Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance

Mechanism of antibiotic resistance spread during sub-lethal ozonation of antibiotic-resistant bacteria with different resistance targets

The increase and spread of antibiotic-resistant bacteria (ARB) in aquatic environments and the dissemination of antibiotic resistance genes (ARGs) greatly impact environmental and human health. It is necessary to understand the mechanism of action of ARB and ARGs to formulate measures to solve this problem. This study aimed to determine the mechanism of antibiotic resistance spread during sub-lethal ozonation of ARB with different antibiotic resistance targets, including proteins, cell walls, and cell membranes. ARB conjugation and transformation frequencies increased after exposure to 0-1.0 mg/L ozone for 10 min. During sub-lethal ozonation, compared with control groups not stimulated by ozone, the conjugative transfer frequencies of E. coli DH5α (CTX), E. coli DH5α (MCR), and E. coli DH5α (GEN) increased by 1.35-2.02, 1.13-1.58, and 1.32-2.12 times, respectively; the transformation frequencies of E. coli DH5α (MCR) and E. coli DH5α (GEN) increased by 1.49-3.02 and 1.45-1.92 times, respectively. When target inhibitors were added, the conjugative transfer frequencies of antibiotics targeting cell wall and membrane synthesis decreased 0.59-0.75 and 0.43-0.76 times, respectively, while that for those targeting protein synthesis increased by 1-1.38 times. After inhibitor addition, the transformation frequencies of bacteria resistant to antibiotics targeting the cell membrane and proteins decreased by 0.76-0.89 and 0.69-0.78 times, respectively. Cell morphology, cell membrane permeability, reactive oxygen species, and antioxidant enzymes changed with different ozone concentrations. Expression of most genes related to regulating different antibiotic resistance targets was up-regulated when bacteria were exposed to sub-lethal ozonation, further confirming the target genes playing a crucial role in the inactivation of different target bacteria. These results will help guide the careful utilization of ozonation for bacterial inactivation, providing more detailed reference information for ozonation oxidation treatment of ARB and ARGs in aquatic environments.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Preventing multi-resistance: New insights for managing fungal adaptation

Sustainable crop protection is vital for food security, yet it is under threat due to the adaptation of a diverse and evolving pathogen population. Resistance can be managed by maximising the diversity of selection pressure through dose variation and the spatial and temporal combination of active ingredients. This study explores the interplay between operational drivers for maximising the sustainability of management strategies in relation to the resistance status of fungal populations. We applied an experimental evolution approach to three artificial populations of Zymoseptoria tritici, an economically significant wheat pathogen, each differing in initial resistance status. Our findings reveal that diversified selection pressure curtails the selection of resistance in naïve populations and those with low frequencies of single resistance. Increasing the number of modes of action most effectively delays resistance development, surpassing the increase in the number of fungicides, fungicide choice based on resistance risk, and temporal variation in fungicide exposure. However, this approach favours generalism in the evolved populations. The prior presence of multiple resistant isolates and their subsequent selection in populations override the effects of diversity in management strategies, thereby invalidating any universal ranking. Therefore, the initial resistance composition must be specifically considered in sustainable resistance management to address real-world field situations.

In Vitro Microevolution and Co-Selection Assessment of Amoxicillin and Cefotaxime Impact on Escherichia coli Resistance Development

The global spread of antimicrobial resistance has become a prominent issue in both veterinary and public health in the 21st century. The extensive use of amoxicillin, a beta-lactam antibiotic, and consequent resistance development are particularly alarming in food-producing animals, with a focus on the swine and poultry sectors. Another beta-lactam, cefotaxime, is widely utilized in human medicine, where the escalating resistance to third- and fourth-generation cephalosporins is a major concern. The aim of this study was to simulate the development of phenotypic and genotypic resistance to beta-lactam antibiotics, focusing on amoxicillin and cefotaxime. The investigation of the minimal inhibitory concentrations (MIC) of antibiotics was performed at 1×, 10×, 100×, and 1000× concentrations using the modified microbial evolution and growth arena (MEGA-plate) method. Our results indicate that amoxicillin significantly increased the MIC values of several tested antibiotics, except for oxytetracycline and florfenicol. In the case of cefotaxime, this increase was observed in all classes. A total of 44 antimicrobial resistance genes were identified in all samples. Chromosomal point mutations, particularly concerning cefotaxime, revealed numerous complex mutations, deletions, insertions, and single nucleotide polymorphisms (SNPs) that were not experienced in the case of amoxicillin. The findings suggest that, regarding amoxicillin, the point mutation of the acrB gene could explain the observed MIC value increases due to the heightened activity of the acrAB-tolC efflux pump system. However, under the influence of cefotaxime, more intricate processes occurred, including complex amino acid substitutions in the ampC gene promoter region, increased enzyme production induced by amino acid substitutions and SNPs, as well as mutations in the acrR and robA repressor genes that heightened the activity of the acrAB-tolC efflux pump system. These changes may contribute to the significant MIC increases observed for all tested antibiotics. The results underscore the importance of understanding cross-resistance development between individual drugs when choosing clinical alternative drugs. The point mutations in the mdtB and emrR genes may also contribute to the increased activity of the mdtABC-tolC and emrAB-tolC pump systems against all tested antibiotics. The exceptionally high mutation rate induced by cephalosporins justifies further investigations to clarify the exact mechanism behind.

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

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

Trade-offs constrain adaptive pathways to the type VI secretion system survival

The Type VI Secretion System (T6SS) is a nano-harpoon used by many bacteria to inject toxins into neighboring cells. While much is understood about mechanisms of T6SS-mediated toxicity, less is known about the ways that competitors can defend themselves against this attack, especially in the absence of their own T6SS. Here we subjected eight replicate populations of Escherichia coli to T6SS attack by Vibrio cholerae. Over ∼500 generations of competition, isolates of the E. coli populations evolved to survive T6SS attack an average of 27-fold better, through two convergently evolved pathways: apaH was mutated in six of the eight replicate populations, while the other two populations each had mutations in both yejM and yjeP. However, the mutations we identified are pleiotropic, reducing cellular growth rates, and increasing susceptibility to antibiotics and elevated pH. These trade-offs help us understand how the T6SS shapes the evolution of bacterial interactions.

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 β-lactam antibiotics with which to treat the simulated E. coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

Diverse evolutionary pathways challenge the use of collateral sensitivity as a strategy to suppress resistance

Drug resistance remains a major obstacle to malaria control and eradication efforts, necessitating the development of novel therapeutic strategies to treat this disease. Drug combinations based on collateral sensitivity, wherein resistance to one drug causes increased sensitivity to the partner drug, have been proposed as an evolutionary strategy to suppress the emergence of resistance in pathogen populations. In this study, we explore collateral sensitivity between compounds targeting the Plasmodium dihydroorotate dehydrogenase (DHODH). We profiled the cross-resistance and collateral sensitivity phenotypes of several DHODH mutant lines to a diverse panel of DHODH inhibitors. We focus on one compound, TCMDC-125334, which was active against all mutant lines tested, including the DHODH C276Y line, which arose in selections with the clinical candidate DSM265. In six selections with TCMDC-125334, the most common mechanism of resistance to this compound was copy number variation of the dhodh locus, although we did identify one mutation, DHODH I263S, which conferred resistance to TCMDC-125334 but not DSM265. We found that selection of the DHODH C276Y mutant with TCMDC-125334 yielded additional genetic changes in the dhodh locus. These double mutant parasites exhibited decreased sensitivity to TCMDC-125334 and were highly resistant to DSM265. Finally, we tested whether collateral sensitivity could be exploited to suppress the emergence of resistance in the context of combination treatment by exposing wildtype parasites to both DSM265 and TCMDC-125334 simultaneously. This selected for parasites with a DHODH V532A mutation which were cross-resistant to both compounds and were as fit as the wildtype parent in vitro. The emergence of these cross-resistant, evolutionarily fit parasites highlights the mutational flexibility of the DHODH enzyme.

Ciprofloxacin and Tetracycline Resistance Cause Collateral Sensitivity to Aminoglycosides in Salmonella Typhimurium

The objective of this study was to evaluate collateral sensitivity and cross-resistance of antibiotic-induced resistant Salmonella Typhimurium to various antibiotics. S. Typhimurium ATCC 19585 (STWT) was exposed to ciprofloxacin, gentamicin, kanamycin, and tetracycline to induce antibiotic resistance, respectively, assigned as STCIP, STGEN, STKAN, and STTET. The susceptibilities of the antibiotic-induced resistant mutants to cefotaxime, chloramphenicol, ciprofloxacin, gentamicin, kanamycin, polymyxin B, streptomycin, tetracycline, and tobramycin were determined in the absence and presence of CCCP and PAβN. STCIP showed the cross-resistance to tetracycline and collateral sensitivity to gentamicin (1/2 fold) and kanamycin (1/4 fold). STTET was also cross-resistant to ciprofloxacin (128-fold) and collateral sensitive to gentamicin (1/4-fold) and kanamycin (1/8-fold). The cross-resistance and collateral sensitivity of STCIP and STTET were associated with the AcrAB-TolC efflux pump and outer membrane porin proteins (OmpC). This study provides new insight into the collateral sensitivity phenomenon, which can be used for designing effective antibiotic treatment regimens to control antibiotic-resistant bacteria.

Alteration of adeS Contributes to Tigecycline Resistance and Collateral Sensitivity to Sulbactam in Acinetobacter baumannii

The treatment of extensively drug-resistant (XDR) A. baumannii has emerged as a major problem. Tigecycline (TGC) and sulbactam (SUL) are both effective antibiotics against XDR A. baumannii. Here, we investigated the in-host evolution and mechanism of collateral sensitivity (CS) phenomenon in development of tigecycline resistance accompanied by a concomitant increase of sulbactam susceptibility. A total of four XDR A. baumannii strains were sequentially isolated from the same patient suffering from bacteremia. Core-genome multilocus sequence typing separated all the strains into two clusters. Comparative analysis of isolate pair 1 revealed that multiplication of blaOXA-23 within Tn2006 on the chromosome contributed to the change in the antimicrobial susceptibility phenotype of isolate pair 1. Additionally, we observed the emergence of CS to sulbactam in isolate pair 2, as demonstrated by an 8-fold increase in the TGC MIC with a simultaneous 4-fold decrease in the SUL MIC. Compared to the parental strain Ab-3557, YZM-0406 showed partial deletion in the two-component system sensor adeS. Reconstruction of the adeS mutant in Ab-3557 in situ suggested that TGC resistance and CS to SUL were mainly caused by the mutation of adeS. Overall, our study reported a novel CS combination of TGC and SUL in A. baumannii and further revealed a mechanism of CS attributed to the mutation of adeS. This study provides a valuable foundation for developing effective regimens and sequential combinations of tigecycline and sulbactam against XDR A. baumannii. IMPORTANCE Collateral sensitivity (CS) has become an increasingly common evolutionary trade-off during adaptive bacterial evolution. Here, we report a novel combination of tigecycline (TGC) resistance and CS to sulbactam (SUL) in A. baumannii. TGC and SUL are both effective antibiotics against XDR A. baumannii, and it is essential to reveal the mechanism of CS between TGC and SUL. In our study, the partial deletion of adeS, a two-component system sensor, was confirmed to be the key factor contributing to this CS phenomenon. This study provides a valuable foundation for developing effective regimens and sequential combinations of tigecycline and sulbactam against XDR A. baumannii.

Collateral sensitivity profiling in drug-resistant Escherichia coli identifies natural products suppressing cephalosporin resistance

The rapid emergence of antimicrobial resistance presents serious health challenges to the management of infectious diseases, a problem that is further exacerbated by slowing rates of antimicrobial drug discovery in recent years. The phenomenon of collateral sensitivity (CS), whereby resistance to one drug is accompanied by increased sensitivity to another, provides new opportunities to address both these challenges. Here, we present a high-throughput screening platform termed Collateral Sensitivity Profiling (CSP) to map the difference in bioactivity of large chemical libraries across 29 drug-resistant strains of E. coli. CSP screening of 80 commercial antimicrobials demonstrated multiple CS interactions. Further screening of a 6195-member natural product library revealed extensive CS relationships in nature. In particular, we report the isolation of known and new analogues of borrelidin A with potent CS activities against cephalosporin-resistant strains. Co-dosing ceftazidime with borrelidin A slows broader cephalosporin resistance with no recognizable resistance to borrelidin A itself.

Collateral Sensitivity to Fosfomycin of Tobramycin-Resistant Mutants of Pseudomonas aeruginosa Is Contingent on Bacterial Genomic Background

Understanding the consequences in bacterial physiology of the acquisition of drug resistance is needed to identify and exploit the weaknesses derived from it. One of them is collateral sensitivity, a potentially exploitable phenotype that, unfortunately, is not always conserved among different isolates. The identification of robust, conserved collateral sensitivity patterns is then relevant for the translation of this knowledge into clinical practice. We have previously identified a robust fosfomycin collateral sensitivity pattern of Pseudomonas aeruginosa that emerged in different tobramycin-resistant clones. To go one step further, here, we studied if the acquisition of resistance to tobramycin is associated with robust collateral sensitivity to fosfomycin among P. aeruginosa isolates. To that aim, we analyzed, using adaptive laboratory evolution approaches, 23 different clinical isolates of P. aeruginosa presenting diverse mutational resistomes. Nine of them showed collateral sensitivity to fosfomycin, indicating that this phenotype is contingent on the genetic background. Interestingly, collateral sensitivity to fosfomycin was linked to a larger increase in tobramycin minimal inhibitory concentration. Further, we unveiled that fosA low expression, rendering a higher intracellular accumulation of fosfomycin, and a reduction in the expression of the P. aeruginosa alternative peptidoglycan-recycling pathway enzymes, might be on the basis of the collateral sensitivity phenotype.

Tackling antibiotic resistance by inducing transient and robust collateral sensitivity

Collateral sensitivity (CS) is an evolutionary trade-off traditionally linked to the mutational acquisition of antibiotic resistance (AR). However, AR can be temporally induced, and the possibility that this causes transient, non-inherited CS, has not been addressed. Mutational acquisition of ciprofloxacin resistance leads to robust CS to tobramycin in pre-existing antibiotic-resistant mutants of Pseudomonas aeruginosa. Further, the strength of this phenotype is higher when nfxB mutants, over-producing the efflux pump MexCD-OprJ, are selected. Here, we induce transient nfxB-mediated ciprofloxacin resistance by using the antiseptic dequalinium chloride. Notably, non-inherited induction of AR renders transient tobramycin CS in the analyzed antibiotic-resistant mutants and clinical isolates, including tobramycin-resistant isolates. Further, by combining tobramycin with dequalinium chloride we drive these strains to extinction. Our results support that transient CS could allow the design of new evolutionary strategies to tackle antibiotic-resistant infections, avoiding the acquisition of AR mutations on which inherited CS depends.

Adaptive laboratory evolution of antimicrobial resistance in bacteria for genetic and phenotypic analyses

Adaptive laboratory evolution (ALE) of bacteria has the potential to provide many insights like revealing novel mechanisms of resistance and elucidating the impact of drug combinations and concentrations on AMR evolution. Here, we describe a step-by-step ALE protocol for the model bacterium Escherichia coli that can be easily adapted to answer questions related to evolution and genetics of AMR in diverse bacteria. Key issues to consider when designing ALE experiments as well as some downstream mutation mapping analyses are described. For complete details on the use and execution of this protocol, please refer to Patel and Matange (2021)1 and Matange et al. (2019).2.

Collateral sensitivity between tetracyclines and aminoglycosides constrains resistance evolution in carbapenem-resistant Klebsiella pneumoniae

AIMS

The acquisition of resistance to one antibiotic may confer an increased sensitivity to another antibiotic in bacteria, which is an evolutionary trade-off between different resistance mechanisms, defined as collateral sensitivity (CS). Exploiting the role of CS in treatment design could be an effective method to suppress or even reverse resistance evolution.

METHODS

Using experimental evolution, we systematically studied the CS between aminoglycosides and tetracyclines in carbapenem-resistant Klebsiella pneumoniae (CRKP) and explored the underlying mechanisms through genomic and transcriptome analyses. The application of CS-based therapies for resistance suppression, including combination therapy and alternating antibiotic therapy, was further evaluated in vitro and in vivo.

RESULTS

Reciprocal CS existed between tetracyclines and aminoglycosides in CRKP. The increased sensitivity of aminoglycoside-resistant strains to tetracyclines was associated with the alteration of bacterial membrane potential, whereas the unbalanced oxidation-reduction process of tetracycline-resistant strains may lead to an increased bacterial sensitivity to aminoglycosides. CS-based combination therapy could efficiently constrain the evolution of CRKP resistance in vitro and in vivo. In addition, alternating antibiotic therapy can re-sensitize CRKP to previously resistant drugs, thereby maintaining the trade-off.

CONCLUSIONS

These results provide new insights into constraining the evolution of CRKP resistance through CS-based therapies.

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.

Rapid Phenotypic Convergence towards Collateral Sensitivity in Clinical Isolates of Pseudomonas aeruginosa Presenting Different Genomic Backgrounds

Collateral sensitivity (CS) is an evolutionary trade-off by which acquisition of resistance to an antibiotic leads to increased susceptibility to another. This Achilles' heel of antibiotic resistance could be exploited to design evolution-based strategies for treating bacterial infections. To date, most studies in the field have focused on the identification of CS patterns in model strains. However, one of the main requirements for the clinical application of this trade-off is that it must be robust and has to emerge in different genomic backgrounds, including preexisting drug-resistant isolates, since infections are frequently caused by pathogens already resistant to antibiotics. Here, we report the first analysis of CS robustness in clinical strains of Pseudomonas aeruginosa presenting different ab initio mutational resistomes. We identified a robust CS pattern associated with short-term evolution in the presence of ciprofloxacin of clinical P. aeruginosa isolates, including representatives of high-risk epidemic clones belonging to sequence type (ST) 111, ST175, and ST244. We observed the acquisition of different ciprofloxacin resistance mutations in strains presenting varied STs and different preexisting mutational resistomes. Importantly, despite these genetic differences, the use of ciprofloxacin led to a robust CS to aztreonam and tobramycin. In addition, we describe the possible application of this evolutionary trade-off to drive P. aeruginosa infections to extinction by using the combination of ciprofloxacin-tobramycin or ciprofloxacin-aztreonam. Our results support the notion that the identification of robust patterns of CS may establish the basis for developing evolution-informed treatment strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens. IMPORTANCE Collateral sensitivity (CS) is a trade-off of antibiotic resistance evolution that could be exploited to design strategies for treating bacterial infections. Clinical application of CS requires it to robustly emerge in different genomic backgrounds. In this study, we performed an analysis to identify robust patterns of CS associated with the use of ciprofloxacin in clinical isolates of P. aeruginosa presenting different mutational resistomes and including high-risk epidemic clones (ST111, ST175, and ST244). We demonstrate the robustness of CS to tobramycin and aztreonam and the potential application of this evolutionary observation to drive P. aeruginosa infections to extinction. Our results support the notion that the identification of robust CS patterns may establish the basis for developing evolutionary strategies to tackle bacterial infections, including those due to antibiotic-resistant pathogens.

Bacterial meningitis in Africa

Bacterial meningitis differs globally, and the incidence and case fatality rates vary by region, country, pathogen, and age group; being a life-threatening disease with a high case fatality rate and long-term complications in low-income countries. Africa has the most significant prevalence of bacterial meningitis illness, and the outbreaks typically vary with the season and the geographic location, with a high incidence in the meningitis belt of the sub-Saharan area from Senegal to Ethiopia. Streptococcus pneumoniae (pneumococcus) and Neisseria meningitidis (meningococcus) are the main etiological agents of bacterial meningitis in adults and children above the age of one. Streptococcus agalactiae (group B Streptococcus), Escherichia coli, and Staphylococcus aureus are neonatal meningitis's most common causal agents. Despite efforts to vaccinate against the most common causes of bacterial neuro-infections, bacterial meningitis remains a significant cause of mortality and morbidity in Africa, with children below 5 years bearing the heaviest disease burden. The factors attributed to this continued high disease burden include poor infrastructure, continued war, instability, and difficulty in diagnosis of bacterial neuro-infections leading to delay in treatment and hence high morbidity. Despite having the highest disease burden, there is a paucity of African data on bacterial meningitis. In this article, we discuss the common etiologies of bacterial neuroinfectious diseases, diagnosis and the interplay between microorganisms and the immune system, and the value of neuroimmune changes in diagnostics and therapeutics.

Co-occurrence of antibiotic and metal resistance in long-term sewage sludge-amended soils: influence of application rates and pedo-climatic conditions

Urban sewage sludge (USS) is increasingly being used as an alternative organic amendment in agriculture. Because USS originates mostly from human excreta, partially metabolized pharmaceuticals have also been considered in risk assessment studies after reuse. In this regard, we investigated the cumulative effect of five annual USS applications on the spread of antibiotic-resistant bacteria (ARB) and their subsequent resistance to toxic metals in two unvegetated soils. Eventually, USS contained bacterial strains resistant to all addressed antibiotics with indices of resistance varying between 0.25 for gentamicin to 38% for ampicillin and azithromycin. Sludge-amended soils showed also the emergence of resistome for all tested antibiotics compared to non-treated controls. In this regard, the increase of sludge dose generally correlated with ARB counts, while soil texture had no influence. On the other hand, the multi-antibiotic resistance (MAR) of 52 isolates selected from USS and different soil treatments was investigated for 10 most prescribed antibiotics. Nine isolates showed significant MAR index (≥ 0.3) and co-resistance to Cd, As and Be as well. However, events including an extreme flash flood and the termination of USS applications significantly disrupted ARB communities in all soil treatments. In any case, this study highlighted the risks of ARB spread in sludge-amended soils and a greater concern with the recent exacerbation of antibiotic overuse following COVID-19 outbreak.

Resistance-resistant antibacterial treatment strategies

Antibiotic resistance is a major danger to public health that threatens to claim the lives of millions of people per year within the next few decades. Years of necessary administration and excessive application of antibiotics have selected for strains that are resistant to many of our currently available treatments. Due to the high costs and difficulty of developing new antibiotics, the emergence of resistant bacteria is outpacing the introduction of new drugs to fight them. To overcome this problem, many researchers are focusing on developing antibacterial therapeutic strategies that are "resistance-resistant"-regimens that slow or stall resistance development in the targeted pathogens. In this mini review, we outline major examples of novel resistance-resistant therapeutic strategies. We discuss the use of compounds that reduce mutagenesis and thereby decrease the likelihood of resistance emergence. Then, we examine the effectiveness of antibiotic cycling and evolutionary steering, in which a bacterial population is forced by one antibiotic toward susceptibility to another antibiotic. We also consider combination therapies that aim to sabotage defensive mechanisms and eliminate potentially resistant pathogens by combining two antibiotics or combining an antibiotic with other therapeutics, such as antibodies or phages. Finally, we highlight promising future directions in this field, including the potential of applying machine learning and personalized medicine to fight antibiotic resistance emergence and out-maneuver adaptive pathogens.

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.

Comparison of bacterial suppression by phage cocktails, dual-receptor generalists, and coevolutionarily trained phages

The evolution and spread of antibiotic-resistant bacteria have renewed interest in phage therapy, the use of bacterial viruses (phages) to combat bacterial infections. The delivery of phages in cocktails where constituent phages target different modalities (e.g., receptors) may improve treatment outcomes by making it more difficult for bacteria to evolve resistance. However, the multipartite nature of cocktails may lead to unintended evolutionary and ecological outcomes. Here, we compare a 2-phage cocktail with a largely unconsidered group of phages: generalists that can infect through multiple, independent receptors. We find that λ phage generalists and cocktails that target the same receptors (LamB and OmpF) suppress Escherichia coli similarly for ~2 days. Yet, a "trained" generalist phage, which previously adapted to its host via 28 days of coevolution, demonstrated superior suppression. To understand why the trained generalist was more effective, we measured the resistance of bacteria against each of our phages. We find that, when bacteria were assailed by two phages in the cocktail, they evolved mutations in manXYZ, a host inner-membrane transporter that λ uses to move its DNA across the periplasmic space and into the cell for infection. This provided cross-resistance against the cocktail and untrained generalist. However, these mutations were ineffective at blocking the trained generalist because, through coevolutionary training, it evolved to bypass manXYZ resistance. The trained generalist's past experiences in training make it exceedingly difficult for bacteria to evolve resistance, further demonstrating the utility of coevolutionary phage training for improving the therapeutic properties of phages.

Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity

Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.

Modeling-informed Engineered Genetic Incompatibility strategies to overcome resistance in the invasive Drosophila suzukii

Engineered Genetic Incompatibility (EGI) is an engineered extreme underdominance genetic system wherein hybrid animals are not viable, functioning as a synthetic speciation event. There are several strategies in which EGI could be leveraged for genetic biocontrol of pest populations. We used an agent-based model of Drosophila suzukii (Spotted Wing Drosophila) to determine how EGI would fare with high rates of endemic genetic resistance alleles. We discovered a surprising failure mode wherein field-generated females convert an incompatible male release program into a population replacement gene drive. Local suppression could still be attained in two seasons by tailoring the release strategy to take advantage of this effect, or alternatively in one season by altering the genetic design of release agents. We show in this work that data from modeling can be utilized to recognize unexpected emergent phenomena and a priori inform genetic biocontrol treatment design to increase efficacy.

Drug Combinations to Prevent Antimicrobial Resistance: Various Correlations and Laws, and Their Verifications, Thus Proposing Some Principles and a Preliminary Scheme

Antimicrobial resistance (AMR) has been a serious threat to human health, and combination therapy is proved to be an economic and effective strategy for fighting the resistance. However, the abuse of drug combinations conversely accelerates the spread of AMR. In our previous work, we concluded that the mutant selection indexes (SIs) of one agent against a specific bacterial strain are closely related to the proportions of two agents in a drug combination. To discover probable correlations, predictors and laws for further proposing feasible principles and schemes guiding the AMR-preventing practice, here, three aspects were further explored. First, the power function (y = axb, a > 0) correlation between the SI (y) of one agent and the ratio (x) of two agents in a drug combination was further established based on the mathematical and statistical analyses for those experimental data, and two rules a1 × MIC1 = a2 × MIC2 and b1 + b2 = −1 were discovered from both equations of y = a1xb1 and y = a2xb2 respectively for two agents in drug combinations. Simultaneously, it was found that one agent with larger MPC alone for drug combinations showed greater potency for narrowing itself MSW and preventing the resistance. Second, a new concept, mutation-preventing selection index (MPSI) was proposed and used for evaluating the mutation-preventing potency difference of two agents in drug combination; a positive correlation between the MPSI and the mutant prevention concentration (MPC) or minimal inhibitory concentration (MIC) was subsequently established. Inspired by this, the significantly positive correlation, contrary to previous reports, between the MIC and the corresponding MPC of antimicrobial agents against pathogenic bacteria was established using 181 data pairs reported. These results together for the above three aspects indicate that the MPCs in alone and combination are very important indexes for drug combinations to predict the mutation-preventing effects and the trajectories of collateral sensitivity, and while the MPC of an agent can be roughly calculated from its corresponding MIC. Subsequently, the former conclusion was further verified and improved via antibiotic exposure to 43 groups designed as different drug concentrations and various proportions. The results further proposed that the C/MPC for the agent with larger proportion in drug combinations can be considered as a predictor and is the key to judge whether the resistance and the collateral sensitivity occur to two agents. Based on these above correlations, laws, and their verification experiments, some principles were proposed, and a diagram of the mutation-preventing effects and the resistant trajectories for drug combinations with different concentrations and ratios of two agents was presented. Simultaneously, the reciprocal of MPC alone (1/MPC), proposed as the stress factors of two agents in drug combinations, together with their SI in combination, is the key to predict the mutation-preventing potency and control the trajectories of collateral sensitivity. Finally, a preliminary scheme for antimicrobial combinations preventing AMR was further proposed for subsequent improvement research and clinic popularization, based on the above analyses and discussion. Moreover, some similar conclusions were speculated for triple or multiple drug combinations.

Evolutionary history and strength of selection determine the rate of antibiotic resistance adaptation

Bacterial adaptation to stressful environments often produces evolutionary constraints whereby increases in resistance are associated with reduced fitness in a different environment. The exploitation of this resistance-cost trade-off has been proposed as the basis of rational antimicrobial treatment strategies designed to limit the evolution of drug resistance in bacterial pathogens. Recent theoretical, laboratory, and clinical studies have shown that fluctuating selection can maintain drug efficacy and even restore drug susceptibility, but can also increase the rate of adaptation and promote cross-resistance to other antibiotics. In this paper, we combine mathematical modeling, experimental evolution, and whole-genome sequencing to follow evolutionary trajectories towards β-lactam resistance under fluctuating selective conditions. Our experimental model system consists of eight populations of Escherichia coli K12 evolving in parallel to a serial dilution protocol designed to dynamically control the strength of selection for resistance. We implemented adaptive ramps with mild and strong selection, resulting in evolved populations with similar levels of resistance, but with different evolutionary dynamics and diverging genotypic profiles. We found that mutations that emerged under strong selection are unstable in the absence of selection, in contrast to resistance mutations previously selected in the mild selection regime that were stably maintained in drug-free environments and positively selected for when antibiotics were reintroduced. Altogether, our population dynamics model and the phenotypic and genomic analysis of the evolved populations show that the rate of resistance adaptation is contingent upon the strength of selection, but also on evolutionary constraints imposed by prior drug exposures.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

The EnvZ/OmpR Two-Component System Regulates the Antimicrobial Activity of TAT-RasGAP317-326 and the Collateral Sensitivity to Other Antibacterial Agents

The rapid emergence of antibiotic-resistant bacteria poses a serious threat to public health worldwide. Antimicrobial peptides (AMPs) are promising antibiotic alternatives; however, little is known about bacterial mechanisms of AMP resistance and the interplay between AMP resistance and the bacterial response to other antimicrobials. In this study, we identified Escherichia coli mutants resistant to the TAT-RasGAP_317-326 antimicrobial peptide and found that resistant bacteria show collateral sensitivity to other AMPs and antibacterial agents. We determined that resistance to TAT-RasGAP_317-326 peptide arises through mutations in the histidine kinase EnvZ, a member of the EnvZ/OmpR two-component system responsible for osmoregulation in E. coli. In particular, we found that TAT-RasGAP_317-326 binding and entry is compromised in E. coli peptide-resistant mutants. We showed that peptide resistance is associated with transcriptional regulation of a number of pathways and EnvZ-mediated resistance is dependent on the OmpR response regulator but is independent of the OmpC and OmpF outer membrane porins. Our findings provide insight into the bacterial mechanisms of TAT-RasGAP_317-326 resistance and demonstrate that resistance to this AMP is associated with collateral sensitivity to other antibacterial agents. IMPORTANCE Antimicrobial peptides (AMP) are promising alternatives to classical antibiotics in the fight against antibiotic resistance. Resistance toward antimicrobial peptides can occur, but little is known about the mechanisms driving this phenomenon. Moreover, there is limited knowledge on how AMP resistance relates to the bacterial response to other antimicrobial agents. Here, we address these questions in the context of the antimicrobial peptide TAT-RasGAP_317-326. We show that resistant Escherichia coli strains can be selected and do not show resistance to other antimicrobial agents. Resistance is caused by a mutation in a regulatory pathway, which lowers binding and entry of the peptide in E. coli. Our results highlight a mechanism of resistance that is specific to TAT-RasGAP_317-326. Further research is required to characterize these mechanisms and to evaluate the potential of antimicrobial combinations to curb the development of antimicrobial resistance.

Strong selective environments determine evolutionary outcome in time‐dependent fitness seascapes

The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time‐dependent selection in so‐called fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question using a comprehensive set of regimes are lacking. Here, we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time‐invariant and time‐dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time‐invariant environments caused stronger directional selection for resistances compared to time‐dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross‐resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time‐invariant environments and as a function of exposure epoch in time‐dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution.

Modulating the evolutionary trajectory of tolerance using antibiotics with different metabolic dependencies

Antibiotic tolerance, or the ability of bacteria to survive antibiotic treatment in the absence of genetic resistance, has been linked to chronic and recurrent infections. Tolerant cells are often characterized by a low metabolic state, against which most clinically used antibiotics are ineffective. Here, we show that tolerance readily evolves against antibiotics that are strongly dependent on bacterial metabolism, but does not arise against antibiotics whose efficacy is only minimally affected by metabolic state. We identify a mechanism of tolerance evolution in E. coli involving deletion of the sodium-proton antiporter gene nhaA, which results in downregulated metabolism and upregulated stress responses. Additionally, we find that cycling of antibiotics with different metabolic dependencies interrupts evolution of tolerance in vitro, increasing the lifetime of treatment efficacy. Our work highlights the potential for limiting the occurrence and extent of tolerance by accounting for antibiotic dependencies on bacterial metabolism.

Mutational background influences P. aeruginosa ciprofloxacin resistance evolution but preserves collateral sensitivity robustness

Bacterial adaptation to the presence of an antibiotic often involves evolutionary trade-offs, such as increased susceptibility to other drugs (collateral sensitivity). Its exploitation to design improved therapeutic strategies is only feasible if collateral sensitivity is robust, reproducible, and emerges in resistant mutants; these issues are rarely addressed in available publications. We describe a robust collateral sensitivity phenotype that emerges in different antibiotic-resistance mutational backgrounds, due to different genetic events, and propose therapeutic strategies effective for treating infections caused by Pseudomonas aeruginosa antibiotic-resistant mutants. Since conserved collateral sensitivity phenotypes do not confer adaptation to the presence of antibiotics, our results are also relevant for understanding convergent evolution processes in which the force selecting the emerging phenotype remains unclear.

Collateral sensitivity is an evolutionary trade-off whereby acquisition of the adaptive phenotype of resistance to an antibiotic leads to the nonadaptive increased susceptibility to another. The feasibility of harnessing such a trade-off to design evolutionary-based approaches for treating bacterial infections has been studied using model strains. However, clinical application of collateral sensitivity requires its conservation among strains presenting different mutational backgrounds. Particularly relevant is studying collateral sensitivity robustness of already-antibiotic-resistant mutants when challenged with a new antimicrobial, a common situation in clinics that has hardly been addressed. We submitted a set of diverse Pseudomonas aeruginosa antibiotic-resistant mutants to short-term evolution in the presence of different antimicrobials. Ciprofloxacin selects different clinically relevant resistance mutations in the preexisting resistant mutants, which gave rise to the same, robust, collateral sensitivity to aztreonam and tobramycin. We then experimentally determined that alternation of ciprofloxacin with aztreonam is more efficient than ciprofloxacin–tobramycin alternation in driving the extinction of the analyzed antibiotic-resistant mutants. Also, we show that the combinations ciprofloxacin–aztreonam or ciprofloxacin–tobramycin are the most effective strategies for eliminating the tested P. aeruginosa antibiotic-resistant mutants. These findings support that the identification of conserved collateral sensitivity patterns may guide the design of evolution-based strategies to treat bacterial infections, including those due to antibiotic-resistant mutants. Besides, this is an example of phenotypic convergence in the absence of parallel evolution that, beyond the antibiotic-resistance field, could facilitate the understanding of evolution processes, where the selective forces giving rise to new, not clearly adaptive phenotypes remain unclear.

A Phage Foundry Framework to Systematically Develop Viral Countermeasures to Combat Antibiotic-Resistant Bacterial Pathogens

At its current rate, the rise of antimicrobial-resistant (AMR) infections is predicted to paralyze our industries and healthcare facilities while becoming the leading global cause of loss of human life. With limited new antibiotics on the horizon, we need to invest in alternative solutions. Bacteriophages (phages)—viruses targeting bacteria—offer a powerful alternative approach to tackle bacterial infections. Despite recent advances in using phages to treat recalcitrant AMR infections, the field lacks systematic development of phage therapies scalable to different applications. We propose a Phage Foundry framework to establish metrics for phage characterization and to fill the knowledge and technological gaps in phage therapeutics. Coordinated investment in AMR surveillance, sampling, characterization, and data sharing procedures will enable rational exploitation of phages for treatments. A fully realized Phage Foundry will enhance the sharing of knowledge, technology, and viral reagents in an equitable manner and will accelerate the biobased economy.

Rapid decline of ceftazidime resistance in antibiotic-free and sub-lethal environments is contingent on genetic background

Trade-offs of antibiotic resistance evolution, such as fitness cost and collateral sensitivity (CS), could be exploited to drive evolution toward antibiotic susceptibility. Decline of resistance may occur when resistance to other drug leads to CS to the first one and when compensatory mutations, or genetic reversion of the original ones, reduce fitness cost. Here we describe the impact of antibiotic-free and sublethal environments on declining ceftazidime resistance in different Pseudomonas aeruginosa resistant mutants. We determined that decline of ceftazidime resistance occurs within 450 generations, which is caused by newly acquired mutations and not by reversion of the original ones, and that the original CS of these mutants is preserved. In addition, we observed that the frequency and degree of this decline is contingent on genetic background. Our results are relevant to implement evolution-based therapeutic approaches, as well as to redefine global policies of antibiotic use, such as drug cycling.

Rapid expansion and extinction of antibiotic resistance mutations during treatment of acute bacterial respiratory infections

Acute bacterial infections are often treated empirically, with the choice of antibiotic therapy updated during treatment. The effects of such rapid antibiotic switching on the evolution of antibiotic resistance in individual patients are poorly understood. Here we find that low-frequency antibiotic resistance mutations emerge, contract, and even go to extinction within days of changes in therapy. We analyzed Pseudomonas aeruginosa populations in sputum samples collected serially from 7 mechanically ventilated patients at the onset of respiratory infection. Combining short- and long-read sequencing and resistance phenotyping of 420 isolates revealed that while new infections are near-clonal, reflecting a recent colonization bottleneck, resistance mutations could emerge at low frequencies within days of therapy. We then measured the in vivo frequencies of select resistance mutations in intact sputum samples with resistance-targeted deep amplicon sequencing (RETRA-Seq), which revealed that rare resistance mutations not detected by clinically used culture-based methods can increase by nearly 40-fold over 5–12 days in response to antibiotic changes. Conversely, mutations conferring resistance to antibiotics not administered diminish and even go to extinction. Our results underscore how therapy choice shapes the dynamics of low-frequency resistance mutations at short time scales, and the findings provide a possibility for driving resistance mutations to extinction during early stages of infection by designing patient-specific antibiotic cycling strategies informed by deep genomic surveillance.

It remains unclear how rapid antibiotic switching affects the evolution of antibiotic resistance in individual patients. Here, Chung et al. combine short- and long-read sequencing and resistance phenotyping of 420 serial isolates of Pseudomonas aeruginosa collected from the onset of respiratory infection, and show that rare resistance mutations can increase by nearly 40-fold over 5–12 days in response to antibiotic changes, while mutations conferring resistance to antibiotics not administered diminish and even go to extinction.

The population genetics of collateral resistance and sensitivity

Resistance mutations against one drug can elicit collateral sensitivity against other drugs. Multi-drug treatments exploiting such trade-offs can help slow down the evolution of resistance. However, if mutations with diverse collateral effects are available, a treated population may evolve either collateral sensitivity or collateral resistance. How to design treatments robust to such uncertainty is unclear. We show that many resistance mutations in Escherichia coli against various antibiotics indeed have diverse collateral effects. We propose to characterize such diversity with a joint distribution of fitness effects (JDFE) and develop a theory for describing and predicting collateral evolution based on simple statistics of the JDFE. We show how to robustly rank drug pairs to minimize the risk of collateral resistance and how to estimate JDFEs. In addition to practical applications, these results have implications for our understanding of evolution in variable environments.

Drugs known as antibiotics are the main treatment for most serious infections caused by bacteria. However, many bacteria are acquiring genetic mutations that make them resistant to the effects of one or more types of antibiotics, making them harder to eliminate.

One way to tackle drug-resistant bacteria is to develop new types of antibiotics; however, in recent years, the rate at which new antibiotics have become available has been dwindling. Using two or more existing drugs, one after another, can also be an effective way to eliminate resistant bacteria. The success of any such ‘multi-drug’ treatment lies in being able to predict whether mutations that make the bacteria resistant to one drug simultaneously make it sensitive to another, a phenomenon known as collateral sensitivity.

Different resistance mutations may have different collateral effects: some may increase the bacteria’s sensitivity to the second drug, while others might make the bacteria more resistant. However, it is currently unclear how to design robust multi-drug treatments that take this diversity of collateral effects into account.

Here, Ardell and Kryazhimskiy used a concept called JDFE (short for the joint distribution of fitness effects) to describe the diversity of collateral effects in a population of bacteria exposed to a single drug. This information was then used to mathematically model how collateral effects evolved in the population over time.

Ardell and Kryazhimskiy showed that this approach can predict how likely a population is to become collaterally sensitive or collaterally resistant to a second antibiotic. Drug pairs can then be ranked according to the risk of collateral resistance emerging, so long as information on the variety of resistance mutations available to the bacteria are included in the model.

Each year, more than 700,000 people die from infections caused by bacteria that are resistant to one or more antibiotics. The findings of Ardell and Kryazhimskiy may eventually help clinicians design multi-drug treatments that effectively eliminate bacterial infections and help to prevent more bacteria from evolving resistance to antibiotics. However, to achieve this goal, more research is needed to fully understand the range collateral effects caused by resistance mutations.

Synthesis, Structure Elucidation, Antibacterial Activities, and Synergistic Effects of Novel Juglone and Naphthazarin Derivatives Against Clinical Methicillin-Resistant Staphylococcus aureus Strains

Infections caused by drug-resistant bacteria are a serious threat to human and global public health. Moreover, in recent years, very few antibiotics have been discovered and developed by pharmaceutical companies. Therefore, there is an urgent need to discover and develop new antibacterial agents to combat multidrug-resistant bacteria. In this study, two novel series of juglone/naphthazarin derivatives (43 compounds) were synthesized and evaluated for their antibacterial properties against various clinical and reference Gram-positive MSSA, clinical Gram-positive MRSA, and clinical and reference Gram-negative bacteria E. coli and P. aeruginosa. These strains are of clinical importance because they belong to ESKAPE pathogens. Compounds 3al, 5ag, and 3bg showed promising activity against clinical and reference MSSA (MIC: 1-8 µg/ml) and good efficacy against clinical MRSA (MIC: 2-8 µg/ml) strains. 5am and 3bm demonstrated better activity on both MSSA (MIC: 0.5 µg/ml) and MRSA (MIC: 2 µg/ml) strains. Their MICs were similar to those of cloxacillin against clinical MRSA strains. The synergistic effects of active compounds 3al, 5ag, 5am, 3bg, and 3bm were evaluated with reference antibiotics, and it was found that the antibiotic combination with 3bm efficiently enhanced the antimicrobial activity. Compound 3bm was found to restore the sensitivity of clinical MRSA to cloxacillin and enhanced the antibacterial activity of vancomycin when they were added together. In the presence of 3bm, the MIC values of vancomycin and cloxacillin were lowered up to 1/16th of the original MIC with an FIC index of 0.313. Moreover, compounds 3al, 5ag, 5am, 3bg, and 3bm did not present hemolytic activity on sheep red blood cells. In silico prediction of ADME profile parameter results for 3bm is promising and encouraging for further development.

Advantages of laboratory natural selection in the applied sciences

In the past three decades, laboratory natural selection has become a widely used technique in biological research. Most studies which have utilized this technique are in the realm of basic science, often testing hypotheses related to mechanisms of evolutionary change or ecological dynamics. While laboratory natural selection is currently utilized heavily in this setting, there is a significant gap with its usage in applied studies, especially when compared to the other selection experiment methodologies like artificial selection and directed evolution. This is despite avenues of research in the applied sciences which seem well suited to laboratory natural selection. In this review, we place laboratory natural selection in context with other selection experiments, identify the characteristics which make it well suited for particular kinds of applied research and briefly cover key examples of the usefulness of selection experiments within applied science. Finally, we identify three promising areas of inquiry for laboratory natural selection in the applied sciences: bioremediation technology, identifying mechanisms of drug resistance and optimizing biofuel production. Although laboratory natural selection is currently less utilized in applied science when compared to basic research, the method has immense promise in the field moving forward.

Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results

Background

Some antibiotic pairs display a property known as collateral sensitivity in which the evolution of resistance to one antibiotic increases sensitivity to the other. Alternating between collaterally sensitive antibiotics has been proposed as a sustainable solution to the problem of antibiotic resistance. We aimed to identify antibiotic pairs that could be considered for treatment strategies based on alternating antibiotics.

Methods

We did a retrospective analysis of 448 563 antimicrobial susceptibility test results acquired over a 4-year period (Jan 1, 2015, to Dec 31, 2018) from 23 hospitals in the University of Pittsburgh Medical Center (Pittsburgh, PA, USA) hospital system. We used a score based on mutual information to identify pairs of antibiotics displaying disjoint resistance, wherein resistance to one antibiotic is commonly associated with susceptibility to the other and vice versa. We applied this approach to the six most frequently isolated bacterial pathogens (Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, and Proteus mirabilis) and subpopulations of each created by conditioning on resistance to individual antibiotics. To identify higher-order antibiotic interactions, we predicted rates of multidrug resistance for triplets of antibiotics using Markov random fields and compared these to the observed rates.

Findings

We identified 69 antibiotic pairs displaying varying degrees of disjoint resistance for subpopulations of the six bacterial species. However, disjoint resistance was rarely conserved at the species level, with only 6 (0·7%) of 875 antibiotic pairs showing evidence of disjoint resistance. Instead, more than half of antibiotic pairs (465 [53·1%] of 875) exhibited signatures of concurrent resistance, whereby resistance to one antibiotic is associated with resistance to another. We found concurrent resistance to extend to more than two antibiotics, with observed rates of resistance to three antibiotics being higher than predicted from pairwise information alone.

Interpretation

The high frequency of concurrent resistance shows that bacteria have means of counteracting multiple antibiotics at a time. The almost complete absence of disjoint resistance at the species level implies that treatment strategies based on alternating between antibiotics might require subspecies level pathogen identification and be limited to a few antibiotic pairings.

Funding

US National Institutes of Health.

Ciprofloxacin induced antibiotic resistance in Salmonella Typhimurium mutants and genome analysis

Antibiotic resistance of Salmonella species is well reported. Ciprofloxacin is the frontline antibiotic for salmonellosis. The repeated exposure to ciprofloxacin leads to resistant strains. After 20 cycles of antibiotic exposure, resistant bacterial clones were evaluated. The colony size of the mutants was small and had an extended lag phase compared to parent strain. The whole genome sequencing showed 40,513 mutations across the genome. Small percentage (5.2%) of mutations was non-synonymous. Four-fold more transitions were observed than transversions. Ratio of < 1 transition vs transversion showed a positive selection for antibiotic resistant trait. Mutation distribution across the genome was uniform. The native plasmid was an exception and 2 mutations were observed on 90 kb plasmid. The important genes like dnaE, gyrA, iroC, metH and rpoB involved in antibiotic resistance had point mutations. The genome analysis revealed most of the metabolic pathways were affected.

Design principles of collateral sensitivity-based dosing strategies

Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance.

Evolutionary Dynamics of Treatment-Induced Resistance in Cancer Informs Understanding of Rapid Evolution in Natural Systems

Rapid evolution is ubiquitous in nature. We briefly review some of this quite broadly, particularly in the context of response to anthropogenic disturbances. Nowhere is this more evident, replicated and accessible to study than in cancer. Curiously cancer has been late - relative to fisheries, antibiotic resistance, pest management and evolution in human dominated landscapes - in recognizing the need for evolutionarily informed management strategies. The speed of evolution matters. Here, we employ game-theoretic modeling to compare time to progression with continuous maximum tolerable dose to that of adaptive therapy where treatment is discontinued when the population of cancer cells gets below half of its initial size and re-administered when the cancer cells recover, forming cycles with and without treatment. We show that the success of adaptive therapy relative to continuous maximum tolerable dose therapy is much higher if the population of cancer cells is defined by two cell types (sensitive vs. resistant in a polymorphic population). Additionally, the relative increase in time to progression increases with the speed of evolution. These results hold with and without a cost of resistance in cancer cells. On the other hand, treatment-induced resistance can be modeled as a quantitative trait in a monomorphic population of cancer cells. In that case, when evolution is rapid, there is no advantage to adaptive therapy. Initial responses to therapy are blunted by the cancer cells evolving too quickly. Our study emphasizes how cancer provides a unique system for studying rapid evolutionary changes within tumor ecosystems in response to human interventions; and allows us to contrast and compare this system to other human managed or dominated systems in nature.

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.

Laboratory evolution of Mycobacterium on agar plates for analysis of resistance acquisition and drug sensitivity profiles

Drug-resistant tuberculosis (TB) is a growing public health problem. There is an urgent need for information regarding cross-resistance and collateral sensitivity relationships among drugs and the genetic determinants of anti-TB drug resistance for developing strategies to suppress the emergence of drug-resistant pathogens. To identify mutations that confer resistance to anti-TB drugs in Mycobacterium species, we performed the laboratory evolution of nonpathogenic Mycobacterium smegmatis, which is closely related to Mycobacterium tuberculosis, against ten anti-TB drugs. Next, we performed whole-genome sequencing and quantified the resistance profiles of each drug-resistant strain against 24 drugs. We identified the genes with novel meropenem (MP) and linezolid (LZD) resistance-conferring mutation, which also have orthologs, in M. tuberculosis H37Rv. Among the 240 possible drug combinations, we identified 24 pairs that confer cross-resistance and 18 pairs that confer collateral sensitivity. The acquisition of bedaquiline or linezolid resistance resulted in collateral sensitivity to several drugs, while the acquisition of MP resistance led to multidrug resistance. The MP-evolved strains showed cross-resistance to rifampicin and clarithromycin owing to the acquisition of a mutation in the intergenic region of the Rv2864c ortholog, which encodes a penicillin-binding protein, at an early stage. These results provide a new insight to tackle drug-resistant TB.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Convergent phenotypic evolution towards fosfomycin collateral sensitivity of Pseudomonas aeruginosa antibiotic-resistant mutants

The rise of antibiotic resistance and the reduced amount of novel antibiotics support the need of developing novel strategies to fight infections, based on improving the use of the antibiotics we already have. Collateral sensitivity is an evolutionary trade‐off associated with the acquisition of antibiotic resistance that can be exploited to tackle this relevant health problem. However, different works have shown that patterns of collateral sensitivity are not always conserved, thus precluding the exploitation of this evolutionary trade‐off to fight infections. In this work, we identify a robust pattern of collateral sensitivity to fosfomycin in Pseudomonas aeruginosa antibiotic‐resistant mutants, selected by antibiotics belonging to different structural families. We characterize the underlying mechanism of the collateral sensitivity observed, which is a reduced expression of the genes encoding the peptidoglycan‐recycling pathway, which preserves the peptidoglycan synthesis in situations where its de novo synthesis is blocked, and a reduced expression of fosA, encoding a fosfomycin‐inactivating enzyme. We propose that the identification of robust collateral sensitivity patterns, as well as the understanding of the molecular mechanisms behind these phenotypes, would provide valuable information to design evolution‐based strategies to treat bacterial infections.

Overview on the role of heavy metals tolerance on developing antibiotic resistance in both Gram-negative and Gram-positive bacteria

Environmental health is a critical concern, continuously contaminated by physical and biological components (viz., anthropogenic activity), which adversely affect on biodiversity, ecosystems and human health. Nonetheless, environmental pollution has great impact on microbial communities, especially bacteria, which try to evolve in changing environment. For instance, during the course of adaptation, bacteria easily become resistance to antibiotics and heavy metals. Antibiotic resistance genes are now one of the most vital pollutants, provided as a source of frequent horizontal gene transfer. In this review, the environmental cause of multidrug resistance (MDR) that was supposed to be driven by either heavy metals or combination of environmental factors was essentially reviewed, especially focussed on the correlation between accumulation of heavy metals and development of MDR by bacteria. This kind of correlation was seemed to be non-significant, i.e. paradoxical. Gram-positive bacteria accumulating much of toxic heavy metal (i.e. highly stress tolerance) were unlikely to become MDR, whereas Gram-negative bacteria that often avoid accumulation of toxic heavy metal by efflux pump systems were come out to be more prone to MDR. So far, other than antibiotic contaminant, no such available data strongly support the direct influence of heavy metals in bacterial evolution of MDR; combinations of factors may drive the evolution of antibiotic resistance. Therefore, Gram-positive bacteria are most likely to be an efficient member in treatment of industrial waste water, especially in the removal of heavy metals, perhaps inducing the less chance of antibiotic resistance pollution in the environment.

Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations and one chemosensitive population that has a higher baseline fitness but is not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner's Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of this three-component system along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. A key parameter determines whether the two drugs interact synergistically, additively, or antagonistically. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design evolutionary cycles (closed loops) in the three-component phase space by shaping the fitness landscape of the cell populations (i.e., altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs. We describe two key bifurcations associated with our drug interaction parameter which help explain why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.