The population genetics of collateral resistance and sensitivity. - Related Documents




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937201.0000The population genetics of collateral resistance and sensitivity. Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.202134889185
937710.9997Experimental Evolution of the TolC-Receptor Phage U136B Functionally Identifies a Tail Fiber Protein Involved in Adsorption through Strong Parallel Adaptation. Bacteriophages have received recent attention for their therapeutic potential to treat antibiotic-resistant bacterial infections. One particular idea in phage therapy is to use phages that not only directly kill their bacterial hosts but also rely on particular bacterial receptors, such as proteins involved in virulence or antibiotic resistance. In such cases, the evolution of phage resistance would correspond to the loss of those receptors, an approach termed evolutionary steering. We previously found that during experimental evolution, phage U136B can exert selection pressure on Escherichia coli to lose or modify its receptor, the antibiotic efflux protein TolC, often resulting in reduced antibiotic resistance. However, for TolC-reliant phages like U136B to be used therapeutically, we also need to study their own evolutionary potential. Understanding phage evolution is critical for the development of improved phage therapies as well as the tracking of phage populations during infection. Here, we characterized phage U136B evolution in 10 replicate experimental populations. We quantified phage dynamics that resulted in five surviving phage populations at the end of the 10-day experiment. We found that phages from all five surviving populations had evolved higher rates of adsorption on either ancestral or coevolved E. coli hosts. Using whole-genome and whole-population sequencing, we established that these higher rates of adsorption were associated with parallel molecular evolution in phage tail protein genes. These findings will be useful in future studies to predict how key phage genotypes and phenotypes influence phage efficacy and survival despite the evolution of host resistance. IMPORTANCE Antibiotic resistance is a persistent problem in health care and a factor that may help maintain bacterial diversity in natural environments. Bacteriophages ("phages") are viruses that specifically infect bacteria. We previously discovered and characterized a phage called U136B, which infects bacteria through TolC. TolC is an antibiotic resistance protein that helps bacteria pump antibiotics out of the cell. Over short timescales, phage U136B can be used to evolutionarily "steer" bacterial populations to lose or modify the TolC protein, sometimes reducing antibiotic resistance. In this study, we investigate whether U136B itself evolves to better infect bacterial cells. We discovered that the phage can readily evolve specific mutations that increase its infection rate. This work will be useful for understanding how phages can be used to treat bacterial infections.202337191555
961320.9997Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics. Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.202031851309
947130.9997Systematic analysis of putative phage-phage interactions on minimum-sized phage cocktails. The application of bacteriophages as antibacterial agents has many benefits in the "post-antibiotic age". To increase the number of successfully targeted bacterial strains, phage cocktails, instead of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.202235165352
889640.9997Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance. Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.201830169679
949250.9997The Search for 'Evolution-Proof' Antibiotics. The effectiveness of antibiotics has been widely compromised by the evolution of resistance among pathogenic bacteria. It would be restored by the development of antibiotics to which bacteria cannot evolve resistance. We first discuss two kinds of 'evolution-proof' antibiotic. The first comprises literally evolution-proof antibiotics to which bacteria cannot become resistant by mutation or horizontal gene transfer. The second category comprises agents to which resistance may arise, but so rarely that it does not become epidemic. The likelihood that resistance to a novel agent will spread is evaluated here by a simple model that includes biological and therapeutic parameters governing the evolution of resistance within hosts and the transmission of resistant strains between hosts. This model leads to the conclusion that epidemic spread is unlikely if the frequency of mutations that confer resistance falls below a defined minimum value, and it identifies potential targets for intervention to prevent the evolution of resistance. Whether or not evolution-proof antibiotics are ever found, searching for them is likely to improve the deployment of new and existing agents by advancing our understanding of how resistance evolves.201829191398
937460.9997Mathematical modelling of antibiotic interaction on evolution of antibiotic resistance: an analytical approach. BACKGROUND: The emergence and spread of antibiotic-resistant pathogens have led to the exploration of antibiotic combinations to enhance clinical effectiveness and counter resistance development. Synergistic and antagonistic interactions between antibiotics can intensify or diminish the combined therapy's impact. Moreover, these interactions can evolve as bacteria transition from wildtype to mutant (resistant) strains. Experimental studies have shown that the antagonistically interacting antibiotics against wildtype bacteria slow down the evolution of resistance. Interestingly, other studies have shown that antibiotics that interact antagonistically against mutants accelerate resistance. However, it is unclear if the beneficial effect of antagonism in the wildtype bacteria is more critical than the detrimental effect of antagonism in the mutants. This study aims to illuminate the importance of antibiotic interactions against wildtype bacteria and mutants on the deacceleration of antimicrobial resistance. METHODS: To address this, we developed and analyzed a mathematical model that explores the population dynamics of wildtype and mutant bacteria under the influence of interacting antibiotics. The model investigates the relationship between synergistic and antagonistic antibiotic interactions with respect to the growth rate of mutant bacteria acquiring resistance. Stability analysis was conducted for equilibrium points representing bacteria-free conditions, all-mutant scenarios, and coexistence of both types. Numerical simulations corroborated the analytical findings, illustrating the temporal dynamics of wildtype and mutant bacteria under different combination therapies. RESULTS: Our analysis provides analytical clarification and numerical validation that antibiotic interactions against wildtype bacteria exert a more significant effect on reducing the rate of resistance development than interactions against mutants. Specifically, our findings highlight the crucial role of antagonistic antibiotic interactions against wildtype bacteria in slowing the growth rate of resistant mutants. In contrast, antagonistic interactions against mutants only marginally affect resistance evolution and may even accelerate it. CONCLUSION: Our results emphasize the importance of considering the nature of antibiotic interactions against wildtype bacteria rather than mutants when aiming to slow down the acquisition of antibiotic resistance.202438426146
427670.9997Phages limit the evolution of bacterial antibiotic resistance in experimental microcosms. The evolution of multi-antibiotic resistance in bacterial pathogens, often resulting from de novo mutations, is creating a public health crisis. Phages show promise for combating antibiotic-resistant bacteria, the efficacy of which, however, may also be limited by resistance evolution. Here, we suggest that phages may be used as supplements to antibiotics in treating initially sensitive bacteria to prevent resistance evolution, as phages are unaffected by most antibiotics and there should be little cross-resistance to antibiotics and phages. In vitro experiments using the bacterium Pseudomonas fluorescens, a lytic phage, and the antibiotic kanamycin supported this prediction: an antibiotic-phage combination dramatically decreased the chance of bacterial population survival that indicates resistance evolution, compared with antibiotic treatment alone, whereas the phage alone did not affect bacterial survival. This effect of the combined treatment in preventing resistance evolution was robust to immigration of bacteria from an untreated environment, but not to immigration from environment where the bacteria had coevolved with the phage. By contrast, an isogenic hypermutable strain constructed from the wild-type P. fluorescens evolved resistance to all treatments regardless of immigration, but typically suffered very large fitness costs. These results suggest that an antibiotic-phage combination may show promise as an antimicrobial strategy.201223028398
960780.9997Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution. Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.201728217741
961290.9996Using experimental evolution to explore natural patterns between bacterial motility and resistance to bacteriophages. Resistance of bacteria to phages may be gained by alteration of surface proteins to which phages bind, a mechanism that is likely to be costly as these molecules typically have critical functions such as movement or nutrient uptake. To address this potential trade-off, we combine a systematic study of natural bacteria and phage populations with an experimental evolution approach. We compare motility, growth rate and susceptibility to local phages for 80 bacteria isolated from horse chestnut leaves and, contrary to expectation, find no negative association between resistance to phages and bacterial motility or growth rate. However, because correlational patterns (and their absence) are open to numerous interpretations, we test for any causal association between resistance to phages and bacterial motility using experimental evolution of a subset of bacteria in both the presence and absence of naturally associated phages. Again, we find no clear link between the acquisition of resistance and bacterial motility, suggesting that for these natural bacterial populations, phage-mediated selection is unlikely to shape bacterial motility, a key fitness trait for many bacteria in the phyllosphere. The agreement between the observed natural pattern and the experimental evolution results presented here demonstrates the power of this combined approach for testing evolutionary trade-offs.201121509046
8987100.9996Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Alternating antibiotic therapy, in which pairs of drugs are cycled during treatment, has been suggested as a means to inhibit the evolution of de novo resistance while avoiding the toxicity associated with more traditional combination therapy. However, it remains unclear under which conditions and by what means such alternating treatments impede the evolution of resistance. Here, we tracked multistep evolution of resistance in replicate populations of Staphylococcus aureus during 22 d of continuously increasing single-, mixed-, and alternating-drug treatment. In all three tested drug pairs, the alternating treatment reduced the overall rate of resistance by slowing the acquisition of resistance to one of the two component drugs, sometimes as effectively as mixed treatment. This slower rate of evolution is reflected in the genome-wide mutational profiles; under alternating treatments, bacteria acquire mutations in different genes than under corresponding single-drug treatments. To test whether this observed constraint on adaptive paths reflects trade-offs in which resistance to one drug is accompanied by sensitivity to a second drug, we profiled many single-step mutants for cross-resistance. Indeed, the average cross-resistance of single-step mutants can help predict whether or not evolution was slower in alternating drugs. Together, these results show that despite the complex evolutionary landscape of multidrug resistance, alternating-drug therapy can slow evolution by constraining the mutational paths toward resistance.201425246554
9611110.9996Parallel evolution of Pseudomonas aeruginosa phage resistance and virulence loss in response to phage treatment in vivo and in vitro. With rising antibiotic resistance, there has been increasing interest in treating pathogenic bacteria with bacteriophages (phage therapy). One limitation of phage therapy is the ease at which bacteria can evolve resistance. Negative effects of resistance may be mitigated when resistance results in reduced bacterial growth and virulence, or when phage coevolves to overcome resistance. Resistance evolution and its consequences are contingent on the bacteria-phage combination and their environmental context, making therapeutic outcomes hard to predict. One solution might be to conduct 'in vitro evolutionary simulations' using bacteria-phage combinations from the therapeutic context. Overall, our aim was to investigate parallels between in vitro experiments and in vivo dynamics in a human participant. Evolutionary dynamics were similar, with high levels of resistance evolving quickly with limited evidence of phage evolution. Resistant bacteria-evolved in vitro and in vivo-had lower virulence. In vivo, this was linked to lower growth rates of resistant isolates, whereas in vitro phage resistant isolates evolved greater biofilm production. Population sequencing suggests resistance resulted from selection on de novo mutations rather than sorting of existing variants. These results highlight the speed at which phage resistance can evolve in vivo, and how in vitro experiments may give useful insights for clinical evolutionary outcomes.202235188102
9373120.9996Dynamics of the emergence of genetic resistance to biocides among asexual and sexual organisms. A stochastic, agent based, evolutionary algorithm, modeling mating, reproduction, genetic variation, phenotypic expression and selection was used to study the dynamic interactions affecting a multiple-gene system. The results suggest that strong irreversible constraints affect the evolution of resistance to biocides. Resistant genes evolve differently in asexual organisms compared with sexual ones in response to various patterns of biocide applications. Asexual populations (viruses and bacteria) are less likely to develop genetic resistance in response to multiple pesticides or if pesticides are used at low doses, whereas sexual populations (insects for example) are more likely to become resistant to pesticides if susceptibility to the pesticide relates to mate selection. The adaptation of genes not related to the emergence of resistance will affect the dynamics of the evolution of resistance. Increasing the number of pesticides reduces the probability of developing resistance to any of them in asexual organisms but much less so in sexual organisms. Sequential applications of toxins, were slightly less efficient in slowing emergence of resistance compared with simultaneous application of a mix in both sexual and asexual organisms. Targeting only one sex of the pest speeds the development of resistance. The findings are consistent to most of the published analytical models but are closer to known experimental results, showing that nonlinear, agent based simulation models are more powerful in explaining complex processes.19979344733
4271130.9996Multi-step vs. single-step resistance evolution under different drugs, pharmacokinetics, and treatment regimens. The success of antimicrobial treatment is threatened by the evolution of drug resistance. Population genetic models are an important tool in mitigating that threat. However, most such models consider resistance emergence via a single mutational step. Here, we assembled experimental evidence that drug resistance evolution follows two patterns: (i) a single mutation, which provides a large resistance benefit, or (ii) multiple mutations, each conferring a small benefit, which combine to yield high-level resistance. Using stochastic modeling, we then investigated the consequences of these two patterns for treatment failure and population diversity under various treatments. We find that resistance evolution is substantially limited if more than two mutations are required and that the extent of this limitation depends on the combination of drug type and pharmacokinetic profile. Further, if multiple mutations are necessary, adaptive treatment, which only suppresses the bacterial population, delays treatment failure due to resistance for a longer time than aggressive treatment, which aims at eradication.202134001313
9693140.9996Antibody-mediated crosslinking of gut bacteria hinders the spread of antibiotic resistance. The body is home to a diverse microbiota, mainly in the gut. Resistant bacteria are selected by antibiotic treatments, and once resistance becomes widespread in a population of hosts, antibiotics become useless. Here, we develop a multiscale model of the interaction between antibiotic use and resistance spread in a host population, focusing on an important aspect of within-host immunity. Antibodies secreted in the gut enchain bacteria upon division, yielding clonal clusters of bacteria. We demonstrate that immunity-driven bacteria clustering can hinder the spread of a novel resistant bacterial strain in a host population. We quantify this effect both in the case where resistance preexists and in the case where acquiring a new resistance mutation is necessary for the bacteria to spread. We further show that the reduction of spread by clustering can be countered when immune hosts are silent carriers, and are less likely to get treated, and/or have more contacts. We demonstrate the robustness of our findings to including stochastic within-host bacterial growth, a fitness cost of resistance, and its compensation. Our results highlight the importance of interactions between immunity and the spread of antibiotic resistance, and argue in the favor of vaccine-based strategies to combat antibiotic resistance.201930957218
9614150.9996Antibiotic-Independent Adaptive Effects of Antibiotic Resistance Mutations. Antibiotic usage selects for the accumulation and spread of antibiotic-resistant bacteria. However, resistance can also accumulate in the absence of antibiotic exposure. Antibiotics are often designed to target widely distributed regulatory housekeeping genes. The targeting of such genes enables these antibiotics to be useful against a wider variety of pathogens. This review highlights work suggesting that regulatory housekeeping genes of the type targeted by many antibiotics function as hubs of adaptation to conditions unrelated to antibiotic exposure. As a result of this, some mutations to the regulatory housekeeping gene targets of antibiotics confer both antibiotic resistance and an adaptive effect unrelated to antibiotic exposure. Such antibiotic-independent adaptive effects of resistance mutations may substantially affect the dynamics of antibiotic resistance accumulation and spread.201728629950
9485160.9996Evolution of Drug Resistance in Bacteria. Resistance to antibiotics is an important and timely problem of contemporary medicine. Rapid evolution of resistant bacteria calls for new preventive measures to slow down this process, and a longer-term progress cannot be achieved without a good understanding of the mechanisms through which drug resistance is acquired and spreads in microbial populations. Here, we discuss recent experimental and theoretical advances in our knowledge how the dynamics of microbial populations affects the evolution of antibiotic resistance . We focus on the role of spatial and temporal drug gradients and show that in certain situations bacteria can evolve de novo resistance within hours. We identify factors that lead to such rapid onset of resistance and discuss their relevance for bacterial infections.201627193537
4273170.9996Mathematical modeling on bacterial resistance to multiple antibiotics caused by spontaneous mutations. We formulate a mathematical model that describes the population dynamics of bacteria exposed to multiple antibiotics simultaneously, assuming that acquisition of resistance is through mutations due to antibiotic exposure. Qualitative analysis reveals the existence of a free-bacteria equilibrium, resistant-bacteria equilibrium and an endemic equilibrium where both bacteria coexist.201424467935
9376180.9996Historical Contingency Drives Compensatory Evolution and Rare Reversal of Phage Resistance. Bacteria and lytic viruses (phages) engage in highly dynamic coevolutionary interactions over time, yet we have little idea of how transient selection by phages might shape the future evolutionary trajectories of their host populations. To explore this question, we generated genetically diverse phage-resistant mutants of the bacterium Pseudomonas syringae. We subjected the panel of mutants to prolonged experimental evolution in the absence of phages. Some populations re-evolved phage sensitivity, whereas others acquired compensatory mutations that reduced the costs of resistance without altering resistance levels. To ask whether these outcomes were driven by the initial genetic mechanisms of resistance, we next evolved independent replicates of each individual mutant in the absence of phages. We found a strong signature of historical contingency: some mutations were highly reversible across replicate populations, whereas others were highly entrenched. Through whole-genome sequencing of bacteria over time, we also found that populations with the same resistance gene acquired more parallel sets of mutations than populations with different resistance genes, suggesting that compensatory adaptation is also contingent on how resistance initially evolved. Our study identifies an evolutionary ratchet in bacteria-phage coevolution and may explain previous observations that resistance persists over time in some bacterial populations but is lost in others. We add to a growing body of work describing the key role of phages in the ecological and evolutionary dynamics of their host communities. Beyond this specific trait, our study provides a new insight into the genetic architecture of historical contingency, a crucial component of interpreting and predicting evolution.202235994371
9241190.9996Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance. Antibiotics target essential cellular functions but bacteria can become resistant by acquiring either exogenous resistance genes or chromosomal mutations. Resistance mutations typically occur in genes encoding essential functions; these mutations are therefore generally detrimental in the absence of drugs. However, bacteria can reduce this handicap by acquiring additional mutations, known as compensatory mutations. Genetic interactions (epistasis) either with the background or between resistances (in multiresistant bacteria) dramatically affect the fitness cost of antibiotic resistance and its compensation, therefore shaping dissemination of antibiotic resistance mutations. This Review summarizes current knowledge on the evolutionary mechanisms influencing maintenance of resistance mediated by chromosomal mutations, focusing on their fitness cost, compensatory evolution, epistasis, and the effect of the environment on these processes.201829439838