Is selection relevant in the evolutionary emergence of drug resistance?

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into 6 classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Small-Molecule Antibiotic Drug Development: Need and Challenges

The need for new antibiotics is urgent. Antimicrobial resistance is rising, although currently, many more people die from drug-sensitive bacterial infections. The continued evolution of drug resistance is inevitable, fueled by pathogen population size and exposure to antibiotics. Additionally, opportunistic pathogens will always pose a threat to vulnerable patients whose immune systems cannot efficiently fight them even if they are sensitive to available antibiotics, according to clinical microbiology tests. These problems are intertwined and will worsen as human populations age, increase in density, and experience disruptions such as war, extreme weather events, or declines in standard of living. The development of appropriate drugs to treat all the world's bacterial infections should be a priority, and future success will likely require combinations of multiple approaches. However, the highest burden of bacterial infection is in Low- and Middle-Income Countries, where limited medical infrastructure is a major challenge. For effectively managing infections in these contexts, small-molecule-based treatments offer significant advantages. Unfortunately, support for ongoing small-molecule antibiotic discovery has recently suffered from significant challenges related both to the scientific difficulties in treating bacterial infections and to market barriers. Nevertheless, small-molecule antibiotics remain essential and irreplaceable tools for fighting infections, and efforts to develop novel and improved versions deserve ongoing investment. Here, we first describe the global historical context of antibiotic treatment and then highlight some of the challenges surrounding small-molecule development and potential solutions. Many of these challenges are likely to be common to all modalities of antibacterial treatment and should be addressed directly.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

The future of evolutionary medicine: sparking innovation in biomedicine and public health

Evolutionary medicine - i.e. the application of insights from evolution and ecology to biomedicine - has tremendous untapped potential to spark transformational innovation in biomedical research, clinical care and public health. Fundamentally, a systematic mapping across the full diversity of life is required to identify animal model systems for disease vulnerability, resistance, and counter-resistance that could lead to novel clinical treatments. Evolutionary dynamics should guide novel therapeutic approaches that target the development of treatment resistance in cancers (e.g., via adaptive or extinction therapy) and antimicrobial resistance (e.g., via innovations in chemistry, antimicrobial usage, and phage therapy). With respect to public health, the insight that many modern human pathologies (e.g., obesity) result from mismatches between the ecologies in which we evolved and our modern environments has important implications for disease prevention. Life-history evolution can also shed important light on patterns of disease burden, for example in reproductive health. Experience during the COVID-19 (SARS-CoV-2) pandemic has underlined the critical role of evolutionary dynamics (e.g., with respect to virulence and transmissibility) in predicting and managing this and future pandemics, and in using evolutionary principles to understand and address aspects of human behavior that impede biomedical innovation and public health (e.g., unhealthy behaviors and vaccine hesitancy). In conclusion, greater interdisciplinary collaboration is vital to systematically leverage the insight-generating power of evolutionary medicine to better understand, prevent, and treat existing and emerging threats to human, animal, and planetary health.

Hygiene may attenuate selection for antibiotic resistance by changing microbial community structure

Good hygiene, in both health care and the community, is central to containing the rise of antibiotic resistance, as well as to infection control more generally. But despite the well-known importance, the ecological mechanisms by which hygiene (or other transmission control measures) affect the evolution of resistance remain to be elucidated. Using metacommunity ecology theory, we here propose that hygiene attenuates the effect of antibiotic selection pressure. Specifically, we predict that hygiene limits the scope for antibiotics to induce competitive release of resistant bacteria within treated hosts, and that this is due to an effect of hygiene on the distribution of resistant and sensitive strains in the host population. We show this in a mathematical model of bacterial metacommunity dynamics, and test the results against data on antibiotic resistance, antibiotic treatment, and the use of alcohol-based hand rub in long-term care facilities. The data are consistent with hand rub use attenuating the resistance promoting effect of antibiotic treatment. Our results underscore the importance of hygiene, and point to a concrete way to weaken the link between antibiotic use and increasing resistance.

The persistent, bioaccumulative, toxic, and resistance (PBTR) risk assessment framework of antibiotics in the drinking water sources

Antibiotics are emerging pollutants largely considered to have a lower risk based on persistent, bioaccumulative, toxic (PBT) risk assessments. However, an increasing number of studies have illustrated that antibiotics are responsible for the global increase in antimicrobial resistance (AMR), which suggests that the risk of antibiotics has been largely underestimated by using PBT risk assessment. Here, we designed an integrated innovation risk assessment framework of persistent, bioaccumulative, toxic, and resistance (PBTR) that accounts for antibiotic resistance to better represent the antibiotic environmental risk. This novel antibiotic risk assessment framework was further verified via application to 39 target antibiotics in the 23 drinking water sources of the lower Yangtze River (LYR), China, during the normal and flood seasons. In contrast with the PBT assessment, single toxicity assessment and single resistance assessment, in the PBTR assessment, 7 of 39 target antibiotics with bacterial insensitivity were observed to represent a more prominent risk, as were the sites sampled during the flood season with low concentrations but high pollution loads, which confirmed that the sensitivity of PBTR risk assessment was instructive. The PBTR risk assessment for the screened priority antibiotics contributes not only representative data but also an innovative approach for identifying resistance risks. Using the positive matrix factorization (PMF) model, the sources of priority antibiotics can be predicted and thus supported the corresponding policy. Overall, this study first constructed a PBTR risk assessment framework, then applied it to facilitate the accurate management of antibiotic pollution at the basin level.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

Genetic Diversity of Plasmodium falciparum and Distribution of Antimalarial Drug Resistance Mutations in Symptomatic and Asymptomatic Infections

Malaria control relies on passive case detection, and this strategy fails detecting asymptomatic infections. In addition, infections in endemic areas harbor multiple parasite genotypes that could affect case management and malaria epidemiology.

Allogenous Selection of Mutational Collateral Resistance: Old Drugs Select for New Resistance Within Antibiotic Families

Allogeneous selection occurs when an antibiotic selects for resistance to more advanced members of the same family. The mechanisms of allogenous selection are (a) collateral expansion, when the antibiotic expands the gene and gene-containing bacterial populations favoring the emergence of other mutations, inactivating the more advanced antibiotics; (b) collateral selection, when the old antibiotic selects its own resistance but also resistance to more modern drugs; (c) collateral hyper-resistance, when resistance to the old antibiotic selects in higher degree for populations resistant to other antibiotics of the family than to itself; and (d) collateral evolution, when the simultaneous or sequential use of antibiotics of the same family selects for new mutational combinations with novel phenotypes in this family, generally with higher activity (higher inactivation of the antibiotic substrates) or broader spectrum (more antibiotics of the family are inactivated). Note that in some cases, collateral selection derives from collateral evolution. In this article, examples of allogenous selection are provided for the major families of antibiotics. Improvements in minimal inhibitory concentrations with the newest drugs do not necessarily exclude “old” antibiotics of the same family of retaining some selective power for resistance to the newest agents. If this were true, the use of older members of the same drug family would facilitate the emergence of mutational resistance to the younger drugs of the family, which is frequently based on previously established resistance traits. The extensive use of old drugs (particularly in low-income countries and in farming) might be significant for the emergence and selection of resistance to the novel members of the family, becoming a growing source of variation and selection of resistance to the whole family. In terms of future research, it could be advisable to focus antimicrobial drug discovery more on the identification of new targets and new (unique) classes of antimicrobial agents, than on the perpetual chemical exploitation of classic existing ones.

Experimental and mathematical insights on the interactions between poliovirus and a defective interfering genome

During replication, RNA viruses accumulate genome alterations, such as mutations and deletions. The interactions between individual variants can determine the fitness of the virus population and, thus, the outcome of infection. To investigate the effects of defective interfering genomes (DI) on wild-type (WT) poliovirus replication, we developed an ordinary differential equation model, which enables exploring the parameter space of the WT and DI competition. We also experimentally examined virus and DI replication kinetics during co-infection, and used these data to infer model parameters. Our model identifies, and our experimental measurements confirm, that the efficiencies of DI genome replication and encapsidation are two most critical parameters determining the outcome of WT replication. However, an equilibrium can be established which enables WT to replicate, albeit to reduced levels.

Drug-induced resistance evolution necessitates less aggressive treatment

Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.

Dawning of a new ERA: Environmental Risk Assessment of antibiotics and their potential to select for antimicrobial resistance

Antibiotics and antimicrobials are used, misused and overused in human and veterinary medicine, animal husbandry and aquaculture. These compounds can persist in both human and animal waste and then enter the environment through a variety of mechanisms. Though generally measured environmental concentrations (MECs) of antibiotics in aquatic systems are significantly lower than point of therapeutic use concentrations, there is increasing evidence that suggests these concentrations may still enrich antimicrobial resistant bacteria. In light of this evidence, a rigorous and standardised novel methodology needs to be developed which can perform environmental risk assessment (ERA) of antimicrobials in terms of their selective potential as well as their environmental impact, to ensure that diffuse and point source discharges are safe. This review summarises and critically appraises the current methodological approaches that study selection at below point of therapeutic use, or sub-inhibitory, concentrations of antibiotics. We collate and compare selective concentration data generated to date. We recommend how these data can be interpreted in line with current ERA guidelines; outlining and describing novel concepts unique to risk assessment of AMR (such as direct selection of AMR or increased persistence of AMR). We consolidate terminology used thus far into a single framework that could be adopted moving forward, by proposing predicted no effect concentrations for resistance (PNECRs) and predicted no effect concentrations for persistence (PNECPs) be determined in AMR risk assessment. Such a framework will contribute to antibiotic stewardship and by extension, protection of human health, food security and the global economy.

Dosage strategies for delaying resistance emergence in heterogeneous tumors

Drug resistance in cancer treatments is a frequent problem that, when it arises, leads to failure in therapeutic efforts. Tumor heterogeneity is the primary reason for resistance emergence and a precise treatment design that takes heterogeneity into account is required to postpone the rise of resistant subpopulations in the tumor environment. In this paper, we present a mathematical framework involving clonal evolution modeling of drug-sensitive and drug-resistant clones. Using our framework, we examine delaying the rise of resistance in heterogeneous tumors during control phase of therapy in a containment treatment approach. We apply pharmacokinetic/pharmacodynamic (PKPD) modeling and show that dosage strategies can be designed to control the resistant subpopulation. Our results show that the drug dosage and schedule determine the relative dynamics of sensitive and resistant clones. We present an optimal control problem that finds the dosing strategy that maximizes the delay in resistance emergence for a given period of containment treatment.

The Antibiotic Dosage of Fastest Resistance Evolution: gene amplifications underpinning the inverted-U

To determine the dosage at which antibiotic resistance evolution is most rapid, we treated Escherichia coli in vitro, deploying the antibiotic erythromycin at dosages ranging from zero to high. Adaptation was fastest just below erythromycin’s minimal inhibitory concentration (MIC) and genotype-phenotype correlations determined from whole genome sequencing revealed the molecular basis: simultaneous selection for copy number variation in three resistance mechanisms which exhibited an “inverted-U” pattern of dose-dependence, as did several insertion sequences and an integron. Many genes did not conform to this pattern, however, reflecting changes in selection as dose increased: putative media adaptation polymorphisms at zero antibiotic dosage gave way to drug target (ribosomal RNA operon) amplification at mid dosages whereas prophage-mediated drug efflux amplifications dominated at the highest dosages. All treatments exhibited E. coli increases in the copy number of efflux operons acrAB and emrE at rates that correlated with increases in population density. For strains where the inverted-U was no longer observed following the genetic manipulation of acrAB, it could be recovered by prolonging the antibiotic treatment at subMIC dosages.

An adjunctive therapy administered with an antibiotic prevents enrichment of antibiotic-resistant clones of a colonizing opportunistic pathogen

A key challenge in antibiotic stewardship is figuring out how to use antibiotics therapeutically without promoting the evolution of antibiotic resistance. Here, we demonstrate proof of concept for an adjunctive therapy that allows intravenous antibiotic treatment without driving the evolution and onward transmission of resistance. We repurposed the FDA-approved bile acid sequestrant cholestyramine, which we show binds the antibiotic daptomycin, as an 'anti-antibiotic' to disable systemically-administered daptomycin reaching the gut. We hypothesized that adjunctive cholestyramine could enable therapeutic daptomycin treatment in the bloodstream, while preventing transmissible resistance emergence in opportunistic pathogens colonizing the gastrointestinal tract. We tested this idea in a mouse model of Enterococcus faecium gastrointestinal tract colonization. In mice treated with daptomycin, adjunctive cholestyramine therapy reduced the fecal shedding of daptomycin-resistant E. faecium by up to 80-fold. These results provide proof of concept for an approach that could reduce the spread of antibiotic resistance for important hospital pathogens.

Evolutionary Approaches to Combat Antibiotic Resistance: Opportunities and Challenges for Precision Medicine

The rise of antimicrobial resistance (AMR) in bacterial pathogens is acknowledged by the WHO as a major global health crisis. It is estimated that in 2050 annually up to 10 million people will die from infections with drug resistant pathogens if no efficient countermeasures are implemented. Evolution of pathogens lies at the core of this crisis, which enables rapid adaptation to the selective pressures imposed by antimicrobial usage in both medical treatment and agriculture, consequently promoting the spread of resistance genes or alleles in bacterial populations. Approaches developed in the field of Evolutionary Medicine attempt to exploit evolutionary insight into these adaptive processes, with the aim to improve diagnostics and the sustainability of antimicrobial therapy. Here, we review the concept of evolutionary trade-offs in the development of AMR as well as new therapeutic approaches and their impact on host-microbiome-pathogen interactions. We further discuss the possible translation of evolution-informed treatments into clinical practice, considering both the rapid cure of the individual patients and the prevention of AMR.

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.

Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

The emergence of antibiotic resistance poses a critical threat to the efficacy of antibiotic treatments. A resistant bacterial population must originally arise from a single cell that mutates or acquires a resistance gene. This single cell may, by chance, fail to successfully reproduce before it dies, leading to loss of the nascent resistant lineage. Here, we show that antibiotic concentrations that selectively favor resistance are nonetheless sufficient to reduce the chance of outgrowth from a single cell to a very low probability. Our findings suggest that lower antibiotic concentrations than those required to clear a large resistant population may be sufficient to prevent, with high probability, outgrowth of initially rare resistant mutants.

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favored over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using two Pseudomonas aeruginosa strains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as one-eighth of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.

Effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

BACKGROUND AND OBJECTIVES

There is a significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model.

METHODOLOGY

In a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose or timing of treatment (day post-infection) with the antimalarial drug atovaquone.

RESULTS

The likelihood of high-level resistance emergence increased with atovaquone dose. When varying the timing of treatment, treating either very early or late in infection reduced the risk of resistance. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, which correlated with the largest population sizes at time of treatment.

CONCLUSIONS AND IMPLICATIONS

(i) Higher doses do not always minimize resistance emergence and can promote the emergence of high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, likely due to the size of the population at the time of treatment, although we did not test the effect of immunity whose influence may have been important in the case of late treatment. (iii) Finding the 'right' dose and 'right' time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

LAY SUMMARY

In a mouse model of malaria, higher drug doses led to increases in drug resistance. The timing of drug treatment also impacted resistance emergence, likely due to the size of the population at the time of treatment.

Molecular assessment of kelch13 non-synonymous mutations in Plasmodium falciparum isolates from Central African Republic (2017-2019)

Background

Over the last decade, artemisinin-based combination therapy (ACT) has contributed substantially to the decrease in malaria-related morbidity and mortality. The emergence of Plasmodium falciparum parasites resistant to artemisinin derivatives in Southeast Asia and the risk of their spread or of local emergence in sub-Saharan Africa are a major threat to public health. This study thus set out to estimate the proportion of P. falciparum isolates, with Pfkelch13 gene mutations associated with artemisinin resistance previously detected in Southeast Asia.

Methods

Blood samples were collected in two sites of Bangui, the capital of the Central African Republic (CAR) from 2017 to 2019. DNA was extracted and nested PCR were carried out to detect Plasmodium species and mutations in the propeller domain of the Pfkelch13 gene for P. falciparum samples.

Results

A total of 255 P. falciparum samples were analysed. Plasmodium ovale DNA was found in four samples (1.57%, 4/255). Among the 187 samples with interpretable Pfkelch13 sequences, four samples presented a mutation (2.1%, 4/187), including one non-synonymous mutation (Y653N) (0.5%, 1/187). This mutation has never been described as associated with artemisinin resistance in Southeast Asia and its in vitro phenotype is unknown.

Conclusion

This preliminary study indicates the absence of Pfkelch13 mutant associated with artemisinin resistance in Bangui. However, this limited study needs to be extended by collecting samples across the whole country along with the evaluation of in vitro and in vivo phenotype profiles of Pfkelch13 mutant parasites to estimate the risk of artemisinin resistance in the CAR.

Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations

Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, a containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here, we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant Escherichia coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density-the acceptable bacterial burden-is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.

The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

Persistent insecticides sprayed onto house walls, and incorporated into insecticide-treated bednets, provide long-acting, cost-effective control of vector-borne diseases such as malaria and leishmaniasis. The high concentrations that occur immediately postdeployment may kill both resistant and susceptible insects. However, insecticide concentration, and therefore killing ability, declines in the months after deployment. As concentrations decline, resistant insects start to survive, while susceptible insects are still killed. The period of time after deployment, within which the mortality of resistant individuals is lower than that of susceptible ones, has been termed the "window of selection" in other contexts. It is recognized as driving resistance in bacteria and malaria parasites, both of which are predominantly haploid. We argue that paying more attention to these mortality differences can help understand the evolution of insecticide resistance. Because insects are diploid, resistance encoded by single genes generates heterozygotes. This gives the potential for a narrower "window of dominance," within the window of selection, where heterozygote mortality is lower than that of susceptible homozygotes. We explore the general properties of windows of selection and dominance in driving resistance. We quantify their likely effect using data from new laboratory experiments and published data from the laboratory and field. These windows can persist months or years after insecticide deployments. Differential mortalities of resistant, susceptible and heterozygous genotypes, after public health deployments, constitute a major challenge to controlling resistance. Greater attention to mortality differences by genotype would inform strategies to reduce the evolution of resistance to existing and new insecticides.

The effect of enrofloxacin on enteric Escherichia coli: Fitting a mathematical model to in vivo data

Antimicrobial drugs administered systemically may cause the emergence and dissemination of antimicrobial resistance among enteric bacteria. To develop logical, research-based recommendations for food animal veterinarians, we must understand how to maximize antimicrobial drug efficacy while minimizing risk of antimicrobial resistance. Our objective is to evaluate the effect of two approved dosing regimens of enrofloxacin (a single high dose or three low doses) on Escherichia coli in cattle. We look specifically at bacteria above and below the epidemiological cutoff (ECOFF), above which the bacteria are likely to have an acquired or mutational resistance to enrofloxacin. We developed a differential equation model for the antimicrobial drug concentrations in plasma and colon, and bacteria populations in the feces. The model was fit to animal data of drug concentrations in the plasma and colon obtained using ultrafiltration probes. Fecal E. coli counts and minimum inhibitory concentrations were measured for the week after receiving the antimicrobial drug. We predict that the antimicrobial susceptibility of the bacteria above the ECOFF pre-treatment strongly affects the composition of the bacteria following treatment. Faster removal of the antimicrobial drugs from the colon throughout the study leads to improved clearance of bacteria above the ECOFF in the low dose regimen. If we assume a fitness cost is associated with bacteria above the ECOFF, the increased fitness costs leads to reduction of bacteria above the ECOFF in the low dose study. These results suggest the initial E. coli susceptibility is a strong indicator of how steers respond to antimicrobial drug treatment.

Targeting antibiotic resistant bacteria with phage reduces bacterial density in an insect host

Phage therapy is attracting growing interest among clinicians as antibiotic resistance continues becoming harder to control. However, clinical trials and animal model studies on bacteriophage treatment are still scarce and results on the efficacy vary. Recent research suggests that using traditional antimicrobials in concert with phage could have desirable synergistic effects that hinder the evolution of resistance. Here, we present a novel insect gut model to study phage-antibiotic interaction in a system where antibiotic resistance initially exists in very low frequency and phage specifically targets the resistance bearing cells. We demonstrate that while phage therapy could not reduce the frequency of target bacteria in the population during positive selection by antibiotics, it alleviated the antibiotic induced blooming by lowering the overall load of resistant cells. The highly structured gut environment had pharmacokinetic effects on both phage and antibiotic dynamics compared with in vitro: antibiotics did not reduce the overall amount of bacteria, demonstrating a simple turnover of gut microbiota from non-resistant to resistant population with little cost. The results imply moderate potential for using phage as an aid to target antibiotic resistant gut infections, and question the usefulness of in vitro inferences.

Selection for antimicrobial resistance is reduced when embedded in a natural microbial community

Antibiotic resistance has emerged as one of the most pressing, global threats to public health. In single-species experiments selection for antibiotic resistance occurs at very low antibiotic concentrations. However, it is unclear how far these findings can be extrapolated to natural environments, where species are embedded within complex communities. We competed isogenic strains of Escherichia coli, differing exclusively in a single chromosomal resistance determinant, in the presence and absence of a pig faecal microbial community across a gradient of antibiotic concentration for two relevant antibiotics: gentamicin and kanamycin. We show that the minimal selective concentration was increased by more than one order of magnitude for both antibiotics when embedded in the community. We identified two general mechanisms were responsible for the increase in minimal selective concentration: an increase in the cost of resistance and a protective effect of the community for the susceptible phenotype. These findings have implications for our understanding of the evolution and selection of antibiotic resistance, and can inform future risk assessment efforts on antibiotic concentrations.

Suppression of bacteriocin resistance using live, heterospecific competitors

Rapidly spreading antibiotic resistance has led to the need for novel alternatives and sustainable strategies for antimicrobial use. Bacteriocins are a class of proteinaceous anticompetitor toxins under consideration as novel therapeutic agents. However, bacteriocins, like other antimicrobial agents, are susceptible to resistance evolution and will require the development of sustainable strategies to prevent or decelerate the evolution of resistance. Here, we conduct proof-of-concept experiments to test whether introducing a live, heterospecific competitor along with a bacteriocin dose can effectively suppress the emergence of bacteriocin resistance in vitro. Previous work with conventional chemotherapeutic agents suggests that competition between conspecific sensitive and resistant pathogenic cells can effectively suppress the emergence of resistance in pathogenic populations. However, the threshold of sensitive cells required for such competitive suppression of resistance may often be too high to maintain host health. Therefore, here we aim to ask whether the principle of competitive suppression can be effective if a heterospecific competitor is used. Our results show that a live competitor introduced in conjunction with low bacteriocin dose can effectively control resistance and suppress sensitive cells. Further, this efficacy can be matched by using a bacteriocin-producing competitor without any additional bacteriocin. These results provide strong proof of concept for the effectiveness of competitive suppression using live, heterospecific competitors. Currently used probiotic strains or commensals may provide promising candidates for the therapeutic use of bacteriocin-mediated competitive suppression.

Five rules for resistance management in the antibiotic apocalypse, a road map for integrated microbial management

Resistance to new antimicrobials can become widespread within 2-3 years. Resistance problems are particularly acute for bacteria that can experience selection as both harmless commensals and pathogenic hospital-acquired infections. New drugs, although welcome, cannot tackle the antimicrobial resistance crisis alone: new drugs must be partnered with more sustainable patterns of use. However, the broader experience of resistance management in other disciplines, and the assumptions on which resistance rests, is not widely appreciated in clinical and microbiological disciplines. Improved awareness of the field of resistance management could improve clinical outcomes and help shape novel solutions. Here, the aim is to develop a pragmatic approach to developing a sustainable integrated means of using antimicrobials, based on an interdisciplinary synthesis of best practice, recent theory and recent clinical data. This synthesis emphasizes the importance of pre-emptive action and the value of reducing the supply of genetic novelty to bacteria under selection. The weight of resistance management experience also cautions against strategies that over-rely on the fitness costs of resistance or low doses. The potential (and pitfalls) of shorter courses, antibiotic combinations and antibiotic mixing or cycling are discussed in depth. Importantly, some of variability in the success of clinical trials of mixing approaches can be explained by the number and diversity of drugs in a trial, as well as whether trials encompass single wards or the wider transmission network that is a hospital. Consideration of the importance of data, and of the initially low frequency of resistance, leads to a number of additional recommendations. Overall, reduction in selection pressure, interference with the transmission of problematic genotypes and multidrug approaches (combinations, mixing or cycling) are all likely to be required for sustainability and the protection of forthcoming drugs.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

Mutations in gyrB play an important role in ciprofloxacin-resistant Pseudomonas aeruginosa

Purpose

To investigate the main molecular resistance mechanisms to fluoroquinolones (FQs) in Pseudomonas aeruginosa and also to investigate the effect of time and concentration on mutations in resistance genes.

Materials and methods

The clinical isolates of P. aeruginosa which are sensitive to ciprofloxacin (CIP) or levofloxacin (LEV) were collected. The isolates were incubated with different concentrations of CIP or LEV for 5 days and the minimal inhibitory concentrations (MICs) of CIP, LEV and ofloxacin (OFX) were measured. The MIC of FQs to P. aeruginosa was measured by the agar dilution method. FQ resistance determining regions of gyrA, gyrB, parC and parE were amplified by PCR, and mutations in four genes were explored using sequence analysis with the Snapgene software. The relative expression levels of two efflux pumps genes (mexA and mexE) were measured by quantitative reverse transcription PCR.

Results

A total of eleven isolates were collected from the Second Hospital of Shanxi Medical University. Amino acid alterations in gyrA and gyrB were mainly detected in resistant mutants, and the percentage of strains with amino acid alterations in gyrB was significantly higher than that in gyrA (P<0.001). MICs of strains with mutations both in gyrA and gyrB were not significantly higher than those of strains with mutations only in gyrB (P>0.05). No amino acid alterations were detected in genes of parC and parE. In both gyrA and gyrB, the number of amino acid alterations increased with incubation time prolonged and increased with increasing incubation concentration.

Conclusion

CIP was more competent than LEV in making P. aeruginosa resistant to in vitro selection. Mutations occurring in gyrB played an important role in FQ resistance of P. aeruginosa in vitro selection.

Cooperation among cancer cells: applying game theory to cancer

Cell cooperation promotes many of the hallmarks of cancer via the secretion of diffusible factors that can affect cancer cells or stromal cells in the tumour microenvironment. This cooperation cannot be explained simply as the collective action of cells for the benefit of the tumour because non-cooperative subclones can constantly invade and free-ride on the diffusible factors produced by the cooperative cells. A full understanding of cooperation among the cells of a tumour requires methods and concepts from evolutionary game theory, which has been used successfully in other areas of biology to understand similar problems but has been underutilized in cancer research. Game theory can provide insights into the stability of cooperation among cells in a tumour and into the design of potentially evolution-proof therapies that disrupt this cooperation.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

The impact of within-host ecology on the fitness of a drug-resistant parasite

Background and objectives

The rate of evolution of drug resistance depends on the fitness of resistant pathogens. The fitness of resistant pathogens is reduced by competition with sensitive pathogens in untreated hosts and so enhanced by competitive release in drug-treated hosts. We set out to estimate the magnitude of those effects on a variety of fitness measures, hypothesizing that competitive suppression and competitive release would have larger impacts when resistance was rarer to begin with.

Methodology

We infected mice with varying densities of drug-resistant Plasmodium chabaudi malaria parasites in a fixed density of drug-sensitive parasites and followed infection dynamics using strain-specific quantitative PCR.

Results

Competition with susceptible parasites reduced the absolute fitness of resistant parasites by 50–100%. Drug treatment increased the absolute fitness from 2- to >10 000-fold. The ecological context and choice of fitness measure was responsible for the wide variation in those estimates. Initial population growth rates poorly predicted parasite abundance and transmission probabilities.

Conclusions and implications

(i) The sensitivity of estimates of pathogen fitness to ecological context and choice of fitness measure make it difficult to derive field-relevant estimates of the fitness costs and benefits of resistance from experimental settings. (ii) Competitive suppression can be a key force preventing resistance from emerging when it is rare, as it is when it first arises. (iii) Drug treatment profoundly affects the fitness of resistance. Resistance evolution could be slowed by developing drug use policies that consider in-host competition.

Community interactions and spatial structure shape selection on antibiotic resistant lineages

Polymicrobial interactions play an important role in shaping the outcome of antibiotic treatment, yet how multispecies communities respond to antibiotic assault is still little understood. Here we use an individual-based simulation model of microbial biofilms to investigate how competitive and mutualistic interactions between an antibiotic-resistant and a susceptible strain (or species) influence the two-lineage community response to antibiotic exposure. Our model predicts that while increasing competition and antibiotics leads to increasing competitive release of the antibiotic-resistant strain, hitting a mutualistic community of cross-feeding species with antibiotics leads to a mutualistic suppression effect where both susceptible and resistant species are harmed. We next show that the impact of antibiotics is further governed by emergent spatial feedbacks within communities. Mutualistic cross-feeding communities can rescue susceptible members by subsidizing their growth inside the biofilm despite lack of access to the nutrient-rich and high-antibiotic growing front. Moreover, we show that antibiotic detoxification by resistant cells can protect nearby susceptible cells, but such cross-protection is more effective in mutualistic communities because mutualism drives mixing of resistant and susceptible cells. In contrast, competition leads to segregation, which ultimately prevents susceptible cells to profit from detoxification. Understanding how the interplay between microbial metabolic interactions and community spatial structuring shapes the outcome of antibiotic treatment can be key to effectively leverage the power of antibiotics and promote microbiome health.

Pathogens -microorganisms that make us sick- often live within dynamic and complex multispecies communities, where they may not only compete for limiting resources but also exchange beneficial resources or services with other resident species. While antibiotics are commonly used to get rid of such harmful microbes, the community-wide effects of antibiotic treatment and its consequences for antibiotic resistance are still not well understood. How do competitive or mutually beneficial interactions between antibiotic resistant and susceptible species influence community resistance to antibiotics? Here we investigate this question using a computational model. We find that antibiotic exposure favours the resistant lineage when resistant and susceptible strains are competitors but harms both types when they are mutualists. With antibiotic-detoxifying resistant cells, cross-protection of susceptible cells is more effective in mutualistic communities because mutualism drives mixing of susceptible and resistant cells. In contrast, competition leads to their segregation, precluding susceptible cells to profit from their competitor’s local detoxification. Our findings highlight that knowing not only what species are present but also how they interact with each other and arrange themselves in space is central to understanding antibiotic resistance and to informing the development of strategies that promote microbiome health.

Sexual Conflict: Mechanisms and Emerging Themes in Resistance Biology

Sexual conflict is acknowledged as pervasive, with the potential to generate and maintain genetic variation. Mechanistic studies of conflict have been important in providing direct evidence for the existence of sexual conflict. They have also led to the growing realization that there is a striking phenotypic diversity of adaptations whose evolution can be shaped by sexually antagonistic selection. The mechanisms involved range from the use of genital spines, claspers, songs, and smells to ejaculate molecules. In one well-studied example, sexual conflict can occur over the sexually antagonistic effects of seminal fluid proteins in Drosophila melanogaster. However, an important puzzle remains, namely, why seminal fluid proteins are so numerous and complex, hence whether all or some are involved in mediating sexual conflict. I hypothesize that this rich diversity and the complexity of traits subject to sexually antagonistic selection in general may arise, at least in part, due to the deployment of sexually antagonistic adaptations in males in a way that lessens the probability of broadscale, strong resistance evolution in females. In elaborating this hypothesis, I explore how research into the evolution of resistance to insecticides, antimicrobials, and vaccines might be used to provide insights into the evolution of female resistance to the effects of sexually antagonistic manipulative traits of males. In this manner, the manipulative traits of males can be resistance-proofed.

Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

The stress-induced mutagenesis hypothesis postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to better withstand the stress. This has implications for antibiotic treatment: exposure to subinhibitory doses of antibiotics has been reported to increase bacterial mutation rates and thus probably the rate at which resistance mutations appear and lead to treatment failure. More generally, the hypothesis posits that stress increases evolvability (the ability of a population to generate adaptive genetic diversity) and thus accelerates evolution. Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet subinhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus providing more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress. We developed a system based on plasmid segregation that allows us to measure death and division rates simultaneously in bacterial populations. Using this system, we found that a substantial death rate occurs at the tested subinhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover, even when antibiotics increase mutation rate, we show that subinhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics. We conclude that antibiotic-induced mutagenesis is overestimated because of death and that understanding evolvability under stress requires accounting for the effects of stress on population dynamics as much as on mutation rate. Our goal here is dual: we show that population dynamics and, in particular, the numbers of cell divisions are crucial but neglected parameters in the evolvability of a population, and we provide experimental and computational tools and methods to study evolvability under stress, leading to a reassessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

The effect of environmental stress on bacterial mutagenesis has been a paradigm-shift discovery. Recent developments include evidence that various antibiotics increase mutation rates in bacteria when used at subinhibitory concentrations. It is therefore suggested that such treatments promote resistance evolution because they increase the generation of genetic variation on which natural selection can act. However, existing methods to compute mutation rate neglect the effect of stress on death and population dynamics. Developing new experimental and computational tools, we find that taking death into account significantly lowers the signal for stress-induced mutagenesis. Moreover, we show that treatments that increase mutation rate do not always lead to increased genetic diversity, which questions the standard paradigm of increased evolvability under stress.

Beyond dose: Pulsed antibiotic treatment schedules can maintain individual benefit while reducing resistance

The emergence of treatment-resistant microbes is a key challenge for disease treatment and a leading threat to human health and wellbeing. New drugs are always in development, but microbes regularly and rapidly acquire resistance. We must consider if altering how we administer drugs at the individual level could slow development of resistance. Here we use mathematical models to show that exposing microbes to drug pulses could greatly reduce resistance without increasing individual pathogen load. Our results stem from two key factors: the presence of antibiotics creates a selection pressure for antibiotic resistant microbes, and large populations of bacteria are more likely to harbor drug resistance than small populations. Drug pulsing targets these factors simultaneously. Short duration pulses minimize the time during which there is selection for resistance, and high drug concentrations minimize pathogen abundance. Our work provides a theoretical basis for the design of in vitro and in vivo experiments to test how drug pulsing might reduce the impact of drug resistant infections.

Host-mediated selection impacts the diversity of Plasmodium falciparum antigens within infections

Host immunity exerts strong selective pressure on pathogens. Population-level genetic analysis can identify signatures of this selection, but these signatures reflect the net selective effect of all hosts and vectors in a population. In contrast, analysis of pathogen diversity within hosts provides information on individual, host-specific selection pressures. Here, we combine these complementary approaches in an analysis of the malaria parasite Plasmodium falciparum using haplotype sequences from thousands of natural infections in sub-Saharan Africa. We find that parasite genotypes show preferential clustering within multi-strain infections in young children, and identify individual amino acid positions that may contribute to strain-specific immunity. Our results demonstrate that natural host defenses to P. falciparum act in an allele-specific manner to block specific parasite haplotypes from establishing blood-stage infections. This selection partially explains the extreme amino acid diversity of many parasite antigens and suggests that vaccines targeting such proteins should account for allele-specific immunity.

Host immune responses exert selective pressure on Plasmodium falciparum. Here, the authors show that allele-specific immunity impacts the antigenic diversity of individual malaria infections. This process partially explains the extreme amino acid diversity of many parasite antigens and suggests that vaccines should account for allele-specific immunity.

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration

Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient (sc) that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [κ]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well (R² ≥ 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ≤ κ ≤ 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc, indicating the importance of fitness differences between strains for determining the MSC. The MSC can be >1 order of magnitude lower than the MIC, typically by the factor sc^κ This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

A nutrient mediates intraspecific competition between rodent malaria parasites in vivo

Hosts are often infected with multiple strains of a single parasite species. Within-host competition between parasite strains can be intense and has implications for the evolution of traits that impact patient health, such as drug resistance and virulence. Yet the mechanistic basis of within-host competition is poorly understood. Here, we demonstrate that a parasite nutrient, para-aminobenzoic acid (pABA), mediates competition between a drug resistant and drug susceptible strain of the malaria parasite, Plasmodium chabaudi. We further show that increasing pABA supply to hosts infected with the resistant strain worsens disease and changes the relationship between parasite burden and pathology. Our experiments demonstrate that, even when there is profound top-down regulation (immunity), bottom-up regulation of pathogen populations can occur and that its importance may vary during an infection. The identification of resources that can be experimentally controlled opens up the opportunity to manipulate competitive interactions between parasites and hence their evolution.

Reconciling Pasteur and Darwin to control infectious diseases

The continual emergence of new pathogens and the increased spread of antibiotic resistance in bacterial populations remind us that microbes are living entities that evolve at rates that impact public health interventions. Following the historical thread of the works of Pasteur and Darwin shows how reconciling clinical microbiology, ecology, and evolution can be instrumental to understanding pathology, developing new therapies, and prolonging the efficiency of existing ones.

Resource limitation prevents the emergence of drug resistance by intensifying within-host competition

Slowing the evolution of antimicrobial resistance is essential if we are to continue to successfully treat infectious diseases. Whether a drug-resistant mutant grows to high densities, and so sickens the patient and spreads to new hosts, is determined by the competitive interactions it has with drug-susceptible pathogens within the host. Competitive interactions thus represent a good target for resistance management strategies. Using an in vivo model of malaria infection, we show that limiting a resource that is disproportionately required by resistant parasites retards the evolution of drug resistance by intensifying competitive interactions between susceptible and resistant parasites. Resource limitation prevented resistance emergence regardless of whether resistant mutants arose de novo or were experimentally added before drug treatment. Our work provides proof of principle that chemotherapy paired with an "ecological" intervention can slow the evolution of resistance to antimicrobial drugs, even when resistant pathogens are present at high frequencies. It also suggests that a broad range of previously untapped compounds could be used for treating infectious diseases.

Integrating human and environmental health in antibiotic risk assessment: A critical analysis of protection goals, species sensitivity and antimicrobial resistance

Antibiotics are vital in the treatment of bacterial infectious diseases but when released into the environment they may impact non-target organisms that perform vital ecosystem services and enhance antimicrobial resistance development with significant consequences for human health. We evaluate whether the current environmental risk assessment regulatory guidance is protective of antibiotic impacts on the environment, protective of antimicrobial resistance, and propose science-based protection goals for antibiotic manufacturing discharges. A review and meta-analysis was conducted of aquatic ecotoxicity data for antibiotics and for minimum selective concentration data derived from clinically relevant bacteria. Relative species sensitivity was investigated applying general linear models, and predicted no effect concentrations were generated for toxicity to aquatic organisms and compared with predicted no effect concentrations for resistance development. Prokaryotes were most sensitive to antibiotics but the range of sensitivities spanned up to several orders of magnitude. We show reliance on one species of (cyano)bacteria and the 'activated sludge respiration inhibition test' is not sufficient to set protection levels for the environment. Individually, neither traditional aquatic predicted no effect concentrations nor predicted no effect concentrations suggested to safeguard for antimicrobial resistance, protect against environmental or human health effects (via antimicrobial resistance development). Including data from clinically relevant bacteria and also more species of environmentally relevant bacteria in the regulatory framework would help in defining safe discharge concentrations for antibiotics for patient use and manufacturing that would protect environmental and human health. It would also support ending unnecessary testing on metazoan species.

The 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.

Exploiting ecology in drug pulse sequences in favour of population reduction

A deterministic population dynamics model involving birth and death for a two-species system, comprising a wild-type and more resistant species competing via logistic growth, is subjected to two distinct stress environments designed to mimic those that would typically be induced by temporal variation in the concentration of a drug (antibiotic or chemotherapeutic) as it permeates through the population and is progressively degraded. Different treatment regimes, involving single or periodical doses, are evaluated in terms of the minimal population size (a measure of the extinction probability), and the population composition (a measure of the selection pressure for resistance or tolerance during the treatment). We show that there exist timescales over which the low-stress regime is as effective as the high-stress regime, due to the competition between the two species. For multiple periodic treatments, competition can ensure that the minimal population size is attained during the first pulse when the high-stress regime is short, which implies that a single short pulse can be more effective than a more protracted regime. Our results suggest that when the duration of the high-stress environment is restricted, a treatment with one or multiple shorter pulses can produce better outcomes than a single long treatment. If ecological competition is to be exploited for treatments, it is crucial to determine these timescales, and estimate for the minimal population threshold that suffices for extinction. These parameters can be quantified by experiment.

Suppressive drug combinations and their potential to combat antibiotic resistance

Antibiotic effectiveness often changes when two or more such drugs are administered simultaneously and unearthing antibiotic combinations with enhanced efficacy (synergy) has been a longstanding clinical goal. However, antibiotic resistance, which undermines individual drugs, threatens such combined treatments. Remarkably, it has emerged that antibiotic combinations whose combined effect is lower than that of at least one of the individual drugs can slow or even reverse the evolution of resistance. We synthesize and review studies of such so-called 'suppressive interactions' in the literature. We examine why these interactions have been largely disregarded in the past, the strategies used to identify them, their mechanistic basis, demonstrations of their potential to reverse the evolution of resistance and arguments for and against using them in clinical treatment. We suggest future directions for research on these interactions, aiming to expand the basic body of knowledge on suppression and to determine the applicability of suppressive interactions in the clinic.