including climate change.

Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoter

Genotype-to-phenotype maps and the related fitness landscapes that include epistatic interactions are difficult to measure because of their high dimensional structure. Here we construct such a map using the recently collected corpora of high-throughput sequence data from the 75 base pairs long mutagenized E. coli lac promoter region, where each sequence is associated with its phenotype, the induced transcriptional activity measured by a fluorescent reporter. We find that the additive (non-epistatic) contributions of individual mutations account for about two-thirds of the explainable phenotype variance, while pairwise epistasis explains about 7% of the variance for the full mutagenized sequence and about 15% for the subsequence associated with protein binding sites. Surprisingly, there is no evidence for third order epistatic contributions, and our inferred fitness landscape is essentially single peaked, with a small amount of antagonistic epistasis. There is a significant selective pressure on the wild type, which we deduce to be multi-objective optimal for gene expression in environments with different nutrient sources. We identify transcription factor (CRP) and RNA polymerase binding sites in the promotor region and their interactions without difficult optimization steps. In particular, we observe evidence for previously unexplored genetic regulatory mechanisms, possibly kinetic in nature. We conclude with a cautionary note that inferred properties of fitness landscapes may be severely influenced by biases in the sequence data.

Variance in epistasis links gene regulation and evolutionary rate in the yeast genetic interaction network

Organisms face a constantly shifting landscape of environmental conditions and internal physiological states. How gene regulation and cellular functions are maintained across genetic and environmental variation is therefore a fundamental question in biology. Here, we analyze the Saccharomyces cerevisiae genetic interaction network to understand how the yeast cell maintains regulatory capacity across genetic backgrounds and environmental conditions. We used the recently characterized synthetic sick/lethal network in yeast, which measures the fitness effects of knocking out pairs of genes, to analyze interactions among 4,364 genes. Genes with large variance in epistatic effects on fitness are highly and ubiquitously expressed (with open chromatin conformations in their promoter regions) and evolve more slowly than genes with weak effects on fitness. Thus, rather than being the elements responsible for the regulation and responsiveness of the genetic network, genes with large epistatic effects tend to be more mundane “housekeeping” genes whose consistent expression is critical to fitness under all environments and that are thereby deeply embedded within the regulatory structure of the network. Our analysis shows that the yeast cell has evolved a system whereby a physical mechanism of regulation (nucleosome occupancy) buffers key genes from the variability experienced by the cell as a whole.

The environment affects epistatic interactions to alter the topology of an empirical fitness landscape

The fitness effect of mutations can be influenced by their interactions with the environment, other mutations, or both. Previously, we constructed 32 ( = 2⁵) genotypes that comprise all possible combinations of the first five beneficial mutations to fix in a laboratory-evolved population of Escherichia coli. We found that (i) all five mutations were beneficial for the background on which they occurred; (ii) interactions between mutations drove a diminishing returns type epistasis, whereby epistasis became increasingly antagonistic as the expected fitness of a genotype increased; and (iii) the adaptive landscape revealed by the mutation combinations was smooth, having a single global fitness peak. Here we examine how the environment influences epistasis by determining the interactions between the same mutations in two alternative environments, selected from among 1,920 screened environments, that produced the largest increase or decrease in fitness of the most derived genotype. Some general features of the interactions were consistent: mutations tended to remain beneficial and the overall pattern of epistasis was of diminishing returns. Other features depended on the environment; in particular, several mutations were deleterious when added to specific genotypes, indicating the presence of antagonistic interactions that were absent in the original selection environment. Antagonism was not caused by consistent pleiotropic effects of individual mutations but rather by changing interactions between mutations. Our results demonstrate that understanding adaptation in changing environments will require consideration of the combined effect of epistasis and pleiotropy across environments.

The fitness effect of beneficial mutations can depend on how they interact with their genetic and external environment. The form of these interactions is important because it can alter adaptive outcomes, selecting for or against certain combinations of beneficial mutations. Here, we examine how interactions between beneficial mutations favored during adaptation of a lab strain of Escherichia coli to one simple environment are altered when the strain is grown in two novel environments. We found that fitness effects were greatly influenced by both the genetic and external environments. In several instances a change in environment reversed the effect of a mutation from beneficial to deleterious or caused combinations of beneficial mutations to become deleterious. Our results suggest that a complex or fluctuating environment may favor combinations of mutations whose interactions may be less sensitive to external conditions.

Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution

The premise of genetic analysis is that a causal link exists between phenotypic and allelic variation. However, it has long been documented that mutant phenotypes are not a simple result of a single DNA lesion, but are instead due to interactions of the focal allele with other genes and the environment. Although an experimentally rigorous approach focused on individual mutations and isogenic control strains has facilitated amazing progress within genetics and related fields, a glimpse back suggests that a vast complexity has been omitted from our current understanding of allelic effects. Armed with traditional genetic analyses and the foundational knowledge they have provided, we argue that the time and tools are ripe to return to the underexplored aspects of gene function and embrace the context-dependent nature of genetic effects. We assert that a broad understanding of genetic effects and the evolutionary dynamics of alleles requires identifying how mutational outcomes depend upon the 'wild type' genetic background. Furthermore, we discuss how best to exploit genetic background effects to broaden genetic research programs.

Evolution of Escherichia coli rifampicin resistance in an antibiotic-free environment during thermal stress

BACKGROUND

Beneficial mutations play an essential role in bacterial adaptation, yet little is known about their fitness effects across genetic backgrounds and environments. One prominent example of bacterial adaptation is antibiotic resistance. Until recently, the paradigm has been that antibiotic resistance is selected by the presence of antibiotics because resistant mutations confer fitness costs in antibiotic free environments. In this study we show that it is not always the case, documenting the selection and fixation of resistant mutations in populations of Escherichia coli B that had never been exposed to antibiotics but instead evolved for 2000 generations at high temperature (42.2°C).

RESULTS

We found parallel mutations within the rpoB gene encoding the beta subunit of RNA polymerase. These amino acid substitutions conferred different levels of rifampicin resistance. The resistant mutations typically appeared, and were fixed, early in the evolution experiment. We confirmed the high advantage of these mutations at 42.2°C in glucose-limited medium. However, the rpoB mutations had different fitness effects across three genetic backgrounds and six environments.

CONCLUSIONS

We describe resistance mutations that are not necessarily costly in the absence of antibiotics or compensatory mutations but are highly beneficial at high temperature and low glucose. Their fitness effects depend on the environment and the genetic background, providing glimpses into the prevalence of epistasis and pleiotropy.

Can you sequence ecology? Metagenomics of adaptive diversification

Few areas of science have benefited more from the expansion in sequencing capability than the study of microbial communities. Can sequence data, besides providing hypotheses of the functions the members possess, detect the evolutionary and ecological processes that are occurring? For example, can we determine if a species is adapting to one niche, or if it is diversifying into multiple specialists that inhabit distinct niches? Fortunately, adaptation of populations in the laboratory can serve as a model to test our ability to make such inferences about evolution and ecology from sequencing. Even adaptation to a single niche can give rise to complex temporal dynamics due to the transient presence of multiple competing lineages. If there are multiple niches, this complexity is augmented by segmentation of the population into multiple specialists that can each continue to evolve within their own niche. For a known example of parallel diversification that occurred in the laboratory, sequencing data gave surprisingly few obvious, unambiguous signs of the ecological complexity present. Whereas experimental systems are open to direct experimentation to test hypotheses of selection or ecological interaction, the difficulty in "seeing ecology" from sequencing for even such a simple system suggests translation to communities like the human microbiome will be quite challenging. This will require both improved empirical methods to enhance the depth and time resolution for the relevant polymorphisms and novel statistical approaches to rigorously examine time-series data for signs of various evolutionary and ecological phenomena within and between species.

Adaptively evolved yeast mutants on galactose show trade-offs in carbon utilization on glucose

Adaptive evolution offers many opportunities in metabolic engineering; however, several constraints still exist as evolutionary trade-offs may impose collateral cost to obtain new traits. The application of adaptive evolution for strains development could be further improved by elucidating the molecular mechanisms. In this study, adaptively evolved yeast mutants with improved galactose utilization ability showed impaired glucose utilization. The molecular genetic basis of this trade-off was investigated using a systems biology approach. Transcriptional and metabolic changes resulting from the improvement of galactose utilization were found maintained during growth on glucose. Moreover, glucose repression related genes showed conserved expression patterns during growth on both sugars. Mutations in the RAS2 gene that were identified as beneficial for galactose utilization in evolved mutants exhibited significant correlation with attenuation of glucose utilization. These results indicate that antagonistic pleiotropy is the dominant mechanism in the observed trade-off, and it is likely realized by changes in glucose signaling.

The dynamics of adapting, unregulated populations and a modified fundamental theorem

A population in a novel environment will accumulate adaptive mutations over time, and the dynamics of this process depend on the underlying fitness landscape: the fitness of and mutational distance between possible genotypes in the population. Despite its fundamental importance for understanding the evolution of a population, inferring this landscape from empirical data has been problematic. We develop a theoretical framework to describe the adaptation of a stochastic, asexual, unregulated, polymorphic population undergoing beneficial, neutral and deleterious mutations on a correlated fitness landscape. We generate quantitative predictions for the change in the mean fitness and within-population variance in fitness over time, and find a simple, analytical relationship between the distribution of fitness effects arising from a single mutation, and the change in mean population fitness over time: a variant of Fisher's 'fundamental theorem' which explicitly depends on the form of the landscape. Our framework can therefore be thought of in three ways: (i) as a set of theoretical predictions for adaptation in an exponentially growing phase, with applications in pathogen populations, tumours or other unregulated populations; (ii) as an analytically tractable problem to potentially guide theoretical analysis of regulated populations; and (iii) as a basis for developing empirical methods to infer general features of a fitness landscape.

Small changes in enzyme function can lead to surprisingly large fitness effects during adaptive evolution of antibiotic resistance

In principle, evolutionary outcomes could be largely predicted if all of the relevant physicochemical variants of a particular protein function under selection were known and integrated into an appropriate physiological model. We have tested this principle by generating a family of variants of the tetracycline resistance protein TetX2 and identified the physicochemical properties most correlated with organismal fitness. Surprisingly, small changes in the K(m(MCN)), less than twofold, were sufficient to produce highly successful adaptive mutants over clinically relevant drug concentrations. We then built a quantitative model directly relating the in vitro physicochemical properties of the mutant enzymes to the growth rates of bacteria carrying a single chromosomal copy of the tet(X2) variants over a wide range of minocycline (MCN) concentrations. Importantly, this model allows the prediction of enzymatic properties directly from cellular growth rates as well as the physicochemical-fitness landscape of TetX2. Using experimental evolution and deep sequencing to monitor the allelic frequencies of the seven most biochemically efficient TetX2 mutants in 10 independently evolving populations, we showed that the model correctly predicted the success of the two most beneficial variants tet(X2)(T280A) and tet(X2)(N371I). The structure of the most efficient variant, TetX2(T280A), in complex with MCN at 2.7 Å resolution suggests an indirect effect on enzyme kinetics. Taken together, these findings support an important role for readily accessible small steps in protein evolution that can, in turn, greatly increase the fitness of an organism during natural selection.

Adaptive evolution: evaluating empirical support for theoretical predictions

Adaptive evolution is shaped by the interaction of population genetics, natural selection and underlying network and biochemical constraints. Variation created by mutation, the raw material for evolutionary change, is translated into phenotypes by flux through metabolic pathways and by the topography and dynamics of molecular networks. Finally, the retention of genetic variation and the efficacy of selection depend on population genetics and demographic history. Emergent high-throughput experimental methods and sequencing technologies allow us to gather more evidence and to move beyond the theory in different systems and populations. Here we review the extent to which recent evidence supports long-established theoretical principles of adaptation.

Chromosomal duplication is a transient evolutionary solution to stress

Aneuploidy, an abnormal number of chromosomes, is a widespread phenomenon found in unicellulars such as yeast, as well as in plants and in mammalians, especially in cancer. Aneuploidy is a genome-scale aberration that imposes a severe burden on the cell, yet under stressful conditions specific aneuploidies confer a selective advantage. This dual nature of aneuploidy raises the question of whether it can serve as a stable and sustainable evolutionary adaptation. To clarify this, we conducted a set of laboratory evolution experiments in yeast and followed the long-term dynamics of aneuploidy under diverse conditions. Here we show that chromosomal duplications are first acquired as a crude solution to stress, yet only as transient solutions that are eliminated and replaced by more efficient solutions obtained at the individual gene level. These transient dynamics of aneuploidy were repeatedly observed in our laboratory evolution experiments; chromosomal duplications gained under stress were eliminated not only when the stress was relieved, but even if it persisted. Furthermore, when stress was applied gradually rather than abruptly, alternative solutions appear to have emerged, but not aneuploidy. Our findings indicate that chromosomal duplication is a first evolutionary line of defense, that retains survivability under strong and abrupt selective pressures, yet it merely serves as a "quick fix," whereas more refined and sustainable solutions take over. Thus, in the perspective of genome evolution trajectory, aneuploidy is a useful yet short-lived intermediate that facilitates further adaptation.

Hidden epistastic interactions can favour the evolution of sex and recombination

Deleterious mutations can have a strong influence on the outcome of evolution. The nature of this influence depends on how mutations combine together to affect fitness. “Negative epistasis” occurs when a new deleterious mutation causes the greatest loss in fitness in a genome that already contains many deleterious mutations. Negative epistasis is a key ingredient for some of the leading hypotheses regarding the evolution of recombination, the evolution of sex, and a variety of other phenomena. In general, laboratory studies have not supported the idea that negative epistasis is ubiquitous, and this has led to doubts about its importance in biological evolution. Here, we show that these experimental results may be misleading, because negative epistasis can produce evolutionary advantages for sex and recombination while simultaneously being almost impossible to detect using current experimental methods. Under asexual reproduction, such hidden epistasis influences evolutionary outcomes only if the fittest individuals are present in substantial numbers, while also forming a very small proportion of the population as a whole. This implies that our results for asexuals will apply only for very large populations, and also limits the extent of the fitness benefits that hidden epistasis can provide. Despite these caveats, our results show that the fitness consequences of sex and recombination cannot always be inferred from observable epistasis alone.

The basis of antagonistic pleiotropy in hfq mutations that have opposite effects on fitness at slow and fast growth rates

Mutations beneficial in one environment may cause costs in different environments, resulting in antagonistic pleiotropy. Here, we describe a novel form of antagonistic pleiotropy that operates even within the same environment, where benefits and deleterious effects exhibit themselves at different growth rates. The fitness of hfq mutations in Escherichia coli affecting the RNA chaperone involved in small-RNA regulation is remarkably sensitive to growth rate. E. coli populations evolving in chemostats under nutrient limitation acquired beneficial mutations in hfq during slow growth (0.1 h(-1)) but not in populations growing sixfold faster. Four identified hfq alleles from parallel populations were beneficial at 0.1 h(-1) and deleterious at 0.6 h(-1). The hfq mutations were beneficial, deleterious or neutral at an intermediate growth rate (0.5 h(-1)) and one changed from beneficial to deleterious within a 36 min difference in doubling time. The benefit of hfq mutations was due to the greater transport of limiting nutrient, which diminished at higher growth rates. The deleterious effects of hfq mutations at 0.6 h(-1) were less clear, with decreased viability a contributing factor. The results demonstrate distinct pleiotropy characteristics in the alleles of the same gene, probably because the altered residues in Hfq affected the regulation of expression of different genes in distinct ways. In addition, these results point to a source of variation in experimental measurement of the selective advantage of a mutation; estimates of fitness need to consider variation in growth rate impacting on the magnitude of the benefit of mutations and on their fitness distributions.

Mechanisms and selection of evolvability: experimental evidence

The vast number of species we see around us today, all stemming from a common ancestor, clearly demonstrates the capacity of organisms to adapt to new environments. Understanding the underlying basis of differences in the capacity of genotypes to adapt - that is, their evolvability - has become a major research field. Several mechanisms have been demonstrated to influence evolvability, including differences in mutation rate, mutational robustness, and some kinds of gene interactions. However, the benefits of increased evolvability are indirect, so that the conditions required for selection of evolvability traits are expected to be more limited than for traits that confer immediately beneficial phenotypes. Moreover, just because a trait can affect evolvability does not mean that it actually does so in a particular environment. Instead, some other function of the trait may better explain its success. Nevertheless, there is accumulating experimental evidence that some traits can increase the evolvability of a genotype and that these traits do influence evolutionary outcomes. We discuss recent theory and experiments that demonstrate the potential for traits that influence evolvability to arise and be selected.

Functional evolution of an anthocyanin pathway enzyme during a flower color transition

Dissecting the genetic basis for the evolution of species differences requires a combination of phylogenetic and molecular genetic perspectives. By mapping the genetic changes and their phenotypic effects onto the phylogeny, it is possible to distinguish changes that may have been directly responsible for a new character state from those that fine tune the transition. Here, we use phylogenetic and functional methods to trace the evolution of substrate specificity in dihydroflavonol-4-reductase (Dfr), an anthocyanin pathway gene known to be involved in the transition from blue to red flowers in Iochroma. Ancestral state reconstruction indicates that three substitutions occurred during the flower color transition, whereas several additional substitutions followed the transition. Comparisons of enzymatic function between ancestral proteins in blue- and red-flowered lineages and proteins from present-day taxa demonstrate that evolution of specificity for red pigment precursors was caused by the first three substitutions, which were fixed by positive selection and which differ from previously documented mutations affecting specificity. Two inferred substitutions subsequent to the initial flower color transition were also adaptive and resulted in an additional increase in specificity for red precursors. Epistatic interactions among both sets of substitutions may have limited the order of substitutions along branches of the phylogeny leading from blue-pigmented ancestors to the present-day red-flowered taxa. These results suggest that the species differences in DFR specificity may arise by a combination of selection on flower color and selection for improved pathway efficiency but that the exact series of genetic changes resulting in the evolution of specificity is likely to be highly contingent on the starting state.

Interaction-based feature selection and classification for high-dimensional biological data

MOTIVATION

Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion.

RESULTS

We present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by considering interactions. Secondly, a sizable portion of genes identified by our method for breast cancer metastasis overlaps with those reported in gene-to-system breast cancer (G2SBC) database as disease associated and some of them have interesting biological implication. In summary, interaction-based methods may lead to substantial gain in biological insights as well as more accurate prediction.

The effect of bacterial recombination on adaptation on fitness landscapes with limited peak accessibility

There is ample empirical evidence revealing that fitness landscapes are often complex: the fitness effect of a newly arisen mutation can depend strongly on the allelic state at other loci. However, little is known about the effects of recombination on adaptation on such fitness landscapes. Here, we investigate how recombination influences the rate of adaptation on a special type of complex fitness landscapes. On these landscapes, the mutational trajectories from the least to the most fit genotype are interrupted by genotypes with low relative fitness. We study the dynamics of adapting populations on landscapes with different compositions and numbers of low fitness genotypes, with and without recombination. Our results of the deterministic model (assuming an infinite population size) show that recombination generally decelerates adaptation on these landscapes. However, in finite populations, this deceleration is outweighed by the accelerating Fisher-Muller effect under certain conditions. We conclude that recombination has complex effects on adaptation that are highly dependent on the particular fitness landscape, population size and recombination rate.

The emergence and persistence of recombination is a long-standing open question in evolutionary biology. Most previous theoretical studies assumed relatively simple fitness landscapes, i.e., simple relationships between allelic states at different loci and fitness. By contrast, empirically determined bacterial and viral fitness landscapes reveal pervasive complex interactions between alleles at different loci. In this study, we explore the effect of recombination on adaptation on fitness landscapes where some trajectories leading to a global fitness peak are interrupted by genotypes of very low fitness. We find that in infinitely large populations, recombination generally reduces the rate of adaptation. However, in finite populations and under certain conditions, recombination can substantially speed up adaptation. Our study provides insights into the effect of recombination on more realistic fitness landscapes. Moreover, it helps gain a better understanding of the dynamics of the spread of adaptive genes in recombining bacterial populations during niche expansion and colonization of new habitats.

Understanding specialism when the Jack of all trades can be the master of all

Specialism is widespread in nature, generating and maintaining diversity, but recent work has demonstrated that generalists can be equally fit as specialists in some shared environments. This no-cost generalism challenges the maxim that 'the jack of all trades is the master of none', and requires evolutionary genetic mechanisms explaining the existence of specialism and no-cost generalism, and the persistence of specialism in the face of selection for generalism. Examining three well-described mechanisms with respect to epistasis and pleiotropy indicates that sign (or antagonistic) pleiotropy without epistasis cannot explain no-cost generalism and that magnitude pleiotropy without epistasis (including directional selection and mutation accumulation) cannot explain the persistence of specialism. However, pleiotropy with epistasis can explain all. Furthermore, epistatic pleiotropy may allow past habitat use to influence future use of novel environments, thereby affecting disease emergence and populations' responses to habitat change.

The genomics of adaptation

The amount and nature of genetic variation available to natural selection affect the rate, course and outcome of evolution. Consequently, the study of the genetic basis of adaptive evolutionary change has occupied biologists for decades, but progress has been hampered by the lack of resolution and the absence of a genome-level perspective. Technological advances in recent years should now allow us to answer many long-standing questions about the nature of adaptation. The data gathered so far are beginning to challenge some widespread views of the way in which natural selection operates at the genomic level. Papers in this Special Feature of Proceedings of the Royal Society B illustrate various aspects of the broad field of adaptation genomics. This introductory article sets up a context and, on the basis of a few selected examples, discusses how genomic data can advance our understanding of the process of adaptation.

Engineering ecosystems and synthetic ecologies

Microbial ecosystems play an important role in nature. Engineering these systems for industrial, medical, or biotechnological purposes are important pursuits for synthetic biologists and biological engineers moving forward. Here we provide a review of recent progress in engineering natural and synthetic microbial ecosystems. We highlight important forward engineering design principles, theoretical and quantitative models, new experimental and manipulation tools, and possible applications of microbial ecosystem engineering. We argue that simply engineering individual microbes will lead to fragile homogenous populations that are difficult to sustain, especially in highly heterogeneous and unpredictable environments. Instead, engineered microbial ecosystems are likely to be more robust and able to achieve complex tasks at the spatial and temporal resolution needed for truly programmable biology.

Cellular cooperation: insights from microbes

Cooperation between cells is a widespread phenomenon in nature, found across diverse systems ranging from microbial populations to multicellular organisms. For cooperation to evolve and be maintained within a population of cells, costs due to competition have to be outweighed by the benefits gained through cooperative actions. Because cooperation generally confers a cost to the cooperating cells, defector cells that do not cooperate but reap the benefits of cooperation can thrive and eventually drive the cooperating phenotypes to extinction. Here we summarize recent advances made in understanding how cooperation and multicellularity can evolve in microbial populations in the face of such conflicts and discuss parallels with cell populations within multicellular organisms.

Cancer in light of experimental evolution

Cancer initiation, progression, and the emergence of therapeutic resistance are evolutionary phenomena of clonal somatic cell populations. Studies in microbial experimental evolution and the theoretical work inspired by such studies are yielding deep insights into the evolutionary dynamics of clonal populations, yet there has been little explicit consideration of the relevance of this rapidly growing field to cancer biology. Here, we examine how the understanding of mutation, selection, and spatial structure in clonal populations that is emerging from experimental evolution may be applicable to cancer. Along the way, we discuss some significant ways in which cancer differs from the model systems used in experimental evolution. Despite these differences, we argue that enhanced prediction and control of cancer may be possible using ideas developed in the context of experimental evolution, and we point out some prospects for future research at the interface between these traditionally separate areas.

The consistency of beneficial fitness effects of mutations across diverse genetic backgrounds

Parallel and convergent evolution have been remarkably common observations in molecular adaptation but primarily in the context of the same genotype adapting to the same conditions. These phenomena therefore tell us about the stochasticity and limitations of adaptation. The limited data on convergence and parallelism in the adaptation of different genotypes conflict as to the importance of such events. If the effects of beneficial mutations are highly context dependent (i.e., if they are epistatic), different genotypes should adapt through different mutations. Epistasis for beneficial mutations has been investigated but mainly through measurement of interactions between individually beneficial mutations for the same genotype. We examine epistasis for beneficial mutations at a broader genetic scale by measuring the fitness effects of two mutations beneficial for the ssDNA bacteriophage ID11 in eight different, related genotypes showing 0.3-3.7% nucleotide divergence from ID11. We found no evidence for sign epistasis, but the mutations tended to have much smaller or no effects on fitness in the new genotypes. We found evidence for diminishing-returns epistasis; the effects were more beneficial for lower-fitness genotypes. The patterns of epistasis were not determined by phylogenetic relationships to the original genotype. To improve our understanding of the patterns of epistasis, we fit the data to a model in which each mutation had a constant, nonepistatic phenotypic effect across genotypes and the phenotype-fitness map had a single optimum. This model fit the data well, suggesting that epistasis for these mutations was due to nonlinearity in the phenotype-fitness mapping and that the likelihood of parallel evolution depends more on phenotype than on genotype.

Recovery of phenotypes obtained by adaptive evolution through inverse metabolic engineering

In a previous study, system level analysis of adaptively evolved yeast mutants showing improved galactose utilization revealed relevant mutations. The governing mutations were suggested to be in the Ras/PKA signaling pathway and ergosterol metabolism. Here, site-directed mutants having one of the mutations RAS2(Lys77), RAS2(Tyr112), and ERG5(Pro370) were constructed and evaluated. The mutants were also combined with overexpression of PGM2, earlier proved as a beneficial target for galactose utilization. The constructed strains were analyzed for their gross phenotype, transcriptome and targeted metabolites, and the results were compared to those obtained from reference strains and the evolved strains. The RAS2(Lys77) mutation resulted in the highest specific galactose uptake rate among all of the strains with an increased maximum specific growth rate on galactose. The RAS2(Tyr112) mutation also improved the specific galactose uptake rate and also resulted in many transcriptional changes, including ergosterol metabolism. The ERG5(Pro370) mutation only showed a small improvement, but when it was combined with PGM2 overexpression, the phenotype was almost the same as that of the evolved mutants. Combination of the RAS2 mutations with PGM2 overexpression also led to a complete recovery of the adaptive phenotype in galactose utilization. Recovery of the gross phenotype by the reconstructed mutants was achieved with much fewer changes in the genome and transcriptome than for the evolved mutants. Our study demonstrates how the identification of specific mutations by systems biology can direct new metabolic engineering strategies for improving galactose utilization by yeast.

Genetic background affects epistatic interactions between two beneficial mutations

The phenotypic effect of mutations can depend on their genetic background, a phenomenon known as epistasis. Many experimental studies have found that epistasis is pervasive, and some indicate that it may follow a general pattern dependent on the fitness effect of the interacting mutations. These studies have, however, typically examined the effect of interactions between a small number of focal mutations in a single genetic background. Here, we extend this approach by considering how the interaction between two beneficial mutations that were isolated from a population of laboratory evolved Escherichia coli changes when they are added to divergent natural isolate strains of E. coli. We find that interactions between the focal mutations and the different genetic backgrounds are common. Moreover, the pair-wise interaction between the focal mutations also depended on their genetic background, being more negative in backgrounds with higher absolute fitness. Together, our results indicate the presence of interactions between focal mutations, but also caution that these interactions depend quantitatively on the wider genetic background.

Epistasis from functional dependence of fitness on underlying traits

Epistasis between mutations in two genes is thought to reflect an interdependence of their functions. While sometimes epistasis is predictable using mechanistic models, its roots seem, in general, hidden in the complex architecture of biological networks. Here, we ask how epistasis can be quantified based on the mathematical dependence of a system-level trait (e.g. fitness) on lower-level traits (e.g. molecular or cellular properties). We first focus on a model in which fitness is the difference between a benefit and a cost trait, both pleiotropically affected by mutations. We show that despite its simplicity, this model can be used to analytically predict certain properties of the ensuing distribution of epistasis, such as a global negative bias, resulting in antagonism between beneficial mutations, and synergism between deleterious ones. We next extend these ideas to derive a general expression for epistasis given an arbitrary functional dependence of fitness on other traits. This expression demonstrates how epistasis relative to fitness can emerge despite the absence of epistasis relative to lower level traits, leading to a formalization of the concept of independence between biological processes. Our results suggest that epistasis may be largely shaped by the pervasiveness of pleiotropic effects and modular organization in biological networks.

Experimental evolution

Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work.

Evolution of clonal populations approaching a fitness peak

Populations facing novel environments are expected to evolve through the accumulation of adaptive substitutions. The dynamics of adaptation depend on the fitness landscape and possibly on the genetic background on which new mutations arise. Here, we model the dynamics of adaptive evolution at the phenotypic and genotypic levels, focusing on a Fisherian landscape characterized by a single peak. We find that Fisher's geometrical model of adaptation, extended to allow for small random environmental variations, is able to explain several features made recently in experimentally evolved populations. Consistent with data on populations evolving under controlled conditions, the model predicts that mean population fitness increases rapidly when populations face novel environments and then achieves a dynamic plateau, the rate of molecular evolution is remarkably constant over long periods of evolution, mutators are expected to invade and patterns of epistasis vary along the adaptive walk. Negative epistasis is expected in the initial steps of adaptation but not at later steps, a prediction that remains to be tested. Furthermore, populations are expected to exhibit high levels of phenotypic diversity at all times during their evolution. This implies that populations are possibly able to adapt rapidly to novel abiotic environments.