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

Conserved Evolutionary Trajectory Can Be Perturbed to Prevent Resistance Evolution under Norfloxacin Pressure by Forcing Mycobacterium smegmatis on Alternate Evolutionary Paths

Antibiotic resistance is a pressing health issue, with the emergence of resistance in bacteria outcompeting the discovery of novel drug candidates. While many studies have used Adaptive Laboratory Evolution (ALE) to understand the determinants of resistance, the influence of the drug dosing profile on the evolutionary trajectory remains understudied. In this study, we employed ALE on Mycobacterium smegmatis exposed to various concentrations of Norfloxacin using both cyclic constant and stepwise increasing drug dosages to examine their impact on the resistance mechanisms selected. Mutations in an efflux pump regulator, LfrR, were found in all of the evolved populations irrespective of the drug profile and population bottleneck, indicating a conserved efflux-based resistance mechanism. This mutation appeared early in the evolutionary trajectory, providing low-level resistance when present alone, with a further increase in resistance resulting from successive accumulation of other mutations. Notably, drug target mutations, similar to those observed in clinical isolates, were only seen above a threshold of greater than 4× the minimum inhibitory concentration (MIC). A combination of three mutations in the genes, lfrR, MSMEG_1959, and MSMEG_5045, was conserved across multiple lineages, leading to high-level resistance and preceding the appearance of drug target mutations. Interestingly, in populations evolved from parental strains lacking the lfrA efflux pump, the primary target of the lfrR regulator, no lfrR gene mutations are selected. Furthermore, evolutional trajectories originating from the ΔlfrA strain displayed early arrest in some lineages and the absence of target gene mutations in those that evolved, albeit delayed. Thus, blocking or inhibiting the expression of efflux pumps can arrest or delay the fixation of drug target mutations, potentially limiting the maximum attainable resistance levels.

Mutational robustness and the role of buffer genes in evolvability

Organisms rely on mutations to fuel adaptive evolution. However, many mutations impose a negative effect on fitness. Cells may have therefore evolved mechanisms that affect the phenotypic effects of mutations, thus conferring mutational robustness. Specifically, so-called buffer genes are hypothesized to interact directly or indirectly with genetic variation and reduce its effect on fitness. Environmental or genetic perturbations can change the interaction between buffer genes and genetic variation, thereby unmasking the genetic variation's phenotypic effects and thus providing a source of variation for natural selection to act on. This review provides an overview of our understanding of mutational robustness and buffer genes, with the chaperone gene HSP90 as a key example. It discusses whether buffer genes merely affect standing variation or also interact with de novo mutations, how mutational robustness could influence evolution, and whether mutational robustness might be an evolved trait or rather a mere side-effect of complex genetic interactions.

Environment-independent distribution of mutational effects emerges from microscopic epistasis

Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, where the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global-epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.

The evolution of diverse antimicrobial responses in vancomycin-intermediate Staphylococcus aureus and its therapeutic implications

Staphylococcus aureus causes endocarditis, osteomyelitis, and bacteremia. Clinicians often prescribe vancomycin as an empiric therapy to account for methicillin-resistant S. aureus (MRSA) and narrow treatment based on culture susceptibility results. However, these results reflect a single time point before empiric treatment and represent a limited subset of the total bacterial population within the patient. Thus, while they may indicate that the infection is susceptible to a particular drug, this recommendation may no longer be accurate during therapy. Here, we addressed how antibiotic susceptibility changes over time by accounting for evolution. We evolved 18 methicillin-susceptible S. aureus (MSSA) populations under increasing vancomycin concentrations until they reached intermediate resistance levels. Sequencing revealed parallel mutations that affect cell membrane stress response and cell-wall biosynthesis. The populations exhibited repeated cross-resistance to daptomycin and varied responses to meropenem, gentamicin, and nafcillin. We accounted for this variability by deriving likelihood estimates that express a population's probability of exhibiting a drug response following vancomycin treatment. Our results suggest antistaphylococcal penicillins are preferable first-line treatments for MSSA infections but also highlight the inherent uncertainty that evolution poses to effective therapies. Infections may take varied evolutionary paths; therefore, considering evolution as a probabilistic process should inform our therapeutic choices.

Functional characterization of two TolC in the resistance to drugs and metals and in the virulence of Riemerella anatipestifer

IMPORTANCE

Riemerella anatipestifer (RA) is a notorious duck pathogen, characterized by a multitude of serotypes that exhibit no cross-reaction with one another. Moreover, RA is resistant to various antibacterial agents. Consequently, understanding the mechanisms behind resistance and identifying potential targets for drug development have become pressing needs. In this study, we show that the two TolC proteins play a role in the resistance to different drugs and metals and in the virulence. The results suggest that TolCA has a wider range of efflux substrates than TolCB. Except for gentamicin, neither TolCA nor TolCB was involved in the efflux of the other tested antibiotics. Strikingly, TolCA but not TolCB enhanced the frequency of resistance-conferring mutations. Moreover, TolCA was involved in RA virulence. Given its conservation in RA, TolCA has potential as a drug target for the development of therapeutics against RA infections.

Peptide-based molecules for the disruption of bacterial Hsp70 chaperones

DnaK is a chaperone that aids in nascent protein folding and the maintenance of proteome stability across bacteria. Due to the importance of DnaK in cellular proteostasis, there have been efforts to generate molecules that modulate its function. In nature, both protein substrates and antimicrobial peptides interact with DnaK. However, many of these ligands interact with other cellular machinery as well. Recent work has sought to modify these peptide scaffolds to create DnaK-selective and species-specific probes. Others have reported protein domain mimics of interaction partners to disrupt cellular DnaK function and high-throughput screening approaches to discover clinically-relevant peptidomimetics that inhibit DnaK. The described work provides a foundation for the design of new assays and molecules to regulate DnaK activity.

Selection of Multi-Drug Targets against Drug-Resistant Mycobacterium tuberculosis XDR1219 Using the Hyperbolic Mapping of the Protein Interaction Network

Tuberculosis remains the leading cause of death from a single pathogen. On the other hand, antimicrobial resistance (AMR) makes it increasingly difficult to deal with this disease. We present the hyperbolic embedding of the Mycobacterium tuberculosis protein interaction network (mtbPIN) of resistant strain (MTB XDR1219) to determine the biological relevance of its latent geometry. In this hypermap, proteins with similar interacting partners occupy close positions. An analysis of the hypermap of available drug targets (DTs) and their direct and intermediate interactors was used to identify potentially useful drug combinations and drug targets. We identify rpsA and rpsL as close DTs targeted by different drugs (pyrazinamide and aminoglycosides, respectively) and propose that the combination of these drugs could have a synergistic effect. We also used the hypermap to explain the effects of drugs that affect multiple DTs, for example, forcing the bacteria to deal with multiple stresses like ethambutol, which affects the synthesis of both arabinogalactan and lipoarabinomannan. Our strategy uncovers novel potential DTs, such as dprE1 and dnaK proteins, which interact with two close DT pairs: arabinosyltransferases (embC and embB), Ser/Thr protein kinase (pknB) and RNA polymerase (rpoB), respectively. Our approach provides mechanistic explanations for existing drugs and suggests new DTs. This strategy can also be applied to the study of other resistant strains.

Recruitment of a Middling Promiscuous Enzyme Drives Adaptive Metabolic Evolution in Escherichia coli

A key step in metabolic pathway evolution is the recruitment of promiscuous enzymes to perform new functions. Despite the recognition that promiscuity is widespread in biology, factors dictating the preferential recruitment of one promiscuous enzyme over other candidates are unknown. Escherichia coli contains four sugar kinases that are candidates for recruitment when the native glucokinase machinery is deleted-allokinase (AlsK), manno(fructo)kinase (Mak), N-acetylmannosamine kinase (NanK), and N-acetylglucosamine kinase (NagK). The catalytic efficiencies of these enzymes are 103- to 105-fold lower than native glucokinases, ranging from 2,400 M-1 s-1 for the most active candidate, NagK, to 15 M-1 s-1 for the least active candidate, AlsK. To investigate the relationship between catalytic activities of promiscuous enzymes and their recruitment, we performed adaptive evolution of a glucokinase-deficient E. coli strain to restore glycolytic metabolism. We observed preferential recruitment of NanK via a trajectory involving early mutations that facilitate glucose uptake and amplify nanK transcription, followed by nonsynonymous substitutions in NanK that enhance the enzyme's promiscuous glucokinase activity. These substitutions reduced the native activity of NanK and reduced organismal fitness during growth on an N-acetylated carbon source, indicating that enzyme recruitment comes at a cost for growth on other substrates. Notably, the two most active candidates, NagK and Mak, were not recruited, suggesting that catalytic activity alone does not dictate evolutionary outcomes. The results highlight our lack of knowledge regarding biological drivers of enzyme recruitment and emphasize the need for a systems-wide approach to identify factors facilitating or constraining this important adaptive process.

Genomic alterations involved in fluoroquinolone resistance development in Staphylococcus aureus

AIM

Fluoroquinolone (FQ) is a potent antibiotic class. However, resistance to this class emerges quickly which hinders its application. In this study, mechanisms leading to the emergence of multidrug-resistant (MDR) Staphylococcus aureus (S. aureus) strains under FQ exposure were investigated.

METHODOLOGY

S. aureus ATCC 29213 was serially exposed to ciprofloxacin (CIP), ofloxacin (OFL), or levofloxacin (LEV) at sub-minimum inhibitory concentrations (sub-MICs) for 12 days to obtain S. aureus -1 strains and antibiotic-free cultured for another 10 days to obtain S. aureus-2 strains. The whole genome (WGS) and target sequencing were applied to analyze genomic alterations; and RT-qPCR was used to access the expressions of efflux-related genes, alternative sigma factors, and genes involved in FQ resistance.

RESULTS

A strong and irreversible increase of MICs was observed in all applied FQs (32 to 128 times) in all S. aureus-1 and remained 16 to 32 times in all S. aureus-2. WGS indicated 10 noticeable mutations occurring in all FQ-exposed S. aureus including 2 insdel mutations in SACOL0573 and rimI; a synonymous mutation in hslO; and 7 missense mutations located in an untranslated region. GrlA, was found mutated (R570H) in all S. aureus-1 and -2. Genes encoding for efflux pumps and their regulator (norA, norB, norC, and mgrA); alternative sigma factors (sigB and sigS); acetyltransferase (rimI); methicillin resistance (fmtB); and hypothetical protein BJI72_0645 were overexpressed in FQ-exposed strains.

CONCLUSION

The emergence of MDR S. aureus was associated with the mutations in the FQ-target sequences and the overexpression of efflux pump systems and their regulators.

Quantitative approaches to study phenotypic effects of large-scale genetic perturbations

How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.

Epistasis decreases with increasing antibiotic pressure but not temperature

Predicting mutational effects is essential for the control of antibiotic resistance (ABR). Predictions are difficult when there are strong genotype-by-environment (G × E), gene-by-gene (G × G or epistatic) or gene-by-gene-by-environment (G × G × E) interactions. We quantified G × G × E effects in Escherichia coli across environmental gradients. We created intergenic fitness landscapes using gene knock-outs and single-nucleotide ABR mutations previously identified to vary in the extent of G × E effects in our environments of interest. Then, we measured competitive fitness across a complete combinatorial set of temperature and antibiotic dosage gradients. In this way, we assessed the predictability of 15 fitness landscapes across 12 different but related environments. We found G × G interactions and rugged fitness landscapes in the absence of antibiotic, but as antibiotic concentration increased, the fitness effects of ABR genotypes quickly overshadowed those of gene knock-outs, and the landscapes became smoother. Our work reiterates that some single mutants, like those conferring resistance or susceptibility to antibiotics, have consistent effects across genetic backgrounds in stressful environments. Thus, although epistasis may reduce the predictability of evolution in benign environments, evolution may be more predictable in adverse environments. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

CYP broth: a tool for Yersinia pestis isolation in ancient culture collections and field samples

We developed a simple new selective LB-based medium, named CYP broth, suitable for recovering long-term stored Y. pestis subcultures and for isolation of Y. pestis strains from field-caught samples for the Plague surveillance. It aimed to inhibit the growth contaminating microorganisms and enrich Y. pestis growth through iron supplementation. The performance of CYP broth on microbial growth from different gram-negative and gram-positive strains from American Type Culture Collection (ATCC®) and other clinical isolates, field-caught rodent samples, and more importantly, on several vials of ancient Y. pestis subcultures was evaluated. Additionally, other pathogenic Yersinia species such as Y. pseudotuberculosis and Y. enterocolitica were also successfully isolated with CYP broth. Selectivity tests and bacterial growth performance on CYP broth (LB broth supplemented with Cefsulodine, Irgasan, Novobiocin, nystatin and ferrioxamine E) were evaluated in comparison with LB broth without additive; LB broth/CIN, LB broth/nystatin and with traditional agar media including LB agar without additive, and LB agar and Cefsulodin-Irgasan-Novobiocin Agar (CIN agar) supplemented with 50 µg/mL of nystatin. Of note, the CYP broth had a recovery twofold higher than those of the CIN supplemented media or other regular media. Additionally, selectivity tests and bacterial growth performance were also evaluated on CYP broth in the absence of ferrioxamine E. The cultures were incubated at 28 °C and visually inspected for microbiological growth analysis and O.D._625 nm measurement between 0 and 120 h. The presence and purity of Y. pestis growth were confirmed by bacteriophage and multiplex PCR tests. Altogether, CYP broth provides an enhanced growth of Y. pestis at 28 °C, while inhibiting contaminant microorganisms. The media is a simple, but powerful tool to improve the reactivation and decontamination of ancient Y. pestis culture collections and for the isolation of Y. pestis strains for the Plague surveillance from various backgrounds. KEY POINTS: • The newly described CYP broth improves the recuperation of ancient/contaminated Yersinia pestis culture collections • CYP broth was also efficient in reducing environmental contamination in field-capture samples, improving Y. pestis isolation • CYP broth can also be used for the isolation of Y. enterocolitica and Y. pseudotuberculosis.

Adaptive Laboratory Evolution of Microorganisms: Methodology and Application for Bioproduction

Adaptive laboratory evolution (ALE) is a useful experimental methodology for fundamental scientific research and industrial applications to create microbial cell factories. By using ALE, cells are adapted to the environment that researchers set based on their objectives through the serial transfer of cell populations in batch cultivations or continuous cultures and the fitness of the cells (i.e., cell growth) under such an environment increases. Then, omics analyses of the evolved mutants, including genome sequencing, transcriptome, proteome and metabolome analyses, are performed. It is expected that researchers can understand the evolutionary adaptation processes, and for industrial applications, researchers can create useful microorganisms that exhibit increased carbon source availability, stress tolerance, and production of target compounds based on omics analysis data. In this review article, the methodologies for ALE in microorganisms are introduced. Moreover, the application of ALE for the creation of useful microorganisms as cell factories has also been introduced.

MomL inhibits bacterial antibiotic resistance through the starvation stringent response pathway

Antibiotic resistance in gram-negative pathogens has become one of the most serious global public health threats. The role of the N-acyl homoserine lactone (AHL)-mediated signaling pathway, which is widespread in gram-negative bacteria, in the bacterial resistance process should be studied in depth. Here, we report a degrading enzyme of AHLs, MomL, that inhibits the antibiotic resistance of Pseudomonas aeruginosa through a novel mechanism. The MomL-mediated reactivation of kanamycin is highly associated with the relA-mediated starvation stringent response. The degradation of AHLs by MomL results in the inability of LasR to activate relA, which, in turn, stops the activation of downstream rpoS. Further results show that rpoS directly regulates the type VI secretion system H2-T6SS. Under MomL treatment, inactivated RpoS fails to regulate H2-T6SS; therefore, the expression of effector phospholipase A is reduced, and the adaptability of bacteria to antibiotics is weakened. MomL in combination with kanamycin is effective against a wide range of gram-negative pathogenic bacteria. Therefore, this study reports a MomL-antibiotic treatment strategy on antibiotic-resistant bacteria and reveals its mechanism of action.

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Adaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins enhancing the evolvability, has therefore attracted interest. However, our current understanding of how genes influence adaptation kinetics is limited, partly because accurately measuring adaptation for many cell populations is challenging. We used a high-throughput adaptive laboratory evolution platform to track the adaptation of >18,000 cell populations corresponding to single-gene deletion strains in the haploid yeast deletion collection. We report that the preadaptation fitness of gene knockouts near-perfectly (R2= 0.91) predicts their adaptation to arsenic, leaving at the most a marginal role for dedicated evolvability gene functions. We tracked the adaptation of another >23,000 gene knockout populations to a diverse range of selection pressures and generalized the almost perfect (R2=0.72-0.98) capacity of preadaptation fitness to predict adaptation. We also reconstructed mutations in FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wild-type cells, in gene deletions covering a broad fitness range and show that the predictability of arsenic adaptation can be understood as a by global epistasis, where excluding arsenic is more beneficial to arsenic unfit cells. The paucity of genes with a meaningful evolvability effect on adaptation diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

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

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

Pre-existing chromosomal polymorphisms in pathogenic E. coli potentiate the evolution of resistance to a last-resort antibiotic

Bacterial pathogens show high levels of chromosomal genetic diversity, but the influence of this diversity on the evolution of antibiotic resistance by plasmid acquisition remains unclear. Here, we address this problem in the context of colistin, a 'last line of defence' antibiotic. Using experimental evolution, we show that a plasmid carrying the MCR-1 colistin resistance gene dramatically increases the ability of Escherichia coli to evolve high-level colistin resistance by acquiring mutations in lpxC, an essential chromosomal gene involved in lipopolysaccharide biosynthesis. Crucially, lpxC mutations increase colistin resistance in the presence of the MCR-1 gene, but decrease the resistance of wild-type cells, revealing positive sign epistasis for antibiotic resistance between the chromosomal mutations and a mobile resistance gene. Analysis of public genomic datasets shows that lpxC polymorphisms are common in pathogenic E. coli, including those carrying MCR-1, highlighting the clinical relevance of this interaction. Importantly, lpxC diversity is high in pathogenic E. coli from regions with no history of MCR-1 acquisition, suggesting that pre-existing lpxC polymorphisms potentiated the evolution of high-level colistin resistance by MCR-1 acquisition. More broadly, these findings highlight the importance of standing genetic variation and plasmid/chromosomal interactions in the evolutionary dynamics of antibiotic resistance.

Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes

All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.

New Delhi Metallo-Beta-Lactamase Facilitates the Emergence of Cefiderocol Resistance in Enterobacter cloacae

Cefiderocol is a promising novel siderophore cephalosporin for the treatment of multidrug-resistant Gram-negative bacilli and with stability against degradation by metallo-β-lactamases. Nonetheless, the emergence of cefiderocol in metallo-β-lactamase-producing Enterobacterales during therapy has been reported on more than one occasion. To understand the underlying mechanisms and factors facilitating the resistance development, we conducted an in vitro evolution experiment using clinical E. cloacae isolates via serial passaging under cefiderocol pressure. In this study, we showed that the presence of the New Delhi metallo-β-lactamase (NDM) facilitates the emergence of resistance via nonsynonymous mutations of the CirA catecholate siderophore receptor. Inhibition of metallo-β-lactamase activity using dipicolinic acid prevented the emergence of cefiderocol-resistant mutants successfully. This finding implies that caution should be taken when using cefiderocol for the treatment of infections caused by metallo-β-lactamase-producing bacteria.

Revisiting Antibiotic Resistance: Mechanistic Foundations to Evolutionary Outlook

Antibiotics are the pivotal pillar of contemporary healthcare and have contributed towards its advancement over the decades. Antibiotic resistance emerged as a critical warning to public wellbeing because of unsuccessful management efforts. Resistance is a natural adaptive tool that offers selection pressure to bacteria, and hence cannot be stopped entirely but rather be slowed down. Antibiotic resistance mutations mostly diminish bacterial reproductive fitness in an environment without antibiotics; however, a fraction of resistant populations 'accidentally' emerge as the fittest and thrive in a specific environmental condition, thus favouring the origin of a successful resistant clone. Therefore, despite the time-to-time amendment of treatment regimens, antibiotic resistance has evolved relentlessly. According to the World Health Organization (WHO), we are rapidly approaching a 'post-antibiotic' era. The knowledge gap about antibiotic resistance and room for progress is evident and unified combating strategies to mitigate the inadvertent trends of resistance seem to be lacking. Hence, a comprehensive understanding of the genetic and evolutionary foundations of antibiotic resistance will be efficacious to implement policies to force-stop the emergence of resistant bacteria and treat already emerged ones. Prediction of possible evolutionary lineages of resistant bacteria could offer an unswerving impact in precision medicine. In this review, we will discuss the key molecular mechanisms of resistance development in clinical settings and their spontaneous evolution.

The population genetics of collateral resistance and sensitivity

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

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

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

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

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

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

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

Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana

Mutations in DNA sequences that bind transcription factors and thus modulate gene expression are a source of adaptive variation in gene expression. To understand how transcription factor binding sequences evolve in natural populations of the thale cress Arabidopsis thaliana, we integrated genomic polymorphism data for loci bound by transcription factors with in vitro data on binding affinity for these transcription factors. Specifically, we studied 19 different transcription factors, and the allele frequencies of 8,333 genomic loci bound in vivo by these transcription factors in 1,135 A. thaliana accessions. We find that transcription factor binding sequences show very low genetic diversity, suggesting that they are subject to purifying selection. High frequency alleles of such binding sequences tend to bind transcription factors strongly. Conversely, alleles that are absent from the population tend to bind them weakly. In addition, alleles with high frequencies also tend to be the endpoints of many accessible evolutionary paths leading to these alleles. We show that both high affinity and high evolutionary accessibility contribute to high allele frequency for at least some transcription factors. Although binding sequences with stronger affinity are more frequent, we did not find them to be associated with higher gene expression levels. Epistatic interactions among individual mutations that alter binding affinity are pervasive and can help explain variation in accessibility among binding sequences. In summary, combining in vitro binding affinity data with in vivo binding sequence data can help understand the forces that affect the evolution of transcription factor binding sequences in natural populations.

An allosteric inhibitor of bacterial Hsp70 chaperone potentiates antibiotics and mitigates resistance

DnaK is the bacterial homolog of Hsp70, an ATP-dependent chaperone that helps cofactor proteins to catalyze nascent protein folding and salvage misfolded proteins. In the pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), DnaK and its cofactors are proposed antimycobacterial targets, yet few small-molecule inhibitors or probes exist for these families of proteins. Here, we describe the repurposing of a drug called telaprevir that is able to allosterically inhibit the ATPase activity of DnaK and to prevent chaperone function by mimicking peptide substrates. In mycobacterial cells, telaprevir disrupts DnaK- and cofactor-mediated cellular proteostasis, resulting in enhanced efficacy of aminoglycoside antibiotics and reduced resistance to the frontline TB drug rifampin. Hence, this work contributes to a small but growing collection of protein chaperone inhibitors, and it demonstrates that these molecules disrupt bacterial mechanisms of survival in the presence of different antibiotic classes.

Defence by duplication: The relation between phenotypic glyphosate resistance and EPSPS gene copy number variation in Amaranthus palmeri

Gene copy number variation (CNV) has been increasingly associated with organismal responses to environmental stress, but we know little about the quantitative relation between CNV and phenotypic variation. In this study we quantify the relation between variation in EPSPS (5-enolpyruvylshikimate-3-phosphate synthase) copy number using digital drop PCR and variation in phenotypic glyphosate resistance in 22 populations of Amaranthus palmeri (Palmer Amaranth), a range-expanding agricultural weed. Overall, we detected a significant positive relation between population mean copy number and resistance. The majority of populations exhibited high glyphosate resistance yet maintained low-resistance individuals, resulting in bimodality in many populations. We also investigated threshold models for the relation between copy number and resistance, and found evidence for a threshold of ~15 EPSPS copies: there was a steep increase in resistance below the threshold, followed by a much shallower increase. Across 924 individuals, as copy number increased the range of variation in resistance decreased, yielding an increasing frequency of high phenotypic resistance individuals. Among populations we detected a decline in variation (s.d.) as mean phenotypic resistance increased from moderate to high, consistent with the prediction that as phenotypic resistance increases in populations, stabilizing selection decreases variation in the trait. Our study demonstrates that populations of A. palmeri can harbour wide variation in EPSPS copy number and phenotypic glyphosate resistance, reflecting the history of, and template for future, resistance evolution.

Quantitative biology of survival under antibiotic treatments

The mathematical formulation for the dynamics of growth reduction and/or killing under antibiotic treatments has a long history. Even before the extensive use of antibiotics, attempts to model the killing dynamics of biocides were made [1]. Here, we review relatively simple quantitative formulations of the two main modes of survival under antibiotics, resistance and tolerance, as well as their heterogeneity in bacterial populations. We focus on the two main types of heterogeneity that have been described: heteroresistance and antibiotic persistence, each linked to the variation in a different parameter of the antibiotic response dynamics. Finally, we review the effects on survival of combining resistance and tolerance mutations as well as on the mode and tempo of evolution under antibiotic treatments.

The roles of history, chance, and natural selection in the evolution of antibiotic resistance

History, chance, and selection are the fundamental factors that drive and constrain evolution. We designed evolution experiments to disentangle and quantify effects of these forces on the evolution of antibiotic resistance. Previously, we showed that selection of the pathogen Acinetobacter baumannii in both structured and unstructured environments containing the antibiotic ciprofloxacin produced distinct genotypes and phenotypes, with lower resistance in biofilms as well as collateral sensitivity to β-lactam drugs (Santos-Lopez et al., 2019). Here we study how this prior history influences subsequent evolution in new β-lactam antibiotics. Selection was imposed by increasing concentrations of ceftazidime and imipenem and chance differences arose as random mutations among replicate populations. The effects of history were reduced by increasingly strong selection in new drugs, but not erased, at times revealing important contingencies. A history of selection in structured environments constrained resistance to new drugs and led to frequent loss of resistance to the initial drug by genetic reversions and not compensatory mutations. This research demonstrates that despite strong selective pressures of antibiotics leading to genetic parallelism, history can etch potential vulnerabilities to orthogonal drugs.

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.

Development of antibacterial compounds that constrain evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here, we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC₅₀ 50–75 µM against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3-resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC₅₀) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which constrain evolutionary escape routes in pathogens.

The AcrAB-TolC Efflux Pump Impacts Persistence and Resistance Development in Stationary-Phase Escherichia coli following Delafloxacin Treatment

Bacteria have a repertoire of strategies to overcome antibiotics in clinical use, complicating our ability to treat and cure infectious diseases. In addition to evolving resistance, bacteria within genetically clonal cultures can undergo transient phenotypic changes and tolerate high doses of antibiotics. These cells, termed persisters, exhibit heterogeneous phenotypes; the strategies that a bacterial population deploys to overcome one class of antibiotics can be distinct from those needed to survive treatment with drugs with another mode of action. It was previously reported that fluoroquinolones, which target DNA topoisomerases, retain the capacity to kill nongrowing bacteria that tolerate other classes of antibiotics. Here, we show that in Escherichia coli stationary-phase cultures and colony biofilms, persisters that survive treatment with the anionic fluoroquinolone delafloxacin depend on the AcrAB-TolC efflux pump. In contrast, we did not detect this dependence on AcrAB-TolC in E. coli persisters that survive treatment with three other fluoroquinolone compounds. We found that the loss of AcrAB-TolC activity via genetic mutations or chemical inhibition not only reduces delafloxacin persistence in nongrowing E. coli MG1655 or EDL933 (an E. coli O157:H7 strain), but it limits resistance development in progenies derived from delafloxacin persisters that were given the opportunity to recover in nutritive medium following antibiotic treatment. Our findings highlight the heterogeneity in defense mechanisms that persisters use to overcome different compounds within the same class of antibiotics. They further indicate that efflux pump inhibitors can potentiate the activity of delafloxacin against stationary-phase E. coli and block resistance development in delafloxacin persister progenies.

Knowledge and Awareness of Physicians About Rational Antibiotic Use and Antimicrobial Resistance Before and After Graduation: A Cross-Sectional Study Conducted in Malatya Province in Turkey

BACKGROUND

We aimed to evaluate the level of theoretical knowledge of rational antibiotic use and awareness about emergence of multidrug resistance (MDR) among the senior students at a medical school and the actively-working family physicians.

METHODS

This cross-sectional research was carried out between 1 February and 30 April 2019 in Malatya province. Two-hundred twenty-five senior students in the Inonu University Medical School (Group 1) and 230 actively-working family physicians in Malatya primary health care services who were found to be eligible (Group 2) were included in this study. Power analysiscalculated the minimum of 240 participants with a proportion difference of 0.18 between the groups, a type I error of 0.05 and a type II error of 0.20. A questionnaire including seven sections and 28 questions was applied to the participants.

RESULTS

Doctors were more hesitant in pre-graduation and more self-confident in the post-graduation period for the decision to start antibiotic treatment. In addition, doctors forget their theoretical knowledge of antibiotics over time and are unable to follow current developments after graduation. The most important concern in the pre-graduation period was the choice of antibiotics from the wrong group, while in the post-graduation period it is the fear of the presence of unproven infection.

CONCLUSION

Physicians' antibiotic prescribing habits, attitudes and behaviors vary before and after graduation. Sustainable education for antibiotic use for physicians after graduation can contribute positively to reduce of antimicrobial resistance rates and to increase awareness about the use of rational antibiotics.

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.

The DnaK Chaperone System Buffers the Fitness Cost of Antibiotic Resistance Mutations in Mycobacteria

Chaperones aid in protein folding and maintenance of protein integrity. In doing so, they have the unique ability to directly stabilize resistance-conferring amino acid substitutions in drug targets and to counter the stress imparted by these substitutions, thus supporting heritable antimicrobial resistance (AMR). We asked whether chaperones support AMR in Mycobacterium smegmatis, a saprophytic model of Mycobacterium tuberculosis, the causative agent of tuberculosis (TB). We show that DnaK associates with many drug targets and that DnaK associates more with AMR-conferring mutant RNA polymerase (RNAP) than with wild-type RNAP. In addition, frequency-of-resistance (FOR) and fitness studies reveal that the DnaK system of chaperones supports AMR in antimicrobial targets in mycobacteria, including RNAP and the ribosome. These findings highlight chaperones as potential targets for drugs to overcome AMR in mycobacteria, including M. tuberculosis, as well as in other pathogens.IMPORTANCE AMR is a global problem, especially for TB. Here, we show that mycobacterial chaperones support AMR in M. smegmatis, a nonpathogenic model of M. tuberculosis, the causative agent of TB. In particular, the mycobacterial DnaK system of chaperones supports AMR in the antimicrobial targets RNA polymerase and the ribosome. This is the first report showing a role for protein chaperones in mediating AMR in mycobacteria. Given the widespread role of protein chaperones in enabling genomic diversity, we anticipate that our findings can be extended to other microbes.

Digital Insights Into Nucleotide Metabolism and Antibiotic Treatment Failure

Nucleotide metabolism plays a central role in bacterial physiology, producing the nucleic acids necessary for DNA replication and RNA transcription. Recent studies demonstrate that nucleotide metabolism also proactively contributes to antibiotic-induced lethality in bacterial pathogens and that disruptions to nucleotide metabolism contributes to antibiotic treatment failure in the clinic. As antimicrobial resistance continues to grow unchecked, new approaches are needed to study the molecular mechanisms responsible for antibiotic efficacy. Here we review emerging technologies poised to transform understanding into why antibiotics may fail in the clinic. We discuss how these technologies led to the discovery that nucleotide metabolism regulates antibiotic drug responses and why these are relevant to human infections. We highlight opportunities for how studies into nucleotide metabolism may enhance understanding of antibiotic failure mechanisms.

Antimicrobial Resistance in Escherichia coli Strains Isolated from Humans and Pet Animals

Throughout scientific literature, we can find evidence that antimicrobial resistance has become a big problem in the recent years on a global scale. Public healthcare systems all over the world are faced with a great challenge in this respect. Obviously, there are many bacteria that can cause infections in humans and animals alike, but somehow it seems that the greatest threat nowadays comes from the Enterobacteriaceae members, especially Escherichia coli. Namely, we are witnesses to the fact that the systems that these bacteria developed to fight off antibiotics are the strongest and most diverse in Enterobacteriaceae. Our great advantage is in understanding the systems that bacteria developed to fight off antibiotics, so these can help us understand the connection between these microorganisms and the occurrence of antibiotic-resistance both in humans and their pets. Furthermore, unfavorable conditions related to the ease of E. coli transmission via the fecal-oral route among humans, environmental sources, and animals only add to the problem. For all the above stated reasons, it is evident that the epidemiology of E. coli strains and resistance mechanisms they have developed over time are extremely significant topics and all scientific findings in this area will be of vital importance in the fight against infections caused by these bacteria.

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.