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

Synthesis of Mesoporous Catechin Nanoparticles as Biocompatible Drug-Free Antibacterial Mesoformulation

While polyphenolic substances stand as excellent antibacterial agents, their antimicrobial properties rely on the auxiliary support of micro-/nanostructures. Despite offering a novel avenue for enhancing polymer performance, controllable fabrication of mesoporous polymeric nanomaterials encounters significant challenges due to intricate intermolecular forces. In this article, mesoporous catechin nanoparticles have been successfully fabricated using a balanced multivariate interaction approach. The harmonization of the water-ethanol ratio and ionic strength effectively balances the forces of hydrogen bonding and π-π stacking, facilitating the controlled assembly of mesostructures. The mesoporous catechin nanoparticles exhibit a uniform spherical structure (∼100 nm), open mesopores with a diameter of ∼15 nm, and a high surface area of ∼106 m2 g-1. While exhibiting a good biocompatibility and negative surface charge, the mesoporous catechins possess outstanding antibacterial ability and function as an antibiotic mesoformulation without the necessity of loading any drugs. This mesoformulation inhibits 50% in vitro Staphylococcus aureus growth with a low concentration of ∼10 μg mL-1 and achieves complete inhibition at ∼25 μg mL-1. In a mouse wound model, accelerated wound healing and complete closure within 6-8 days are achieved. Proteomics of bacteria reveals that the excellent antibacterial property is attributed to the synergetic effect of mesoformulation's mesostructure and the catechin molecule intervening in bacterial metabolism. Overall, this work may pave a novel way for the future exploration of polymer nanomaterials and antibiotic formulations.

Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning

Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of in silico analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic.

IMPORTANCE

In the age of next-generation sequencing (NGS), while data-driven methods such as genome-wide association study (GWAS) and machine learning (ML) excel at finding patterns, functional validation can be challenging due to the high numbers of candidate variants. We designed an integrative approach combining a GWAS on S. pneumoniae clinical isolates, followed by whole-genome transformation coupled with NGS to functionally characterize a large set of GWAS candidates. Our study validated several phenotypic folA mutations beyond the standard Ile100Leu mutation, and showed that the overexpression of the sulA locus produces trimethoprim (TMP) resistance in Streptococcus pneumoniae. These validated loci, when used to build ML models, were found to be the best inputs for predicting TMP minimal inhibitory concentrations. Integrative approaches can bridge the genotype-phenotype gap by biological insights that can be incorporated in ML models for accurate prediction of drug susceptibility.

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

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

In Vitro Resistance-Predicting Studies and In Vitro Resistance-Related Parameters-A Hit-to-Lead Perspective

Despite the urgent need for new antibiotics, very few innovative antibiotics have recently entered clinics or clinical trials. To provide a constant supply of new drug candidates optimized in terms of their potential to select for resistance in natural settings, in vitro resistance-predicting studies need to be improved and scaled up. In this review, the following in vitro parameters are presented: frequency of spontaneous mutant selection (FSMS), mutant prevention concentration (MPC), dominant mutant prevention concentration (MPC-D), inferior-mutant prevention concentration (MPC-F), and minimal selective concentration (MSC). The utility of various adaptive laboratory evolution (ALE) approaches (serial transfer, continuous culture, and evolution in spatiotemporal microenvironments) for comparing hits in terms of the level and time required for multistep resistance to emerge is discussed. We also consider how the hit-to-lead stage can benefit from high-throughput ALE setups based on robotic workstations, do-it-yourself (DIY) continuous cultivation systems, microbial evolution and growth arena (MEGA) plates, soft agar gradient evolution (SAGE) plates, microfluidic chips, or microdroplet technology. Finally, approaches for evaluating the fitness of in vitro-generated resistant mutants are presented. This review aims to draw attention to newly emerged ideas on how to improve the in vitro forecasting of the potential of compounds to select for resistance in natural settings.

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

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

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.

Multi-armed antibiotics for Gram-positive bacteria

Antibiotic resistance is a serious threat to public health. Here, we propose a multi-armed chemical scaffold (MACS) for antibiotic screening, which refers to multi-armed molecules (MAMs) consisting of a core unit and three or four arms, neither of which is active for pathogens. Based on a structure-activity relationship study of MAMs, we discover a class of multi-armed antibiotics (MAAs) with a core similar to ethylene (E), carbon atom (C), benzene (B), nitrogen atom (N), and triazine (T) and three or four 4-phenylbenzoic acid (PBA) arms, or a B core and three 4-vinylbenzoic acid (VBA) or 4-ethynylbenzoic acid (EBA) arms. They can selectively interact with Gram-positive bacteria and inhibit cell wall assembly by targeting the lipid carriers of cell wall biosynthesis. MAAs have excellent antibacterial activities against Gram-positive bacteria, including clinical multi-drug-resistant (MDR) isolates. Our study provides a chemical scaffold and identifies eight antibacterial lead compounds for the development of antibiotics.

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.

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.

Antibiotics Limit Adaptation of Drug-Resistant Staphylococcus aureus to Hypoxia

Bacterial pathogens are confronted with a range of challenges at the site of infection, including exposure to antibiotic treatment and harsh physiological conditions, that can alter the fitness benefits and costs of acquiring antibiotic resistance. Here, we develop an experimental system to recapitulate resistance gene acquisition by Staphylococcus aureus and test how the subsequent evolution of the resistant bacterium is modulated by antibiotic treatment and oxygen levels, both of which are known to vary extensively at sites of infection. We show that acquiring tetracycline resistance was costly, reducing competitive growth against the isogenic strain without the resistance gene in the absence of the antibiotic, for S. aureus under hypoxic but not normoxic conditions. Treatment with tetracycline or doxycycline drove the emergence of enhanced resistance through mutations in an RluD-like protein-encoding gene and duplications of tetL, encoding the acquired tetracycline-specific efflux pump. In contrast, evolutionary adaptation by S. aureus to hypoxic conditions, which evolved in the absence of antibiotics through mutations affecting gyrB, was impeded by antibiotic treatment. Together, these data suggest that the horizontal acquisition of a new resistance mechanism is merely a starting point for the emergence of high-level resistance under antibiotic selection but that antibiotic treatment constrains pathogen adaptation to other important environmental selective forces such as hypoxia, which in turn could limit the survival of these highly resistant but poorly adapted genotypes after antibiotic treatment is ended.

Investigations into the antibacterial effects and potential mechanism of gambogic acid and neogambogic acid

The growing threat of antibiotic-resistant bacterial infections to public health necessitates the development of novel antibacterial agents. Inhibiting bacterial cell wall synthesis has remained a key focus for antibiotic development. Our search for inhibitors of undecaprenyl diphosphate synthase (UPPS), an essential enzyme required for bacterial cell wall formation, revealed that two primary components of gamboge, gambogic acid (GA) and neogambogic acid (NGA), significantly inhibited the activity of Enterococcus faecalis UPPS (EfaUPPS) with the half maximal inhibitory concentrations (IC₅₀) of 3.08 μM and 3.07 μM, respectively. In the in vitro antibacterial assay, both GA and NGA also exhibited inhibitory activities against E. faecalis with the minimal inhibitory concentrations (MICs) of 2 μg/mL. Using microscale thermophoresis, molecular docking, and enzymatic assays, we further confirmed that GA and NGA occupy the substrate binding pocket of EfaUPPS with micro-molar binding affinity, preventing the natural substrates farnesyl diphosphate (FPP) from entering. Mutagenesis analysis revealed that L91 and L146 are two key residues in the binding between GA/NGA and UPPS. Furthermore, we also demonstrated that GA and NGA can improve E. faecalis-induced undesirable inflammation in a mouse infection model. Taken together, our findings provide a basis for structural optimization of GA/NGA to develop improved antibiotic leads and enhance treatment success rates in clinical practice.

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.

Deploying Gold Nanomaterials in Combating Multi-Drug-Resistant Bacteria

Antibiotic resistance has become a serious threat to human health due to the overuse of antibiotics. Different antibiotics are being developed to treat resistant bacteria, but the development cycle of antibiotics is hard to keep up with the high incidence of antibiotic resistance. Recent advances in antimicrobial nanomaterials have made nanotechnology a powerful solution to this dilemma. Among these nanomaterials, gold nanomaterials have excellent antibacterial efficacy and biosafety, making them alternatives to antibiotics. This review presents strategies that use gold nanomaterials to combat drug-resistant bacteria. We focus on the influence of physicochemical factors such as surface chemistry, size, and shape of gold nanomaterials on their antimicrobial properties and describe the antimicrobial applications of gold nanomaterials in medical devices. Finally, the existing challenges and future directions are discussed.

Identification of quinoline derivatives as growth inhibitors of MDR pathogen Klebsiella pneumoniae

Aims: This study was aimed to identify compounds with significant inhibitory potential against multidrug-resistant (MDR), multidrug-sensitive, and clinical isolates of Klebsiella pneumoniae. Materials & methods: Antibacterial activity of the nitroquinoline derivatives was assessed by micro-plate Alamar Blue assay. Results: Nitroquinoline derivatives 9, 11 and 14 showed inhibitory activity against MDR K. pneumoniae. Docking studies of these compounds with topoisomerase IV of K. pneumonia indicated the interactions of these compounds at the active site residues of enzyme near to cofactor (Mg⁺²). Furthermore, compound 11 was identified as a reactive oxygen species (ROS) inducer. None of the compounds showed hemolytic effect. Conclusion: This study was designed to identify compounds active against MDR K. pneumoniae which causes infections, such as pneumonia and urinary tract infections.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.

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.

Synergistic Activity of Capsaicin and Colistin Against Colistin-Resistant Acinetobacter baumannii: In Vitro/Vivo Efficacy and Mode of Action

Acinetobacter baumannii is an opportunistic pathogen predominantly associated with nosocomial infections. With emerging resistance against polymyxins, synergistic combinations of drugs are being investigated as a new therapeutic approach. Capsaicin is a common constituent of the human diet and is widely used in traditional alternative medicines. The present study evaluated the antibacterial activities of capsaicin in combination with colistin against three unrelated colistin-resistant Acinetobacter baumannii strains in vitro and in vivo, and then further studied their synergistic mechanisms. Using the checkerboard technique and time-kill assays, capsaicin and colistin showed a synergistic effect on colistin-resistant A. baumannii. A mouse bacteremia model confirmed the in vivo effects of capsaicin and colistin. Mechanistic studies shown that capsaicin can inhibit the biofilm formation of both colistin-resistant and non-resistant A. baumannii. In addition, capsaicin decreased the production of intracellular ATP and disrupted the outer membrane of A. baumannii. In summary, the synergy between these drugs may enable a lower concentration of colistin to be used to treat A. baumannii infection, thereby reducing the dose-dependent side effects. Hence, capsaicin–colistin combination therapy may offer a new treatment option for the control of A. baumannii infection.

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.

Drug-induced resistance evolution necessitates less aggressive treatment

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

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.

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.

An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics

Real-time monitoring of gene expression dynamics and population levels in a multispecies microbial community could enable the study of the role of changing gene expression patterns on eco-evolutionary outcomes. Here we report the design and validation of a unique experimental platform with an in situ fluorescence measurement system that has high dynamic range and temporal resolution and is capable of monitoring multiple fluorophores for long-term gene expression and population dynamics experiments. We demonstrate the capability of our system to capture gene expression dynamics in response to external perturbations in two synthetic genetic systems: a simple inducible genetic circuit and a bistable toggle switch. Finally, in exploring the population dynamics of a two species microbial community, we show that our system can capture the switch between competitive exclusion and long-term coexistence in response to different nutrient conditions.

More is less: Creation of pathogenic microbe-related theranostic oriented AIEgens

Theranostic agents based on photo-dynamic therapy exhibited the properties of the noninvasive feature, spatial-temporal control and be free of drug resistance. Herein, based on the principle of "More is Less", a multifunctional nanoprobe for selective lighting-up of fungi and targeted anti-microbes was designed and achieved. The introducing of the hydroxyl groups and alkaline diethylamino moiety facilitate the probe with well aggregation-induced emission feature, good selectivity towards fungi and acid responsiveness. This probe could only light-up fungi with bright fluorescence, and exhibited diversity anti-microbe behavior towards different microbes. Moreover, the in vitro and in vivo eradication of the supergerm of methicillin resistant staphylococcus aureus was achieved by the treatment of the probe. Confidently, this well-designed nanoprobe is anticipated to have great potential in infective theranostic applications in clinic.

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.

Fighting microbial pathogens by integrating host ecosystem interactions and evolution

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Mapping the Role of AcrAB-TolC Efflux Pumps in the Evolution of Antibiotic Resistance Reveals Near-MIC Treatments Facilitate Resistance Acquisition

Antibiotic resistance has become a major public health concern as bacteria evolve to evade drugs, leading to recurring infections and a decrease in antibiotic efficacy. Systematic efforts have revealed mechanisms involved in resistance. Yet, in many cases, how these specific mechanisms accelerate or slow the evolution of resistance remains unclear. Here, we conducted a systematic study of the impact of the AcrAB-TolC efflux pump on the evolution of antibiotic resistance. We mapped how population growth rate and resistance change over time as a function of both the antibiotic concentration and the parent strain's genetic background. We compared the wild-type strain to a strain overexpressing AcrAB-TolC pumps and a strain lacking functional pumps. In all cases, resistance emerged when cultures were treated with chloramphenicol concentrations near the MIC of their respective parent strain. The genetic background of the parent strain also influenced resistance acquisition. The wild-type strain evolved resistance within 24 h through mutations in the acrAB operon and its associated regulators. Meanwhile, the strain overexpressing AcrAB-TolC evolved resistance more slowly than the wild-type strain; this strain achieved resistance in part through point mutations in acrB and the acrAB promoter. Surprisingly, the strain without functional AcrAB-TolC efflux pumps still gained resistance, which it achieved through upregulation of redundant efflux pumps. Overall, our results suggest that treatment conditions just above the MIC pose the largest risk for the evolution of resistance and that AcrAB-TolC efflux pumps impact the pathway by which chloramphenicol resistance is achieved.IMPORTANCE Combatting the rise of antibiotic resistance is a significant challenge. Efflux pumps are an important contributor to drug resistance; they exist across many cell types and can export numerous classes of antibiotics. Cells can regulate pump expression to maintain low intracellular drug concentrations. Here, we explored how resistance emerged depending on the antibiotic concentration, as well as the presence of efflux pumps and their regulators. We found that treatments near antibiotic concentrations that inhibit the parent strain's growth were most likely to promote resistance. While wild-type, pump overexpression, and pump knockout strains were all able to evolve resistance, they differed in the absolute level of resistance evolved, the speed at which they achieved resistance, and the genetic pathways involved. These results indicate that specific treatment regimens may be especially problematic for the evolution of resistance and that the strain background can influence how resistance is achieved.

High-throughput laboratory evolution reveals evolutionary constraints in Escherichia coli

Understanding the constraints that shape the evolution of antibiotic resistance is critical for predicting and controlling drug resistance. Despite its importance, however, a systematic investigation of evolutionary constraints is lacking. Here, we perform a high-throughput laboratory evolution of Escherichia coli under the addition of 95 antibacterial chemicals and quantified the transcriptome, resistance, and genomic profiles for the evolved strains. Utilizing machine learning techniques, we analyze the phenotype-genotype data and identified low dimensional phenotypic states among the evolved strains. Further analysis reveals the underlying biological processes responsible for these distinct states, leading to the identification of trade-off relationships associated with drug resistance. We also report a decelerated evolution of β-lactam resistance, a phenomenon experienced by certain strains under various stresses resulting in higher acquired resistance to β-lactams compared to strains directly selected by β-lactams. These findings bridge the genotypic, gene expression, and drug resistance gap, while contributing to a better understanding of evolutionary constraints for antibiotic resistance.

Humoral and Cell-Mediated Immune Response Validation in Calves after a Live Attenuated Vaccine of Babesia bigemina

The current vaccines to control bovine Babesia bigemina (B. bigemina) infection are not fully protective and vaccination failures incur heavy losses to the cattle industry around the world. Using modified micro-aerophilous stationary phase, we developed a culture-derived attenuated live vaccine against B. bigemina and tested a single subcutaneous inoculation of 2 × 10⁸ infected erythrocytes in calves. The protection was measured after a lethal intravenous challenge with 5 × 10⁸ virulent calf-derived B. bigemina. Our results demonstrated that a single shot of attenuated vaccine was capable of inducing robust humoral and cell-mediated immune responses in calves. We found a significant increase in the IgG antibody titers post-challenge and a strong proliferation of both CD4+ and CD8+ T cells contributing towards the protection. Our vaccine provided complete protection and parasitic clearance, which was followed for more than 100 days post-challenge. This immunity against babesiosis was directly linked to strong humoral responses; however, the parasitic clearance was attributed to significant T cells effector responses in vaccinated calves as compared to the infected control calves. We anticipate that these results will be helpful in the development of more efficient culture-derived vaccines against Babesia infections, thus reducing significant global economic losses to farmers and the cattle industry.

Functional Characterization of a Novel Hybrid Peptide with High Potency against Gram-negative Bacteria

BACKGROUND

Multi-drug resistant infections are a growing worldwide health concern. There is an urgent need to produce alternative antimicrobial agents.

OBJECTIVE

The study aimed to design a new hybrid antimicrobial peptide, and to evaluate its antimicrobial activity alone and in combination with traditional antibiotics.

METHODS

Herein, we designed a novel hybrid peptide (BMR-1) using the primary sequences of the parent peptides Frog Esculentin-1a and Monkey Rhesus cathelicidin (RL-37). The positive net charge was increased, and other physicochemical parameters were optimized. The antimicrobial activities of BMR-1 were tested against control and multi-drug resistant gram-negative bacteria.

RESULTS

BMR-1 adopted a bactericidal behavior with MIC values of 25-30 µM. These values reduced by over 75% upon combination with conventional antibiotics (levofloxacin, chloramphenicol, ampicillin, and rifampicin). The combination showed strong synergistic activities in most cases and particularly against multi-drug resistance P. aeruginosa and E. coli. BMR-1 showed similar potency against all tested strains regardless of their resistant mechanisms. BMR-1 exhibited no hemolytic effect on human red blood cells with the effective MIC values against the tested strains.

CONCLUSION

BMR-1 hybrid peptide is a promising candidate to treat resistant infectious diseases caused by gramnegative bacteria.

How antibiotics work together: molecular mechanisms behind combination therapy

Antibiotics used in combination are an effective strategy for combatting numerous infectious diseases in clinical and veterinary settings, particularly as a last-line therapy for difficult-to-treat cases. Combination therapy can either increase or slow the rate of killing, broaden the antibiotic spectrum, reduce dosage and unwanted side-effects, and even control the emergence of resistance. The administration of antibiotics in combination has been used effectively against bacterial infections for >70 years, first used to treat tuberculosis. However, effective antibiotic combinations and their dosage regimes have been largely determined empirically in the clinic, and the molecular mechanisms underpinning how these combinations work remains surprisingly elusive. This review focuses on studies that have outlined the genetics and molecular mechanisms of action underlying antibiotic combinations, as well as those that examine how resistance develops. We highlight the need for further experimentation and genetic validation to fully realise the potential of combination therapy.

Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance

Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics.

The antibiotic resistance crisis calls for new ways of restricting the ability of bacteria to evolve resistance. Here, Lukačišinová et al. perform highly controlled evolution experiments in E. coli strains to identify genetic perturbations that strongly limit the evolution of antibiotic resistance through epistasis.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Using Selection by Nonantibiotic Stressors to Sensitize Bacteria to Antibiotics

Evolutionary adaptation of bacteria to nonantibiotic selective forces, such as osmotic stress, has been previously associated with increased antibiotic resistance, but much less is known about potentially sensitizing effects of nonantibiotic stressors. In this study, we use laboratory evolution to investigate adaptation of Enterococcus faecalis, an opportunistic bacterial pathogen, to a broad collection of environmental agents, ranging from antibiotics and biocides to extreme pH and osmotic stress. We find that nonantibiotic selection frequently leads to increased sensitivity to other conditions, including multiple antibiotics. Using population sequencing and whole-genome sequencing of single isolates from the evolved populations, we identify multiple mutations in genes previously linked with resistance to the selecting conditions, including genes corresponding to known drug targets or multidrug efflux systems previously tied to collateral sensitivity. Finally, we hypothesized based on the measured sensitivity profiles that sequential rounds of antibiotic and nonantibiotic selection may lead to hypersensitive populations by harnessing the orthogonal collateral effects of particular pairs of selective forces. To test this hypothesis, we show experimentally that populations evolved to a sequence of linezolid (an oxazolidinone antibiotic) and sodium benzoate (a common preservative) exhibit increased sensitivity to more stressors than adaptation to either condition alone. The results demonstrate how sequential adaptation to drug and nondrug environments can be used to sensitize bacteria to antibiotics and highlight new potential strategies for exploiting shared constraints governing adaptation to diverse environmental challenges.

Delayed antibiotic exposure induces population collapse in enterococcal communities with drug-resistant subpopulations

The molecular underpinnings of antibiotic resistance are increasingly understood, but less is known about how these molecular events influence microbial dynamics on the population scale. Here, we show that the dynamics of E. faecalis communities exposed to antibiotics can be surprisingly rich, revealing scenarios where increasing population size or delaying drug exposure can promote population collapse. Specifically, we demonstrate how density-dependent feedback loops couple population growth and antibiotic efficacy when communities include drug-resistant subpopulations, leading to a wide range of behavior, including population survival, collapse, or one of two qualitatively distinct bistable behaviors where survival is favored in either small or large populations. These dynamics reflect competing density-dependent effects of different subpopulations, with growth of drug-sensitive cells increasing but growth of drug-resistant cells decreasing effective drug inhibition. Finally, we demonstrate how populations receiving immediate drug influx may sometimes thrive, while identical populations exposed to delayed drug influx collapse.

Suppression of antibiotic resistance evolution by single-gene deletion

Antibiotic treatment generally results in the selection of resistant bacterial strains, and the dynamics of resistance evolution is dependent on complex interactions between cellular components. To better characterize the mechanisms of antibiotic resistance and evaluate its dependence on gene regulatory networks, we performed systematic laboratory evolution of Escherichia coli strains with single-gene deletions of 173 transcription factors under three different antibiotics. This resulted in the identification of several genes whose deletion significantly suppressed resistance evolution, including arcA and gutM. Analysis of double-gene deletion strains suggested that the suppression of resistance evolution caused by arcA and gutM deletion was not caused by epistatic interactions with mutations known to confer drug resistance. These results provide a methodological basis for combinatorial drug treatments that may help to suppress the emergence of resistant pathogens by inhibiting resistance evolution.

Antibiotic interactions shape short-term evolution of resistance in E. faecalis

Antibiotic combinations are increasingly used to combat bacterial infections. Multidrug therapies are a particularly important treatment option for E. faecalis, an opportunistic pathogen that contributes to high-inoculum infections such as infective endocarditis. While numerous synergistic drug combinations for E. faecalis have been identified, much less is known about how different combinations impact the rate of resistance evolution. In this work, we use high-throughput laboratory evolution experiments to quantify adaptation in growth rate and drug resistance of E. faecalis exposed to drug combinations exhibiting different classes of interactions, ranging from synergistic to suppressive. We identify a wide range of evolutionary behavior, including both increased and decreased rates of growth adaptation, depending on the specific interplay between drug interaction and drug resistance profiles. For example, selection in a dual β-lactam combination leads to accelerated growth adaptation compared to selection with the individual drugs, even though the resulting resistance profiles are nearly identical. On the other hand, populations evolved in an aminoglycoside and β-lactam combination exhibit decreased growth adaptation and resistant profiles that depend on the specific drug concentrations. We show that the main qualitative features of these evolutionary trajectories can be explained by simple rescaling arguments that correspond to geometric transformations of the two-drug growth response surfaces measured in ancestral cells. The analysis also reveals multiple examples where resistance profiles selected by drug combinations are nearly growth-optimized along a contour connecting profiles selected by the component drugs. Our results highlight trade-offs between drug interactions and resistance profiles during the evolution of multi-drug resistance and emphasize evolutionary benefits and disadvantages of particular drug pairs targeting enterococci.

In Situ Construction of a MgSn(OH)6 Perovskite/SnO2 Type-II Heterojunction: A Highly Efficient Photocatalyst towards Photodegradation of Tetracycline

Using solar energy to remove antibiotics from aqueous environments via photocatalysis is highly desirable. In this work, a novel type-II heterojunction photocatalyst, MgSn(OH)₆/SnO_2, was successfully prepared via a facile one-pot in situ hydrothermal method at 220 °C for 24 h. The obtained heterojunctions were characterized via powder X-ray diffraction, Fourier-transform infrared spectroscopy, transmission electron microscopy, and ultraviolet-visible diffuse reflectance spectroscopy. The photocatalytic performance was evaluated for photodegradation of tetracycline solution under ultraviolet irradiation. The initial concentration of tetracycline solution was set to be 20 mg/L. The prepared heterojunctions exhibited superior photocatalytic activity compared with the parent MgSn(OH)₆ and SnO₂ compounds. Among them, the obtained MgSn(OH)₆/SnO₂ heterojunction with MgCl₂·6H₂O:SnCl₄·5H₂O = 4:5.2 (mmol) displayed the highest photocatalytic performance and the photodegradation efficiency conversion of 91% could be reached after 60 min under ultraviolet irradiation. The prepared heterojunction maintained its performance after four successive cycles of use. Active species trapping experiments demonstrated that holes were the dominant active species. Hydroxyl radicals and superoxide ions had minor effects on the photocatalytic oxidation of tetracycline. Photoelectrochemical measurements were used to investigate the photocatalytic mechanism. The enhancement of photocatalytic activity could be assigned to the formation of a type-II junction photocatalytic system, which was beneficial for efficient transfer and separation of photogenerated electrons and holes. This research provides an in situ growth strategy for the design of highly efficient photocatalysts for environmental restoration.

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.

Spatial and temporal variation of antibiotic resistance in marine fish cage-culture area of Guangdong, China

The rapid emergence and dissemination of antibiotic resistance poses a threat to human health and to the marine environment. We have investigated the abundance and diversity of antibiotic resistance genes (ARGs) and of antibiotic-resistant bacteria (ARB), during the seedling period, rearing period, and harvesting period in seven marine fish cage-culture areas in Guangdong. Spatial and temporal variations of AGRs and ARB were also analyzed. Culture-based methods and quantitative PCR were used to detect ARB and ARGs. Bacterial resistance rates were no significantly different within farming periods. The proportion of antibiotic-resistant bacteria was extremely low (average on 1.15%), except for oxytetracycline-resistant bacteria (average on 34.15%). Vibrio was the most common ARB. Sul1, tetB, and ermB, had the highest relative abundance. The abundance of ARGs in the harvesting period was significant highest. The total abundance of ARGs was highest at Raoping and lowest at Dayawan and Liusha. Most ARGs were associated with opportunistic pathogens. The environmental factors effecting ARB and ARGs are complex, and no key factors were identified. This study provides a theoretical basis for assessing the harmfulness of ARGs and ARB to food safety and human health.

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.

Development of unstable resistance to diminazene aceturate in Babesia bovis

Diminazene aceturate (DA) is commonly used in the treatment of bovine babesiosis caused by Babesia bovis. In this study, we attempted to develop resistance in B. bovis in vitro to DA and clofazimine (CF, a novel antibabesial agent) using short- and long-term drug pressures. In the short term, we found that 6.7 ± 2 (0.54 ± 0.16 μM)-, 12.9 ± 8.6 (1.05 ± 0.7 μM)-, and 14 ± 5.9 (1.14 ± 0.48 μM)-fold increases in the half-maximal inhibitory concentration (IC₅₀) of DA were demonstrated on B. bovis cultivated with 0.04 μM of DA pressure for 4, 8, and 12 days, respectively, as compared to that on parental culture (0.08 ± 0.0065 μM) before drug pressure was initiated. However, in B. bovis cultivated with 0.04 μM of DA pressure after 16 days, the parasites could not tolerate 0.8 μM of DA. In the long term, 7.6 ± 3.5-, 20.5 ± 0.1-, and 26.8 ± 5.5-fold increases in the IC₅₀ of DA were demonstrated on parasites from subcultures at days 8, 3, and 5 post-cultivation, respectively, in a drug-free medium, where these subcultures were obtained from B. bovis cultivated with DA pressure with changing doses for 30, 60, and 90 days, respectively. However, the second and third times, no increase was demonstrated on B. bovis from these subcultures at days 15 and 30 post-cultivation in a drug-free medium. In addition, in B. bovis cultivated with drug pressure after 90 days, the parasites tolerate up to 0.64 μM DA. All findings demonstrated that DA resistance in B. bovis is unstable and lost within 15 days of drug withdrawal. However, treatment with subtherapeutic doses of DA in cattle might result in the development of resistance in B. bovis, which may not even respond to subsequent treatments with high doses of DA. Thus, if the bovine babesiosis caused by B. bovis is unresponsive to DA, treatment with other antibabesial agents might be recommended.

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.

Are We There Yet? How and When Specific Biotechnologies Will Improve Human Health

Patient X: A 67-year-old Caucasian man slips on a patch of ice. He has abrasions to his hands and has sustained significant damage to his hip. At the emergency room, he informs clinicians he takes atorvastatin, metformin, and glimepiride to treat hypertension and Type 2 Diabetes Mellitus (T2DM). X-rays reveal a fractured hip, which will require total hip replacement surgery.

A bio-based pro-antimicrobial polymer network via degradable acetal linkages

The synthesis of a fully degradable, bio-based, sustained release, pro-antimicrobial polymer network comprised of degradable acetals (PANDA) is reported. The active antimicrobial agent - p-anisaldehyde (pA) (an extract from star anise) - was converted into a UV curable acetal containing pro-antimicrobial monomer and subsequently photopolymerized into a homogenous thiol-ene network. Under neutral to acidic conditions (pH < 8), the PANDAs undergo surface erosion and exhibit sustained release of pA over 38 days. The release of pA from PANDAs was shown to be effective against both bacterial and fungal pathogens. From a combination of confocal microscopy and transmission electron microscopy, we observed that the released pA disrupts the cell membrane. Additionally, we demonstrated that PANDAs have minimal cytotoxicity towards both epithelial cells and macrophages. Although a model platform, these results point to promising pathways for the design of fully degradable sustained-release antimicrobial systems with potential applications in agriculture, pharmaceuticals, cosmetics, household/personal care, and food industries.

STATEMENT OF SIGNIFICANCE

With the increasing number of patients prescribed immunosuppressants coupled with the rise in antibiotic resistance - life-threatening microbial infections are a looming global threat. With limited success within the antibiotic pipeline, nature-based essential oils (EOs) are being investigated for their multimodal effectiveness against microbes. Despite the promising potential of EOs, difficulties in their encapsulation, limited water solubility, and high volatility limit their use. Various studies have shown that covalent attachment of these EO derivatives to polymers can mitigate these limitations. The current study presents the synthesis of a fully-degradable, sustained release, cytocompatible, pro-antimicrobial acetal network derived from p-anisaldehyde. This polymer network design provides a pathway toward application-specific EO releasing materials with quantitative encapsulation efficiencies, sustained release, and broad-spectrum antimicrobial activity.

Prediction of Cross-resistance and Collateral Sensitivity by Gene Expression profiles and Genomic Mutations

In adaptive evolution, an increase in fitness to an environment is frequently accompanied by changes in fitness to other environmental conditions, called cross-resistance and sensitivity. Although the networks between fitness changes affect the course of evolution substantially, the mechanisms underlying such fitness changes are yet to be fully elucidated. Herein, we performed high-throughput laboratory evolution of Escherichia coli under various stress conditions using an automated culture system, and quantified how the acquisition of resistance to one stressor alters the resistance to other stressors. We demonstrated that resistance changes could be quantitatively predicted based on changes in the transcriptome of the resistant strains. We also identified several genes and gene functions, for which mutations were commonly fixed in the strains resistant to the same stress, which could partially explain the observed cross-resistance and collateral sensitivity. The integration of transcriptome and genome data enabled us to clarify the bacterial stress resistance mechanisms.