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

Genome and transcriptomic analysis of the adaptation of Escherichia coli to environmental stresses

In natural niches, bacteria are forced to spend most of their lives under various environmental stresses, such as nutrient limitation, heavy metal pollution, heat and antibiotic stress. To cope with adverse environments, bacterial genome can during the life cycle, produce potential adaptive mutants. The genomic changes, especially mutations, in the genes that encode RNA polymerase and transcription factors, might lead to variations in the transcriptome. These variations enable bacteria to cope with environmental stresses through physiological adaptation in response to stress. This paper reviews the recent contributions of genomic and transcriptomic analyses in understanding the adaption mechanism of Escherichia coli to environmental stresses. Various genomic changes have been observed in E. coli strains in laboratory or under natural stresses, including starvation, heavy metals, acidic conditions, heat shock and antibiotics. The mutations include slight changes (one to several nucleotides), deletions, insertions, chromosomal rearrangements and variations in copy numbers. The transcriptome of E. coli largely changes due to genomic mutations. However, the transcriptional profiles vary due to variations in stress selections. Cellular adaptation to the selections is associated with transcriptional changes resulting from genomic mutations. Changes in genome and transcriptome are cooperative and jointly affect the adaptation of E. coli to different environments. This comprehensive review reveals that coordination of genome mutations and transcriptional variations needs to be explored further to provide a better understanding of the mechanisms of bacterial adaptation to stresses.

In vitro development of resistance against antipseudomonal agents: comparison of novel β-lactam/β-lactamase inhibitor combinations and other β-lactam agents

We subjected seven P. aeruginosa isolates to a 10-day serial passaging against five antipseudomonal agents to evaluate resistance levels post-exposure and putative resistance mechanisms in terminal mutants were analyzed by whole-genome sequencing analysis. Meropenem (mean, 38-fold increase), cefepime (14.4-fold), and piperacillin-tazobactam (52.9-fold) terminal mutants displayed high minimum inhibitory concentration (MIC) values compared to those obtained after exposure to ceftolozane-tazobactam (11.4-fold) and ceftazidime-avibactam (5.7-fold). Fewer isolates developed elevated MIC values for other β-lactams and agents belonging to other classes when exposed to meropenem in comparison to other agents. Alterations in nalC and nalD, involved in the upregulation of the efflux pump system MexAB-OprM, were common and observed more frequently in isolates exposed to ceftazidime-avibactam and meropenem. These alterations, along with ones in mexR and amrR, provided resistance to most β-lactams and levofloxacin but not imipenem. The second most common gene altered was mpl, which is involved in the recycling of the cell wall peptidoglycan. These alterations were mainly noted in isolates exposed to ceftolozane-tazobactam and piperacillin-tazobactam but also in one cefepime-exposed isolate. Alterations in other genes known to be involved in β-lactam resistance (ftsI, oprD, phoP, pepA, and cplA) and multiple genes involved in lipopolysaccharide biosynthesis were also present. The data generated here suggest that there is a difference in the mechanisms selected for high-level resistance between newer β-lactam/β-lactamase inhibitor combinations and older agents. Nevertheless, the isolates exposed to all agents displayed elevated MIC values for other β-lactams (except imipenem) and quinolones tested mainly due to alterations in the MexAB-OprM regulators that extrude these agents.

Determinants of rapid adaptation in species with large variance in offspring production

The speed of population adaptation to changing biotic and abiotic environments is determined by the interaction between genetic drift, positive selection and linkage effects. Many marine species (fish, crustaceans), invertebrates and pathogens of humans and crops, exhibit sweepstakes reproduction characterized by the production of a very large amount of offspring (fecundity phase) from which only a small fraction may survive to the next generation (viability phase). Using stochastic simulations, we investigate whether the occurrence of sweepstakes reproduction affects the efficiency of a positively selected unlinked locus, and thus, the speed of adaptation since fecundity and/or viability have distinguishable consequences on mutation rate, probability and fixation time of advantageous alleles. We observe that the mean number of mutations at the next generation is always the function of the population size, but the variance increases with stronger sweepstakes reproduction when mutations occur in the parents. On the one hand, stronger sweepstakes reproduction magnifies the effect of genetic drift thus increasing the probability of fixation of neutral allele and decreasing that of selected alleles. On the other hand, the time to fixation of advantageous (as well as neutral) alleles is shortened by stronger sweepstakes reproduction. Importantly, fecundity and viability selection exhibit different probabilities and times to fixation of advantageous alleles under intermediate and weak sweepstakes reproduction. Finally, alleles under both strong fecundity and viability selection display a synergistic efficiency of selection. We conclude that measuring and modelling accurately fecundity and/or viability selection are crucial to predict the adaptive potential of species with sweepstakes reproduction.

Rational Design and Antimicrobial Potency Assessment of Abaecin Analogues

The widespread and escalating emergence of multidrug resistance is now recognized as one of the most severe global threats to human health. To address the urgent issue of drug-resistant bacteria and the limitation of effective clinical treatments, antimicrobial peptides (AMPs) have been developed as promising substituents of conventional antibiotics. In this study, rational design strategies were employed to acquire seven cationic and α-helical engineered peptides based on the original template of Abaecin. After investigation, we found that AC7 (LLRRWKKLFKKIIRWPRPLPNPGH) demonstrated potent and broad-spectrum antimicrobial activity. Additionally, it demonstrated low cytotoxicity and hemolysis while maintaining good stability. Notably, AC7 displays the antibacterial mechanism with superior abilities in cell membrane disruption and potential DNA binding in vitro, as well as effectively disrupting biofilms. Moreover, the murine skin wound model infected with drug-resistant Pseudomonas aeruginosa was employed to evaluate the anti-infective efficacy and therapeutic potential of AC7. It was observed that AC7 displays a remarkable capacity to inhibit wound colonization, reduce levels of inflammatory cytokines (TNF-α) and inflammatory cells (white blood cells (WBC), monocytes (MONO), lymphocytes (LYMPH), neutrophils (GRAN)), promote the levels of IL-10 and VEGF, and enhance wound healing. Overall, these findings demonstrate the potential of AC7 as a viable alternative to traditional antibiotics.

Mutators can drive the evolution of multi-resistance to antibiotics

Antibiotic combination therapies are an approach used to counter the evolution of resistance; their purported benefit is they can stop the successive emergence of independent resistance mutations in the same genome. Here, we show that bacterial populations with 'mutators', organisms with defects in DNA repair, readily evolve resistance to combination antibiotic treatment when there is a delay in reaching inhibitory concentrations of antibiotic-under conditions where purely wild-type populations cannot. In populations of Escherichia coli subjected to combination treatment, we detected a diverse array of acquired mutations, including multiple alleles in the canonical targets of resistance for the two drugs, as well as mutations in multi-drug efflux pumps and genes involved in DNA replication and repair. Unexpectedly, mutators not only allowed multi-resistance to evolve under combination treatment where it was favoured, but also under single-drug treatments. Using simulations, we show that the increase in mutation rate of the two canonical resistance targets is sufficient to permit multi-resistance evolution in both single-drug and combination treatments. Under both conditions, the mutator allele swept to fixation through hitch-hiking with single-drug resistance, enabling subsequent resistance mutations to emerge. Ultimately, our results suggest that mutators may hinder the utility of combination therapy when mutators are present. Additionally, by raising the rates of genetic mutation, selection for multi-resistance may have the unwanted side-effect of increasing the potential to evolve resistance to future antibiotic treatments.

Antibacterial activity of metal-phenanthroline complexes against multidrug-resistant Irish clinical isolates: a whole genome sequencing approach

Antimicrobial resistance (AMR) is one of the serious global health challenges of our time. There is now an urgent need to develop novel therapeutic agents that can overcome AMR, preferably through alternative mechanistic pathways from conventional treatments. The antibacterial activity of metal complexes (metal = Cu(II), Mn(II), and Ag(I)) incorporating 1,10-phenanthroline (phen) and various dianionic dicarboxylate ligands, along with their simple metal salt and dicarboxylic acid precursors, against common AMR pathogens were investigated. Overall, the highest level of antibacterial activity was evident in compounds that incorporate the phen ligand compared to the activities of their simple salt and dicarboxylic acid precursors. The chelates incorporating both phen and the dianion of 3,6,9-trioxaundecanedioic acid (tdda) were the most effective, and the activity varied depending on the metal centre. Whole-genome sequencing (WGS) was carried out on the reference Pseudomonas aeruginosa strain, PAO1. This strain was exposed to sub-lethal doses of lead metal-tdda-phen complexes to form mutants with induced resistance properties with the aim of elucidating their mechanism of action. Various mutations were detected in the mutant P. aeruginosa genome, causing amino acid changes to proteins involved in cellular respiration, the polyamine biosynthetic pathway, and virulence mechanisms. This study provides insights into acquired resistance mechanisms of pathogenic organisms exposed to Cu(II), Mn(II), and Ag(I) complexes incorporating phen with tdda and warrants further development of these potential complexes as alternative clinical therapeutic drugs to treat AMR infections.

The Effects of Antibiotic Combination Treatments on Pseudomonas aeruginosa Tolerance Evolution and Coexistence with Stenotrophomonas maltophilia

The Pseudomonas aeruginosa bacterium is a common pathogen of cystic fibrosis (CF) patients due to its ability to evolve resistance to antibiotics during treatments. While P. aeruginosa resistance evolution is well-characterized in monocultures, it is less well-understood in polymicrobial CF infections. Here, we investigated how exposure to ciprofloxacin, colistin, or tobramycin antibiotics, administered at sub-minimum inhibitory concentration (MIC) doses, both alone and in combination, shaped the tolerance evolution of P. aeruginosa (PAO1 lab and clinical CF LESB58 strains) in the absence and presence of a commonly co-occurring species, Stenotrophomonas maltophilia. The increases in antibiotic tolerances were primarily driven by the presence of that antibiotic in the treatment. We observed a reciprocal cross-tolerance between ciprofloxacin and tobramycin, and, when combined, the selected antibiotics increased the MICs for all of the antibiotics. Though the presence of S. maltophilia did not affect the tolerance or the MIC evolution, it drove P. aeruginosa into extinction more frequently in the presence of tobramycin due to its relatively greater innate tobramycin tolerance. In contrast, P. aeruginosa dominated and drove S. maltophilia extinct in most other treatments. Together, our findings suggest that besides driving high-level antibiotic tolerance evolution, sub-MIC antibiotic exposure can alter competitive bacterial interactions, leading to target pathogen extinctions in multispecies communities. IMPORTANCE Cystic fibrosis (CF) is a genetic condition that results in thick mucus secretions in the lungs that are susceptible to chronic bacterial infections. The bacterial pathogen Pseudomonas aeruginosa is often associated with morbidity in CF and is difficult to treat due to its high resistance to antibiotics. The resistance evolution of Pseudomonas aeruginosa is poorly understood in polymicrobial infections that are typical of CF. To study this, we exposed P. aeruginosa to sublethal concentrations of ciprofloxacin, colistin, or tobramycin antibiotics in the absence and presence of a commonly co-occurring CF species, Stenotrophomonas maltophilia. We found that low-level antibiotic concentrations selected for high-level antibiotic resistance. While P. aeruginosa dominated in most antibiotic treatments, S. maltophilia drove it into extinction in the presence of tobramycin due to an innately higher tobramycin resistance. Our findings suggest that, besides driving high-level antibiotic tolerance evolution, sublethal antibiotic exposure can magnify competition in bacterial communities, which can lead to target pathogen extinctions in multispecies communities.

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.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

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

Evolution under low antibiotic concentrations: a risk for the selection of Pseudomonas aeruginosa multidrug-resistant mutants in nature

Antibiotic pollution of non-clinical environments might have a relevant impact on human health if resistant pathogens are selected. However, this potential risk is often overlooked, since drug concentrations in nature are usually below their minimal inhibitory concentrations (MICs). Albeit, antibiotic resistant bacteria can be selected even at sub-MIC concentrations, in a range known as the sub-MIC selective window. Using short-term evolution experiments, we have determined the sub-MIC selective windows of the opportunistic pathogen Pseudomonas aeruginosa for seven antibiotics of clinical relevance, finding the ones of quinolones to be the widest, and the ones of polymyxin B and imipenem, the narrowest. Clinically relevant multidrug-resistant mutants arose within the sub-MIC selective windows of most antibiotics tested, being some of these phenotypes mediated by efflux pumps' activity. The fact that the concentration of antibiotics reported in aquatic ecosystems - colonizable by P. aeruginosa - are, in occasions, higher than the ones that select multidrug-resistant mutants in our assays, has implications for understanding the role of different ecosystems and conditions in the emergence of antibiotic resistance from a One-Health perspective. Further, it reinforces the importance of procuring accurate information on the sub-MIC selective windows for drugs of clinical value in pathogens with environmental niches.

Targeting Polyphosphate Kinases in the Fight against Pseudomonas aeruginosa

Polyphosphate (polyP) is a universally conserved molecule that plays critical roles in managing bacterial stress responses, in addition to biofilm formation and virulence. The enzymes that make polyphosphate molecules are called polyphosphate kinases (PPKs). Since these enzymes are not conserved in higher eukaryotes, PPKs make excellent therapeutic targets. In a recent paper in mBio, Neville and colleagues described gallein, a commercially available G-protein antagonist, as a novel dual-specificity inhibitor against two families of PPK enzymes in Pseudomonas aeruginosa. In this commentary, we discuss the impact of this discovery, outline potential challenges of implementing gallein use in the clinic, and describe how gallein will serve as a fantastic new tool to further fundamental PPK and polyP research in bacteria.

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.

Coming from the Wild: Multidrug Resistant Opportunistic Pathogens Presenting a Primary, Not Human-Linked, Environmental Habitat

The use and misuse of antibiotics have made antibiotic-resistant bacteria widespread nowadays, constituting one of the most relevant challenges for human health at present. Among these bacteria, opportunistic pathogens with an environmental, non-clinical, primary habitat stand as an increasing matter of concern at hospitals. These organisms usually present low susceptibility to antibiotics currently used for therapy. They are also proficient in acquiring increased resistance levels, a situation that limits the therapeutic options for treating the infections they cause. In this article, we analyse the most predominant opportunistic pathogens with an environmental origin, focusing on the mechanisms of antibiotic resistance they present. Further, we discuss the functions, beyond antibiotic resistance, that these determinants may have in the natural ecosystems that these bacteria usually colonize. Given the capacity of these organisms for colonizing different habitats, from clinical settings to natural environments, and for infecting different hosts, from plants to humans, deciphering their population structure, their mechanisms of resistance and the role that these mechanisms may play in natural ecosystems is of relevance for understanding the dissemination of antibiotic resistance under a One-Health point of view.

Genomic and Phenotypic Evolution of Achromobacter xylosoxidans during Chronic Airway Infections of Patients with Cystic Fibrosis

Bacterial pathogens evolve during chronic colonization of the human host by selection for pathoadaptive mutations. One of the emerging and understudied bacterial species causing chronic airway infections in patients with cystic fibrosis (CF) is Achromobacter xylosoxidans. It can establish chronic infections in patients with CF, but the genetic and phenotypic changes associated with adaptation during these infections are not completely understood. In this study, we analyzed the whole-genome sequences of 55 clinical A. xylosoxidans isolates longitudinally collected from the sputum of 6 patients with CF. Four genes encoding regulatory proteins and two intergenic regions showed convergent evolution, likely driven by positive selection for pathoadaptive mutations, across the different clones of A. xylosoxidans. Most of the evolved isolates had lower swimming motility and were resistant to multiple classes of antibiotics, while fewer of the evolved isolates had slower growth or higher biofilm production than the first isolates. Using a genome-wide association study method, we identified several putative genetic determinants of biofilm formation, motility and β-lactam resistance in this pathogen. With respect to antibiotic resistance, we discovered that a combination of mutations in pathoadaptive genes (phoQ and bigR) and two other genes encoding regulatory proteins (spoT and cpxA) were associated with increased resistance to meropenem and ceftazidime. Altogether, our results suggest that genetic changes within regulatory loci facilitate within-host adaptation of A. xylosoxidans and the emergence of adaptive phenotypes, such as antibiotic resistance or biofilm formation. IMPORTANCE A thorough understanding of bacterial pathogen adaptation is essential for the treatment of chronic bacterial infections. One unique challenge in the analysis and interpretation of genomics data is identifying the functional impact of mutations accumulated in the bacterial genome during colonization in the human host. Here, we investigated the genomic and phenotypic evolution of A. xylosoxidans in chronic airway infections of patients with CF and identified several mutations associated with the phenotypic evolution of this pathogen using genome-wide associations. Identification of phenotypes under positive selection and the associated mutations can enlighten the adaptive processes of this emerging pathogen in human infections and pave the way for novel therapeutic interventions.

Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance

During antibiotic treatment, the evolution of bacterial pathogens is fundamentally affected by bottlenecks and varying selection levels imposed by the drugs. Bottlenecks-that is, reductions in bacterial population size-lead to an increased influence of random effects (genetic drift) during bacterial evolution, and varying antibiotic concentrations during treatment may favour distinct resistance variants. Both aspects influence the process of bacterial evolution during antibiotic therapy and thereby treatment outcome. Surprisingly, the joint influence of these interconnected factors on the evolution of antibiotic resistance remains largely unexplored. Here we combine evolution experiments with genomic and genetic analyses to demonstrate that bottleneck size and antibiotic-induced selection reproducibly impact the evolutionary path to resistance in pathogenic Pseudomonas aeruginosa, one of the most problematic opportunistic human pathogens. Resistance is favoured-expectedly-under high antibiotic selection and weak bottlenecks, but-unexpectedly-also under low antibiotic selection and severe bottlenecks. The latter is likely to result from a reduced probability of losing favourable variants through drift under weak selection. Moreover, the absence of high resistance under low selection and weak bottlenecks is caused by the spread of low-resistance variants with high competitive fitness under these conditions. We conclude that bottlenecks, in combination with drug-induced selection, are currently neglected key determinants of pathogen evolution and outcome of antibiotic treatment.