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

Laboratory Evolution of Antimicrobial Resistance in Bacteria to Develop Rational Treatment Strategies

Laboratory evolution studies, particularly with Escherichia coli, have yielded invaluable insights into the mechanisms of antimicrobial resistance (AMR). Recent investigations have illuminated that, with repetitive antibiotic exposures, bacterial populations will adapt and eventually become tolerant and resistant to the drugs. Through intensive analyses, these inquiries have unveiled instances of convergent evolution across diverse antibiotics, the pleiotropic effects of resistance mutations, and the role played by loss-of-function mutations in the evolutionary landscape. Moreover, a quantitative analysis of multidrug combinations has shed light on collateral sensitivity, revealing specific drug combinations capable of suppressing the acquisition of resistance. This review article introduces the methodologies employed in the laboratory evolution of AMR in bacteria and presents recent discoveries concerning AMR mechanisms derived from laboratory evolution. Additionally, the review outlines the application of laboratory evolution in endeavors to formulate rational treatment strategies.

Chance Favors the Prepared Genomes: Horizontal Transfer Shapes the Emergence of Antibiotic Resistance Mutations in Core Genes

Bacterial lineages acquire novel traits at diverse rates in part because the genetic background impacts the successful acquisition of novel genes by horizontal transfer. Yet, how horizontal transfer affects the subsequent evolution of core genes remains poorly understood. Here, we studied the evolution of resistance to quinolones in Escherichia coli accounting for population structure. We found 60 groups of genes whose gain or loss induced an increase in the probability of subsequently becoming resistant to quinolones by point mutations in the gyrase and topoisomerase genes. These groups include functions known to be associated with direct mitigation of the effect of quinolones, with metal uptake, cell growth inhibition, biofilm formation, and sugar metabolism. Many of them are encoded in phages or plasmids. Although some of the chronologies may reflect epidemiological trends, many of these groups encoded functions providing latent phenotypes of antibiotic low-level resistance, tolerance, or persistence under quinolone treatment. The mutations providing resistance were frequent and accumulated very quickly. Their emergence was found to increase the rate of acquisition of other antibiotic resistances setting the path for multidrug resistance. Hence, our findings show that horizontal gene transfer shapes the subsequent emergence of adaptive mutations in core genes. In turn, these mutations further affect the subsequent evolution of resistance by horizontal gene transfer. Given the substantial gene flow within bacterial genomes, interactions between horizontal transfer and point mutations in core genes may be a key to the success of adaptation processes.

Identification of combinatorial mutations associated with colistin resistance in Shewanella algae

Colistin is a last-resort antibiotic used to treat infections caused by drug-resistant gram-negative bacteria. However, the genetic mechanisms underlying colistin resistance in Shewanella algae are not well understood. In this study, we sequenced and compared the genomes of 23 mcr-negative colistin-resistant and sensitive S. algae samples from various sources. We applied a computational approach to identify combinatorial mutations associated with colistin resistance. Our analysis revealed a combination of three mutations (PmrB 451, PmrE168, PmrH292) that were strongly associated with colistin resistance in S. algae. This study provides insights into the genetic mechanisms of colistin resistance in S. algae and demonstrates the utility of a computational approach for identifying epistatic interactions among mutations. Identifying the genetic mutations responsible for colistin resistance in S. algae can inform the development of new treatments or strategies to combat infections caused by this emerging pathogen.

The fitness cost of horizontally transferred and mutational antimicrobial resistance in Escherichia coli

Antimicrobial resistance (AMR) in bacteria implies a tradeoff between the benefit of resistance under antimicrobial selection pressure and the incurred fitness cost in the absence of antimicrobials. The fitness cost of a resistance determinant is expected to depend on its genetic support, such as a chromosomal mutation or a plasmid acquisition, and on its impact on cell metabolism, such as an alteration in an essential metabolic pathway or the production of a new enzyme. To provide a global picture of the factors that influence AMR fitness cost, we conducted a systematic review and meta-analysis focused on a single species, Escherichia coli. By combining results from 46 high-quality studies in a multilevel meta-analysis framework, we find that the fitness cost of AMR is smaller when provided by horizontally transferable genes such as those encoding beta-lactamases, compared to mutations in core genes such as those involved in fluoroquinolone and rifampicin resistance. We observe that the accumulation of acquired AMR genes imposes a much smaller burden on the host cell than the accumulation of AMR mutations, and we provide quantitative estimates of the additional cost of a new gene or mutation. These findings highlight that gene acquisition is more efficient than the accumulation of mutations to evolve multidrug resistance, which can contribute to the observed dominance of horizontally transferred genes in the current AMR epidemic.

Revisiting Antibiotic Resistance: Mechanistic Foundations to Evolutionary Outlook

Antibiotics are the pivotal pillar of contemporary healthcare and have contributed towards its advancement over the decades. Antibiotic resistance emerged as a critical warning to public wellbeing because of unsuccessful management efforts. Resistance is a natural adaptive tool that offers selection pressure to bacteria, and hence cannot be stopped entirely but rather be slowed down. Antibiotic resistance mutations mostly diminish bacterial reproductive fitness in an environment without antibiotics; however, a fraction of resistant populations 'accidentally' emerge as the fittest and thrive in a specific environmental condition, thus favouring the origin of a successful resistant clone. Therefore, despite the time-to-time amendment of treatment regimens, antibiotic resistance has evolved relentlessly. According to the World Health Organization (WHO), we are rapidly approaching a 'post-antibiotic' era. The knowledge gap about antibiotic resistance and room for progress is evident and unified combating strategies to mitigate the inadvertent trends of resistance seem to be lacking. Hence, a comprehensive understanding of the genetic and evolutionary foundations of antibiotic resistance will be efficacious to implement policies to force-stop the emergence of resistant bacteria and treat already emerged ones. Prediction of possible evolutionary lineages of resistant bacteria could offer an unswerving impact in precision medicine. In this review, we will discuss the key molecular mechanisms of resistance development in clinical settings and their spontaneous evolution.

Lanza V, Rodríguez-Beltrán J, Galán JC, San Millán A, Cantón R, Coque TM. Evolutionary Pathways and Trajectories in Antibiotic Resistance

Evolution is the hallmark of life. Descriptions of the evolution of microorganisms have provided a wealth of information, but knowledge regarding "what happened" has precluded a deeper understanding of "how" evolution has proceeded, as in the case of antimicrobial resistance. The difficulty in answering the "how" question lies in the multihierarchical dimensions of evolutionary processes, nested in complex networks, encompassing all units of selection, from genes to communities and ecosystems. At the simplest ontological level (as resistance genes), evolution proceeds by random (mutation and drift) and directional (natural selection) processes; however, sequential pathways of adaptive variation can occasionally be observed, and under fixed circumstances (particular fitness landscapes), evolution is predictable. At the highest level (such as that of plasmids, clones, species, microbiotas), the systems' degrees of freedom increase dramatically, related to the variable dispersal, fragmentation, relatedness, or coalescence of bacterial populations, depending on heterogeneous and changing niches and selective gradients in complex environments. Evolutionary trajectories of antibiotic resistance find their way in these changing landscapes subjected to random variations, becoming highly entropic and therefore unpredictable. However, experimental, phylogenetic, and ecogenetic analyses reveal preferential frequented paths (highways) where antibiotic resistance flows and propagates, allowing some understanding of evolutionary dynamics, modeling and designing interventions. Studies on antibiotic resistance have an applied aspect in improving individual health, One Health, and Global Health, as well as an academic value for understanding evolution. Most importantly, they have a heuristic significance as a model to reduce the negative influence of anthropogenic effects on the environment.

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.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono- and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.

The Genetic Background Modulates the Evolution of Fluoroquinolone-Resistance in Mycobacterium tuberculosis

Fluoroquinolones (FQ) form the backbone in experimental treatment regimens against drug-susceptible tuberculosis. However, little is known on whether the genetic variation present in natural populations of Mycobacterium tuberculosis (Mtb) affects the evolution of FQ-resistance (FQ-R). To investigate this question, we used nine genetically distinct drug-susceptible clinical isolates of Mtb and measured their frequency of resistance to the FQ ofloxacin (OFX) in vitro. We found that the Mtb genetic background led to differences in the frequency of OFX-resistance (OFX-R) that spanned two orders of magnitude and substantially modulated the observed mutational profiles for OFX-R. Further, in vitro assays showed that the genetic background also influenced the minimum inhibitory concentration and the fitness effect conferred by a given OFX-R mutation. To test the clinical relevance of our in vitro work, we surveyed the mutational profile for FQ-R in publicly available genomic sequences from clinical Mtb isolates, and found substantial Mtb lineage-dependent variability. Comparison of the clinical and the in vitro mutational profiles for FQ-R showed that 51% and 39% of the variability in the clinical frequency of FQ-R gyrA mutation events in Lineage 2 and Lineage 4 strains, respectively, can be attributed to how Mtb evolves FQ-R in vitro. As the Mtb genetic background strongly influenced the evolution of FQ-R in vitro, we conclude that the genetic background of Mtb also impacts the evolution of FQ-R in the clinic.

On the evolutionary ecology of multidrug resistance in bacteria

Resistance against different antibiotics appears on the same bacterial strains more often than expected by chance, leading to high frequencies of multidrug resistance. There are multiple explanations for this observation, but these tend to be specific to subsets of antibiotics and/or bacterial species, whereas the trend is pervasive. Here, we consider the question in terms of strain ecology: explaining why resistance to different antibiotics is often seen on the same strain requires an understanding of the competition between strains with different resistance profiles. This work builds on models originally proposed to explain another aspect of strain competition: the stable coexistence of antibiotic sensitivity and resistance observed in a number of bacterial species. We first identify a partial structural similarity in these models: either strain or host population structure stratifies the pathogen population into evolutionarily independent sub-populations and introduces variation in the fitness effect of resistance between these sub-populations, thus creating niches for sensitivity and resistance. We then generalise this unified underlying model to multidrug resistance and show that models with this structure predict high levels of association between resistance to different drugs and high multidrug resistance frequencies. We test predictions from this model in six bacterial datasets and find them to be qualitatively consistent with observed trends. The higher than expected frequencies of multidrug resistance are often interpreted as evidence that these strains are out-competing strains with lower resistance multiplicity. Our work provides an alternative explanation that is compatible with long-term stability in resistance frequencies.

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.

Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution

In experimental evolution, laboratory-controlled conditions select for the adaptation of species, which can be monitored in real time. Despite the current popularity of such experiments, nature's most pervasive biological force was long believed to be observable only on time scales that transcend a researcher's life-span, and studying evolution by natural selection was therefore carried out solely by comparative means. Eventually, microorganisms' propensity for fast evolutionary changes proved us wrong, displaying strong evolutionary adaptations over a limited time, nowadays massively exploited in laboratory evolution experiments. Here, we formulate a guide to experimental evolution with microorganisms, explaining experimental design and discussing evolutionary dynamics and outcomes and how it is used to assess ecoevolutionary theories, improve industrially important traits, and untangle complex phenotypes. Specifically, we give a comprehensive overview of the setups used in experimental evolution. Additionally, we address population dynamics and genetic or phenotypic diversity during evolution experiments and expand upon contributing factors, such as epistasis and the consequences of (a)sexual reproduction. Dynamics and outcomes of evolution are most profoundly affected by the spatiotemporal nature of the selective environment, where changing environments might lead to generalists and structured environments could foster diversity, aided by, for example, clonal interference and negative frequency-dependent selection. We conclude with future perspectives, with an emphasis on possibilities offered by fast-paced technological progress. This work is meant to serve as an introduction to those new to the field of experimental evolution, as a guide to the budding experimentalist, and as a reference work to the seasoned expert.

The Evolution of Gene Expression in cis and trans

There is abundant variation in gene expression between individuals, populations, and species. The evolution of gene regulation and expression within and between species is thought to frequently contribute to adaptation. Yet considerable evidence suggests that the primary evolutionary force acting on variation in gene expression is stabilizing selection. We review here the results of recent studies characterizing the evolution of gene expression occurring in cis (via linked polymorphisms) or in trans (through diffusible products of other genes) and their contribution to adaptation and response to the environment. We review the evidence for buffering of variation in gene expression at the level of both transcription and translation, and the possible mechanisms for this buffering. Lastly, we summarize unresolved questions about the evolution of gene regulation.

Predictable Phenotypes of Antibiotic Resistance Mutations

Antibiotic-resistant bacteria represent a major threat to our ability to treat bacterial infections. Two factors that determine the evolutionary success of antibiotic resistance mutations are their impact on resistance level and the fitness cost. Recent studies suggest that resistance mutations commonly show epistatic interactions, which would complicate predictions of their stability in bacterial populations. We analyzed 13 different chromosomal resistance mutations and 10 host strains of Salmonella enterica and Escherichia coli to address two main questions. (i) Are there epistatic interactions between different chromosomal resistance mutations? (ii) How does the strain background and genetic distance influence the effect of chromosomal resistance mutations on resistance and fitness? Our results show that the effects of combined resistance mutations on resistance and fitness are largely predictable and that epistasis remains rare even when up to four mutations were combined. Furthermore, a majority of the mutations, especially target alteration mutations, demonstrate strain-independent phenotypes across different species. This study extends our understanding of epistasis among resistance mutations and shows that interactions between different resistance mutations are often predictable from the characteristics of the individual mutations.

The spread of antibiotic-resistant bacteria imposes an urgent threat to public health. The ability to forecast the evolutionary success of resistant mutants would help to combat dissemination of antibiotic resistance. Previous studies have shown that the phenotypic effects (fitness and resistance level) of resistance mutations can vary substantially depending on the genetic context in which they occur. We conducted a broad screen using many different resistance mutations and host strains to identify potential epistatic interactions between various types of resistance mutations and to determine the effect of strain background on resistance phenotypes. Combinations of several different mutations showed a large amount of phenotypic predictability, and the majority of the mutations displayed strain-independent phenotypes. However, we also identified a few outliers from these patterns, illustrating that the choice of host organism can be critically important when studying antibiotic resistance mutations.

Epistasis and the Evolution of Antimicrobial Resistance

The fitness effects of a mutation can depend, sometimes dramatically, on genetic background; this phenomenon is often referred to as “epistasis.” Epistasis can have important practical consequences in the context of antimicrobial resistance (AMR). For example, genetic background plays an important role in determining the costs of resistance, and hence in whether resistance will persist in the absence of antibiotic pressure. Furthermore, interactions between resistance mutations can have important implications for the evolution of multi-drug resistance. I argue that there is a need to better characterize the extent and nature of epistasis for mutations and horizontally transferred elements conferring AMR, particularly in clinical contexts. Furthermore, I suggest that epistasis should be an important consideration in attempts to slow or limit the evolution of AMR.

Evolution by flight and fight: diverse mechanisms of adaptation by actively motile microbes

Evolutionary adaptation can be achieved by mechanisms accessible to all organisms, including faster growth and interference competition, but self-generated motility offers additional possibilities. We tested whether 55 populations of the bacterium Myxococcus xanthus that underwent selection for increased fitness at the leading edge of swarming colonies adapted by swarming faster toward unused resources or by other means. Populations adapted greatly but diversified markedly in both swarming phenotypes and apparent mechanisms of adaptation. Intriguingly, although many adapted populations swarm intrinsically faster than their ancestors, numerous others do not. Some populations evolved interference competition toward their ancestors, whereas others gained the ability to facultatively increase swarming rate specifically upon direct interaction with ancestral competitors. Our results both highlight the diverse range of mechanisms by which actively motile organisms can adapt evolutionarily and help to explain the high levels of swarming-phenotype diversity found in local soil populations of M. xanthus.

Do tradeoffs structure antibiotic inhibition, resistance, and resource use among soil-borne Streptomyces?

Background

Tradeoffs among competing traits are believed to be crucial to the maintenance of diversity in complex communities. The production of antibiotics to inhibit competitors and resistance to antibiotic inhibition are two traits hypothesized to be critical to microbial fitness in natural habitats, yet data on costs or tradeoffs associated with these traits are limited. In this work we characterized tradeoffs between antibiotic inhibition or resistance capacities and growth efficiencies or niche widths for a broad collection of Streptomyces from soil.

Results

Streptomyces isolates tended to have either very little or very high inhibitory capacity. In contrast, Streptomyces isolates were most commonly resistant to antibiotic inhibition by an intermediate number of other isolates. Streptomyces with either very high antibiotic inhibitory or resistance capacities had less efficient growth and utilized a smaller number of resources for growth (smaller niche width) than those with low inhibition or resistance capacities, suggesting tradeoffs between antibiotic inhibitory or resistance and resource use phenotypes.

Conclusions

This work suggests that life-history tradeoffs may be crucial to the maintenance of the vast diversity of antibiotic inhibitory and resistance phenotypes found among Streptomyces in natural communities.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0470-6) contains supplementary material, which is available to authorized users.

Potential for adaptation overrides cost of resistance

AIM

To investigate the cost of antibiotic resistance versus the potential for resistant clones to adapt in maintaining polymorphism for resistance.

MATERIALS & METHODS

Experimental evolution of Escherichia coli carrying different resistance alleles was performed under an environment devoid of antibiotics and evolutionary parameters estimated from their frequencies along time.

RESULTS & CONCLUSION

Costly resistance mutations were found to coexist with lower cost resistances for hundreds of generations, contrary to the hypothesis that the cost of a resistance dictates its extinction. Estimated evolutionary parameters for the different resistance backgrounds suggest a higher adaptive potential of clones with costly antibiotic resistance mutations, overriding their initial cost of resistance and allowing their maintenance in the absence of drugs.

Alarmingly High Segregation Frequencies of Quinolone Resistance Alleles within Human and Animal Microbiomes Are Not Explained by Direct Clinical Antibiotic Exposure

Antibiotic resistance poses a major threat to human health. It is therefore important to characterize the frequency of resistance within natural bacterial environments. Many studies have focused on characterizing the frequencies with which horizontally acquired resistance genes segregate within natural bacterial populations. Yet, very little is currently understood regarding the frequency of segregation of resistance alleles occurring within the housekeeping targets of antibiotics. We surveyed a large number of metagenomic datasets extracted from a large variety of host-associated and non host-associated environments for such alleles conferring resistance to three groups of broad spectrum antibiotics: streptomycin, rifamycins, and quinolones. We find notable segregation frequencies of resistance alleles occurring within the target genes of each of the three antibiotics, with quinolone resistance alleles being the most frequent and rifamycin resistance alleles being the least frequent. Resistance allele frequencies varied greatly between different phyla and as a function of environment. The frequency of quinolone resistance alleles was especially high within host-associated environments, where it averaged an alarming ∼40%. Within host-associated environments, resistance to quinolones was most often conferred by a specific resistance allele. High frequencies of quinolone resistance alleles were also found within hosts that were not directly treated with antibiotics. Therefore, the high segregation frequency of quinolone resistance alleles occurring within the housekeeping targets of antibiotics in host-associated environments does not seem to be the sole result of clinical antibiotic usage.

Tackling Drug Resistant Infection Outbreaks of Global Pandemic Escherichia coli ST131 Using Evolutionary and Epidemiological Genomics

High-throughput molecular screening is required to investigate the origin and diffusion of antimicrobial resistance in pathogen outbreaks. The most frequent cause of human infection is Escherichia coli, which is dominated by sequence type 131 (ST131)-a set of rapidly radiating pandemic clones. The highly infectious clades of ST131 originated firstly by a mutation enhancing conjugation and adhesion. Secondly, single-nucleotide polymorphisms occurred enabling fluoroquinolone-resistance, which is near-fixed in all ST131. Thirdly, broader resistance through beta-lactamases has been gained and lost frequently, symptomatic of conflicting environmental selective effects. This flexible approach to gene exchange is worrying and supports the proposition that ST131 will develop an even wider range of plasmid and chromosomal elements promoting antimicrobial resistance. To stop ST131, deep genome sequencing is required to understand the origin, evolution and spread of antimicrobial resistance genes. Phylogenetic methods that decipher past events can predict future patterns of virulence and transmission based on genetic signatures of adaptation and gene exchange. Both the effect of partial antimicrobial exposure and cell dormancy caused by variation in gene expression may accelerate the development of resistance. High-throughput sequencing can decode measurable evolution of cell populations within patients associated with systems-wide changes in gene expression during treatments. A multi-faceted approach can enhance assessment of antimicrobial resistance in E. coli ST131 by examining transmission dynamics between hosts to achieve a goal of pre-empting resistance before it emerges by optimising antimicrobial treatment protocols.

Lytic phages obscure the cost of antibiotic resistance in Escherichia coli

The long-term persistence of antibiotic-resistant bacteria depends on their fitness relative to other genotypes in the absence of drugs. Outside the laboratory, viruses that parasitize bacteria (phages) are ubiquitous, but costs of antibiotic resistance are typically studied in phage-free experimental conditions. We used a mathematical model and experiments with Escherichia coli to show that lytic phages strongly affect the incidence of antibiotic resistance in drug-free conditions. Under phage parasitism, the likelihood that antibiotic-resistant genetic backgrounds spread depends on their initial frequency, mutation rate and intrinsic growth rate relative to drug-susceptible genotypes, because these parameters determine relative rates of phage-resistance evolution on different genetic backgrounds. Moreover, the average cost of antibiotic resistance in terms of intrinsic growth in the antibiotic-free experimental environment was small relative to the benefits of an increased mutation rate in the presence of phages. This is consistent with our theoretical work indicating that, under phage selection, typical costs of antibiotic resistance can be outweighed by realistic increases in mutability if drug resistance and hypermutability are genetically linked, as is frequently observed in clinical isolates. This suggests the long-term distribution of antibiotic resistance depends on the relative rates at which different lineages adapt to other types of selection, which in the case of phage parasitism is probably extremely common, as well as costs of resistance inferred by classical in vitro methods.

Sensing cytosolic RpsL by macrophages induces lysosomal cell death and termination of bacterial infection

The intracellular bacterial pathogen Legionella pneumophila provokes strong host responses and has proven to be a valuable model for the discovery of novel immunosurveillance pathways. Our previous work revealed that an environmental isolate of L. pneumophila induces a noncanonical form of cell death, leading to restriction of bacterial replication in primary mouse macrophages. Here we show that such restriction also occurs in infections with wild type clinical isolates. Importantly, we found that a lysine to arginine mutation at residue 88 (K88R) in the ribosome protein RpsL that not only confers bacterial resistance to streptomycin, but more importantly, severely attenuated the induction of host cell death and enabled L. pneumophila to replicate in primary mouse macrophages. Although conferring similar resistance to streptomycin, a K43N mutation in RpsL does not allow productive intracellular bacterial replication. Further analysis indicated that RpsL is capable of effectively inducing macrophage death via a pathway involved in lysosomal membrane permeabilization; the K88R mutant elicits similar responses but is less potent. Moreover, cathepsin B, a lysosomal protease that causes cell death after being released into the cytosol upon the loss of membrane integrity, is required for efficient RpsL-induced macrophage death. Furthermore, despite the critical role of cathepsin B in delaying RpsL-induced cell death, macrophages lacking cathepsin B do not support productive intracellular replication of L. pneumophila harboring wild type RpsL. This suggests the involvement of other yet unidentified components in the restriction of bacterial replication. Our results identified RpsL as a regulator in the interactions between bacteria such as L. pneumophila and primary mouse macrophages by triggering unique cellular pathways that restrict intracellular bacterial replication.

The death of the host cell during infection can be triggered by one or more microbial molecules; this “live or die” selection provides effective means for the dissection of immune recognition mechanisms as well as for the identification of the microbial molecules responsible for such responses. We found that infection of primary mouse macrophages by Legionella pneumophila strains harboring wild type RpsL, the S12 component of the bacterial ribosome, causes macrophage death by a mechanism independent of the three inflammatory caspases, caspase 1, 7 and 11. Importantly, although both confer resistance to streptomycin at indistinguishable effectiveness, the K88R, but not the K43N mutation in RpsL enables L. pneumophila to replicate in macrophages. Purified RpsL and RpsL_K43N physically delivered into macrophages cause cell death by inducing damage to lysosomal membranes and the release of cathepsins. We also found that the lysosomal protease cathepsin B is required for efficient RpsL-induced cell death but its absence is not sufficient for macrophages to support intracellular bacterial replication. Thus, RpsL functions as an immune induction molecule to trigger one or more signaling cascades that leads to lysosomal cell death as well as the termination of bacterial replication.

Fighting microbial drug resistance: a primer on the role of evolutionary biology in public health

Although microbes have been evolving resistance to antimicrobials for millennia, the spread of resistance in pathogen populations calls for the development of new drugs and treatment strategies. We propose that successful, long-term resistance management requires a better understanding of how resistance evolves in the first place. This is an opportunity for evolutionary biologists to engage in public health, a collaboration that has substantial precedent. Resistance evolution has been an important tool for developing and testing evolutionary theory, especially theory related to the genetic basis of new traits and constraints on adaptation. The present era is no exception. The articles in this issue highlight the breadth of current research on resistance evolution and also its challenges. In this introduction, we review the conceptual advances that have been achieved from studying resistance evolution and describe a path forward.

GWAMAR: genome-wide assessment of mutations associated with drug resistance in bacteria

Background

Development of drug resistance in bacteria causes antibiotic therapies to be less effective and more costly. Moreover, our understanding of the process remains incomplete. One promising approach to improve our understanding of how resistance is being acquired is to use whole-genome comparative approaches for detection of drug resistance-associated mutations.

Results

We present GWAMAR, a tool we have developed for detecting of drug resistance-associated mutations in bacteria through comparative analysis of whole-genome sequences. The pipeline of GWAMAR comprises several steps. First, for a set of closely related bacterial genomes, it employs eCAMBer to identify homologous gene families. Second, based on multiple alignments of the gene families, it identifies mutations among the strains of interest. Third, it calculates several statistics to identify which mutations are the most associated with drug resistance.

Conclusions

Based on our analysis of two large datasets retrieved from publicly available data for M. tuberculosis, we identified a set of novel putative drug resistance-associated mutations. As a part of this work, we present also an application of our tool to detect putative compensatory mutations.

Testing the role of genetic background in parallel evolution using the comparative experimental evolution of antibiotic resistance

Parallel evolution is the independent evolution of the same phenotype or genotype in response to the same selection pressure. There are examples of parallel molecular evolution across divergent genetic backgrounds, suggesting that genetic background may not play an important role in determining the outcome of adaptation. Here, we measure the influence of genetic background on phenotypic and molecular adaptation by combining experimental evolution with comparative analysis. We selected for resistance to the antibiotic rifampicin in eight strains of bacteria from the genus Pseudomonas using a short term selection experiment. Adaptation occurred by 47 mutations at conserved sites in rpoB, the target of rifampicin, and due to the high diversity of possible mutations the probability of within-strain parallel evolution was low. The probability of between-strain parallel evolution was only marginally lower, because different strains substituted similar rpoB mutations. In contrast, we found that more than 30% of the phenotypic variation in the growth rate of evolved clones was attributable to among-strain differences. Parallel molecular evolution across strains resulted in divergent phenotypic evolution because rpoB mutations had different effects on growth rate in different strains. This study shows that genetic divergence between strains constrains parallel phenotypic evolution, but had little detectable impact on the molecular basis of adaptation in this system.

You cannot tell a book by looking at the cover: Cryptic complexity in bacterial evolution

Do genetically closely related organisms under identical, but strong selection pressure converge to a common resistant genotype or will they diverge to different genomic solutions? This question gets at the heart of how rough is the fitness landscape in the local vicinity of two closely related strains under stress. We chose a Growth Advantage in Stationary Phase (GASP) E scherichia coli strain to address this question because the GASP strain has very similar fitness to the wild-type (WT) strain in the absence of metabolic stress but in the presence of metabolic stress continues to divide and does not enter into stationary phase. We find that under strong antibiotic selection pressure by the fluoroquinolone antibiotic ciprofloxacin in a complex ecology that the GASP strain rapidly evolves in under 20 h missense mutation in gyrA only 2 amino acids removed from the WT strain indicating a convergent solution, yet does not evolve the other 3 mutations of the WT strain. Further the GASP strain evolves a prophage e14 excision which completely inhibits biofilm formation in the mutant strain, revealing the hidden complexity of E. coli evolution to antibiotics as a function of selection pressure. We conclude that there is a cryptic roughness to fitness landscapes in the absence of stress.

Experimental evolution as an efficient tool to dissect adaptive paths to antibiotic resistance

Antibiotic treatments increasingly fail due to rapid dissemination of drug resistance. Comparative genomics of clinical isolates highlights the role of de novo adaptive mutations and horizontal gene transfer (HGT) in the acquisition of resistance. Yet it cannot fully describe the selective pressures and evolutionary trajectories that yielded today's problematic strains. Experimental evolution offers a compelling addition to such studies because the combination of replicated experiments under tightly controlled conditions with genomics of intermediate time points allows real-time reconstruction of evolutionary trajectories. Recent studies thus established causal links between antibiotic deployment therapies and the course and timing of mutations, the cost of resistance and the likelihood of compensating mutations. They particularly underscored the importance of long-term effects. Similar investigations incorporating horizontal gene transfer (HGT) are wanting, likely because of difficulties associated with its integration into experiments. In this review, we describe current advances in experimental evolution of antibiotic resistance and reflect on ways to incorporate horizontal gene transfer into the approach. We contend it provides a powerful tool for systematic and highly controlled dissection of evolutionary paths to antibiotic resistance that needs to be taken into account for the development of sustainable anti-bacterial treatment strategies.