Mutators can drive the evolution of multi-resistance to antibiotics.

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Environmental and genetic influence on the rate and spectrum of spontaneous mutations in Escherichia coli.

Spontaneous mutations are the ultimate source of novel genetic variation on which evolution operates. Although mutation rate is often discussed as a single parameter in evolution, it comprises multiple distinct types of changes at the level of DNA. Moreover, the rates of these distinct changes can be independently influenced by genomic background and environmental conditions. Using fluctuation tests, we characterized the spectrum of spontaneous mutations in Escherichia coli grown in low and high glucose environments. These conditions are known to affect the rate of spontaneous mutation in wild-type MG1655, but not in a ΔluxS deletant strain - a gene with roles in both quorum sensing and the recycling of methylation products used in E. coli's DNA repair process. We find an increase in AT>GC transitions in the low glucose environment, suggesting that processes relating to the production or repair of this mutation could drive the response of overall mutation rate to glucose concentration. Interestingly, this increase in AT>GC transitions is maintained by the glucose non-responsive ΔluxS deletant. Instead, an elevated rate of GC>TA transversions, more common in a high glucose environment, leads to a net non-responsiveness of overall mutation rate for this strain. Our results show how relatively subtle changes, such as the concentration of a carbon substrate or loss of a regulatory gene, can substantially influence the amount and nature of genetic variation available to selection.

Competition delays multi-drug resistance evolution during combination therapy.

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

Spontaneous mutation rate is a plastic trait associated with population density across domains of life.

Rates of random, spontaneous mutation can vary plastically, dependent upon the environment. Such plasticity affects evolutionary trajectories and may be adaptive. We recently identified an inverse plastic association between mutation rate and population density at 1 locus in 1 species of bacterium. It is unknown how widespread this association is, whether it varies among organisms, and what molecular mechanisms of mutagenesis or repair are required for this mutation-rate plasticity. Here, we address all 3 questions. We identify a strong negative association between mutation rate and population density across 70 years of published literature, comprising hundreds of mutation rates estimated using phenotypic markers of mutation (fluctuation tests) from all domains of life and viruses. We test this relationship experimentally, determining that there is indeed density-associated mutation-rate plasticity (DAMP) at multiple loci in both eukaryotes and bacteria, with up to 23-fold lower mutation rates at higher population densities. We find that the degree of plasticity varies, even among closely related organisms. Nonetheless, in each domain tested, DAMP requires proteins scavenging the mutagenic oxidised nucleotide 8-oxo-dGTP. This implies that phenotypic markers give a more precise view of mutation rate than previously believed: having accounted for other known factors affecting mutation rate, controlling for population density can reduce variation in mutation-rate estimates by 93%. Widespread DAMP, which we manipulate genetically in disparate organisms, also provides a novel trait to use in the fight against the evolution of antimicrobial resistance. Such a prevalent environmental association and conserved mechanism suggest that mutation has varied plastically with population density since the early origins of life.

Mathematical modelling for antibiotic resistance control policy: do we know enough?

BACKGROUND: Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base. MAIN TEXT: One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy. CONCLUSIONS: We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Environmental pleiotropy and demographic history direct adaptation under antibiotic selection.

Evolutionary rescue following environmental change requires mutations permitting population growth in the new environment. If change is severe enough to prevent most of the population reproducing, rescue becomes reliant on mutations already present. If change is sustained, the fitness effects in both environments, and how they are associated-termed 'environmental pleiotropy'-may determine which alleles are ultimately favoured. A population's demographic history-its size over time-influences the variation present. Although demographic history is known to affect the probability of evolutionary rescue, how it interacts with environmental pleiotropy during severe and sustained environmental change remains unexplored. Here, we demonstrate how these factors interact during antibiotic resistance evolution, a key example of evolutionary rescue fuelled by pre-existing mutations with pleiotropic fitness effects. We combine published data with novel simulations to characterise environmental pleiotropy and its effects on resistance evolution under different demographic histories. Comparisons among resistance alleles typically revealed no correlation for fitness-i.e., neutral pleiotropy-above and below the sensitive strain's minimum inhibitory concentration. Resistance allele frequency following experimental evolution showed opposing correlations with their fitness effects in the presence and absence of antibiotic. Simulations demonstrated that effects of environmental pleiotropy on allele frequencies depended on demographic history. At the population level, the major influence of environmental pleiotropy was on mean fitness, rather than the probability of evolutionary rescue or diversity. Our work suggests that determining both environmental pleiotropy and demographic history is critical for predicting resistance evolution, and we discuss the practicalities of this during in vivo evolution.

Divergent evolution peaks under intermediate population bottlenecks during bacterial experimental evolution.

There is growing evidence that parallel molecular evolution is common, but its causes remain poorly understood. Demographic parameters such as population bottlenecks are predicted to be major determinants of parallelism. Here, we test the hypothesis that bottleneck intensity shapes parallel evolution by elucidating the genomic basis of adaptation to antibiotic-supplemented media in hundreds of populations of the bacterium Pseudomonas fluorescens Pf0-1. As expected, bottlenecking decreased the rate of phenotypic and molecular adaptation. Surprisingly, bottlenecking had no impact on the likelihood of parallel adaptive molecular evolution at a genome-wide scale. However, bottlenecking had a profound impact on the genes involved in antibiotic resistance. Specifically, under either intense or weak bottlenecking, resistance predominantly evolved by strongly beneficial mutations which provide high levels of antibiotic resistance. In contrast with intermediate bottlenecking regimes, resistance evolved by a greater diversity of genetic mechanisms, significantly reducing the observed levels of parallel genetic evolution. Our results demonstrate that population bottlenecking can be a major predictor of parallel evolution, but precisely how may be more complex than many simple theoretical predictions.

Here's to the losers: evolvable residents accelerate the evolution of high-fitness invaders.

Recent work has shown that evolvability plays a key role in determining the long-term population dynamics of asexual clones. However, simple considerations suggest that the evolvability of a focal lineage of bacteria should also be influenced by the evolvability of its competitors. First, evolvable competitors should accelerate evolution by impeding the fixation of the focal lineage through a clonal interference-like mechanism. Second, evolvable competitors should increase the strength of selection by rapidly degrading the environment, increasing selection for adaptive mutations. Here we tested these ideas by allowing a high-fitness clone of the bacterium Pseudomonas aeruginosa to invade populations of two low-fitness resident clones that differ in their evolvability. Both competition from mutations in the resident lineage and environmental degradation lead to faster adaptation in the invader through fixing single mutations with a greater fitness advantage. The results suggest that competition from mutations in both the successful invader and the unsuccessful resident shapes the adaptive trajectory of the invader through both direct competition and indirect environmental effects. Therefore, to predict evolutionary outcomes, it will be necessary to consider the evolvability of all members of the community and the effects of adaptation on the quality of the environment. This is particularly relevant to mixed microbial communities where lineages differ in their adaptive potential, a common feature of chronic infections.

Adaptation at the Extremes of Life: Experimental Evolution with the Extremophile Archaeon Sulfolobus acidocaldarius.

The archaeon Sulfolobus acidocaldarius has emerged as a promising thermophilic model system. Investigating how thermophiles adapt to changing temperatures is a key requirement, not only for understanding fundamental evolutionary processes but also for developing S. acidocaldarius as a chassis for bioengineering. One major obstacle to conducting experimental evolution with thermophiles is the expense of equipment maintenance and energy usage of traditional incubators for high-temperature growth. To address this challenge, a comprehensive experimental protocol for conducting experimental evolution in S. acidocaldarius is presented, utilizing low-cost and energy-efficient bench-top thermomixers. The protocol involves a batch culture technique with relatively small volumes (1.5 mL), enabling tracking of adaptation in multiple independent lineages. This method is easily scalable through the use of additional thermomixers. Such an approach increases the accessibility of S. acidocaldarius as a model system by reducing both initial investment and ongoing costs associated with experimental investigations. Moreover, the technique is transferable to other microbial systems for exploring adaptation to diverse environmental conditions.

Modelling colony population growth in the filamentous fungus Aspergillus nidulans.

Filamentous fungi are ubiquitous in nature and have high societal significance, being both major (food-borne) pathogens and important industrial organisms in the production of antibiotics and enzymes. In addition, fungi are important model organisms for fundamental research, such as studies in genetics and evolutionary biology. However, mechanistic models for population growth that would help understand fungal biology and fundamental processes are almost entirely missing. Here we present such a mechanistic model for the species Aspergillus nidulans as an exemplar of models for other filamentous fungi. The model is based on physiological parameters that influence colony growth, namely mycelial growth rate and sporulation rate, to predict the number of individual nuclei present in a colony through time. Using population size data for colonies of differing ages, we find that our mechanistic model accurately predicts the number of nuclei for two growth environments, and show that fungal population size is most dependent on changes in mycelial growth rate.

Model and test in a fungus of the probability that beneficial mutations survive drift.

Determining the probability of fixation of beneficial mutations is critically important for building predictive models of adaptive evolution. Despite considerable theoretical work, models of fixation probability have stood untested for nearly a century. However, recent advances in experimental and theoretical techniques permit the development of models with testable predictions. We developed a new model for the probability of surviving genetic drift, a major component of fixation probability, for novel beneficial mutations in the fungus Aspergillus nidulans, based on the life-history characteristics of its colony growth on a solid surface. We tested the model by measuring the probability of surviving drift in 11 adapted strains introduced into wild-type populations of different densities. We found that the probability of surviving drift increased with mutant invasion fitness, and decreased with wild-type density, as expected. The model accurately predicted the survival probability for the majority of mutants, yielding one of the first direct tests of the extinction probability of beneficial mutations.

The properties of adaptive walks in evolving populations of fungus.

The rarity of beneficial mutations has frustrated efforts to develop a quantitative theory of adaptation. Recent models of adaptive walks, the sequential substitution of beneficial mutations by selection, make two compelling predictions: adaptive walks should be short, and fitness increases should become exponentially smaller as successive mutations fix. We estimated the number and fitness effects of beneficial mutations in each of 118 replicate lineages of Aspergillus nidulans evolving for approximately 800 generations at two population sizes using a novel maximum likelihood framework, the results of which were confirmed experimentally using sexual crosses. We find that adaptive walks do indeed tend to be short, and fitness increases become smaller as successive mutations fix. Moreover, we show that these patterns are associated with a decreasing supply of beneficial mutations as the population adapts. We also provide empirical distributions of fitness effects among mutations fixed at each step. Our results provide a first glimpse into the properties of multiple steps in an adaptive walk in asexual populations and lend empirical support to models of adaptation involving selection towards a single optimum phenotype. In practical terms, our results suggest that the bulk of adaptation is likely to be accomplished within the first few steps.

Measuring Microbial Mutation Rates with the Fluctuation Assay.

Fluctuation assays are widely used for estimating mutation rates in microbes growing in liquid environments. Many cultures are each inoculated with a few thousand cells, each sensitive to a selective marker that can be assayed phenotypically. These parallel cultures grow for many generations in the absence of the phenotypic marker. A subset of cultures is used to estimate the total number of cells at risk of mutations (i.e., the population size at the end of the growth period, or Nt). The remaining cultures are plated onto the selective agar. The distribution of observed resistant mutants among parallel cultures is then used to estimate the expected number of mutational events, m, using a mathematical model. Dividing m by Nt gives the estimate of the mutation rate per locus per generation. The assay has three critical aspects: the chosen phenotypic marker, the chosen volume of parallel cultures, and ensuring that the surface on the selective agar is completely dry before the incubation. The assay is relatively inexpensive and only needs standard laboratory equipment. It is also less laborious than alternative approaches, such as mutation accumulation and single-cell assays. The assay works on organisms that go through many generations rapidly and it depends on assumptions about the fitness effects of markers and cell death. However, recently developed tools and theoretical studies mean these issues can now be addressed analytically. The assay allows mutation rate estimation of different phenotypic markers in cells with different genotypes growing in isolation or in a community. By conducting multiple assays in parallel, assays can be used to study how an organism's environmental context affects spontaneous mutation rate, which is crucial for understanding antimicrobial resistance, carcinogenesis, aging, and evolution.

Identifying and exploiting genes that potentiate the evolution of antibiotic resistance.

There is an urgent need to develop novel approaches for predicting and preventing the evolution of antibiotic resistance. Here, we show that the ability to evolve de novo resistance to a clinically important ß-lactam antibiotic, ceftazidime, varies drastically across the genus Pseudomonas. This variation arises because strains possessing the ampR global transcriptional regulator evolve resistance at a high rate. This does not arise because of mutations in ampR. Instead, this regulator potentiates evolution by allowing mutations in conserved peptidoglycan biosynthesis genes to induce high levels of ß-lactamase expression. Crucially, blocking this evolutionary pathway by co-administering ceftazidime with the ß-lactamase inhibitor avibactam can be used to eliminate pathogenic P. aeruginosa populations before they can evolve resistance. In summary, our study shows that identifying potentiator genes that act as evolutionary catalysts can be used to both predict and prevent the evolution of antibiotic resistance.

Opposing effects of final population density and stress on Escherichia coli mutation rate.

Evolution depends on mutations. For an individual genotype, the rate at which mutations arise is known to increase with various stressors (stress-induced mutagenesis-SIM) and decrease at high final population density (density-associated mutation-rate plasticity-DAMP). We hypothesised that these two forms of mutation-rate plasticity would have opposing effects across a nutrient gradient. Here we test this hypothesis, culturing Escherichia coli in increasingly rich media. We distinguish an increase in mutation rate with added nutrients through SIM (dependent on error-prone polymerases Pol IV and Pol V) and an opposing effect of DAMP (dependent on MutT, which removes oxidised G nucleotides). The combination of DAMP and SIM results in a mutation rate minimum at intermediate nutrient levels (which can support 7 × 108 cells ml-1). These findings demonstrate a strikingly close and nuanced relationship of ecological factors-stress and population density-with mutation, the fuel of all evolution.

Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover.

The critical mutation rate (CMR) determines the shift between survival-of-the-fittest and survival of individuals with greater mutational robustness ("flattest"). We identify an inverse relationship between CMR and sequence length in an in silico system with a two-peak fitness landscape; CMR decreases to no more than five orders of magnitude above estimates of eukaryotic per base mutation rate. We confirm the CMR reduces exponentially at low population sizes, irrespective of peak radius and distance, and increases with the number of genetic crossovers. We also identify an inverse relationship between CMR and the number of genes, confirming that, for a similar number of genes to that for the plant Arabidopsis thaliana (25,000), the CMR is close to its known wild-type mutation rate; mutation rates for additional organisms were also found to be within one order of magnitude of the CMR. This is the first time such a simulation model has been assigned input and produced output within range for a given biological organism. The decrease in CMR with population size previously observed is maintained; there is potential for the model to influence understanding of populations undergoing bottleneck, stress, and conservation strategy for populations near extinction.

Epistatic interactions between ancestral genotype and beneficial mutations shape evolvability in Pseudomonas aeruginosa.

The idea that interactions between mutations influence adaptation by driving populations to low and high fitness peaks on adaptive landscapes is deeply ingrained in evolutionary theory. Here, we investigate the impact of epistasis on evolvability by challenging populations of two Pseudomonas aeruginosa clones bearing different initial mutations (in rpoB conferring rifampicin resistance, and the type IV pili gene network) to adaptation to a medium containing l-serine as the sole carbon source. Despite being initially indistinguishable in fitness, populations founded by the two ancestral genotypes reached different fitness following 300 generations of evolution. Genome sequencing revealed that the difference could not be explained by acquiring mutations in different targets of selection; the majority of clones from both ancestors converged on one of the following two strategies: (1) acquiring mutations in either PA2449 (gcsR, an l-serine-metabolism RpoN enhancer binding protein) or (2) protease genes. Additionally, populations from both ancestors converged on loss-of-function mutations in the type IV pili gene network, either due to ancestral or acquired mutations. No compensatory or reversion mutations were observed in RNA polymerase (RNAP) genes, in spite of the large fitness costs typically associated with mutations in rpoB. Although current theory points to sign epistasis as the dominant constraint on evolvability, these results suggest that the role of magnitude epistasis in constraining evolvability may be underappreciated. The contribution of magnitude epistasis is likely to be greatest under the biologically relevant mutation supply rates that make back mutations probabilistically unlikely.

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.

Parasite diversity drives rapid host dynamics and evolution of resistance in a bacteria-phage system.

Host-parasite evolutionary interactions are typically considered in a pairwise species framework. However, natural infections frequently involve multiple parasites. Altering parasite diversity alters ecological and evolutionary dynamics as parasites compete and hosts resist multiple infection. We investigated the effects of parasite diversity on host-parasite population dynamics and evolution using the pathogen Pseudomonas aeruginosa and five lytic bacteriophage parasites. To manipulate parasite diversity, bacterial populations were exposed for 24 hours to either phage monocultures or diverse communities containing up to five phages. Phage communities suppressed host populations more rapidly but also showed reduced phage density, likely due to interphage competition. The evolution of resistance allowed rapid bacterial recovery that was greater in magnitude with increases in phage diversity. We observed no difference in the extent of resistance with increased parasite diversity, but there was a profound impact on the specificity of resistance; specialized resistance evolved to monocultures through mutations in a diverse set of genes. In summary, we demonstrate that parasite diversity has rapid effects on host-parasite population dynamics and evolution by selecting for different resistance mutations and affecting the magnitude of bacterial suppression and recovery. Finally, we discuss the implications of phage diversity for their use as biological control agents.