measuring-microbial-mutation-rates-with-the-fluctuation-assay

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 ...

adaptation-at-extremes-life-experimental-evolution-with-extremophile

Adaptation at the Extremes of Life: Experimental Evolution with the Extremophile Archaeon <i>Sulfolobus acidocaldarius</i>

The archaeon Sulfolobus acidocaldarius has emerged as a promising thermophilic model system. Investigating how thermophiles adapt to changing temperatures ...

The University of Manchester

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.

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

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.

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

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.

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

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 β-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 β-lactamase expression. Crucially, blocking this evolutionary pathway by co-administering ceftazidime with the β-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.

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

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 × 10 cells ml). These findings demonstrate a strikingly close and nuanced relationship of ecological factors-stress and population density-with mutation, the fuel of all evolution.

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

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.

Environmental pleiotropy and demographic history direct adaptation under antibiotic selection.

Life on the frontline reveals constraints.

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.

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

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.

Competition delays multi-drug resistance evolution during combination therapy.

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.

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

Within-host environments are likely to present a challenging and stressful environment for opportunistic pathogenic bacteria colonizing from the external environment. How populations of pathogenic bacteria respond to such environmental challenges and how this varies between strains is not well understood. Oxidative stress is one of the defences adopted by the human immune system to confront invading bacteria. In this study, we show that strains of the opportunistic pathogenic bacterium  vary in their eco-evolutionary responses to hydrogen peroxide stress. By quantifying their 24 h growth kinetics across hydrogen peroxide gradients we show that a transmissible epidemic strain isolated from a chronic airway infection of a cystic fibrosis patient, LESB58, is much more susceptible to hydrogen peroxide than either of the reference strains, PA14 or PAO1, with PAO1 showing the lowest susceptibility. Using a 12 day serial passaging experiment combined with a mathematical model, we then show that short-term susceptibility controls the longer-term survival of populations exposed to subinhibitory levels of hydrogen peroxide, but that phenotypic evolutionary responses can delay population extinction. Our model further suggests that hydrogen peroxide driven extinctions are more likely with higher rates of population turnover. Together, these findings suggest that hydrogen peroxide is likely to be an effective defence in host niches where there is high population turnover, which may explain the counter-intuitively high susceptibility of a strain isolated from chronic lung infection, where such ecological dynamics may be slower.

Eco-evolutionary dynamics of experimental  populations under oxidative stress.

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  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 Δ deletant strain - a gene with roles in both quorum sensing and the recycling of methylation products used in '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 Δ 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.

ArticleTotal : 2	Year
Publication title Cited by 35	2019
Publication title	2024

Article	Year
Epistatic interactions between ancestral genotype and beneficial mutations shape evolvability in Pseudomonas aeruginosa. Evolution; international journal of organic evolution\| PubMed ID: 27230588	2016
Spontaneous mutation rate is a plastic trait associated with population density across domains of life. PLoS biology\| PubMed ID: 28837573	2017
Critical Mutation Rate has an Exponential Dependence on Population Size for Eukaryotic-length Genomes with Crossover. Scientific reports\| PubMed ID: 29138394	2017
Identifying and exploiting genes that potentiate the evolution of antibiotic resistance. Nature ecology & evolution\| PubMed ID: 29686236	2018
Opposing effects of final population density and stress on Escherichia coli mutation rate. The ISME journal\| PubMed ID: 30087411	2018
Environmental pleiotropy and demographic history direct adaptation under antibiotic selection. Heredity\| PubMed ID: 30190561	2018
Life on the frontline reveals constraints. Nature ecology & evolution\| PubMed ID: 31611675	2019
Mathematical modelling for antibiotic resistance control policy: do we know enough? BMC infectious diseases\| PubMed ID: 31783803	2019
Competition delays multi-drug resistance evolution during combination therapy. Journal of theoretical biology\| PubMed ID: 33049229	2021
Mutators can drive the evolution of multi-resistance to antibiotics. PLoS genetics\| PubMed ID: 37311005	2023
Eco-evolutionary dynamics of experimental populations under oxidative stress. Microbiology (Reading, England)\| PubMed ID: 37943284	2023
Environmental and genetic influence on the rate and spectrum of spontaneous mutations in . Microbiology (Reading, England)\| PubMed ID: 38687010	2024

Danna R. Gifford

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