Quantifying the adaptive potential of an antibiotic resistance enzyme

For a quantitative understanding of the process of adaptation, we need to understand its "raw material," that is, the frequency and fitness effects of beneficial mutations. At present, most empirical evidence suggests an exponential distribution of fitness effects of beneficial mutations, as predicted for Gumbel-domain distributions by extreme value theory. Here, we study the distribution of mutation effects on cefotaxime (Ctx) resistance and fitness of 48 unique beneficial mutations in the bacterial enzyme TEM-1 β-lactamase, which were obtained by screening the products of random mutagenesis for increased Ctx resistance. Our contributions are threefold. First, based on the frequency of unique mutations among more than 300 sequenced isolates and correcting for mutation bias, we conservatively estimate that the total number of first-step mutations that increase Ctx resistance in this enzyme is 87 [95% CI 75-189], or 3.4% of all 2,583 possible base-pair substitutions. Of the 48 mutations, 10 are synonymous and the majority of the 38 non-synonymous mutations occur in the pocket surrounding the catalytic site. Second, we estimate the effects of the mutations on Ctx resistance by determining survival at various Ctx concentrations, and we derive their fitness effects by modeling reproduction and survival as a branching process. Third, we find that the distribution of both measures follows a Fréchet-type distribution characterized by a broad tail of a few exceptionally fit mutants. Such distributions have fundamental evolutionary implications, including an increased predictability of evolution, and may provide a partial explanation for recent observations of striking parallel evolution of antibiotic resistance.     

Epistasis buffers the fitness effects of rifampicin- resistance mutations in Pseudomonas aeruginosa

Epistatic interactions between resistance mutations in antibiotic-free environments potentially play a crucial role in the spread of resistance in pathogen populations by determining the fitness cost associated with resistance. We used an experimental evolution approach to test for epistatic interactions between 14 different pairs of rifampicin mutations in the pathogenic bacterium Pseudomonas aeruginosa in 42 different rifampicin-free environments. First, we show that epistasis between rifampicin-resistance mutations tends to be antagonistic: the fitness effect of having two mutations is generally smaller than that predicted from the effects of individual mutations on the wild-type. Second, we show that sign epistasis between resistance mutations is both common and strong; most notably, pairs of deleterious resistance mutations often partially or completely compensate for each others' costs, revealing a novel mechanism for compensatory adaptation. These results suggest that antagonistic epistasis between intragenic resistance mutations may be a key determinant of the cost of antibiotic resistance and compensatory adaptation in pathogen populations.     

Mutational neighbourhood and mutation supply rate constrain adaptation in Pseudomonas aeruginosa

Understanding adaptation by natural selection requires understanding the genetic factors that determine which beneficial mutations are available for selection. Here, using experimental evolution of rifampicin-resistant Pseudomonas aeruginosa, we show that different genotypes vary in their capacity for adaptation to the cost of antibiotic resistance. We then use sequence data to show that the beneficial mutations associated with fitness recovery were specific to particular genetic backgrounds, suggesting that genotypes had access to different sets of beneficial mutations. When we manipulated the supply rate of beneficial mutations, by altering effective population size during evolution, we found that it constrained adaptation in some selection lines by restricting access to rare beneficial mutations, but that the effect varied among the genotypes in our experiment. These results suggest that mutational neighbourhood varies even among genotypes that differ by a single amino acid change, and this determines their capacity for adaptation as well as the influence of population biology processes that alter mutation supply rate.     

The Distribution of Fitness Effects of Beneficial Mutations in Pseudomonas aeruginosa

Understanding how beneficial mutations affect fitness is crucial to our understanding of adaptation by natural selection. Here, using adaptation to the antibiotic rifampicin in the opportunistic pathogen Pseudomonas aeruginosa as a model system, we investigate the underlying distribution of fitness effects of beneficial mutations on which natural selection acts. Consistent with theory, the effects of beneficial mutations are exponentially distributed where the fitness of the wild type is moderate to high. However, when the fitness of the wild type is low, the data no longer follow an exponential distribution, because many beneficial mutations have large effects on fitness. There is no existing population genetic theory to explain this bias towards mutations of large effects, but it can be readily explained by the underlying biochemistry of rifampicin–RNA polymerase interactions. These results demonstrate the limitations of current population genetic theory for predicting adaptation to severe sources of stress, such as antibiotics, and they highlight the utility of integrating statistical and biophysical approaches to adaptation.     

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

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10^−4.7 to 10⁻⁴ CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

This study shows that simulation-based inference of evolutionary dynamics using neural networks can yield parameter values for fitness and mutation rate that are difficult to determine experimentally, including those of copy number variants (CNVs) during experimental adaptive evolution of yeast.     

AMELIORATION OF THE DELETERIOUS PLEIOTROPIC EFFECTS OF AN ADAPTIVE MUTATION IN BACILLUS SUBTILIS

The deleterious pleiotropic effects of an adaptive mutation may be ameliorated by one of two modes of evolution: (1) by replacement, in which an adaptive mutation with harmful pleiotropic effects is replaced by one that confers an equal benefit but at less cost; or (2) by compensatory evolution, in which natural selection favors modifiers at other loci that compensate for the deleterious effects of the mutant allele. In this study, we have measured the potential of these two modes of evolution to ameliorate the deleterious pleiotropic effects of resistance to the antibiotic rifampicin in the soil bacterium Bacillus subtilis. One approach was to measure the fitness cost of a series of spontaneous rifampicin-resistance mutations from each of several strains. The potential for amelioration by the replacement mode was estimated by the variation in fitness cost among the mutants of a single strain. Another approach was to introduce a series of different rifampicin-resistance alleles into a diversity of strains, and to measure the fitness cost of rifampicin resistance for each allele-by-strain combination. The potential for amelioration by the replacement mode was estimated by the variation in fitness costs among rifampicin-resistance alleles; the potential for compensatory evolution was estimated by variation in the fitness cost of rifampicin resistance among strains. This study has shown that the cost of rifampicin resistance may be ameliorated by both the compensatory and replacement modes.     

Diverse phenotypic and genetic responses to short‐term selection in evolving Escherichia coli populations

Beneficial mutations fuel adaptation by altering phenotypes that enhance the fit of organisms to their environment. However, the phenotypic effects of mutations often depend on ecological context, making the distribution of effects across multiple environments essential to understanding the true nature of beneficial mutations. Studies that address both the genetic basis and ecological consequences of adaptive mutations remain rare. Here, we characterize the direct and pleiotropic fitness effects of a collection of 21 first‐step beneficial mutants derived from naïve and adapted genotypes used in a long‐term experimental evolution of Escherichia coli. Whole‐genome sequencing was able to identify the majority of beneficial mutations. In contrast to previous studies, we find diverse fitness effects of mutations selected in a simple environment and few cases of genetic parallelism. The pleiotropic effects of these mutations were predominantly positive but some mutants were highly antagonistic in alternative environments. Further, the fitness effects of mutations derived from the adapted genotypes were dramatically reduced in nearly all environments. These findings suggest that many beneficial variants are accessible from a single point on the fitness landscape, and the fixation of alternative beneficial mutations may have dramatic consequences for niche breadth reduction via metabolic erosion.     

A method to infer positive selection from marker dynamics in an asexual population

MOTIVATION: The observation of positive selection acting on a mutant indicates that the corresponding mutation has some form of functional relevance. Determining the fitness effects of mutations thus has relevance to many interesting biological questions. One means of identifying beneficial mutations in an asexual population is to observe changes in the frequency of marked subsets of the population. We here describe a method to estimate the establishment times and fitnesses of beneficial mutations from neutral marker frequency data. RESULTS: The method accurately reproduces complex marker frequency trajectories. In simulations for which positive selection is close to 5% per generation, we obtain correlations upwards of 0.91 between correct and inferred haplotype establishment times. Where mutation selection coefficients are exponentially distributed, the inferred distribution of haplotype fitnesses is close to being correct. Applied to data from a bacterial evolution experiment, our method reproduces an observed correlation between evolvability and initial fitness defect.     

Genotype‐by‐environment interactions due to antibiotic resistance and adaptation in Escherichia coli

Mutations that are beneficial in one environment can have different fitness effects in other environments. In the context of antibiotic resistance, the resulting genotype‐by‐environment interactions potentially make selection on resistance unpredictable in heterogeneous environments. Furthermore, resistant bacteria frequently fix additional mutations during evolution in the absence of antibiotics. How do these two types of mutations interact to determine the bacterial phenotype across different environments? To address this, I used Escherichia coli as a model system, measuring the effects of nine different rifampicin resistance mutations on bacterial growth in 31 antibiotic‐free environments. I did this both before and after approximately 200 generations of experimental evolution in antibiotic‐free conditions (LB medium), and did the same for the antibiotic‐sensitive wild type after adaptation to the same environment. The following results were observed: (i) bacteria with and without costly resistance mutations adapted to experimental conditions and reached similar levels of competitive fitness; (ii) rifampicin resistance mutations and adaptation to LB both indirectly altered growth in other environments; and (iii) resistant‐evolved genotypes were more phenotypically different from the ancestor and from each other than resistant‐nonevolved and sensitive‐evolved genotypes. This suggests genotype‐by‐environment interactions generated by antibiotic resistance mutations, observed previously in short‐term experiments, are more pronounced after adaptation to other types of environmental variation, making it difficult to predict long‐term selection on resistance mutations from fitness effects in a single environment.     

ANTIBIOTIC RESISTANCE AND STRESS IN THE LIGHT OF FISHER'S MODEL

The role of mutations in evolution depends upon the distribution of their effects on fitness. This distribution is likely to depend on the environment. Indeed genotype‐by‐environment interactions are key for the process of local adaptation and ecological specialization. An important trait in bacterial evolution is antibiotic resistance, which presents a clear case of change in the direction of selection between environments with and without antibiotics. Here, we study the distribution of fitness effects of mutations, conferring antibiotic resistance to Escherichia coli, in benign and stressful environments without drugs. We interpret the distributions in the light of a fitness landscape model that assumes a single fitness peak. We find that mutation effects (s) are well described by a shifted gamma distribution, with a shift parameter that reflects the distance to the fitness peak and varies across environments. Consistent with the theoretical predictions of Fisher's geometrical model, with a Gaussian relationship between phenotype and fitness, we find that the main effect of stress is to increase the variance in s. Our findings are in agreement with the results of a recent meta‐analysis, which suggest that a simple fitness landscape model may capture the variation of mutation effects across species and environments.     

Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria

In the absence of the selecting drugs, chromosomal mutations for resistance to antibiotics and other chemotheraputic agents commonly engender a cost in the fitness of microorganisms. Recent in vivo and in vitro experimental studies of the adaptation to these "costs of resistance" in Escherichia coli, HIV, and Salmonella typhimurium found that evolution in the absence of these drugs commonly results in the ascent of mutations that ameliorate these costs, rather than higher-fitness, drug-sensitive revertants. To ascertain the conditions under which this compensatory evolution, rather than reversion, will occur, we did computer simulations, in vitro experiments, and DNA sequencing studies with low-fitness rpsL (streptomycin-resistant) mutants of E. coli with and without mutations that compensate for the fitness costs of these ribosomal protein mutations. The results of our investigation support the hypothesis that in these experiments, the ascent of intermediate-fitness compensatory mutants, rather than high-fitness revertants, can be attributed to higher rates of compensatory mutations relative to that of reversion and to the numerical bottlenecks associated with serial passage. We argue that these bottlenecks are intrinsic to the population dynamics of parasitic and commensal microbes and discuss the implications of these results to the problem of drug resistance and adaptive evolution in parasitic and commmensal microorganisms in general.     

Compensatory adaptation to the deleterious effect of antibiotic resistance in Salmonella typhimurium

Most chromosomal mutations that cause antibiotic resistance impose fitness costs on the bacteria. This biological cost can often be reduced by compensatory mutations. In Salmonella typhimurium, the nucleotide substitution AAA42 --> AAC in the rpsL gene confers resistance to streptomycin. The resulting amino acid substitution (K42N) in ribosomal protein S12 causes an increased rate of ribosomal proofreading and, as a result, the rate of protein synthesis, bacterial growth and virulence are decreased. Eighty-one independent lineages of the low-fitness, K42N mutant were evolved in the absence of antibiotic to ameliorate the costs. From the rate of fixation of compensated mutants and their fitness, the rate of compensatory mutations was estimated to be > or = 10-7 per cell per generation. The size of the population bottleneck during evolution affected fitness of the adapted mutants: a larger bottleneck resulted in higher average fitness. Only four of the evolved lineages contained streptomycin-sensitive revertants. The remaining 77 lineages contained mutants that were still fully streptomycin resistant, had retained the original resistance mutation and also acquired compensatory mutations. Most of the compensatory mutations, resulting in at least 35 different amino acid substitutions, were novel single-nucleotide substitutions in the rpsD, rpsE, rpsL or rplS genes encoding the ribosomal proteins S4, S5, S12 and L19 respectively. Our results show that the deleterious effects of a resistance mutation can be compensated by an unexpected variety of mutations.     

Distributions of beneficial fitness effects in RNA

Beneficial mutations are the driving force of evolution by natural selection. Yet, relatively little is known about the distribution of the fitness effects of beneficial mutations in populations. Recent work of Gillespie and Orr suggested some of the first generalizations for the distributions of beneficial fitness effects and, surprisingly, they depend only weakly on biological details. In particular, the theory suggests that beneficial mutations obey an exponential distribution of fitness effects, with the same exponential parameter across different regions of genotype space, provided only that few possible beneficial mutations are available to that genotype. Here we tested this hypothesis with a quasi-empirical model of RNA evolution in which fitness is based on the secondary structures of molecules and their thermodynamic stabilities. The fitnesses of randomly selected genotypes appeared to follow a Gumbel-type distribution and thus conform to a basic assumption of adaptation theory. However, the observed distributions of beneficial fitness effects conflict with specific predictions of the theory. In particular, the distributions of beneficial fitness effects appeared exponential only when the vast majority of small-effect beneficial mutations were ignored. Additionally, the distribution of beneficial fitness effects varied with the fitness of the parent genotype. We believe that correlation of the fitness values among similar genotypes is likely the cause of the departure from the predictions of recent adaptation theory. Although in conflict with the current theory, these results suggest that more complex statistical generalizations about beneficial mutations may be possible.     

Long-term adaptation of epistatic genetic networks

Gene networks are likely to govern most traits in nature. Mutations at these genes often show functional epistatic interactions that lead to complex genetic architectures and variable fitness effects in different genetic backgrounds. Understanding how epistatic genetic systems evolve in nature remains one of the great challenges in evolutionary biology. Here we combine an analytical framework with individual-based simulations to generate novel predictions about long-term adaptation of epistatic networks. We find that relative to traits governed by independently evolving genes, adaptation with epistatic gene networks is often characterized by longer waiting times to selective sweeps, lower standing genetic variation, and larger fitness effects of adaptive mutations. This may cause epistatic networks to either adapt more slowly or more quickly relative to a nonepistatic system. Interestingly, epistatic networks may adapt faster even when epistatic effects of mutations are on average deleterious. Further, we study the evolution of epistatic properties of adaptive mutations in gene networks. Our results show that adaptive mutations with small fitness effects typically evolve positive synergistic interactions, whereas adaptive mutations with large fitness effects evolve positive synergistic and negative antagonistic interactions at approximately equal frequencies. These results provide testable predictions for adaptation of traits governed by epistatic networks and the evolution of epistasis within networks.     

Source-sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost

Understanding the conditions that favour the evolution and maintenance of antibiotic resistance is the central goal of epidemiology. A crucial feature explaining the adaptation to harsh, or 'sink', environments is the supply of beneficial mutations via migration from a 'source' population. Given that antibiotic resistance is frequently associated with antagonistic pleiotropic fitness costs, increased migration rate is predicted not only to increase the rate of resistance evolution but also to increase the probability of fixation of resistance mutations with minimal fitness costs. Here we report in vitro experiments using the nosocomial pathogenic bacterium Pseudomonas aeruginosa that support these predictions: increasing rate of migration into environments containing antibiotics increased the rate of resistance evolution and decreased the associated costs of resistance. Consistent with previous theoretical work, we found that resistance evolution arose more rapidly in the presence of a single antibiotic than two. Evolution of resistance was also more rapid when bacteria were subjected to sequential exposure with two antibiotics (cycling therapy) compared with simultaneous exposure (bi-therapy). Furthermore, pleiotropic fitness costs of resistance to two antibiotics were higher than for one antibiotic, and were also higher under bi-therapy than cycling therapy, although the cost of resistance depended on the order of the antibiotics through time. These results may be relevant to the clinical setting where immigration is known to be important between chemotherapeutically treated patients, and demonstrate the importance of ecological and evolutionary dynamics in the control of antibiotic resistance.     

Pleiotropic effects of beneficial mutations in Escherichia coli

Micromutational models of adaptation have placed considerable weight on antagonistic pleiotropy as a mechanism that prevents mutations of large effect from achieving fixation. However, there are few empirical studies of the distribution of pleiotropic effects, and no studies that have examined this distribution for a large number of adaptive mutations. Here we examine the form and extent of pleiotropy associated with beneficial mutations in Escherichia coli. To do so, we used a collection of independently evolved genotypes, each of which contains a beneficial mutation that confers increased fitness in a glucose-limited environment. To determine the pleiotropic effects of these mutations, we examined the fitnesses of the mutants in five novel resource environments. Our results show that the majority of mutations had significant fitness effects in alternative resources, such that pleiotropy was common. The predominant form of this pleiotropy was positive--that is, most mutations that conferred increased fitness in glucose also conferred increased fitness in novel resources. We did detect some deleterious pleiotropic effects, but they were primarily limited to one of the five resources, and within this resource, to only a subset of mutants. Although pleiotropic effects were generally positive, fitness levels were lower and more variable on resources that differed most in their mechanisms of uptake and catabolism from that of glucose. Positive pleiotropic effects were strongly correlated in magnitude with their direct effects, but no such correlation was found among mutants with deleterious pleiotropic effects. Whereas previous studies of populations evolved on glucose for longer periods of time showed consistent declines on some of the resources used here, our results suggest that deleterious pleiotropic effects were limited to only a subset of the beneficial mutations available.     

Beneficial fitness effects are not exponential for two viruses

The distribution of fitness effects for beneficial mutations is of paramount importance in determining the outcome of adaptation. It is generally assumed that fitness effects of beneficial mutations follow an exponential distribution, for example, in theoretical treatments of quantitative genetics, clonal interference, experimental evolution, and the adaptation of DNA sequences. This assumption has been justified by the statistical theory of extreme values, because the fitnesses conferred by beneficial mutations should represent samples from the extreme right tail of the fitness distribution. Yet in extreme value theory, there are three different limiting forms for right tails of distributions, and the exponential describes only those of distributions in the Gumbel domain of attraction. Using beneficial mutations from two viruses, we show for the first time that the Gumbel domain can be rejected in favor of a distribution with a right-truncated tail, thus providing evidence for an upper bound on fitness effects. Our data also violate the common assumption that small-effect beneficial mutations greatly outnumber those of large effect, as they are consistent with a uniform distribution of beneficial effects.     

Environmental variation alters the fitness effects of rifampicin resistance mutations in Pseudomonas aeruginosa

The fitness effects of antibiotic resistance mutations in antibiotic‐free conditions play a key role in determining the long‐term maintenance of resistance. Although resistance is usually associated with a cost, the impact of environmental variation on the cost of resistance is poorly understood. Here, we test the impact of heterogeneity in temperature and resource availability on the fitness effects of antibiotic resistance using strains of the pathogenic bacterium Pseudomonas aeruginosa carrying clinically important rifampicin resistance mutations. Although the rank order of fitness was generally maintained across environments, fitness effects relative to the wild type differed significantly. Changes in temperature had a profound impact on the fitness effects of resistance, whereas changes in carbon substrate had only a weak impact. This suggests that environmental heterogeneity may influence whether the costs of resistance are likely to be ameliorated by second‐site compensatory mutations or by reversion to wild‐type rpoB. Our results highlight the need to consider environmental heterogeneity and genotype‐by‐environment interactions for fitness in models of resistance evolution.     

Escape from growth restriction in small colony variants of Salmonella typhimurium by gene amplification and mutation

Antibiotic resistance in bacteria is generally associated with fitness costs that often can be reduced by second-site compensatory mutations. Here, we examined how a protamine-resistant small colony variant of Salmonella typhimurium adapts to the growth reduction conferred by a resistance mutation in hemC (encoding a haem-biosynthesis enzyme). We show that adaptation occurs in a multi-step process where fitness is successively increased. Thus, the initial adaptive response was selection for an unstable gene amplification of the mutant hemC gene that provided a small fitness increase. Fitness was increased further by a mutation that restored HemC function in one gene copy, relaxing selection for the amplification. Subsequently, the amplification segregated back to the haploid state and even higher fitness. The end result was in most cases mutant strains with a hemC sequence different from that of the wild-type strain. These findings suggest that gene amplification facilitates adaptive evolution. A higher gene dosage increases the target size for compensatory mutations and improves fitness of the cell, thereby allowing an increase in the population size, further increasing the probability of a subsequent stable mutation. Our results provide a novel genetic basis for growth compensation in small colony variants.