Temporal variation in antibiotic environments slows down resistance evolution in pathogenic Pseudomonas aeruginosa

Heterogeneous Distribution of Proton Motive Force in Nonheritable Antibiotic Resistance

Bacterial infections that are difficult to eradicate are often treated by sequentially exposing the bacteria to different antibiotics. Although effective, this approach can give rise to epigenetic or other phenomena that may help some cells adapt to and tolerate the antibiotics. Characteristics of such adapted cells are dormancy and low energy levels, which promote survival without lending long-term genetic resistance against antibiotics. In this work, we quantified motility in cells of Escherichia coli that adapted and survived sequential exposure to lethal doses of antibiotics. In populations that adapted to transcriptional inhibition by rifampicin, we observed that ~1 of 3 cells continued swimming for several hours in the presence of lethal concentrations of ampicillin. As motility is powered by proton motive force (PMF), our results suggested that many adapted cells retained a high PMF. Single-cell growth assays revealed that the high-PMF cells resuscitated and divided upon the removal of ampicillin, just as the low-PMF cells did, a behavior reminiscent of persister cells. Our results are consistent with the notion that cells in a clonal population may employ multiple different mechanisms to adapt to antibiotic stresses. Variable PMF is likely a feature of a bet-hedging strategy: a fraction of the adapted cell population lies dormant while the other fraction retains high PMF to be able to swim out of the deleterious environment. IMPORTANCE Bacterial cells with low PMF may survive antibiotic stress due to dormancy, which favors nonheritable resistance without genetic mutations or acquisitions. On the other hand, cells with high PMF are less tolerant, as PMF helps in the uptake of certain antibiotics. Here, we quantified flagellar motility as an indirect measure of the PMF in cells of Escherichia coli that had adapted to ampicillin. Despite the disadvantage of maintaining a high PMF in the presence of antibiotics, we observed high PMF in ~30% of the cells, as evidenced by their ability to swim rapidly for several hours. These and other results were consistent with the idea that antibiotic tolerance can arise via different mechanisms in a clonal population.

Fast drug rotation reduces bacterial resistance evolution in a microcosm experiment

Drug rotation (cycling), in which multiple drugs are administrated alternatively, has the potential for limiting resistance evolution in pathogens. The frequency of drug alternation could be a major factor to determine the effectiveness of drug rotation. Drug rotation practices often have low frequency of drug alternation, with an expectation of resistance reversion. Here we, based on evolutionary rescue and compensatory evolution theories, suggest that fast drug rotation can limit resistance evolution in the first place. This is because fast drug rotation would give little time for the evolutionarily rescued populations to recover in population size and genetic diversity, and thus decrease the chance of future evolutionary rescue under alternate environmental stresses. We experimentally tested this hypothesis using the bacterium Pseudomonas fluorescens and two antibiotics (chloramphenicol and rifampin). Increasing drug rotation frequency reduced the chance of evolutionary rescue, and most of the finally surviving bacterial populations were resistant to both drugs. Drug resistance incurred significant fitness costs, which did not differ among the drug treatment histories. A link between population sizes during the early stages of drug treatment and the end-point fates of populations (extinction vs survival) suggested that population size recovery and compensatory evolution before drug shift increase the chance of population survival. Our results therefore advocate fast drug rotation as a promising approach to reduce bacterial resistance evolution, which in particular could be a substitute for drug combination when the latter has safety risks.

Antifungal alternation can be beneficial for durability but at the cost of generalist resistance

The evolution of resistance to pesticides is a major burden in agriculture. Resistance management involves maximizing selection pressure heterogeneity, particularly by combining active ingredients with different modes of action. We tested the hypothesis that alternation may delay the build-up of resistance not only by spreading selection pressure over longer periods, but also by decreasing the rate of evolution of resistance to alternated fungicides, by applying an experimental evolution approach to the economically important crop pathogen Zymoseptoria tritici. Our results show that alternation is either neutral or slows the overall resistance evolution rate, relative to continuous fungicide use, but results in higher levels of generalism in evolved lines. We demonstrate that the nature of the fungicides, and therefore their relative intrinsic risk of resistance may underly this trade-off, more so than the number of fungicides and the rhythm of alternation. This trade-off is also dynamic over the course of resistance evolution. These findings open up new possibilities for tailoring resistance management effectively while optimizing interplay between alternation components.

Sequential antibiotic therapy in the laboratory and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.

Evaluation of a Sequential Antibiotic Treatment Regimen of Ampicillin, Ciprofloxacin and Fosfomycin against Escherichia coli CFT073 in the Hollow Fiber Infection Model Compared with Simultaneous Combination Treatment

OBJECTIVE

Employ the hollow fiber infection model (HFIM) to study sequential antibiotic administration (ampicillin, ciprofloxacin and fosfomycin) using human pharmacokinetic profiles to measure changes in the rate of antibiotic resistance development and compare this to simultaneous combination therapy with the same antibiotic combinations.

METHODS

Escherichia coli CFT073, a clinical uropathogenic strain, was exposed individually to clinically relevant pharmacokinetic concentrations of ampicillin on day 1, ciprofloxacin on day 2 and fosfomycin on day 3. This sequence was continued for 10 days in the HFIM. Bacterial samples were collected at different time points to enumerate total and resistant bacterial populations. The results were compared with the simultaneous combination therapy previously studied.

RESULTS

Sequential antibiotic treatment (ampicillin-ciprofloxacin-fosfomycin sequence) resulted in the early emergence of single and multi-antibiotic-resistant subpopulations, while the simultaneous treatment regimen significantly delayed or prevented the emergence of resistant subpopulations.

CONCLUSION

Sequential administration of these antibiotic monotherapies did not significantly delay the emergence of resistant subpopulations compared to simultaneous treatment with combinations of the same antibiotics. Further studies are warranted to evaluate different sequences of the same antibiotics in delaying emergent resistance.

Resistance is futile? Mucosal immune mechanisms in the context of microbial ecology and evolution

In the beginning it was simple: we injected a protein antigen and studied the immune responses against the purified protein. This elegant toolbox uncovered thousands of mechanisms via which immune cells are activated. However, when we consider immune responses against real infectious threats, this elegant simplification misses half of the story: the infectious agents are typically evolving orders-of-magnitude faster than we are. Nowhere is this more pronounced than in the mammalian large intestine. A bacterium representing only 0.1% of the human gut microbiota will have a population size of 109 clones, each actively replicating. Moreover, the evolutionary pressure from other microbes is at least as profound as direct effects of the immune system. Therefore, to really understand intestinal immune mechanisms, we need to understand both the host response and how rapid microbial evolution alters the apparent outcome of the response. In this review we use the examples of intestinal inflammation and secretory immunoglobulin A (SIgA) to highlight what is already known (Fig. 1). Further, we will explore how these interactions can inform immunotherapy and prophylaxis. This has major implications for how we design effective mucosal vaccines against increasingly drug-resistant bacterial pathogens Fig. 1 THE IMMUNE RESPONSE SHAPES THE FITNESS LANDSCAPE IN THE GASTRO-INTESTINAL TRACT.: The red arrows depict possible evolutionary paths of a novel colonizer along adaptive peaks in the intestinal fitness landscapes that change with the status of the host immune system. The flat surfaces represent the non-null fitness baselines (values x or y) at which a bacterium can establish at minimum carrying capacity. a In the healthy gut, metabolic competence, resistance to aggressions by competitors and predators, swift adaptation to rapid fluctuations as well as surviving acidic pH and the flow of the intestinal content, represent potent selective pressures and as many opportunities for bacteria to increase fitness by phenotypic or genetic variations. b When pathogens trigger acute inflammation, bacteria must adapt to iron starvation, killing by immune cells and antimicrobial peptides, and oxidative stress, while new metabolic opportunities emerge. c When high-affinity SIgA are produced against a bacterium, e.g., after oral vaccination, escape of SIgA by altering or losing surface epitopes becomes crucial for maximum fitness. However, escaping polyvalent SIgA responses after vaccination with "evolutionary trap" vaccines leads to evolutionary trade-offs: A fitness maximum is reached in the vaccinated host gut that represents a major disadvantage for transmission into naïve hosts (fitness diminished below x) (d).

Seeking patterns of antibiotic resistance in ATLAS, an open, raw MIC database with patient metadata

Antibiotic resistance represents a growing medical concern where raw, clinical datasets are under-exploited as a means to track the scale of the problem. We therefore sought patterns of antibiotic resistance in the Antimicrobial Testing Leadership and Surveillance (ATLAS) database. ATLAS holds 6.5M minimal inhibitory concentrations (MICs) for 3,919 pathogen-antibiotic pairs isolated from 633k patients in 70 countries between 2004 and 2017. We show most pairs form coherent, although not stationary, timeseries whose frequencies of resistance are higher than other databases, although we identified no systematic bias towards including more resistant strains in ATLAS. We sought data anomalies whereby MICs could shift for methodological and not clinical or microbiological reasons and found artefacts in over 100 pathogen-antibiotic pairs. Using an information-optimal clustering methodology to classify pathogens into low and high antibiotic susceptibilities, we used ATLAS to predict changes in resistance. Dynamics of the latter exhibit complex patterns with MIC increases, and some decreases, whereby subpopulations' MICs can diverge. We also identify pathogens at risk of developing clinical resistance in the near future.

Can We Arrest the Evolution of Antibiotic Resistance? The Differences between the Effects of Silver Nanoparticles and Silver Ions

Silver nanoparticles (AgNPs) are effective antimicrobial substances that show promise in combatting multidrug resistance. The potential application and release of AgNPs into the environment may neutralize the selective advantage of antibiotic resistance. Systemic knowledge regarding the effect of NPs on the evolution of antibiotic resistance is lacking. Our results showed that bacteria slowly developed adaptive tolerance to ciprofloxacin (CIP) under cyclic CIP and silver ion (Ag⁺) cotreatment, and no resistance/tolerance was discernible when CIP and AgNP exposure was alternated. In contrast, rapid CIP resistance was induced under continuous selection by treatment with only CIP. To combat the effects of CIP and Ag⁺, bacteria developed convergent evolutionary strategies with similar adaptive mechanisms, including anaerobic respiration transitioning (to reduce oxidative stress) and stringent response (to survive harsh environments). Alternating AgNP exposure impeded evolutionary resistance by accelerating B12-dependent folate and methionine cycles, which reestablished DNA synthesis and partially offset high oxidative stress levels, in contrast with the effect of CIP-directed evolutionary pressure. Nevertheless, CIP/AgNP treatment was ineffective in attenuating virulence, and CIP/Ag⁺ exposure even induced the virulence-critical type III secretion system. Our results increase the basic understanding of the impacts of NPs on evolutionary biology and suggest prospective nanotechnology applications for arresting evolutionary antibiotic resistance.

The cost of evolved constitutive lac gene expression is usually, but not always, maintained during evolution of generalist populations

Beneficial mutations can become costly following an environmental change. Compensatory mutations can relieve these costs, while not affecting the selected function, so that the benefits are retained if the environment shifts back to be similar to the one in which the beneficial mutation was originally selected. Compensatory mutations have been extensively studied in the context of antibiotic resistance, responses to specific genetic perturbations, and in the determination of interacting gene network components. Few studies have focused on the role of compensatory mutations during more general adaptation, especially as the result of selection in fluctuating environments where adaptations to different environment components may often involve trade-offs. We examine whether costs of a mutation in lacI, which deregulated the expression of the lac operon in evolving populations of Escherichia coli bacteria, were compensated. This mutation occurred in multiple replicate populations selected in environments that fluctuated between growth on lactose, where the mutation was beneficial, and on glucose, where it was deleterious. We found that compensation for the cost of the lacI mutation was rare, but, when it did occur, it did not negatively affect the selected benefit. Compensation was not more likely to occur in a particular evolution environment. Compensation has the potential to remove pleiotropic costs of adaptation, but its rarity indicates that the circumstances to bring about the phenomenon may be peculiar to each individual or impeded by other selected mutations.

Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling

Hospital-acquired bacterial infections lead to prolonged hospital stays and increased mortality. The problem is exacerbated by antibiotic-resistant strains that delay or impede effective treatment. To ensure successful therapy and to manage antibiotic resistance, treatment protocols that draw on several different antibiotics might be used. This includes the administration of drug cocktails to individual patients (combination therapy) but also the random assignment of drugs to different patients (mixing) and a regular switch in the default drug used in the hospital from drug A to drug B and back (cycling). For more than 20 years, mathematical models have been used to assess the prospects of antibiotic combination therapy, mixing and cycling. But while tendencies in their ranking across studies have emerged, the picture remains surprisingly inconclusive and incomplete. In this article, we review existing modelling studies and demonstrate by means of examples how methodological factors complicate the emergence of a consistent picture. These factors include the choice of the criterion by which the effects of the protocols are compared, the model implementation and its analysis. We thereafter discuss how progress can be made and suggest future modelling directions.

High potency of sequential therapy with only β-lactam antibiotics

Evolutionary adaptation is a major source of antibiotic resistance in bacterial pathogens. Evolution-informed therapy aims to constrain resistance by accounting for bacterial evolvability. Sequential treatments with antibiotics that target different bacterial processes were previously shown to limit adaptation through genetic resistance trade-offs and negative hysteresis. Treatment with homogeneous sets of antibiotics is generally viewed to be disadvantageous as it should rapidly lead to cross-resistance. We here challenged this assumption by determining the evolutionary response of Pseudomonas aeruginosa to experimental sequential treatments involving both heterogenous and homogeneous antibiotic sets. To our surprise, we found that fast switching between only β-lactam antibiotics resulted in increased extinction of bacterial populations. We demonstrate that extinction is favored by low rates of spontaneous resistance emergence and low levels of spontaneous cross-resistance among the antibiotics in sequence. The uncovered principles may help to guide the optimized use of available antibiotics in highly potent, evolution-informed treatment designs.

Overuse of antibiotic drugs is leading to the appearance of antibiotic-resistant bacteria; this is, bacteria with mutations that allow them to survive treatment with specific antibiotics. This has made some bacterial infections difficult or impossible to treat. Learning more about how bacteria evolve resistance to antibiotics could help scientists find ways to prevent it and develop more effective treatments.

Changing antibiotics frequently may be one way to prevent bacteria from evolving resistance. That way if a bacterium acquires mutations that allow it to escape one antibiotic, another antibiotic will kill it, stopping it from dividing and preventing the appearance of descendants with resistance to several antibiotics. In order to use this approach, testing is needed to find the best sequences of antibiotics to apply and the optimal timings of treatment.

To find out more, Batra, Roemhild et al. grew bacteria in the laboratory and exposed them to different sequences of antibiotics, switching antibiotics at different time intervals. This showed that sequential treatments with different antibiotics can limit bacterial evolution, especially when antibiotics are switched quickly. Unexpectedly, one of the most effective sequences used very similar antibiotics. This was surprising because using similar antibiotics should lead to the evolution of cross-resistance, which is when a drug causes changes that make the bacterium less sensitive to other treatments. However, in the tested case, cross-resistance did not evolve when antibiotics were switched quickly, thereby ensuring efficiency of treatment.

Batra et al. show that alternating sequences of antibiotics may be an effective strategy to prevent drug resistance. Because the experiments were done in a laboratory setting it will be important to verify the results in studies in animals and humans before the approach can be used in medical or veterinary settings. If the results are confirmed, it could reduce the need to develop new antibiotics, which is expensive and time consuming.

Interplay of population size and environmental fluctuations: A new explanation for fitness cost rarity in asexuals

Theoretical models of ecological specialisation commonly assume that adaptation to one environment leads to fitness reductions (costs) in others. However, experiments often fail to detect such costs. We addressed this conundrum using experimental evolution with Escherichia coli in several constant and fluctuating environments at multiple population sizes. We found that in fluctuating environments, smaller populations paid significant costs, but larger ones avoided them altogether. Contrastingly, in constant environments, larger populations paid more costs than the smaller ones. Overall, large population sizes and fluctuating environments led to cost avoidance only when present together. Mutational frequency distributions obtained from whole-genome whole-population sequencing revealed that the primary mechanism of cost avoidance was the enrichment of multiple beneficial mutations within the same lineage. Since the conditions revealed by our study for avoiding costs are widespread, it provides a novel explanation of the conundrum of why the costs expected in theory are rarely detected in experiments.

The Genomic Basis of Rapid Adaptation to Antibiotic Combination Therapy in Pseudomonas aeruginosa

Combination therapy is a common antibiotic treatment strategy that aims at minimizing the risk of resistance evolution in several infectious diseases. Nonetheless, evidence supporting its efficacy against the nosocomial opportunistic pathogen Pseudomonas aeruginosa remains elusive. Identification of the possible evolutionary paths to resistance in multidrug environments can help to explain treatment outcome. For this purpose, we here performed whole-genome sequencing of 127 previously evolved populations of P. aeruginosa adapted to sublethal doses of distinct antibiotic combinations and corresponding single-drug treatments, and experimentally characterized several of the identified variants. We found that alterations in the regulation of efflux pumps are the most favored mechanism of resistance, regardless of the environment. Unexpectedly, we repeatedly identified intergenic variants in the adapted populations, often with no additional mutations and usually associated with genes involved in efflux pump expression, possibly indicating a regulatory function of the intergenic regions. The experimental analysis of these variants demonstrated that the intergenic changes caused similar increases in resistance against single and multidrug treatments as those seen for efflux regulatory gene mutants. Surprisingly, we could find no substantial fitness costs for a majority of these variants, most likely enhancing their competitiveness toward sensitive cells, even in antibiotic-free environments. We conclude that the regulation of efflux is a central target of antibiotic-mediated selection in P. aeruginosa and that, importantly, changes in intergenic regions may represent a usually neglected alternative process underlying bacterial resistance evolution, which clearly deserves further attention in the future.

Evolution in alternating environments with tunable interlandscape correlations

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

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.

Pseudomonas aeruginosa populations in the cystic fibrosis lung lose susceptibility to newly applied β-lactams within 3 days

BACKGROUND

Chronic pulmonary infections by Pseudomonas aeruginosa require frequent intravenous antibiotic treatment in cystic fibrosis (CF) patients. Emergence of antimicrobial resistance is common in these patients, which to date has been investigated at long-term intervals only.

OBJECTIVES

To investigate under close to real-time conditions the dynamics of the response by P. aeruginosa to a single course of antibiotic therapy and the potentially associated rapid spread of antimicrobial resistance, as well as the impact on the airway microbiome.

METHODS

We investigated a cohort of adult CF patients that were treated with a single course of antimicrobial combination therapy. Using daily sampling during treatment, we quantified the expression of resistance by P. aeruginosa (median of six isolates per daily sample, 347 isolates in total), measured bacterial load by P. aeruginosa-specific quantitative PCR and characterized the airway microbiome with a 16S rRNA-based approach. WGS was performed to reconstruct intrapatient strain phylogenies.

RESULTS

In two patients, we found rapid and large increases in resistance to meropenem and ceftazidime. Phylogenetic reconstruction of strain relationships revealed that resistance shifts are probably due to de novo evolution and/or the selection of resistant subpopulations. We observed high interindividual variation in the reduction of bacterial load, microbiome composition and antibiotic resistance.

CONCLUSIONS

We show that CF-associated P. aeruginosa populations can quickly respond to antibiotic therapy and that responses are patient specific. Thus, resistance evolution can be a direct consequence of treatment, and drug efficacy can be lost much faster than usually assumed. The consideration of these patient-specific rapid resistance shifts can help to improve treatment of CF-associated infections, for example by deeper sampling of bacteria for diagnostics, repeated monitoring of pathogen susceptibility and switching between drugs.

Evolution of drug-resistant and virulent small colonies in phenotypically diverse populations of the human fungal pathogen Candida glabrata

Antimicrobial resistance frequently carries a fitness cost to a pathogen, measured as a reduction in growth rate compared to the sensitive wild-type, in the absence of antibiotics. Existing empirical evidence points to the following relationship between cost of resistance and virulence. If a resistant pathogen suffers a fitness cost in terms of reduced growth rate it commonly has lower virulence compared to the sensitive wild-type. If this cost is absent so is the reduction in virulence. Here we show, using experimental evolution of drug resistance in the fungal human pathogen Candida glabrata, that reduced growth rate of resistant strains need not result in reduced virulence. Phenotypically heterogeneous populations were evolved in parallel containing highly resistant sub-population small colony variants (SCVs) alongside sensitive sub-populations. Despite their low growth rate in the absence of an antifungal drug, the SCVs did not suffer a marked alteration in virulence compared with the wild-type ancestral strain, or their co-isolated sensitive strains. This contrasts with classical theory that assumes growth rate to positively correlate with virulence. Our work thus highlights the complexity of the relationship between resistance, basic life-history traits and virulence.

Reciprocal antibiotic collateral sensitivity in Burkholderia multivorans

Antibiotic collateral sensitivity (CS) occurs when a bacterium that acquires resistance to a treatment drug exhibits decreased resistance to a different drug. Here we identify reciprocal CS networks and candidate genes in Burkholderia multivorans. Burkholderia multivorans was evolved to become resistant to each of six antibiotics. The antibiogram of the evolved strain was compared with the immediate parental strain to determine CS and cross-resistance. The evolution process was continued for each resistant strain. CS interactions were observed in 170 of 279 evolved strains. CS patterns grouped into two clusters based on the treatment drug being a β-lactam antibiotic or not. Reciprocal pairs of CS antibiotics arose in ≥25% of all evolved strains. A total of 68 evolved strains were subjected to whole-genome sequencing and the resulting mutation patterns were correlated with antibiograms. Analysis revealed there was no single gene responsible for CS and that CS seen in B. multivorans is likely due to a combination of specific and non-specific mutations. The frequency of reciprocal CS, and the degree to which resistance changed, suggests a long-term treatment strategy; when resistance to one drug occurs, switch to use of the other member of the reciprocal pair. This switching could theoretically be continued indefinitely, allowing life-long treatment of chronic infections with just two antibiotics.

Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa

Evolution is at the core of the impending antibiotic crisis. Sustainable therapy must thus account for the adaptive potential of pathogens. One option is to exploit evolutionary trade-offs, like collateral sensitivity, where evolved resistance to one antibiotic causes hypersensitivity to another one. To date, the evolutionary stability and thus clinical utility of this trade-off is unclear. We performed a critical experimental test on this key requirement, using evolution experiments with Pseudomonas aeruginosa, and identified three main outcomes: (i) bacteria commonly failed to counter hypersensitivity and went extinct; (ii) hypersensitivity sometimes converted into multidrug resistance; and (iii) resistance gains frequently caused re-sensitization to the previous drug, thereby maintaining the trade-off. Drug order affected the evolutionary outcome, most likely due to variation in the effect size of collateral sensitivity, epistasis among adaptive mutations, and fitness costs. Our finding of robust genetic trade-offs and drug-order effects can guide design of evolution-informed antibiotic therapy.

Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance

Evolved resistance to one antibiotic may be associated with "collateral" sensitivity to other drugs. Here, we provide an extensive quantitative characterization of collateral effects in Enterococcus faecalis, a gram-positive opportunistic pathogen. By combining parallel experimental evolution with high-throughput dose-response measurements, we measure phenotypic profiles of collateral sensitivity and resistance for a total of 900 mutant-drug combinations. We find that collateral effects are pervasive but difficult to predict because independent populations selected by the same drug can exhibit qualitatively different profiles of collateral sensitivity as well as markedly different fitness costs. Using whole-genome sequencing of evolved populations, we identified mutations in a number of known resistance determinants, including mutations in several genes previously linked with collateral sensitivity in other species. Although phenotypic drug sensitivity profiles show significant diversity, they cluster into statistically similar groups characterized by selecting drugs with similar mechanisms. To exploit the statistical structure in these resistance profiles, we develop a simple mathematical model based on a stochastic control process and use it to design optimal drug policies that assign a unique drug to every possible resistance profile. Stochastic simulations reveal that these optimal drug policies outperform intuitive cycling protocols by maintaining long-term sensitivity at the expense of short-term periods of high resistance. The approach reveals a new conceptual strategy for mitigating resistance by balancing short-term inhibition of pathogen growth with infrequent use of drugs intended to steer pathogen populations to a more vulnerable future state. Experiments in laboratory populations confirm that model-inspired sequences of four drugs reduce growth and slow adaptation relative to naive protocols involving the drugs alone, in pairwise cycles, or in a four-drug uniform cycle.

Burkholderia multivorans Exhibits Antibiotic Collateral Sensitivity

Burkholderia multivorans is a member of the Burkholderia cepacia complex whose members are inherently resistant to many antibiotics and can cause chronic lung infections in patients with cystic fibrosis. A possible treatment for chronic infections arises from the existence of collateral sensitivity (CS)-acquired resistance to a treatment antibiotic results in a decreased resistance to a nontreatment antibiotic. Determining CS patterns for bacteria involved in chronic infections may lead to sustainable treatment regimens that reduce development of multidrug-resistant bacterial strains. CS has been found to occur in Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Here, we report that B. multivorans exhibits antibiotic CS, as well as cross-resistance (CR), describe CS and CR networks for six antibiotics (ceftazidime, chloramphenicol, levofloxacin, meropenem, minocycline, and trimethoprim-sulfamethoxazole), and identify candidate genes involved in CS. Characterization of CS and CR patterns allows antibiotics to be separated into two clusters based on the treatment drug to which the evolved strain developed primary resistance, suggesting an antibiotic therapy strategy of switching between members of these two clusters.

OXA-48-Mediated Ceftazidime-Avibactam Resistance Is Associated with Evolutionary Trade-Offs

Infections due to carbapenemase-producing Gram-negative pathogens are associated with limited treatment options and consequently lead to increased mortality and morbidity. In response, combinations of existing β-lactams and novel β-lactamase inhibitors, such as ceftazidime-avibactam (CAZ-AVI), have been developed as alternative treatment options. To understand the development of resistance and evolutionary trajectories under CAZ-AVI exposure, we studied the effects of ceftazidime (CAZ) and CAZ-AVI on the carbapenemase OXA-48 and the epidemic OXA-48 plasmid in Escherichia coli Exposure of CAZ and CAZ-AVI resulted in single (P68A) and double (P68A,Y211S) amino acid substitutions in OXA-48, respectively. The antimicrobial susceptibility data and enzyme kinetics showed that the P68A substitution was responsible for an increased activity toward CAZ, whereas P68A,Y211S led to a decrease in the inhibitory activity of AVI. X-ray crystallography and molecular modeling of the mutants demonstrated increased flexibility within the active site, which could explain the elevated CAZ hydrolysis and reduced inhibitory activity of AVI. Interestingly, these substitutions resulted in collateral effects compromising the activity of OXA-48 toward carbapenems and penicillins. Moreover, exposure to CAZ-AVI selected for mutations within the OXA-48-encoding plasmid that severely reduced fitness in the absence of antimicrobial selection. These evolutionary trade-offs may contribute to limit the evolution of OXA-48-mediated CAZ and CAZ-AVI resistance, as well as potentially resensitize isolates toward other therapeutic alternatives.IMPORTANCE The recent introduction of novel β-lactam/β-lactamase inhibitor combinations like ceftazidime-avibactam has increased our ability to treat infections caused by multidrug-resistant Gram-negative bacteria, including carbapenemase-producing Enterobacterales However, the increasing number of cases of reported resistance to ceftazidime-avibactam is a concern. OXA-48 is a carbapenemase that has no significant effect on ceftazidime, but is inhibited by avibactam. Since isolates with OXA-48 frequently harbor extended-spectrum β-lactamases that are inhibited by avibactam, it is likely that ceftazidime-avibactam will be used to treat infections caused by OXA-48-producing Enterobacterales. Our data show that exposure to ceftazidime-avibactam can lead to changes in OXA-48, resulting in increased ability to hydrolyze ceftazidime and withstand the inhibitory effect of avibactam. Thus, resistance toward ceftazidime-avibactam among OXA-48-producing Enterobacterales should be monitored. Interestingly, the compromising effect of the amino acid substitutions in OXA-48 on other β-lactams and the effect of ceftazidime-avibactam exposure on the epidemic OXA-48 plasmid indicate that the evolution of ceftazidime-avibactam resistance comes with collateral effects.

Evolutionary ecology meets the antibiotic crisis: Can we control pathogen adaptation through sequential therapy?

The spread of antibiotic resistance is a global challenge that is fueled by evolution and ecological processes. Therefore, the design of new sustainable therapy should take account of these underlying processes—as proposed within the field of evolutionary medicine, yet usually not receiving the necessary attention from national and international health agencies. We here put the spotlight on a currently neglected treatment strategy: sequential therapy. Changes among antibiotics generate fluctuating selection conditions that are in general difficult to counter by any organism. We argue that sequential treatment designs can be specifically optimized by exploiting evolutionary trade-offs, for example collateral sensitivity and/or inducible physiological constraints, such as negative hysteresis, where pre-exposure to one antibiotic induces temporary hyper-sensitivity to another antibiotic. Our commentary provides an overview of sequential treatment strategies and outlines steps towards their further optimization.

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.

Cellular hysteresis as a principle to maximize the efficacy of antibiotic therapy

Antibiotic resistance has become one of the most dramatic threats to global health. While novel treatment options are urgently required, most attempts focus on finding new antibiotic substances. However, their development is costly, and their efficacy is often compromised within short time periods due to the enormous potential of microorganisms for rapid adaptation. Here, we developed a strategy that uses the currently available antibiotics. Our strategy exploits cellular hysteresis, which is the long-lasting, transgenerational change in cellular physiology that is induced by one antibiotic and sensitizes bacteria to another subsequently administered antibiotic. Using evolution experiments, mathematical modeling, genomics, and functional genetic analysis, we demonstrate that sequential treatment protocols with high levels of cellular hysteresis constrain the evolving bacteria by (i) increasing extinction frequencies, (ii) reducing adaptation rates, and (iii) limiting emergence of multidrug resistance. Cellular hysteresis is most effective in fast sequential protocols, in which antibiotics are changed within 12 h or 24 h, in contrast to the less frequent changes in cycling protocols commonly implemented in hospitals. We found that cellular hysteresis imposes specific selective pressure on the bacteria that disfavors resistance mutations. Instead, if bacterial populations survive, hysteresis is countered in two distinct ways, either through a process related to antibiotic tolerance or a mechanism controlled by the previously uncharacterized two-component regulator CpxS. We conclude that cellular hysteresis can be harnessed to optimize antibiotic therapy, to achieve both enhanced bacterial elimination and reduced resistance evolution.

Pharmacodynamic considerations of collateral sensitivity in design of antibiotic treatment regimen

INTRODUCTION

Antibiotics have greatly reduced the morbidity and mortality due to infectious diseases. Although antibiotic resistance is not a new problem, its breadth now constitutes a significant threat to human health. One strategy to help combat resistance is to find novel ways to use existing drugs, even those that display high rates of resistance. For the pathogens Escherichia coli and Pseudomonas aeruginosa, pairs of antibiotics have been identified for which evolution of resistance to drug A increases sensitivity to drug B and vice versa. These research groups have proposed cycling such pairs to treat infections, and similar treatment strategies are being investigated for various cancer forms as well. While an exciting treatment prospect, no cycling experiments have yet been performed with consideration of pharmacokinetics and pharmacodynamics. To test the plausibility of such schemes and optimize them, we create a mathematical model with explicit pharmacokinetic/pharmacodynamic considerations.

MATERIALS AND METHODS

We evaluate antibiotic cycling protocols using pairs of such antibiotics and investigate the speed of ascent of multiply resistant mutants.

RESULTS

Our analyses show that when using low concentrations of antibiotics, treatment failure will always occur due to the rapid ascent and fixation of resistant mutants. However, moderate to high concentrations of some combinations of bacteriostatic and bactericidal antibiotics with multiday cycling prevent resistance from developing and increase the likelihood of treatment success.

CONCLUSION

Our results call for guarded optimism in application and development of such treatment protocols.

Replication of the Ordered, Nonredundant Library of Pseudomonas aeruginosa strain PA14 Transposon Insertion Mutants

Pseudomonas aeruginosa is a phenotypically and genotypically diverse and adaptable Gram-negative bacterium ubiquitous in human environments. P. aeruginosa is able to form biofilms, develop antibiotic resistance, produce virulence factors, and rapidly evolve in the course of a chronic infection. Thus P. aeruginosa can cause both acute and chronic, difficult to treat infections, resulting in significant morbidity in certain patient populations. P. aeruginosa strain PA14 is a human clinical isolate with a conserved genome structure that infects a variety of mammalian and nonvertebrate hosts making PA14 an attractive strain for studying this pathogen. In 2006, a nonredundant transposon insertion mutant library containing 5,459 mutants corresponding to 4,596 predicted PA14 genes was generated. Since then, distribution of the PA14 library has allowed the research community to better understand the function of individual genes and complex pathways of P. aeruginosa. Maintenance of library integrity through the replication process requires proper handling and precise techniques. To that end, this manuscript presents protocols that describe in detail the steps involved in library replication, library quality control and proper storage of individual mutants.

Cellular Biosensors with Engineered Genetic Circuits

An increasing interest in building novel biological devices with designed cellular functionalities has triggered the search of innovative tools for biocomputation. Utilizing the tools of synthetic biology, numerous genetic circuits have been implemented such as engineered logic operation in analog and digital circuits. Whole cell biosensors are widely used biological devices that employ several biocomputation tools to program cells for desired functions. Up to the present date, a wide range of whole-cell biosensors have been designed and implemented for disease theranostics, biomedical applications, and environmental monitoring. In this review, we investigated the recent developments in biocomputation tools such as analog, digital, and mix circuits, logic gates, switches, and state machines. Additionally, we stated the novel applications of biological devices with computing functionalities for diagnosis and therapy of various diseases such as infections, cancer, or metabolic diseases, as well as the detection of environmental pollutants such as heavy metals or organic toxic compounds. Current whole-cell biosensors are innovative alternatives to classical biosensors; however, there is still a need to advance decision making capabilities by developing novel biocomputing devices.

Molecular and cellular bases of adaptation to a changing environment in microorganisms

Environmental heterogeneity constitutes an evolutionary challenge for organisms. While evolutionary dynamics under variable conditions has been explored for decades, we still know relatively little about the cellular and molecular mechanisms involved. It is of paramount importance to examine these molecular bases because they may play an important role in shaping the course of evolution. In this review, we examine the diversity of adaptive mechanisms in the face of environmental changes. We exploit the recent literature on microbial systems because those have benefited the most from the recent emergence of genetic engineering and experimental evolution followed by genome sequencing. We identify four emerging trends: (i) an adaptive molecular change in a pathway often results in fitness trade-off in alternative environments but the effects are dependent on a mutation's genetic background; (ii) adaptive changes often modify transcriptional and signalling pathways; (iii) several adaptive changes may occur within the same molecular pathway but be associated with pleiotropy of different signs across environments; (iv) because of their large associated costs, macromolecular changes such as gene amplification and aneuploidy may be a rapid mechanism of adaptation in the short-term only. The course of adaptation in a variable environment, therefore, depends on the complexity of the environment but also on the molecular relationships among the genes involved and between the genes and the phenotypes under selection.

Alternative Evolutionary Paths to Bacterial Antibiotic Resistance Cause Distinct Collateral Effects

When bacteria evolve resistance against a particular antibiotic, they may simultaneously gain increased sensitivity against a second one. Such collateral sensitivity may be exploited to develop novel, sustainable antibiotic treatment strategies aimed at containing the current, dramatic spread of drug resistance. To date, the presence and molecular basis of collateral sensitivity has only been studied in few bacterial species and is unknown for opportunistic human pathogens such as Pseudomonas aeruginosa. In the present study, we assessed patterns of collateral effects by experimentally evolving 160 independent populations of P. aeruginosa to high levels of resistance against eight commonly used antibiotics. The bacteria evolved resistance rapidly and expressed both collateral sensitivity and cross-resistance. The pattern of such collateral effects differed to those previously reported for other bacterial species, suggesting interspecific differences in the underlying evolutionary trade-offs. Intriguingly, we also identified contrasting patterns of collateral sensitivity and cross-resistance among the replicate populations adapted to the same drug. Whole-genome sequencing of 81 independently evolved populations revealed distinct evolutionary paths of resistance to the selective drug, which determined whether bacteria became cross-resistant or collaterally sensitive towards others. Based on genomic and functional genetic analysis, we demonstrate that collateral sensitivity can result from resistance mutations in regulatory genes such as nalC or mexZ, which mediate aminoglycoside sensitivity in β-lactam-adapted populations, or the two-component regulatory system gene pmrB, which enhances penicillin sensitivity in gentamicin-resistant populations. Our findings highlight substantial variation in the evolved collateral effects among replicates, which in turn determine their potential in antibiotic therapy.

History of antibiotic adaptation influences microbial evolutionary dynamics during subsequent treatment

Antibiotic regimens often include the sequential changing of drugs to limit the development and evolution of resistance of bacterial pathogens. It remains unclear how history of adaptation to one antibiotic can influence the resistance profiles when bacteria subsequently adapt to a different antibiotic. Here, we experimentally evolved Pseudomonas aeruginosa to six 2-drug sequences. We observed drug order-specific effects, whereby adaptation to the first drug can limit the rate of subsequent adaptation to the second drug, adaptation to the second drug can restore susceptibility to the first drug, or final resistance levels depend on the order of the 2-drug sequence. These findings demonstrate how resistance not only depends on the current drug regimen but also the history of past regimens. These order-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria given knowledge of past adaptations and provide support for the need to consider the history of past drug exposure when designing strategies to mitigate resistance and combat bacterial infections.

Stress-induced mutagenesis: Stress diversity facilitates the persistence of mutator genes

Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy.

Many organisms display increased mutation or recombination rates when exposed to a stressful environment, which can increase the probability that the population acquires adaptations that allow it to avoid extinction. Because of this, it has been suggested that the increase in production rate of genetic variation is itself an adaptation. Here, we use a mathematical model to test this hypothesis. We find that this hypothesis is plausible when the environment is variable enough such that populations do not experience particular stresses too often. We provide an explicit expression for the critical time interval between exposures and discuss its implication for the evolution of resistance. Our results highlight how and when this form of evolvability can evolve by natural selection.

Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies

Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes.

Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro

Multi-drug strategies have been attempted to prolong the efficacy of existing antibiotics, but with limited success. Here we show that the evolution of multi-drug-resistant Escherichia coli can be manipulated in vitro by administering pairs of antibiotics and switching between them in ON/OFF manner. Using a multiplexed cell culture system, we find that switching between certain combinations of antibiotics completely suppresses the development of resistance to one of the antibiotics. Using this data, we develop a simple deterministic model, which allows us to predict the fate of multi-drug evolution in this system. Furthermore, we are able to reverse established drug resistance based on the model prediction by modulating antibiotic selection stresses. Our results support the idea that the development of antibiotic resistance may be potentially controlled via continuous switching of drugs.

Fitness costs associated with the acquisition of antibiotic resistance

Acquisition of antibiotic resistance is a relevant problem for human health. The selection and spread of antibiotic-resistant organisms not only compromise the treatment of infectious diseases, but also the implementation of different therapeutic procedures as organ transplantation, advanced surgery or chemotherapy, all of which require proficient methods for avoiding infections. It has been generally accepted that the acquisition of antibiotic resistance will produce a general metabolic burden: in the absence of selection, the resistant organisms would be outcompeted by the susceptible ones. If that was always true, discontinuation of antibiotic use would render the disappearance of resistant microorganisms. However, several studies have shown that, once resistance emerges, the recovery of a fully susceptible population even in the absence of antibiotics is not easy. In the present study, we review updated information on the effect of the acquisition of antibiotic resistance in bacterial physiology as well as on the mechanisms that allow the compensation of the fitness costs associated with the acquisition of resistance.

Adaptive diversification in heterogeneous environments

The role of environmental heterogeneity in the evolution of biological diversity has been studied only for simple types of heterogeneities and dispersals. This article broadens previous results by considering heterogeneities and dispersals that are structured by several environmental factors. It studies the evolution of a metapopulation, living in a network of patches connected by dispersal, under the effects of mutation, selection and migration. First, it is assumed that patches are equally connected and that they carry habitats characterized by several factors exerting selection pressures on several individual traits. Habitat factors may vary in the environment independently or they may be correlated. It is shown that correlations between habitat factors promote adaptive diversification and that this effect may be modified by trait interactions on survival. Then, it is assumed that patches are structured by two crossed factors, called the row and column factors, such that patches are more connected when they occur in the same row or in the same column. Environmental patterns in which each habitat appears in each row the same number of times and appears in each column the same number of times are found to hinder adaptive diversification.

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

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

Synthetic recombinase-based state machines in living cells

State machines underlie the sophisticated functionality behind human-made and natural computing systems that perform order-dependent information processing. We developed a recombinase-based framework for building state machines in living cells by leveraging chemically controlled DNA excision and inversion operations to encode states in DNA sequences. This strategy enables convenient readout of states (by sequencing and/or polymerase chain reaction) as well as complex regulation of gene expression. We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states. These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines. Our scalable framework should enable new strategies for recording and studying how combinational and temporal events regulate complex cell functions and for programming sophisticated cell behaviors.

Complex Interplay of Physiology and Selection in the Emergence of Antibiotic Resistance

Emergence of antibiotic resistance, an evolutionary process of major importance for human health [1], often occurs under changing levels of antibiotics. Selective sweeps, in which resistant cells become dominant in the population, are a critical step in this process [2]. While resistance emergence has been studied in laboratory experiments [3-8], the full progression of selective sweeps under fluctuating stress, from stochastic events in single cells to fixation in populations, has not been characterized. Here, we study fluctuating selection using Escherichia coli populations engineered with a stochastic switch controlling tetracycline resistance. Using microfluidics and live-cell imaging, we treat multiple E. coli populations with the same total amount of tetracycline but administered in different temporal patterns. We find that populations exposed to either short or long antibiotic pulses are likely to develop resistance through selective sweeps, whereas intermediate pulses allow higher growth rates but suppress selective sweeps. On the basis of single-cell measurements and a dynamic growth model, we identify the major determinants of population growth and show that both physiological memory and environmental durations can strongly modulate the emergence of resistance. Our detailed quantification in a model synthetic system provides key lessons on the interaction between single-cell physiology and selection that should inform the design of treatment regimens [9-12] and the analysis of phenotypically diverse populations adapting under fluctuating selection [13-17].