Antibiotic collateral sensitivity is contingent on the repeatability of evolution

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

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

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.

Theoretical modeling of collaterally sensitive drug cycles: shaping heterogeneity to allow adaptive therapy

In previous work, we focused on the optimal therapeutic strategy with a pair of drugs which are collaterally sensitive to each other, that is, a situation in which evolution of resistance to one drug induces sensitivity to the other, and vice versa. Yoona (Bull Math Biol 8:1-34,Yoon et al. 2018) Here, we have extended this exploration to the optimal strategy with a collaterally sensitive drug sequence of an arbitrary length, N. To explore this, we have developed a dynamical model of sequential drug therapies with N drugs. In this model, tumor cells are classified as one of N subpopulations represented as [Formula: see text]. Each subpopulation, [Formula: see text], is resistant to '[Formula: see text]' and each subpopulation, [Formula: see text] (or [Formula: see text], if [Formula: see text]), is sensitive to it, so that [Formula: see text] increases under '[Formula: see text]' as it is resistant to it, and after drug-switching, decreases under '[Formula: see text]' as it is sensitive to that drug(s). Similar to our previous work examining optimal therapy with two drugs, we found that there is an initial period of time in which the tumor is 'shaped' into a specific makeup of each subpopulation, at which time all the drugs are equally effective ([Formula: see text]). After this shaping period, all the drugs are quickly switched with duration relative to their efficacy in order to maintain each subpopulation, consistent with the ideas underlying adaptive therapy. West(Canver Res 80(7):578-589Gatenby et al. 2009) and Gatenby (Cancer Res 67(11):4894-4903West et al. 2020). Additionally, we have developed methodologies to administer the optimal regimen under clinical or experimental situations in which no drug parameters and limited information of trackable populations data (all the subpopulations or only total population) are known. The therapy simulation based on these methodologies showed consistency with the theoretical effect of optimal therapy .

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.

Finding the right sequence of drugs

Rapidly switching between similar antibiotics may help to slow down the evolution of resistance.

Enrichment of potential degrading bacteria accelerates removal of tetracyclines and their epimers from cow manure biochar amended soil

The excessive usage of tetracyclines in animal husbandry and aquaculture invariably leads to deterioration of the microbial quality of nearby soils. We previously reported the accelerated removal of tetracyclines and their intermediates from the cow manure biochar amended soil (CMB). However, little is known about the underlying changes in the microbial community that mediate the accelerated removal of tetracyclines from the CMB. Here, we compared the concentration of parent tetracyclines along with their intermediates, microbial biomass, and microbial (fungal and bacterial) community in CMB and the control soil (CK) on the day of 1, 5, 10, 20, 30, 45, and 60. The biochar amendment accelerated the removal of tetracyclines and their epimers from the soil. Bacterial community composition varied between the CMB and CK. The relative abundance and richness of the bacteria that correlated with the degradation of tetracyclines and their epimers was significantly higher in the CMB as compared to the CK. Specifically, the CMB had a more intricate network of the degrading bacteria with the three keystone genera viz. Acidothermus sp., Sphingomonas sp., and Blastococcus sp., whereas, the CK had a simple network with Sphingomonas sp. as the keystone genus. Overall, the biochar amendment accelerated the removal of tetracyclines and their epimers through the enrichment of potential tetracycline degrading bacteria in the soil; thus, it can be applied for the in situ remediation of soils contaminated with tetracyclines.

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.

Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment with Escherichia coli

Antibiotic resistance is a growing health concern. Efforts to control resistance would benefit from an improved ability to forecast when and how it will evolve. Epistatic interactions between mutations can promote divergent evolutionary trajectories, which complicates our ability to predict evolution. We recently showed that differences between genetic backgrounds can lead to idiosyncratic responses in the evolvability of phenotypic resistance, even among closely related Escherichia coli strains. In this study, we examined whether a strain's genetic background also influences the genotypic evolution of resistance. Do lineages founded by different genotypes take parallel or divergent mutational paths to achieve their evolved resistance states? We addressed this question by sequencing the complete genomes of antibiotic-resistant clones that evolved from several different genetic starting points during our earlier experiments. We first validated our statistical approach by quantifying the specificity of genomic evolution with respect to antibiotic treatment. As expected, mutations in particular genes were strongly associated with each drug. Then, we determined that replicate lines evolved from the same founding genotypes had more parallel mutations at the gene level than lines evolved from different founding genotypes, although these effects were more subtle than those showing antibiotic specificity. Taken together with our previous work, we conclude that historical contingency can alter both genotypic and phenotypic pathways to antibiotic resistance.

Re-Envisioning Anti-Apicomplexan Parasite Drug Discovery Approaches

Parasites of the phylum Apicomplexa impact humans in nearly all parts of the world, causing diseases including to toxoplasmosis, cryptosporidiosis, babesiosis, and malaria. Apicomplexan parasites have complex life cycles comprised of one or more stages characterized by rapid replication and biomass amplification, which enables accelerated evolutionary adaptation to environmental changes, including to drug pressure. The emergence of drug resistant pathogens is a major looming and/or active threat for current frontline chemotherapies, especially for widely used antimalarial drugs. In fact, resistant parasites have been reported against all modern antimalarial drugs within 15 years of clinical introduction, including the current frontline artemisinin-based combination therapies. Chemotherapeutics are a major tool in the public health arsenal for combatting the onset and spread of apicomplexan diseases. All currently approved antimalarial drugs have been discovered either through chemical modification of natural products or through large-scale screening of chemical libraries for parasite death phenotypes, and so far, none have been developed through a gene-to-drug pipeline. However, the limited duration of efficacy of these drugs in the field underscores the need for new and innovative approaches to discover drugs that can counter rapid resistance evolution. This review details both historical and current antimalarial drug discovery approaches. We also highlight new strategies that may be employed to discover resistance-resistant drug targets and chemotherapies in order to circumvent the rapid evolution of resistance in apicomplexan parasites.

Roadmap on biology in time varying environments

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.

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.

Chemotherapy confers a conserved secondary tolerance to EGFR inhibition via AXL-mediated signaling bypass

Drug resistance remains the major culprit of therapy failure in disseminated cancers. Simultaneous resistance to multiple, chemically different drugs feeds this failure resulting in cancer relapse. Here, we investigate co-resistance signatures shared between antimitotic drugs (AMDs) and inhibitors of receptor tyrosine kinases (RTKs) to probe mechanisms of secondary resistance. We map co-resistance ranks in multiple drug pairs and identified a more widespread occurrence of co-resistance to the EGFR-tyrosine kinase inhibitor (TKI) gefitinib in hundreds of cancer cell lines resistant to at least 11 AMDs. By surveying different parameters of genomic alterations, we find that the two RTKs EGFR and AXL displayed similar alteration and expression signatures. Using acquired paclitaxel and epothilone B resistance as first-line AMD failure models, we show that a stable collateral resistance to gefitinib can be relayed by entering a dynamic, drug-tolerant persister state where AXL acts as bypass signal. Delayed AXL degradation rendered this persistence to become stably resistant. We probed this degradation process using a new EGFR-TKI candidate YD and demonstrated that AXL bypass-driven collateral resistance can be suppressed pharmacologically. The findings emphasize that AXL bypass track is employed by chemoresistant cancer cells upon EGFR inhibition to enter a persister state and evolve resistance to EGFR-TKIs.

β-Lactams against the Fortress of the Gram-Positive Staphylococcus aureus Bacterium

The biological diversity of the unicellular bacteria-whether assessed by shape, food, metabolism, or ecological niche-surely rivals (if not exceeds) that of the multicellular eukaryotes. The relationship between bacteria whose ecological niche is the eukaryote, and the eukaryote, is often symbiosis or stasis. Some bacteria, however, seek advantage in this relationship. One of the most successful-to the disadvantage of the eukaryote-is the small (less than 1 μm diameter) and nearly spherical Staphylococcus aureus bacterium. For decades, successful clinical control of its infection has been accomplished using β-lactam antibiotics such as the penicillins and the cephalosporins. Over these same decades S. aureus has perfected resistance mechanisms against these antibiotics, which are then countered by new generations of β-lactam structure. This review addresses the current breadth of biochemical and microbiological efforts to preserve the future of the β-lactam antibiotics through a better understanding of how S. aureus protects the enzyme targets of the β-lactams, the penicillin-binding proteins. The penicillin-binding proteins are essential enzyme catalysts for the biosynthesis of the cell wall, and understanding how this cell wall is integrated into the protective cell envelope of the bacterium may identify new antibacterials and new adjuvants that preserve the efficacy of the β-lactams.

Investigating the mechanism of action of aggregation-inducing antimicrobial Pept-ins

Aggregation can be selectively induced by aggregation-prone regions (APRs) contained in the target proteins. Aggregation-inducing antimicrobial peptides (Pept-ins) contain sequences homologous to APRs of target proteins and exert their bactericidal effect by causing aggregation of a large number of proteins. To better understand the mechanism of action of Pept-ins and the resistance mechanisms, we analyzed the phenotypic, lipidomic, and transcriptomic as well as genotypic changes in laboratory-derived Pept-in-resistant E. coli mutator cells. The analysis showed that the Pept-in resistance mechanism is dominated by a decreased Pept-in uptake, in both laboratory-derived mutator cells and clinical isolates. Our data indicate that Pept-in uptake involves an electrostatic attraction between the Pept-in and the bacterial membrane and follows a complex mechanism potentially involving many transporters. Furthermore, it seems more challenging for bacteria to become resistant toward Pept-ins that are less dependent on electrostatic attraction for uptake, suggesting that future Pept-ins should be selected for this property.

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.

Antibiotic resistance: turning evolutionary principles into clinical reality

Antibiotic resistance is one of the major challenges facing modern medicine worldwide. The past few decades have witnessed rapid progress in our understanding of the multiple factors that affect the emergence and spread of antibiotic resistance at the population level and the level of the individual patient. However, the process of translating this progress into health policy and clinical practice has been slow. Here, we attempt to consolidate current knowledge about the evolution and ecology of antibiotic resistance into a roadmap for future research as well as clinical and environmental control of antibiotic resistance. At the population level, we examine emergence, transmission and dissemination of antibiotic resistance, and at the patient level, we examine adaptation involving bacterial physiology and host resilience. Finally, we describe new approaches and technologies for improving diagnosis and treatment and minimizing the spread of resistance.

Persistence as an Optimal Hedging Strategy

Bacteria invest in a slow-growing subpopulation, called persisters, to ensure survival in the face of uncertainty. This hedging strategy is remarkably similar to financial hedging, where diversifying an investment portfolio protects against economic uncertainty. We provide a new, to our knowledge, theoretical foundation for understanding cellular hedging by unifying the study of biological population dynamics and the mathematics of financial risk management through optimal control theory. Motivated by the widely accepted role of volatility in the emergence of persistence, we consider several models of environmental volatility described by continuous-time stochastic processes. This allows us to study an emergent cellular hedging strategy that maximizes the expected per capita growth rate of the population. Analytical and simulation results probe the optimal persister strategy, revealing results that are consistent with experimental observations and suggest new opportunities for experimental investigation and design. Overall, we provide a new, to our knowledge, way of conceptualizing and modeling cellular decision making in volatile environments by explicitly unifying theory from mathematical biology and finance.

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.

Understanding evolutionary constraints in antibiotic resistance is crucial for prediction and control. Here, the authors use high-throughput laboratory evolution of Escherichia coli alongside machine learning to identify trade-off relationships associated with drug resistance.

Phage-antibiotic combinations: a promising approach to constrain resistance evolution in bacteria

Antibiotic resistance has reached dangerously high levels throughout the world. A growing number of bacteria pose an urgent, serious, and concerning threat to public health. Few new antibiotics are available to clinicians and only few are in development, highlighting the need for new strategies to overcome the antibiotic resistance crisis. Combining existing antibiotics with phages, viruses the infect bacteria, is an attractive and promising alternative to standalone therapies. Phage-antibiotic combinations have been shown to suppress the emergence of resistance in bacteria, and sometimes even reverse it. Here, we discuss the mechanisms by which phage-antibiotic combinations reduce resistance evolution, and the potential limitations these mechanisms have in steering microbial resistance evolution in a desirable direction. We also emphasize the importance of gaining a better understanding of mechanisms behind physiological and evolutionary phage-antibiotic interactions in complex in-patient environments.

Extreme Antagonism Arising from Gene-Environment Interactions

Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.

Antibiotic Resistance: Moving From Individual Health Norms to Social Norms in One Health and Global Health

Antibiotic resistance is a problem for human health, and consequently, its study had been traditionally focused toward its impact for the success of treating human infections in individual patients (individual health). Nevertheless, antibiotic-resistant bacteria and antibiotic resistance genes are not confined only to the infected patients. It is now generally accepted that the problem goes beyond humans, hospitals, or long-term facility settings and that it should be considered simultaneously in human-connected animals, farms, food, water, and natural ecosystems. In this regard, the health of humans, animals, and local antibiotic-resistance-polluted environments should influence the health of the whole interconnected local ecosystem (One Health). In addition, antibiotic resistance is also a global problem; any resistant microorganism (and its antibiotic resistance genes) could be distributed worldwide. Consequently, antibiotic resistance is a pandemic that requires Global Health solutions. Social norms, imposing individual and group behavior that favor global human health and in accordance with the increasingly collective awareness of the lack of human alienation from nature, will positively influence these solutions. In this regard, the problem of antibiotic resistance should be understood within the framework of socioeconomic and ecological efforts to ensure the sustainability of human development and the associated human-natural ecosystem interactions.

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

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

Rapid and robust evolution of collateral sensitivity in Pseudomonas aeruginosa antibiotic-resistant mutants

The analysis of trade-offs, as collateral sensitivity, associated with the acquisition of antibiotic resistance, is mainly based on the use of model strains. However, the possibility of exploiting these trade-offs for fighting already resistant isolates has not been addressed in depth, despite the fact that bacterial pathogens are frequently antibiotic-resistant, forming either homogeneous or heterogeneous populations. Using a set of Pseudomonas aeruginosa-resistant mutants, we found that ceftazidime selects pyomelanogenic tobramycin-hypersusceptible mutants presenting chromosomal deletions in the analyzed genetic backgrounds. Since pyomelanogenic resistant mutants frequently coexist with other morphotypes in patients with cystic fibrosis, we analyzed the exploitation of this trade-off to drive extinction of heterogeneous resistant populations by using tobramycin/ceftazidime alternation. Our work shows that this approach is feasible because phenotypic trade-offs associated with the use of ceftazidime are robust. The identification of conserved collateral sensitivity networks may guide the rational design of evolution-based antibiotic therapies in P. aeruginosa infections.

Effect of Short-Term Antimicrobial Therapy on the Tolerance and Antibiotic Resistance of Multidrug-Resistant Staphylococcus capitis

Background

Bacteria undergo adaptive mutation in the host. However, the specific effect of antimicrobial use on bacterial evolution and genome mutations related to bacterial survival within a patient is unclear.

Materials and Methods

Three S. capitis strains were sequentially isolated from cerebrospinal fluid of a clinical inpatient. Antimicrobial susceptibility, growth rate, biofilm formation and whole blood survival of these strains were measured. Relative fitness was calculated. The virulence was examined in the Galleria mellonella model. Whole-genome sequencing and in silico analysis were performed to explore the genetic mechanisms of the changes in antimicrobial resistance phenotype. Hypothetical proteins are cloned, expressed and characterized by detection the susceptibility to gentamycin.

Results

The first isolate was susceptible to rifampin (MIC=0.25 μg/mL), resistant to gentamicin (MIC=16 μg/mL), while the later two isolates were resistant to rifampin (MIC >64 μg/mL), susceptible to gentamicin (MIC=4 μg/mL). For the latter two strains, compared to the first, frameshift mutation in a hypothetical protein encoding gene and base substitutions (in genes saeR, moaA and rpoB) were discovered. The mutation of rpoB gene caused rifampicin resistance. Mutations in saeR, moaA and hypothetical gene are associated with changes in other biological traits. Amino acid sequence-based structure and function identification of the hypothetical protein indicated that a mutation in the encoding gene might be associated with altered aminoglycoside susceptibility. Growth curve showed that the later two isolates grew faster than the first isolate with a positive fitness advantage of 13.5%, and 14.8%, accordingly. Biofilm form ability and whole blood survival of the derivative mutants were also enhanced. No significant differences of virulence in the G. mellonella model were observed.

Conclusion

We report here for the first time that short-term clinical antibiotic use was associated with resistance mutations, collateral sensitivity, and positive in vivo fitness advantages to S. capitis during infection.

Identifying States of Collateral Sensitivity during the Evolution of Therapeutic Resistance in Ewing's Sarcoma

Advances in the treatment of Ewing's sarcoma (EWS) are desperately needed, particularly in the case of metastatic disease. A deeper understanding of collateral sensitivity, where the evolution of therapeutic resistance to one drug aligns with sensitivity to another drug, may improve our ability to effectively target this disease. For the first time in a solid tumor, we produced a temporal collateral sensitivity map that demonstrates the evolution of collateral sensitivity and resistance in EWS. We found that the evolution of collateral resistance was predictable with some drugs but had significant variation in response to other drugs. Using this map of temporal collateral sensitivity in EWS, we can see that the path toward collateral sensitivity is not always repeatable, nor is there always a clear trajectory toward resistance or sensitivity. Identifying transcriptomic changes that accompany these states of transient collateral sensitivity could improve treatment planning for patients with EWS.

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Ewing's sarcoma cell lines were evolved to become resistant to standard chemotherapy

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A temporal collateral sensitivity map shows response to alternative drugs over time

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Collateral drug response is repeatable in some instances and stochastic in others

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Differential gene expression elucidates potential biomarkers of drug response

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.

Resistance to targeted therapies as a multifactorial, gradual adaptation to inhibitor specific selective pressures

Despite high initial efficacy, targeted therapies eventually fail in advanced cancers, as tumors develop resistance and relapse. In contrast to the substantial body of research on the molecular mechanisms of resistance, understanding of how resistance evolves remains limited. Using an experimental model of ALK positive NSCLC, we explored the evolution of resistance to different clinical ALK inhibitors. We found that resistance can originate from heterogeneous, weakly resistant subpopulations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence for a hybrid scenario involving the gradual, multifactorial adaptation to the inhibitors through acquisition of multiple cooperating genetic and epigenetic adaptive changes. Additionally, we found that during this adaptation tumor cells might present unique, temporally restricted collateral sensitivities, absent in therapy naïve or fully resistant cells, suggesting the potential for new therapeutic interventions, directed against evolving resistance.

Acquired resistance to cancer therapies reflects the ability of cancers to adapt to therapy-imposed selective pressures. Here, the authors elucidate the dynamics of developing resistance to ALK inhibitors in an ALK+ lung cancer cell line showing that resistance originates from drug-specific tolerant cancer cells and it develops as a gradual adaptation.

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.

Reciprocal antibiotic collateral sensitivity in Burkholderia multivorans

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

Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

Recent clinical trials have shown that adaptive drug therapies can be more efficient than a standard cancer treatment based on a continuous use of maximum tolerated doses (MTD). The adaptive therapy paradigm is not based on a preset schedule; instead, the doses are administered based on the current state of tumour. But the adaptive treatment policies examined so far have been largely ad hoc. We propose a method for systematically optimizing adaptive policies based on an evolutionary game theory model of cancer dynamics. Given a set of treatment objectives, we use the framework of dynamic programming to find the optimal treatment strategies. In particular, we optimize the total drug usage and time to recovery by solving a Hamilton-Jacobi-Bellman equation. We compare MTD-based treatment strategy with optimal adaptive treatment policies and show that the latter can significantly decrease the total amount of drugs prescribed while also increasing the fraction of initial tumour states from which the recovery is possible. We conclude that the use of optimal control theory to improve adaptive policies is a promising concept in cancer treatment and should be integrated into clinical trial design.

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.

An uncertainty law for microbial evolution

Medical practice would benefit from a thorough understanding of constraints and uncertainty in microbial evolution. Higher order epistasis refers to unexpected effects of multiple mutations even if both single mutations and pairwise effects have been accounted for. Recent studies show that higher order epistasis is abundant in nature, for bacteria as well as higher organisms. However, the importance of higher order effects has been debated. It has been suggested that such effects cannot be interpreted, and should not be considered. Here, we show conclusively that higher order epistasis changes the adaptive prospects for a population. The conclusion is based on an exhaustive search of 193,270,310 hyper-cube graphs and applications of graph theory. Our results are more precise, yet more universal, than related research since they depend on mathematical theory, rather than sampling or simulations. Moreover, the uncertainty we establish for microbial evolution, due to higher order epistasis is not sensitive for detailed model assumptions, such as the baseline being additive or log-additive fitness.

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.

Molecular mechanisms of collateral sensitivity to the antibiotic nitrofurantoin

Antibiotic resistance increasingly limits the success of antibiotic treatments, and physicians require new ways to achieve efficient treatment despite resistance. Resistance mechanisms against a specific antibiotic class frequently confer increased susceptibility to other antibiotic classes, a phenomenon designated collateral sensitivity (CS). An informed switch of antibiotic may thus enable the efficient treatment of resistant strains. CS occurs in many pathogens, but the mechanisms that generate hypersusceptibility are largely unknown. We identified several molecular mechanisms of CS against the antibiotic nitrofurantoin (NIT). Mutants that are resistant against tigecycline (tetracycline), mecillinam (β-lactam), and protamine (antimicrobial peptide) all show CS against NIT. Their hypersusceptibility is explained by the overexpression of nitroreductase enzymes combined with increased drug uptake rates, or increased drug toxicity. Increased toxicity occurs through interference of the native drug-response system for NIT, the SOS response, with growth. A mechanistic understanding of CS will help to develop drug switches that combat resistance.

Resistance mechanisms against a specific antibiotic class frequently often confer negative cross-resistance to other antibiotic classes, a phenomenon known as collateral sensitivity. This study shows that collateral sensitivity in bacteria can be explained by a combination of several mechanisms that can be exploited to develop drug switches that combat resistance.

Antibiotic Resistance Evolution Is Contingent on the Quorum-Sensing Response in Pseudomonas aeruginosa

Different works have explored independently the evolution toward antibiotic resistance and the role of eco-adaptive mutations in the adaptation to a new habitat (as the infected host) of bacterial pathogens. However, knowledge about the connection between both processes is still limited. We address this issue by comparing the evolutionary trajectories toward antibiotic resistance of a Pseudomonas aeruginosa lasR defective mutant and its parental wild-type strain, when growing in presence of two ribosome-targeting antibiotics. Quorum-sensing lasR defective mutants are selected in P. aeruginosa populations causing chronic infections. Further, we observed they are also selected in vitro as a first adaptation for growing in culture medium. By using experimental evolution and whole-genome sequencing, we found that the evolutionary trajectories of P. aeruginosa in presence of these antibiotics are different in lasR defective and in wild-type backgrounds, both at the phenotypic and the genotypic levels. Recreation of a set of mutants in both genomic backgrounds (either wild type or lasR defective) allowed us to determine the existence of negative epistatic interactions between lasR and antibiotic resistance determinants. These epistatic interactions could lead to mutual contingency in the evolution of antibiotic resistance when P. aeruginosa colonizes a new habitat in presence of antibiotics. If lasR mutants are selected first, this would constraint antibiotic resistance evolution. Conversely, when resistance mutations (at least those studied in the present work) are selected, lasR mutants may not be selected in presence of antibiotics. These results underlie the importance of contingency and epistatic interactions in modulating antibiotic resistance evolution.

Assessment of Phenotype Microarray plates for rapid and high-throughput analysis of collateral sensitivity networks

The crisis of antimicrobial resistance is driving research into the phenomenon of collateral sensitivity. Sometimes, when a bacterium evolves resistance to one antimicrobial, it becomes sensitive to others. In this study, we have investigated the utility of Phenotype Microarray (PM) plates for identifying collateral sensitivities with unprecedented throughput. We assessed the relative resistance/sensitivity phenotypes of nine strains of Staphylococcus aureus (two laboratory strains and seven clinical isolates) towards the 72 antimicrobials contained in three PM plates. In general, the PM plates reported on resistance and sensitivity with a high degree of reproducibility. However, a rigorous comparison of PM growth phenotypes with minimum inhibitory concentration (MIC) measurements revealed a trade-off between throughput and accuracy. Small differences in PM growth phenotype did not necessarily correlate with changes in MIC. Thus, we conclude that PM plates are useful for the rapid and high-throughput assessment of large changes in collateral sensitivity phenotypes during the evolution of antimicrobial resistance, but more subtle examples of cross-resistance or collateral sensitivity cannot be identified reliably using this approach.

Model genotype-phenotype mappings and the algorithmic structure of evolution

Cancers are complex dynamic systems that undergo evolution and selection. Personalized medicine approaches in the clinic increasingly rely on predictions of tumour response to one or more therapies; these predictions are complicated by the inevitable evolution of the tumour. Despite enormous amounts of data on the mutational status of cancers and numerous therapies developed in recent decades to target these mutations, many of these treatments fail after a time due to the development of resistance in the tumour. The emergence of these resistant phenotypes is not easily predicted from genomic data, since the relationship between genotypes and phenotypes, termed the genotype-phenotype (GP) mapping, is neither injective nor functional. We present a review of models of this mapping within a generalized evolutionary framework that takes into account the relation between genotype, phenotype, environment and fitness. Different modelling approaches are described and compared, and many evolutionary results are shown to be conserved across studies despite using different underlying model systems. In addition, several areas for future work that remain understudied are identified, including plasticity and bet-hedging. The GP-mapping provides a pathway for understanding the potential routes of evolution taken by cancers, which will be necessary knowledge for improving personalized therapies.

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.

Evolutionary pathways to antibiotic resistance are dependent upon environmental structure and bacterial lifestyle

Bacterial populations vary in their stress tolerance and population structure depending upon whether growth occurs in well-mixed or structured environments. We hypothesized that evolution in biofilms would generate greater genetic diversity than well-mixed environments and lead to different pathways of antibiotic resistance. We used experimental evolution and whole genome sequencing to test how the biofilm lifestyle influenced the rate, genetic mechanisms, and pleiotropic effects of resistance to ciprofloxacin in Acinetobacter baumannii populations. Both evolutionary dynamics and the identities of mutations differed between lifestyle. Planktonic populations experienced selective sweeps of mutations including the primary topoisomerase drug targets, whereas biofilm-adapted populations acquired mutations in regulators of efflux pumps. An overall trade-off between fitness and resistance level emerged, wherein biofilm-adapted clones were less resistant than planktonic but more fit in the absence of drug. However, biofilm populations developed collateral sensitivity to cephalosporins, demonstrating the clinical relevance of lifestyle on the evolution of resistance.

Burkholderia multivorans Exhibits Antibiotic Collateral Sensitivity

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

Limited Evolutionary Conservation of the Phenotypic Effects of Antibiotic Resistance Mutations

Multidrug-resistant clinical isolates are common in certain pathogens, but rare in others. This pattern may be due to the fact that mutations shaping resistance have species-specific effects. To investigate this issue, we transferred a range of resistance-conferring mutations and a full resistance gene into Escherichia coli and closely related bacteria. We found that resistance mutations in one bacterial species frequently provide no resistance, in fact even yielding drug hypersensitivity in close relatives. In depth analysis of a key gene involved in aminoglycoside resistance (trkH) indicated that preexisting mutations in other genes-intergenic epistasis-underlie such extreme differences in mutational effects between species. Finally, reconstruction of adaptive landscapes under multiple antibiotic stresses revealed that mutations frequently provide multidrug resistance or elevated drug susceptibility (i.e., collateral sensitivity) only with certain combinations of other resistance mutations. We conclude that resistance and collateral sensitivity are contingent upon the genetic makeup of the bacterial population, and such contingency could shape the long-term fate of resistant bacteria. These results underlie the importance of species-specific treatment strategies.

The 2019 mathematical oncology roadmap

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology-defined here simply as the use of mathematics in cancer research-complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.