Antibiotic resistance and stress in the light of Fisher's model

Fisher's Geometric Model as a Tool to Study Speciation

Interactions between alleles and across environments play an important role in the fitness of hybrids and are at the heart of the speciation process. Fitness landscapes capture these interactions and can be used to model hybrid fitness, helping us to interpret empirical observations and clarify verbal models. Here, we review recent progress in understanding hybridization outcomes through Fisher's geometric model, an intuitive and analytically tractable fitness landscape that captures many fitness patterns observed across taxa. We use case studies to show how the model parameters can be estimated from different types of data and discuss how these estimates can be used to make inferences about the divergence history and genetic architecture. We also highlight some areas where the model's predictions differ from alternative incompatibility-based models, such as the snowball effect and outlier patterns in genome scans.

Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition

Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured if the direct effect of ARGs on host fitness is fixed or if it depends on the host strain, perhaps marking some ARG-host combinations as selective refuges that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse Escherichia spp. strains. Three ARGs (blaTEM-116, cat and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost, but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions can cause the success of ARGs to depend on available host strains, and, to a lesser extent, can cause host strain success to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects across different host strains, and especially the potential of refuge strains to allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.

Unpredictability of the Fitness Effects of Antimicrobial Resistance Mutations Across Environments in Escherichia coli

The evolution of antimicrobial resistance (AMR) in bacteria is a major public health concern, and antibiotic restriction is often implemented to reduce the spread of resistance. These measures rely on the existence of deleterious fitness effects (i.e. costs) imposed by AMR mutations during growth in the absence of antibiotics. According to this assumption, resistant strains will be outcompeted by susceptible strains that do not pay the cost during the period of restriction. The fitness effects of AMR mutations are generally studied in laboratory reference strains grown in standard growth environments; however, the genetic and environmental context can influence the magnitude and direction of a mutation's fitness effects. In this study, we measure how three sources of variation impact the fitness effects of Escherichia coli AMR mutations: the type of resistance mutation, the genetic background of the host, and the growth environment. We demonstrate that while AMR mutations are generally costly in antibiotic-free environments, their fitness effects vary widely and depend on complex interactions between the mutation, genetic background, and environment. We test the ability of the Rough Mount Fuji fitness landscape model to reproduce the empirical data in simulation. We identify model parameters that reasonably capture the variation in fitness effects due to genetic variation. However, the model fails to accommodate the observed variation when considering multiple growth environments. Overall, this study reveals a wealth of variation in the fitness effects of resistance mutations owing to genetic background and environmental conditions, which will ultimately impact their persistence in natural populations.

Contrary effects of increasing temperatures on the spread of antimicrobial resistance in river biofilms

River microbial communities regularly act as the first barrier of defense against the spread of antimicrobial resistance genes (ARGs) that enter environmental microbiomes through wastewater. However, how the invasion dynamics of wastewater-borne ARGs into river biofilm communities will shift due to climate change with increasing average and peak temperatures remains unknown. Here, we aimed to elucidate the effects of increasing temperatures on the naturally occurring river biofilm resistome, as well as the invasion success of foreign ARGs entering through wastewater. Natural biofilms were grown in a low-anthropogenic impact river and transferred to artificial laboratory recirculation flume systems operated at three different temperatures (20°C, 25°C, and 30°C). After 1 week of temperature acclimatization, significant increases in the abundance of the naturally occurring ARGs in biofilms were detected at higher temperatures. After this acclimatization period, biofilms were exposed to a single pulse of wastewater, and the invasion dynamics of wastewater-borne ARGs were analyzed over 2 weeks. After 1 day, wastewater-borne ARGs were able to invade the biofilms successfully with no observable effect of temperature on their relative abundance. However, thereafter, ARGs were lost at a far increased rate at 30°C, with ARG levels dropping to the initial natural levels after 14 days. Contrary to the lower temperatures, ARGs were either lost at slower rates or even able to establish themselves in biofilms with stable relative abundances above natural levels. Hence, higher temperatures come with contrary effects on river biofilm resistomes: naturally occurring ARGs increase in abundance, while foreign, invading ARGs are lost at elevated speeds.IMPORTANCEInfections with bacteria that gained resistance to antibiotics are taking millions of lives annually, with the death toll predicted to increase. River microbial communities act as a first defense barrier against the spread of antimicrobial resistance genes (ARGs) that enter the environment through wastewater after enrichment in human and animal microbiomes. The global increase in temperature due to climate change might disrupt this barrier effect by altering microbial community structure and functions. We consequently explored how increasing temperatures alter ARG spread in river microbial communities. At higher temperatures, naturally occurring ARGs increased in relative abundance. However, this coincided with a decreased success rate of invading foreign ARGs from wastewater to establish themselves in the communities. Therefore, to predict the effects of climate change on ARG spread in river microbiomes, it is imperative to consider if the river ecosystem and its resistome are dominated by naturally occurring or invading foreign ARGs.

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Association between antibiotic resistance and increasing ambient temperature in China: An ecological study with nationwide panel data

Background

Antibiotic resistance leads to longer hospital stays, higher medical costs, and increased mortality. However, research into the relationship between climate change and antibiotic resistance remains inconclusive. This study aims to address the gap in the literature by exploring the association of antibiotic resistance with regional ambient temperature and its changes over time.

Methods

Data were obtained from the China Antimicrobial Surveillance Network (CHINET), monitoring the prevalence of carbapenem-resistant Acinetobacter baumannii (CRAB), Klebsiella pneumoniae (CRKP) and Pseudomonas aeruginosa (CRPA) in 28 provinces/regions over the period from 2005 to 2019. Log-linear regression models were established to determine the association between ambient temperature and antibiotic resistance after adjustment for variations in socioeconomic, health service, and environmental factors.

Findings

A 1 °C increase in average ambient temperature was associated with 1.14-fold increase (95%-CI [1.07–1.23]) in CRKP prevalence and 1.06-fold increase (95%-CI [1.03–1.08]) in CRPA prevalence. There was an accumulative effect of year-by-year changes in ambient temperature, with the four-year sum showing the greatest effect on antibiotic resistance. Higher prevalence of antibiotic resistance was also associated with higher antibiotic consumption, lower density of health facilities, higher density of hospital beds and higher level of corruption.

Interpretation

Higher prevalence of antibiotic resistance is associated with increased regional ambient temperature. The development of antibiotic resistance under rising ambient temperature differs across various strains of bacteria.

Funding

The 10.13039/501100012166National Key R&D Program of China (grant number: 2018YFA0606200), 10.13039/501100001809National Natural Science Foundation of China (grant number: 72074234), 10.13039/501100012476Fundamental Scientific Research Funds for Central Universities, P.R. China (grant number: 22qntd4201), 10.13039/100001547China Medical Board (grant number: CMB-OC-19-337).

Collateral Sensitivity Interactions between Antibiotics Depend on Local Abiotic Conditions

Mutations conferring resistance to one antibiotic can increase (cross-resistance) or decrease (collateral sensitivity) resistance to others. Antibiotic combinations displaying collateral sensitivity could be used in treatments that slow resistance evolution. However, lab-to-clinic translation requires understanding whether collateral effects are robust across different environmental conditions. Here, we isolated and characterized resistant mutants of Escherichia coli using five antibiotics, before measuring collateral effects on resistance to other paired antibiotics. During both isolation and phenotyping, we varied conditions in ways relevant in nature (pH, temperature, and bile). This revealed that local abiotic conditions modified expression of resistance against both the antibiotic used during isolation and other antibiotics. Consequently, local conditions influenced collateral sensitivity in two ways: by favoring different sets of mutants (with different collateral sensitivities) and by modifying expression of collateral effects for individual mutants. These results place collateral sensitivity in the context of environmental variation, with important implications for translation to real-world applications. IMPORTANCE When bacteria become resistant to an antibiotic, the genetic changes involved sometimes increase (cross-resistance) or decrease (collateral sensitivity) their resistance to other antibiotics. Antibiotic combinations showing repeatable collateral sensitivity could be used in treatment to slow resistance evolution. However, collateral sensitivity interactions may depend on the local environmental conditions that bacteria experience, potentially reducing repeatability and clinical application. Here, we show that variation in local conditions (pH, temperature, and bile salts) can influence collateral sensitivity in two ways: by favoring different sets of mutants during bacterial resistance evolution (with different collateral sensitivities to other antibiotics) and by modifying expression of collateral effects for individual mutants. This suggests that translation from the lab to the clinic of new approaches exploiting collateral sensitivity will be influenced by local abiotic conditions.

Heterogeneous fitness landscape cues, pknG low expression, and phthiocerol dimycocerosate low production of Mycobacterium tuberculosis ATCC25618 rpoB S450L in enriched broth

Multidrug-resistant tuberculosis (isoniazid/rifampin[RIF]-resistant TB) ravages developing countries. Fitness is critical in clinical outcomes. Previous studies on RIF-resistant TB (RR-TB) showed competitive fitness gains and losses, with rpoB-S450L as the most isolated/fit mutation. This study measured virulence/resistance genes, phthiocerol dimycocerosate (PDIM) levels and their relationship with rpoB S450L ATCC25618 RR-TB strain fitness. After obtaining 10 different RR-TB GenoType MTBDRplus 2.0-genotyped isolates (with nontyped, S441, H445 and S450 positions), only one S450L isolate (R9, rpoB-S450L ATCC 25618, RR 1 μg/mL) was observed, with H445Y being the most common. A competitive fitness in vitro assay with wild-type (wt) ATCC 25618: R9 1:1 in 50 mL Middlebrook 7H9/OADC was performed, and generation time (G) in vitro and relative fitness were obtained. mRNA and PDIM were extracted on log and stationary phases. Fitness decreased in rpoB S450L and H445Y strains, with heterogeneous fitness cues in three biological replicas of rpoB-S450L: one high and two low fitness replicas. S450L strain had significant pknG increase. Compared with S450L, wt-rpoB showed increased polyketide synthase ppsA expression and high PDIM peak measured by HPLC-MS in log phase compared to S450L. This contrasts with previously increased PDIM in other RR-TB isolates.

Longitudinal sampling of external mucosae in farmed European seabass reveals the impact of water temperature on bacterial dynamics

Fish microbiota are intrinsically linked to health and fitness, but they are highly variable and influenced by both biotic and abiotic factors. Water temperature particularly limits bacterial adhesion and growth, impacting microbial diversity and bacterial infections on the skin and gills. Aquaculture is heavily affected by infectious diseases, especially in warmer months, and industry practices often promote stress and microbial dysbiosis, leading to an increased abundance of potentially pathogenic bacteria. In this regard, fish mucosa health is extremely important because it provides a primary barrier against pathogens. We used 16 rRNA V4 metataxonomics to characterize the skin and gill microbiota of the European seabass, Dicentrarchus labrax, and the surrounding water over 12 months, assessing the impact of water temperature on microbial diversity and function. We show that the microbiota of external mucosae are highly dynamic with consistent longitudinal trends in taxon diversity. Several potentially pathogenic genera (Aliivibrio, Photobacterium, Pseudomonas, and Vibrio) were highly abundant, showing complex interactions with other bacterial genera, some of which with recognized probiotic activity, and were also significantly impacted by changes in temperature. The surrounding water temperature influenced fish microbial composition, structure and function over time (days and months). Additionally, dysbiosis was more frequent in warmer months and during transitions between cold/warm months. We also detected a strong seasonal effect in the fish microbiota, which is likely to result from the compound action of several unmeasured environmental factors (e.g., pH, nutrient availability) beyond temperature. Our results highlight the importance of performing longitudinal studies to assess the impact of environmental factors on fish microbiotas.

Antibiotic-Resistant Bacteria in Aquaculture and Climate Change: A Challenge for Health in the Mediterranean Area

Aquaculture is the productive activity that will play a crucial role in the challenges of the millennium, such as the need for proteins that support humans and the respect for the environment. Aquaculture is an important economic activity in the Mediterranean basin. A great impact is presented, however, by aquaculture practices as they involve the use of antibiotics for treatment and prophylaxis. As a consequence of the use of antibiotics in aquaculture, antibiotic resistance is induced in the surrounding bacteria in the column water, sediment, and fish-associated bacterial strains. Through horizontal gene transfer, bacteria can diffuse antibiotic-resistance genes and mobile resistance genes further spreading genetic determinants. Once triggered, antibiotic resistance easily spreads among aquatic microbial communities and, from there, can reach human pathogenic bacteria, making vain the use of antibiotics for human health. Climate change claims a significant role in this context, as rising temperatures can affect cell physiology in bacteria in the same way as antibiotics, causing antibiotic resistance to begin with. The Mediterranean Sea represents a 'hot spot' in terms of climate change and aspects of antibiotic resistance in aquaculture in this area can be significantly amplified, thus increasing threats to human health. Practices must be adopted to counteract negative impacts on human health, with a reduction in the use of antibiotics as a pivotal point. In the meantime, it is necessary to act against climate change by reducing anthropogenic impacts, for example by reducing CO2 emissions into the atmosphere. The One Health type approach, which involves the intervention of different skills, such as veterinary, ecology, and medicine in compliance with the principles of sustainability, is necessary and strongly recommended to face these important challenges for human and animal health, and for environmental safety in the Mediterranean area.

Adaptation at different points along antibiotic concentration gradients

Antibiotic concentrations vary dramatically in the body and the environment. Hence, understanding the dynamics of resistance evolution along antibiotic concentration gradients is critical for predicting and slowing the emergence and spread of resistance. While it has been shown that increasing the concentration of an antibiotic slows resistance evolution, how adaptation to one antibiotic concentration correlates with fitness at other points along the gradient has not received much attention. Here, we selected populations of Escherichia coli at several points along a concentration gradient for three different antibiotics, asking how rapidly resistance evolved and whether populations became specialized to the antibiotic concentration they were selected on. Populations selected at higher concentrations evolved resistance more slowly but exhibited equal or higher fitness across the whole gradient. Populations selected at lower concentrations evolved resistance rapidly, but overall fitness in the presence of antibiotics was lower. However, these populations readily adapted to higher concentrations upon subsequent selection. Our results indicate that resistance management strategies must account not only for the rates of resistance evolution but also for the fitness of evolved strains.

DNA Breaks-Mediated Fitness Cost Reveals RNase HI as a New Target for Selectively Eliminating Antibiotic-Resistant Bacteria

Antibiotic resistance often generates defects in bacterial growth called fitness cost. Understanding the causes of this cost is of paramount importance, as it is one of the main determinants of the prevalence of resistances upon reducing antibiotics use. Here we show that the fitness costs of antibiotic resistance mutations that affect transcription and translation in Escherichia coli strongly correlate with DNA breaks, which are generated via transcription–translation uncoupling, increased formation of RNA–DNA hybrids (R-loops), and elevated replication–transcription conflicts. We also demonstrated that the mechanisms generating DNA breaks are repeatedly targeted by compensatory evolution, and that DNA breaks and the cost of resistance can be increased by targeting the RNase HI, which specifically degrades R-loops. We further show that the DNA damage and thus the fitness cost caused by lack of RNase HI function drive resistant clones to extinction in populations with high initial frequency of resistance, both in laboratory conditions and in a mouse model of gut colonization. Thus, RNase HI provides a target specific against resistant bacteria, which we validate using a repurposed drug. In summary, we revealed key mechanisms underlying the fitness cost of antibiotic resistance mutations that can be exploited to specifically eliminate resistant bacteria.

Using ecological coexistence theory to understand antibiotic resistance and microbial competition

Tackling antibiotic resistance necessitates deep understanding of how resource competition within and between species modulates the fitness of resistant microbes. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and interspecific competition, but the significance of this body of theory to the problem of antibiotic resistance has been largely overlooked. In this Perspective, we draw on emerging ecological theory to illustrate how changes in resource niche overlap can be equally important as changes in competitive ability for understanding costs of resistance and the persistence of resistant pathogens in microbial communities. We then show how different temporal patterns of resource and antibiotic supply, alongside trade-offs in competitive ability at high and low resource concentrations, can have diametrically opposing consequences for the coexistence and exclusion of resistant and susceptible strains. These insights highlight numerous opportunities for innovative experimental and theoretical research into the ecological dimensions of antibiotic resistance.

Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation

In an era of pervasive anthropogenic ecological disturbances, there is a pressing need to understand the factors that constitute community response and resilience. A detailed understanding of disturbance response needs to go beyond associations and incorporate features of disturbances, species traits, rapid evolution and dispersal. Multispecies microbial communities that experience antibiotic perturbation represent a key system with important medical dimensions. However, previous microbiome studies on this theme have relied on high-throughput sequencing data from uncultured species without the ability to explicitly account for the role of species traits and immigration. Here, we serially passage a 34-species defined bacterial community through different levels of pulse antibiotic disturbance, manipulating the presence or absence of species immigration. To understand the ecological community response measured using amplicon sequencing, we combine initial trait data measured for each species separately and metagenome sequencing data revealing adaptive mutations during the experiment. We found that the ecological community response was highly repeatable within the experimental treatments, which could be attributed in part to key species traits (antibiotic susceptibility and growth rate). Increasing antibiotic levels were also coupled with an increasing probability of species extinction, making species immigration critical for community resilience. Moreover, we detected signals of antibiotic-resistance evolution occurring within species at the same time scale, leaving evolutionary changes in communities despite recovery at the species compositional level. Together, these observations reveal a disturbance response that presents as classic species sorting, but is nevertheless accompanied by rapid within-species evolution.

Warmer temperatures enhance beneficial mutation effects

Temperature determines the rates of all biochemical and biophysical processes, and is also believed to be a key driver of macroevolutionary patterns. It is suggested that physiological constraints at low temperatures may diminish the fitness advantages of otherwise beneficial mutations; by contrast, relatively high, benign, temperatures allow beneficial mutations to efficiently show their phenotypic effects. To experimentally test this "mutational effects" mechanism, we examined the fitness effects of mutations across a temperature gradient using bacterial genotypes from the early stage of a mutation accumulation experiment with Escherichia coli. While the incidence of beneficial mutations did not significantly change across environmental temperatures, the number of mutations that conferred strong beneficial fitness effects was greater at higher temperatures. The results therefore support the hypothesis that warmer temperatures increase the chance and magnitude of positive selection, with implications for explaining the geographic patterns in evolutionary rates and understanding contemporary evolution under global warming.

Dysbiosis individualizes the fitness effect of antibiotic resistance in the mammalian gut

In the absence of antibiotics, it is essential that antibiotic resistance has a fitness cost for microorganisms if suspending antibiotics treatment is to be a useful strategy for reducing antibiotic resistance. However, the cost of antibiotic resistance within the complex ecosystem of the mammalian gut is not well understood. Here, using mice, we show that the same antibiotic resistance mutation can reduce fitness in one host, while being neutral or even increasing fitness in other hosts. Such antagonistic pleiotropy is shaped by the microbiota because resistance in germ-free mice is consistently costly across all hosts, and the host-specific effect on antibiotic resistance is reduced in hosts with similar microbiotas. Using an eco-evolutionary model of competition for resources, we identify a general mechanism that underlies between-host variation and predicts that the dynamics of compensatory evolution of resistant bacteria should be host specific, a prediction that was supported by experimental evolution in vivo. The microbiome of each human is close to unique, and our results suggest that the short-term cost of resistances and their long-term within-host evolution are also highly personalized, a finding that may contribute to the observed variable outcome of withdrawing antibiotics to reduce resistance levels.

Compounding Effects of Climate Warming and Antibiotic Resistance

Bacteria have evolved diverse mechanisms to survive environments with antibiotics. Temperature is both a key factor that affects the survival of bacteria in the presence of antibiotics and an environmental trait that is drastically increasing due to climate change. Therefore, it is timely and important to understand links between temperature changes and selection of antibiotic resistance. This review examines these links by synthesizing results from laboratories, hospitals, and environmental studies. First, we describe the transient physiological responses to temperature that alter cellular behavior and lead to antibiotic tolerance and persistence. Second, we focus on the link between thermal stress and the evolution and maintenance of antibiotic resistance mutations. Finally, we explore how local and global changes in temperature are associated with increases in antibiotic resistance and its spread. We suggest that a multidisciplinary, multiscale approach is critical to fully understand how temperature changes are contributing to the antibiotic crisis.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Ecology determines how low antibiotic concentration impacts community composition and horizontal transfer of resistance genes

Low concentrations of antibiotics have numerous effects on bacteria. However, it is unknown whether ecological factors such as trophic interactions and spatial structuring influence the effects of low concentrations of antibiotics on multispecies microbial communities. Here, we address this question by investigating the effects of low antibiotic concentration on community composition and horizontal transfer of an antibiotic resistance plasmid in a 62-strain bacterial community in response to manipulation of the spatial environment and presence of predation. The strong effects of antibiotic treatment on community composition depend on the presence of predation and spatial structuring that have strong community effects on their own. Overall, we find plasmid transfer to diverse recipient taxa. Plasmid transfer is likely to occur to abundant strains, occurs to a higher number of strains in the presence of antibiotic, and also occurs to low-abundance strains in the presence of spatial structures. These results fill knowledge gaps concerning the effects of low antibiotic concentrations in complex ecological settings.

Evolutionary Rescue over a Fitness Landscape

Evolutionary rescue describes a situation where adaptive evolution prevents the extinction of a population facing a stressing environment. Models of evolutionary rescue could in principle be used to predict the level of stress beyond which extinction becomes likely for species of conservation concern, or, conversely, the treatment levels most likely to limit the emergence of resistant pests or pathogens. Stress levels are known to affect both the rate of population decline (demographic effect) and the speed of adaptation (evolutionary effect), but the latter aspect has received less attention. Here, we address this issue using Fisher's geometric model of adaptation. In this model, the fitness effects of mutations depend both on the genotype and the environment in which they arise. In particular, the model introduces a dependence between the level of stress, the proportion of rescue mutants, and their costs before the onset of stress. We obtain analytic results under a strong-selection-weak-mutation regime, which we compare to simulations. We show that the effect of the environment on evolutionary rescue can be summarized into a single composite parameter quantifying the effective stress level, which is amenable to empirical measurement. We describe a narrow characteristic stress window over which the rescue probability drops from very likely to very unlikely as the level of stress increases. This drop is sharper than in previous models, as a result of the decreasing proportion of stress-resistant mutations as stress increases. We discuss how to test these predictions with rescue experiments across gradients of stress.

The fitness costs and benefits of antibiotic resistance in drug-free microenvironments encountered in the human body

The relationship between bacterial drug resistance and growth fitness is a contentious topic, but some antibiotic resistance mutations clearly have a fitness cost in the laboratory. Whether these costs translate into deleterious effects in natural habitats is less certain however. Previously, fitness effects of resistance mutations were mostly characterized in nutrient-rich, fast-growth conditions, which bacteria rarely encounter in natural habitats. Carbon, phosphate, iron or oxygen limitations are conditions met by bacterial pathogens in various compartments of the human body. Here, we measured the fitness of four different rpoB mutations commonly found in rifampicin-resistant bacterial isolates. The fitness properties and the emergence of these and other alleles were studied in Escherichia coli populations growing under nutrient excess and in four different nutrient-limited states. Consistent with previous findings, all four mutations exhibited deleterious fitness effects under nutrient-rich conditions. In stark contrast, we found positive or neutral fitness effects under nutrient-limited conditions. Two particular rpoB alleles had a remarkable fitness increase under phosphate limitation and these alleles arose to high frequencies specifically under phosphate limitation. These findings suggest that it is not meaningful to draw general conclusions on fitness costs without considering bacterial microenvironments in humans and other animals.

Reversing resistance: different routes and common themes across pathogens

Resistance spreads rapidly in pathogen or pest populations exposed to biocides, such as fungicides and antibiotics, and in many cases new biocides are in short supply. How can resistance be reversed in order to prolong the effectiveness of available treatments? Some key parameters affecting reversion of resistance are well known, such as the fitness cost of resistance. However, the population biological processes that actually cause resistance to persist or decline remain poorly characterized, and consequently our ability to manage reversion of resistance is limited. Where do susceptible genotypes that replace resistant lineages come from? What is the epidemiological scale of reversion? What information do we need to predict the mechanisms or likelihood of reversion? Here, we define some of the population biological processes that can drive reversion, using examples from a wide range of taxa and biocides. These processes differ primarily in the origin of revertant genotypes, but also in their sensitivity to factors such as coselection and compensatory evolution that can alter the rate of reversion, and the likelihood that resistance will re-emerge upon re-exposure to biocides. We therefore argue that discriminating among different types of reversion allows for better prediction of where resistance is most likely to persist.

Fisher's geometrical model and the mutational patterns of antibiotic resistance across dose gradients

Fisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in several situations, for example adaptation to antibiotics, but comes with limitations, so that more mechanistic approaches are often preferred to interpret experimental data. It might be possible however to extend FGM assumptions to better account for mutational data. This is theoretically challenging in the context of antibiotic resistance because resistance mutations are assumed to be rare. In this article, we show with Escherichia coli how the fitness effects of resistance mutations screened at different doses of nalidixic acid vary across a dose-gradient. We found experimental patterns qualitatively consistent with the basic FGM (rate of resistance across doses, gamma distributed costs) but also unexpected patterns such as a decreasing mean cost of resistance with increasing screen dose. We show how different extensions involving mutational modules and variations in trait covariance across environments, can be discriminated based on these data. Overall, simple extensions of the FGM accounted well for complex mutational effects of resistance mutations across antibiotic doses.

The Nonstationary Dynamics of Fitness Distributions: Asexual Model with Epistasis and Standing Variation

Various models describe asexual evolution by mutation, selection, and drift. Some focus directly on fitness, typically modeling drift but ignoring or simplifying both epistasis and the distribution of mutation effects (traveling wave models). Others follow the dynamics of quantitative traits determining fitness (Fisher's geometric model), imposing a complex but fixed form of mutation effects and epistasis, and often ignoring drift. In all cases, predictions are typically obtained in high or low mutation rate limits and for long-term stationary regimes, thus losing information on transient behaviors and the effect of initial conditions. Here, we connect fitness-based and trait-based models into a single framework, and seek explicit solutions even away from stationarity. The expected fitness distribution is followed over time via its cumulant generating function, using a deterministic approximation that neglects drift. In several cases, explicit trajectories for the full fitness distribution are obtained for arbitrary mutation rates and standing variance. For nonepistatic mutations, especially with beneficial mutations, this approximation fails over the long term but captures the early dynamics, thus complementing stationary stochastic predictions. The approximation also handles several diminishing returns epistasis models (e.g., with an optimal genotype); it can be applied at and away from equilibrium. General results arise at equilibrium, where fitness distributions display a "phase transition" with mutation rate. Beyond this phase transition, in Fisher's geometric model, the full trajectory of fitness and trait distributions takes a simple form; robust to the details of the mutant phenotype distribution. Analytical arguments are explored regarding why and when the deterministic approximation applies.

Macrophage adaptation leads to parallel evolution of genetically diverse Escherichia coli small-colony variants with increased fitness in vivo and antibiotic collateral sensitivity

Small-colony variants (SCVs) are commonly observed in evolution experiments and clinical isolates, being associated with antibiotic resistance and persistent infections. We recently observed the repeated emergence of Escherichia coli SCVs during adaptation to the interaction with macrophages. To identify the genetic targets underlying the emergence of this clinically relevant morphotype, we performed whole-genome sequencing of independently evolved SCV clones. We uncovered novel mutational targets, not previously associated with SCVs (e.g. cydA, pepP) and observed widespread functional parallelism. All SCV clones had mutations in genes related to the electron-transport chain. As SCVs emerged during adaptation to macrophages, and often show increased antibiotic resistance, we measured SCV fitness inside macrophages and measured their antibiotic resistance profiles. SCVs had a fitness advantage inside macrophages and showed increased aminoglycoside resistance in vitro, but had collateral sensitivity to other antibiotics (e.g. tetracycline). Importantly, we observed similar results in vivo. SCVs had a fitness advantage upon colonization of the mouse gut, which could be tuned by antibiotic treatment: kanamycin (aminoglycoside) increased SCV fitness, but tetracycline strongly reduced it. Our results highlight the power of using experimental evolution as the basis for identifying the causes and consequences of adaptation during host-microbe interactions.

Enhanced Survival of Rifampin- and Streptomycin-Resistant Escherichia coli Inside Macrophages

The evolution of multiple-antibiotic-resistant bacteria is an increasing global problem. Even though mutations causing resistance usually incur a fitness cost in the absence of antibiotics, the magnitude of such costs varies across environments and genomic backgrounds. We studied how the combination of mutations that confer resistance to rifampin (Rif(r)) and streptomycin (Str(r)) affects the fitness of Escherichia coli when it interacts with cells from the immune system, i.e., macrophages (Mϕs). We found that 13 Rif(r) Str(r) doubly resistant genotypes, of the 16 tested, show a survival advantage inside Mϕs, indicating that double resistance can be highly beneficial in this environment. Our results suggest that there are multiple paths to acquire multiple-drug resistance in this context, i.e., if a clone carrying Rif(r) allele H526 or S531 acquires a second mutation conferring Str(r), the resulting double mutant has a high probability of showing increased survival inside Mϕs. On the other hand, we found two cases of sign epistasis between mutations, leading to a significant decrease in bacterial survival. Remarkably, infection of Mϕs with one of these combinations, K88R+H526Y, resulted in an altered pattern of gene expression in the infected Mϕs. This indicates that the fitness effects of resistance may depend on the pattern of gene expression of infected host cells. Notwithstanding the benefits of resistance found inside Mϕs, the Rif(r) Str(r) mutants have massive fitness costs when the bacteria divide outside Mϕs, indicating that the maintenance of double resistance may depend on the time spent within and outside phagocytic cells.

Fisher's geometric model predicts the effects of random mutations when tested in the wild

Fisher's geometric model of adaptation (FGM) has been the conceptual foundation for studies investigating the genetic basis of adaptation since the onset of the neo Darwinian synthesis. FGM describes adaptation as the movement of a genotype toward a fitness optimum due to beneficial mutations. To date, one prediction of FGM, the probability of improvement is related to the distance from the optimum, has only been tested in microorganisms under laboratory conditions. There is reason to believe that results might differ under natural conditions where more mutations likely affect fitness, and where environmental variance may obscure the expected pattern. We chemically induced mutations into a set of 19 Arabidopsis thaliana accessions from across the native range of A. thaliana and planted them alongside the premutated founder lines in two habitats in the mid-Atlantic region of the United States under field conditions. We show that FGM is able to predict the outcome of a set of random induced mutations on fitness in a set of A. thaliana accessions grown in the wild: mutations are more likely to be beneficial in relatively less fit genotypes. This finding suggests that FGM is an accurate approximation of the process of adaptation under more realistic ecological conditions.

Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance

The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the "morbidostat", a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations-an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution.

Detecting epistasis from an ensemble of adapting populations

The role that epistasis plays during adaptation remains an outstanding problem, which has received considerable attention in recent years. Most of the recent empirical studies are based on ensembles of replicate populations that adapt in a fixed, laboratory controlled condition. Researchers often seek to infer the presence and form of epistasis in the fitness landscape from the time evolution of various statistics averaged across the ensemble of populations. Here, we provide a rigorous analysis of what quantities, drawn from time series of such ensembles, can be used to infer epistasis for populations evolving under weak mutation on finite-site fitness landscapes. First, we analyze the mean fitness trajectory-that is, the time course of the ensemble average fitness. We show that for any epistatic fitness landscape and starting genotype, there always exists a non-epistatic fitness landscape that produces the exact same mean fitness trajectory. Thus, the presence of epistasis is not identifiable from the mean fitness trajectory. By contrast, we show that two other ensemble statistics-the time evolution of the fitness variance across populations, and the time evolution of the mean number of substitutions-can detect certain forms of epistasis in the underlying fitness landscape.

Multiple Resistance at No Cost: Rifampicin and Streptomycin a Dangerous Liaison in the Spread of Antibiotic Resistance

Evidence is mounting that epistasis is widespread among mutations. The cost of carrying two deleterious mutations, or the advantage of acquiring two beneficial alleles, is typically lower that the sum of their individual effects. Much less is known on epistasis between beneficial and deleterious mutations, even though this is key to the amount of genetic hitchhiking that may occur during evolution. This is particularly important in the context of antibiotic resistance: Most resistances are deleterious, but some can be beneficial and remarkably rifampicin resistance can emerge de novo in populations evolving without antibiotics. Here we show pervasive positive pairwise epistasis on Escherichia coli fitness between beneficial mutations, which confer resistance to rifampicin, and deleterious mutations, which confer resistance to streptomycin. We find that 65% of double resistant strains outcompete sensitive bacteria in an environment devoid of antibiotics. Weak beneficial mutations may therefore overcome strong deleterious mutations and can even render double mutants strong competitors.

Mutational signatures indicative of environmental stress in bacteria

Evolutionary innovations are dependent on mutations. Mutation rates are increased by adverse conditions in the laboratory, but there is no evidence that stressful environments that do not directly impact on DNA leave a mutational imprint on extant genomes. Mutational spectra in the laboratory are normally determined with unstressed cells but are unavailable with stressed bacteria. To by-pass problems with viability, selection effects, and growth rate differences due to stressful environments, in this study we used a set of genetically engineered strains to identify the mutational spectrum associated with nutritional stress. The strain set members each had a fixed level of the master regulator protein, RpoS, which controls the general stress response of Escherichia coli. By assessing mutations in cycA gene from 485 cycloserine resistant mutants collected from as many independent cultures with three distinct perceived stress (RpoS) levels, we were able establish a dose-dependent relationship between stress and mutational spectra. The altered mutational patterns included base pair substitutions, single base pair indels, longer indels, and transpositions of different insertion sequences. The mutational spectrum of low-RpoS cells closely matches the genome-wide spectrum previously generated in laboratory environments, while the spectra of high RpoS, high perceived stress cells more closely matches spectra found in comparisons of extant genomes. Our results offer an explanation of the uneven mutational profiles such as the transition-transversion biases observed in extant genomes and provide a framework for assessing the contribution of stress-induced mutagenesis to evolutionary transitions and the mutational emergence of antibiotic resistance and disease states.

The Utility of Fisher's Geometric Model in Evolutionary Genetics

The accumulation of data on the genomic bases of adaptation has triggered renewed interest in theoretical models of adaptation. Among these models, Fisher Geometric Model (FGM) has received a lot of attention over the last two decades. FGM is based on a continuous multidimensional phenotypic landscape, but it is for the emerging properties of individual mutation effects that it is mostly used. Despite an apparent simplicity and a limited number of parameters, FGM integrates a full model of mutation and epistatic interactions that allows the study of both beneficial and deleterious mutations, and subsequently the fate of evolving populations. In this review, I present the different properties of FGM and the qualitative and quantitative support they have received from experimental evolution data. I later discuss how to estimate the different parameters of the model and outline some future directions to connect FGM and the molecular determinants of adaptation.

The probability of improvement in Fisher's geometric model: a probabilistic approach

Fisher developed his geometric model to support the micro-mutationalism hypothesis which claims that small mutations are more likely to be beneficial and therefore to contribute to evolution and adaptation. While others have provided a general solution to the model using geometric approaches, we derive an equivalent general solution using a probabilistic approach. Our approach to Fisher's geometric model provides alternative intuition and interpretation of the solution in terms of the model's parameters: for mutation to improve a phenotype, its relative beneficial effect must be larger than the ratio of its total effect and twice the difference between the current phenotype and the optimal one. Our approach provides new insight into this classical model of adaptive evolution.

Different tradeoffs result from alternate genetic adaptations to a common environment

Fitness tradeoffs are often assumed by evolutionary theory, yet little is known about the frequency of fitness tradeoffs during stress adaptation. Even less is known about the genetic factors that confer these tradeoffs and whether alternative adaptive mutations yield contrasting tradeoff dynamics. We addressed these issues using 114 clones of Escherichia coli that were evolved independently for 2,000 generations under thermal stress (42.2 °C). For each clone, we measured their fitness relative to the ancestral clone at 37 °C and 20 °C. Tradeoffs were common at 37 °C but more prevalent at 20 °C, where 56% of clones were outperformed by the ancestor. We also characterized the upper and lower thermal boundaries of each clone. All clones shifted their upper boundary to at least 45 °C; roughly half increased their lower niche boundary concomitantly, representing a shift of thermal niche. The remaining clones expanded their thermal niche by increasing their upper limit without a commensurate increase of lower limit. We associated these niche dynamics with genotypes and confirmed associations by engineering single mutations in the rpoB gene, which encodes the beta subunit of RNA polymerase, and the rho gene, which encodes a termination factor. Single mutations in the rpoB gene exhibit antagonistic pleiotropy, with fitness tradeoffs at 18 °C and fitness benefits at 42.2 °C. In contrast, a mutation within the rho transcriptional terminator, which defines an alternative adaptive pathway from that of rpoB, had no demonstrable effect on fitness at 18 °C. This study suggests that two different genetic pathways toward high-temperature adaptation have contrasting effects with respect to thermal tradeoffs.

Tetracycline modifies competitive interactions in experimental microcosms containing bacteria isolated from freshwater

Interspecific microbial interactions are important for community structure, function and evolution, but it is not fully understood how interactions among bacterial species are influenced by some types of abiotic environmental variation, such as exposure to antibiotics. We tested for the effect of an antibiotic, tetracycline, on interspecific interactions in vitro among four species of aquatic bacteria isolated from European water bodies. Interactions among species in experimental microcosms containing artificial lake water (ALW) supplemented with glucose were largely competitive, as detected by comparing mixed cultures to pure cultures of their constituent species. Sublethal concentrations of tetracycline changed the relative competitive abilities of different species and revealed considerable variation in antibiotic sensitivity, but did not reduce the average strength of competition. Interspecific interactions at a given concentration were largely predictable from growth in pure cultures and indirect interactions with other species. These results suggest that antibiotics such as tetracycline may have important consequences for interactions among bacterial species, but in our experiments this was because species varied in their capacities for growth in the presence of tetracycline, rather than reduced competition at increasing tetracycline concentrations.

Costs of antibiotic resistance - separating trait effects and selective effects

Antibiotic resistance can impair bacterial growth or competitive ability in the absence of antibiotics, frequently referred to as a ‘cost’ of resistance. Theory and experiments emphasize the importance of such effects for the distribution of resistance in pathogenic populations. However, recent work shows that costs of resistance are highly variable depending on environmental factors such as nutrient supply and population structure, as well as genetic factors including the mechanism of resistance and genetic background. Here, we suggest that such variation can be better understood by distinguishing between the effects of resistance mechanisms on individual traits such as growth rate or yield (‘trait effects’) and effects on genotype frequencies over time (‘selective effects’). We first give a brief overview of the biological basis of costs of resistance and how trait effects may translate to selective effects in different environmental conditions. We then review empirical evidence of genetic and environmental variation of both types of effects and how such variation may be understood by combining molecular microbiological information with concepts from evolution and ecology. Ultimately, disentangling different types of costs may permit the identification of interventions that maximize the cost of resistance and therefore accelerate its decline.

Neutral null models for diversity in serial transfer evolution experiments

Evolution experiments with microorganisms coupled with genome-wide sequencing now allow for the systematic study of population genetic processes under a wide range of conditions. In learning about these processes in natural, sexual populations, neutral models that describe the behavior of diversity and divergence summaries have played a pivotal role. It is therefore natural to ask whether neutral models, suitably modified, could be useful in the context of evolution experiments. Here, we introduce coalescent models for polymorphism and divergence under the most common experimental evolution assay, a serial transfer experiment. This relatively simple setting allows us to address several issues that could affect diversity patterns in evolution experiments, whether selection is operating or not: the transient behavior of neutral polymorphism in an experiment beginning from a single clone, the effects of randomness in the timing of cell division and noisiness in population size in the dilution stage. In our analyses and discussion, we emphasize the implications for experiments aimed at measuring diversity patterns and making inferences about population genetic processes based on these measurements.

The reproducibility of adaptation in the light of experimental evolution with whole genome sequencing

A key question in evolutionary biology is the reproducibility of adaptation. This question can now be quantitatively analyzed using experimental evolution coupled to whole genome sequencing (WGS). With complete sequence data, one can assess convergence among replicate populations. In turn, convergence reflects the action of natural selection and also the breadth of the field of possible adaptive solutions. That is, it provides insight into how many genetic solutions or adaptive paths may lead to adaptation in a given environment. Convergence is both a property of an adaptive landscape and, reciprocally, a tool to study that landscape. In this chapter we present the links between convergence and the properties of adaptive landscapes with respect to two types of microbial experimental evolution. The first tries to reconstruct a full adaptive landscape using a handful of carefully identified mutations (the reductionist approach), while the second uses WGS of replicate experiments to infer properties of the adaptive landscape. Reductionist approaches have highlighted the importance of epistasis in shaping the adaptive landscape, but have also uncovered a wide diversity of landscape architectures. The WGS approach has uncovered a very high diversity of beneficial mutations that affect a limited set of genes or functions and also suggests some shortcomings of the reductionist approach. We conclude that convergence may be better defined at an integrated level, such as the genic level or even at a phenotypic level, and that integrated mechanistic models derived from systems biology may offer an interesting perspective for the analysis of convergence at all levels.

Shifting fitness landscapes in response to altered environments

The role of adaptation in molecular evolution has been contentious for decades. Here, we shed light on the adaptive potential in Saccharomyces cerevisiae by presenting systematic fitness measurements for all possible point mutations in a region of Hsp90 under four environmental conditions. Under elevated salinity, we observe numerous beneficial mutations with growth advantages up to 7% relative to the wild type. All of these beneficial mutations were observed to be associated with high costs of adaptation. We thus demonstrate that an essential protein can harbor adaptive potential upon an environmental challenge, and report a remarkable fit of the data to a version of Fisher's geometric model that focuses on the fitness trade-offs between mutations in different environments.

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.

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.

Predicting the evolution of antibiotic resistance

Mutations causing antibiotic resistance are often associated with a cost in the absence of antibiotics. Surprisingly, a new study found that bacteria adapting to increased temperature became resistant to rifampicin. By studying the consequences of the involved mutations in different conditions and genetic backgrounds, the authors illustrate how knowledge of two fundamental genetic properties, pleiotropy and epistasis, may help to predict the evolution of antibiotic resistance.

See research article http://www.biomedcentral.com/1471-2148/13/50

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.