Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

Rapid evolution of colistin resistance in a bioreactor model of infection of Klebsiella pneumoniae

Colistin remains an important antibiotic for the therapeutic management of drug-resistant Klebsiella pneumoniae. Despite the numerous reports of colistin resistance in clinical strains, it remains unclear exactly when and how different mutational events arise resulting in reduced colistin susceptibility. Using a bioreactor model of infection, we modelled the emergence of colistin resistance in a susceptible isolate of K. pneumoniae. Genotypic, phenotypic and mathematical analyses of the antibiotic-challenged and un-challenged population indicates that after an initial decline, the population recovers within 24 h due to a small number of "founder cells" which have single point mutations mainly in the regulatory genes encoding crrB and pmrB that when mutated results in up to 100-fold reduction in colistin susceptibility. Our work underlines the rapid development of colistin resistance during treatment or exposure of susceptible K. pneumoniae infections having implications for the use of cationic antimicrobial peptides as a monotherapy.

Beta-lactamase dependent and independent evolutionary paths to high-level ampicillin resistance

The incidence of beta-lactam resistance among clinical isolates is a major health concern. A key method to study the emergence of antibiotic resistance is adaptive laboratory evolution. However, in the case of the beta-lactam ampicillin, bacteria evolved in laboratory settings do not recapitulate clinical-like resistance levels, hindering efforts to identify major evolutionary paths and their dependency on genetic background. Here, we used the Microbial Evolution and Growth Arena (MEGA) plate to select ampicillin-resistant Escherichia coli mutants with varying degrees of resistance. Whole-genome sequencing of resistant isolates revealed that ampicillin resistance was acquired via a combination of single-point mutations and amplification of the gene encoding beta-lactamase AmpC. However, blocking AmpC-mediated resistance revealed latent adaptive pathways: strains deleted for ampC were able to adapt through combinations of changes in genes involved in multidrug resistance encoding efflux pumps, transcriptional regulators, and porins. Our results reveal that combinations of distinct genetic mutations, accessible at large population sizes, can drive high-level resistance to ampicillin even independently of beta-lactamases.

Cell morphology as biomarker of carbapenem exposure

Characterizing the physiological response of bacterial cells to antibiotics is crucial for designing diagnostic techniques, treatment choices, and drug development. While bacterial cells at sublethal doses of antibiotics are commonly characterized, the impact of exposure to high concentrations of antibiotics on bacteria after long-term serial exposure and their effect on withdrawal need attention for further characterization. This study investigated the effect of increasing imipenem concentrations on carbapenem-susceptible (S) and carbapenem-resistant (R) E. coli on their growth adaptation and cell surface structure. We exposed the bacterial population to increasing imipenem concentrations through 30 exposure cycles. Cell morphology was observed using a 3D laser scanning confocal microscope (LSCM) and transmission electron microscope (TEM). Results showed that the exposure resulted in significant morphological changes in E. coli (S) cells, while minor changes were seen in E. coli (R) cells. The rod-shaped E. coli (S) gradually transformed into round shapes. Further, the exposed E. coli (S) cells' surface area-to-volume ratio (SA/V) was also significantly different from the control, which is non-exposed E. coli (S). Then, the exposed E. coli (S) cells were re-grown in antibiotic-free environment for 100 growth cycles to determine if the changes in cells were reversible. The results showed that their cell morphology remained round, showing that the cell morphology was not reversible. The morphological response of these cells to imipenem can assist in understanding the resistance mechanism in the context of diagnostics and antibacterial therapies.

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

The term 'druggability' describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and 7 β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ('variant vulnerability'), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ('drug applicability'). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Using CO2 level monitoring to adjust the stress conditions of morbidostats

In a conventional morbidostat, cell growth is monitored by measuring OD, and stress conditions are automatically adjusted using OD values. However, phenomena such as biofilm formation, agglomeration, and the presence of opaque substrates or products can result in inaccurate OD measurements of population size, causing morbidostat systems to fail to adjust stress conditions appropriately. This study offers a solution for circumstances where it is impractical to determine vital activity based on OD by developing a novel morbidostat system that adjusts stress conditions based on measurements of exhaust CO2. As a proof of concept, the adaptation of E. coli ATCC 47076 to 48 °C was performed with two morbidostats using this new strategy. Both populations evolved in the morbidostats were confirmed to grow at 48 °C, a temperature their ancestral strain cannot withstand.

Genetic Alternatives for Experimental Adaptation to Colistin in Three Pseudomonas aeruginosa Lineages

Pseudomonas aeruginosa is characterized by a high adaptive potential, developing resistance in response to antimicrobial pressure. We employed a spatiotemporal evolution model to disclose the pathways of adaptation to colistin, a last-resort polymyxin antimicrobial, among three unrelated P. aeruginosa lineages. The P. aeruginosa ATCC-27833 reference strain (Pa_ATCC), an environmental P. aeruginosa isolate (Pa_Environment), and a clinical isolate with multiple drug resistance (Pa_MDR) were grown over an increasing 5-step colistin concentration gradient from 0 to 400 mg/L. Pa_Environment demonstrated the highest growth pace, achieving the 400 mg/L band in 15 days, whereas it took 37 and 60 days for Pa_MDR and Pa_ATCC, respectively. To identify the genome changes that occurred during adaptation to colistin, the isolates selected during the growth of the bacteria (n = 185) were subjected to whole genome sequencing. In total, 17 mutation variants in eight lipopolysaccharide-synthesis-associated genes were detected. phoQ and lpxL/PA0011 were affected in all three lineages, whereas changes in pmrB were found in Pa_Environment and Pa_MDR but not in Pa_ATCC. In addition, mutations were detected in 34 general metabolism genes, and each lineage developed mutations in a unique set of such genes. Thus, the three examined distinct P. aeruginosa strains demonstrated different capabilities and genetic pathways of colistin adaptation.

Quorum-sensing regulation of phenazine production heightens Pseudomonas aeruginosa resistance to ciprofloxacin

Quorum sensing is a type of cell-cell communication that modulates various biological activities of bacteria. Previous studies indicate that quorum sensing contributes to the evolution of bacterial resistance to antibiotics, but the underlying mechanisms are not fully understood. In this study, we grew Pseudomonas aeruginosa in the presence of sub-lethal concentrations of ciprofloxacin, resulting in a large increase in ciprofloxacin minimal inhibitory concentration. We discovered that quorum sensing-mediated phenazine biosynthesis was significantly enhanced in the resistant isolates, where the quinolone circuit was the predominant contributor to this phenomenon. We found that production of pyocyanin changed carbon flux and showed that the effect can be partially inhibited by the addition of pyruvate to cultures. This study illustrates the role of quorum sensing-mediated phenotypic resistance and suggests a strategy for its prevention.

Robust genetic codes enhance protein evolvability

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance

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

Polymyxin B nonapeptide potentiates the eradication of Gram-negative bacterial persisters

Antibiotic-resistant Gram-negative bacteria remain a globally leading cause of bacterial infection-associated mortality, and it is imperative to identify novel therapeutic strategies. Recently, the advantage of using antibacterials selective against Gram-negative bacteria has been demonstrated with polymyxins that specifically target the lipopolysaccharides of Gram-negative bacteria. However, the severe cytotoxicity of polymyxins limits their clinical use. Here, we demonstrate that polymyxin B nonapeptide (PMBN), a polymyxin B derivative without the terminal amino acyl residue, can significantly enhance the effectiveness of commonly used antibiotics against only Gram-negative bacteria and their persister cells. We show that although PMBN itself does not exhibit antibacterial activity or cytotoxicity well above the 100-fold minimum inhibitory concentration of polymyxin B, PMBN can increase the potency of co-treated antibiotics. We also demonstrate that using PMBN in combination with other antibiotics significantly reduces the frequency of resistant mutant formation. Together, this work provides evidence of the utilities of PMBN as a novel potentiator for antibiotics against Gram-negative bacteria and insights for the eradication of bacterial persister cells during antibiotic treatment.

IMPORTANCE

The significance of our study lies in addressing the problem of antibiotic-resistant Gram-negative bacteria, which continue to be a global cause of mortality associated with bacterial infections. Therefore, identifying innovative therapeutic approaches is an urgent need. Recent research has highlighted the potential of selective antibacterials like polymyxins, which specifically target the lipopolysaccharides of Gram-negative bacteria. However, the clinical use of polymyxins is limited by their severe cytotoxicity. This study unveils the effectiveness of polymyxin B nonapeptide (PMBN) in significantly enhancing the eradication of persister cells in Gram-negative bacteria. Although PMBN itself does not exhibit antibacterial activity or cytotoxicity, it remarkably reduces persister cells during the treatment of antibiotics. Moreover, combining PMBN with other antibiotics reduces the emergence of resistant mutants. Our research emphasizes the utility of PMBN as a novel potentiator to decrease persister cells during antibiotic treatments for Gram-negative bacteria.

Unraveling resistance mechanisms in combination therapy: A comprehensive review of recent advances and future directions

Antimicrobial resistance is a global health threat. Misuse and overuse of antimicrobials are the main drivers in developing drug-resistant bacteria. The emergence of the rapid global spread of multi-resistant bacteria requires urgent multisectoral action to generate novel treatment alternatives. Combination therapy offers the potential to exploit synergistic effects for enhanced antibacterial efficacy of drugs. Understanding the complex dynamics and kinetics of drug interactions in combination therapy is crucial. Therefore, this review outlines the current advances in antibiotic resistance's evolutionary and genetic dynamics in combination therapies-exposed bacteria. Moreover, we also discussed four pivotal future research areas to comprehend better the development of antibiotic resistance in bacteria treated with combination strategies.

Susceptible bacteria can survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance

Antibiotic resistance and evasion are incompletely understood and complicated by the fact that murine interval dosing models do not fully recapitulate antibiotic pharmacokinetics in humans. To better understand how gastrointestinal bacteria respond to antibiotics, we colonized germ-free mice with a pan-susceptible genetically barcoded Escherichia coli clinical isolate and administered the antibiotic cefepime via programmable subcutaneous pumps, allowing closer emulation of human parenteral antibiotic dynamics. E. coli was only recovered from intestinal tissue, where cefepime concentrations were still inhibitory. Strikingly, "some" E. coli isolates were not cefepime resistant but acquired mutations in genes involved in polysaccharide capsular synthesis increasing their invasion and survival within human intestinal cells. Deleting wbaP involved in capsular polysaccharide synthesis mimicked this phenotype, allowing increased invasion of colonocytes where cefepime concentrations were reduced. Additionally, "some" mutant strains exhibited a persister phenotype upon further cefepime exposure. This work uncovers a mechanism allowing "select" gastrointestinal bacteria to evade antibiotic treatment.

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

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

Dynamic Adaptation in Extant Porins Facilitates Antibiotic Tolerance in Energetic Escherichia coli

Bacteria can tolerate antibiotics despite lacking the genetic components for resistance. The prevailing notion is that tolerance results from depleted cellular energy or cell dormancy. In contrast to this view, many cells in the tolerant population of Escherichia coli can exhibit motility - a phenomenon that requires cellular energy, specifically, the proton-motive force (PMF). As these motile-tolerant cells are challenging to isolate from the heterogeneous tolerant population, their survival mechanism is unknown. Here, we discovered that motile bacteria segregate themselves from the tolerant population under micro-confinement, owing to their unique ability to penetrate micron-sized channels. Single-cell measurements on the motile-tolerant population showed that the cells retained a high PMF, but they did not survive through active efflux alone. By utilizing growth assays, single-cell fluorescence studies, and chemotaxis assays, we showed that the cells survived by dynamically inhibiting the function of existing porins in the outer membrane. A drug transport model for porin-mediated intake and efflux pump-mediated expulsion suggested that energetic tolerant cells withstand antibiotics by constricting their porins. The novel porin adaptation we have uncovered is independent of gene expression changes and may involve electrostatic modifications within individual porins to prevent extracellular ligand entry.

Evolution Under Thermal Stress Affects Escherichia coli's Resistance to Antibiotics

Exposure to both antibiotics and temperature changes can induce similar physiological responses in bacteria. Thus, changes in growth temperature may affect antibiotic resistance. Previous studies have found that evolution under antibiotic stress causes shifts in the optimal growth temperature of bacteria. However, little is known about how evolution under thermal stress affects antibiotic resistance. We examined 100+ heat-evolved strains of Escherichia coli that evolved under thermal stress. We asked whether evolution under thermal stress affects optimal growth temperature, if there are any correlations between evolving in high temperatures and antibiotic resistance, and if these strains' antibiotic efficacy changes depending on the local environment's temperature. We found that: (1) surprisingly, most of the heat-evolved strains displayed a decrease in optimal growth temperature and overall growth relative to the ancestor strain, (2) there were complex patterns of changes in antibiotic resistance when comparing the heat-evolved strains to the ancestor strain, and (3) there were few significant correlations among changes in antibiotic resistance, optimal growth temperature, and overall growth.

A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.

Pasa: leveraging population pangenome graph to scaffold prokaryote genome assemblies

Whole genome sequencing has increasingly become the essential method for studying the genetic mechanisms of antimicrobial resistance and for surveillance of drug-resistant bacterial pathogens. The majority of bacterial genomes sequenced to date have been sequenced with Illumina sequencing technology, owing to its high-throughput, excellent sequence accuracy, and low cost. However, because of the short-read nature of the technology, these assemblies are fragmented into large numbers of contigs, hindering the obtaining of full information of the genome. We develop Pasa, a graph-based algorithm that utilizes the pangenome graph and the assembly graph information to improve scaffolding quality. By leveraging the population information of the bacteria species, Pasa is able to utilize the linkage information of the gene families of the species to resolve the contig graph of the assembly. We show that our method outperforms the current state of the arts in terms of accuracy, and at the same time, is computationally efficient to be applied to a large number of existing draft assemblies.

Biocidal polymer derived near white light-emitting polymeric carbon particles for antibacterial and bioimaging applications

A growing antimicrobial crisis has increased demand for antimicrobial materials. It has become increasingly popular to convert polymeric macromolecules into polymeric carbon particles (PCP) in order to achieve highly biocompatible materials with unique properties as a result of the ability to synthesize nanomaterials of the right size and add value to existing stable polymers. This work presents the tuning of PCP for antibacterial application by combining a biocidal polymer with one-pot solvothermal synthesis. PCP displayed broad-spectrum antibacterial activity via various mechanisms, including inhibition of bacterial cell walls, ROS generation, and antibiotic resistance. Furthermore, these biocidal PCP were observed to show excitation-independent near-white light emission which on the other hand is generally possible due to mixed sizes, doping, and surface effects. As opposed to the parent biocidal polymer, PCP added ROS-mediated bactericidal activity, increased cytocompatibility, and nanofibers with anti-adhesive effects and potential of imaging bacterial cells.

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

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

Translational PK/PD for the Development of Novel Antibiotics-A Drug Developer's Perspective

Antibiotic development traditionally involved large Phase 3 programs, preceded by Phase 2 studies. Recognizing the high unmet medical need for new antibiotics and, in some cases, challenges to conducting large clinical trials, regulators created a streamlined clinical development pathway in which a lean clinical efficacy dataset is complemented by nonclinical data as supportive evidence of efficacy. In this context, translational Pharmacokinetic/Pharmacodynamic (PK/PD) plays a key role and is a major contributor to a "robust" nonclinical package. The classical PK/PD index approach, proven successful for established classes of antibiotics, is at the core of recent antibiotic approvals and the current antibacterial PK/PD guidelines by regulators. Nevertheless, in the case of novel antibiotics with a novel Mechanism of Action (MoA), there is no prior experience with the PK/PD index approach as the basis for translating nonclinical efficacy to clinical outcome, and additional nonclinical studies and PK/PD analyses might be considered to increase confidence. In this review, we discuss the value and limitations of the classical PK/PD approach and present potential risk mitigation activities, including the introduction of a semi-mechanism-based PK/PD modeling approach. We propose a general nonclinical PK/PD package from which drug developers might choose the studies most relevant for each individual candidate in order to build up a "robust" nonclinical PK/PD understanding.

The Pseudomonas aeruginosa Resistome: Permanent and Transient Antibiotic Resistance, an Overview

One of the most concerning characteristics of Pseudomonas aeruginosa is its low susceptibility to several antibiotics of common use in clinics, as well as its facility to acquire increased resistance levels. Consequently, the study of the antibiotic resistance mechanisms of this bacterium is of relevance for human health. For such a study, different types of resistance should be distinguished. The intrinsic resistome is composed of a set of genes, present in the core genome of P. aeruginosa, which contributes to its characteristic, species-specific, phenotype of susceptibility to antibiotics. Acquired resistance refers to those genetic events, such as the acquisition of mutations or antibiotic resistance genes that reduce antibiotic susceptibility. Finally, antibiotic resistance can be transiently acquired in the presence of specific compounds or under some growing conditions. The current article provides information on methods currently used to analyze intrinsic, mutation-driven, and transient antibiotic resistance in P. aeruginosa.

The stressostat: A novel approach in adaptive laboratory evolution to improve end-product resistance

End-product inhibition in pH-controlled batch cultures, is the major limiting factor for bacterial biomass formation in the starter culture industry as well as in many other biotechnological processes. Adaptive laboratory evolution (ALE) has emerged over the past decades as a powerful tool for phenotype optimization, but none of the existing ALE methods could select for improved end-product resistance. Therefore, we developed the stressostat (STress Resistance Evolution in Substrate Surplus) as a novel continuous ALE method. Stressostat cultivation applies end-product concentrations as constant evolutionary pressure on microorganisms in the presence of substrate surplus. In this study, we improved the lactate resistance of Lactococcus lactis FM03P in 35 days of stressostat cultivations. The lactate concentrations increased over time from 530 to 675 mM, indicating the successful selection for variants with improved lactate resistance. Thirty-four variants were isolated and grouped into four clusters based on their growth rates at high lactate concentrations. In the high-throughput screening without pH control, most isolated variants could grow at high lactate concentrations (870-928 mM), while the wild type was completely inhibited. The variants grew slower than wild type at low lactate media indicating possible evolutionary trade-off. However, in pH-controlled batch cultivations, most variants produced more biomass than the wild type. In conclusion, stressostat cultivation is a valuable method to obtain L. lactis variants with improved end-product resistance and further characterization is needed to elucidate underlying resistance mechanisms and potential industrial applications.

The Mar, Sox, and Rob Systems

Environments inhabited by Enterobacteriaceae are diverse and often stressful. This is particularly true for Escherichia coli and Salmonella during host association in the gastrointestinal systems of animals. There, E. coli and Salmonella must survive exposure to various antimicrobial compounds produced or ingested by their host. A myriad of changes to cellular physiology and metabolism are required to achieve this feat. A central regulatory network responsible for sensing and responding to intracellular chemical stressors like antibiotics are the Mar, Sox, and Rob systems found throughout the Enterobacteriaceae. Each of these distinct regulatory networks controls expression of an overlapping set of downstream genes whose collective effects result in increased resistance to a wide array of antimicrobial compounds. This collection of genes is known as the mar-sox-rob regulon. This review will provide an overview of the mar-sox-rob regulon and molecular architecture of the Mar, Sox, and Rob systems.

Short-term evolution of antibiotic responses in highly dynamic environments favors loss of regulation

Microbes inhabit natural environments that are remarkably dynamic, with sudden environmental shifts that require immediate action by the cell. To cope with changing environments, microbes are equipped with regulated response mechanisms that are only activated when needed. However, when exposed to extreme environments such as clinical antibiotic treatments, complete loss of regulation is frequently observed. Although recent studies suggest that the initial evolution of microbes in new environments tends to favor mutations in regulatory pathways, it is not clear how this evolution is affected by how quickly conditions change (i.e. dynamics), or which mechanisms are commonly used to implement new regulation. Here, we perform experimental evolution on continuous cultures of E. coli carrying the tetracycline resistance tet operon to identify specific types of mutations that adapt drug responses to different dynamical regimens of drug administration. When cultures are evolved under gradually increasing tetracycline concentrations, we observe no mutations in the tet operon, but a predominance of fine-tuning mutations increasing the affinity of alternative efflux pump AcrB to tetracycline. When cultures are instead periodically exposed to large drug doses, all populations developed transposon insertions in repressor TetR, resulting in loss of regulation of efflux pump TetA. We use a mathematical model of the dynamics of antibiotic responses to show that sudden exposure to large drug concentrations can overwhelm regulated responses, which cannot induce resistance fast enough, resulting in fitness advantage for constitutive expression of resistance. These results help explain the loss of regulation of antibiotic resistance by opportunistic pathogens evolving in clinical environments. Our experiment supports the notion that initial evolution in new ecological niches proceeds largely through regulatory mutations and suggests that transposon insertions are a main mechanism driving this process.

A rugged yet easily navigable fitness landscape

Fitness landscape theory predicts that rugged landscapes with multiple peaks impair Darwinian evolution, but experimental evidence is limited. In this study, we used genome editing to map the fitness of >260,000 genotypes of the key metabolic enzyme dihydrofolate reductase in the presence of the antibiotic trimethoprim, which targets this enzyme. The resulting landscape is highly rugged and harbors 514 fitness peaks. However, its highest peaks are accessible to evolving populations via abundant fitness-increasing paths. Different peaks share large basins of attraction that render the outcome of adaptive evolution highly contingent on chance events. Our work shows that ruggedness need not be an obstacle to Darwinian evolution but can reduce its predictability. If true in general, the complexity of optimization problems on realistic landscapes may require reappraisal.

Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance

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

Evolutionary "Crowdsourcing": Alignment of Fitness Landscapes Allows for Cross-species Adaptation of a Horizontally Transferred Gene

Genes that undergo horizontal gene transfer (HGT) evolve in different genomic backgrounds. Despite the ubiquity of cross-species HGT, the effects of switching hosts on gene evolution remains understudied. Here, we present a framework to examine the evolutionary consequences of host-switching and apply this framework to an antibiotic resistance gene commonly found on conjugative plasmids. Specifically, we determined the adaptive landscape of this gene for a small set of mutationally connected genotypes in 3 enteric species. We uncovered that the landscape topographies were largely aligned with minimal host-dependent mutational effects. By simulating gene evolution over the experimentally gauged landscapes, we found that the adaptive evolution of the mobile gene in one species translated to adaptation in another. By simulating gene evolution over artificial landscapes, we found that sufficient alignment between landscapes ensures such "adaptive equivalency" across species. Thus, given adequate landscape alignment within a bacterial community, vehicles of HGT such as plasmids may enable a distributed form of genetic evolution across community members, where species can "crowdsource" adaptation.

Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few mentions of protein space consider how protein phenotypes can be described in terms of their biophysical components, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these components. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [k_{cat}, K_{M}, K_{i}, and T_{m} (melting temperature)]. We then examine how combinations of three mutations (eight alleles in total) display pleiotropy, or unique effects on individual subspace traits. We examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that future applications to bioengineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Competitive interactions facilitate resistance development against antimicrobials

While the evolution of antimicrobial resistance is well studied in free-living bacteria, information on resistance development in dense and diverse biofilm communities is largely lacking. Therefore, we explored how the social interactions in a duo-species biofilm composed of the brewery isolates Pseudomonas rhodesiae and Raoultella terrigena influence the adaptation to the broad-spectrum antimicrobial sulfathiazole. Previously, we showed that the competition between these brewery isolates enhances the antimicrobial tolerance of P. rhodesiae. Here, we found that this enhanced tolerance in duo-species biofilms is associated with a strongly increased antimicrobial resistance development in P. rhodesiae. Whereas P. rhodesiae was not able to evolve resistance against sulfathiazole in monospecies conditions, it rapidly evolved resistance in the majority of the duo-species communities. Although the initial presence of R. terrigena was thus required for P. rhodesiae to acquire resistance, the resistance mechanisms did not depend on the presence of R. terrigena. Whole genome sequencing of resistant P. rhodesiae clones showed no clear mutational hot spots. This indicates that the acquired resistance phenotype depends on complex interactions between low-frequency mutations in the genetic background of the strains. We hypothesize that the increased tolerance in duo-species conditions promotes resistance by enhancing the selection of partially resistant mutants and opening up novel evolutionary trajectories that enable such genetic interactions. This hypothesis is reinforced by experimentally excluding potential effects of increased initial population size, enhanced mutation rate, and horizontal gene transfer. Altogether, our observations suggest that the community mode of life and the social interactions therein strongly affect the accessible evolutionary pathways toward antimicrobial resistance.IMPORTANCEAntimicrobial resistance is one of the most studied bacterial properties due to its enormous clinical and industrial relevance; however, most research focuses on resistance development of a single species in isolation. In the present study, we showed that resistance evolution of brewery isolates can differ greatly between single- and mixed-species conditions. Specifically, we observed that the development of antimicrobial resistance in certain species can be significantly enhanced in co-culture as compared to the single-species conditions. Overall, the current study emphasizes the need of considering the within bacterial interactions in microbial communities when evaluating antimicrobial treatments and resistance evolution.

Evolution of haploid and diploid populations reveals common, strong, and variable pleiotropic effects in non-home environments

Adaptation is driven by the selection for beneficial mutations that provide a fitness advantage in the specific environment in which a population is evolving. However, environments are rarely constant or predictable. When an organism well adapted to one environment finds itself in another, pleiotropic effects of mutations that made it well adapted to its former environment will affect its success. To better understand such pleiotropic effects, we evolved both haploid and diploid barcoded budding yeast populations in multiple environments, isolated adaptive clones, and then determined the fitness effects of adaptive mutations in 'non-home' environments in which they were not selected. We find that pleiotropy is common, with most adaptive evolved lineages showing fitness effects in non-home environments. Consistent with other studies, we find that these pleiotropic effects are unpredictable: they are beneficial in some environments and deleterious in others. However, we do find that lineages with adaptive mutations in the same genes tend to show similar pleiotropic effects. We also find that ploidy influences the observed adaptive mutational spectra in a condition-specific fashion. In some conditions, haploids and diploids are selected with adaptive mutations in identical genes, while in others they accumulate mutations in almost completely disjoint sets of genes.

Two Classes of DNA Gyrase Inhibitors Elicit Distinct Evolutionary Trajectories Toward Resistance in Gram-Negative Pathogens

Comprehensive knowledge of mechanisms driving the acquisition of antimicrobial resistance is essential for the development of new drugs with minimized resistibility. To gain this knowledge, we combine experimental evolution in a continuous culturing device, the morbidostat, with whole genome sequencing of evolving cultures followed by characterization of drug-resistant isolates. Here, this approach was used to assess evolutionary dynamics of resistance acquisition against DNA gyrase/topoisomerase TriBE inhibitor GP6 in Escherichia coli and Acinetobacter baumannii. The evolution of GP6 resistance in both species was driven by a combination of two classes of mutational events: (i) amino acid substitutions near the ATP-binding site of the GyrB subunit of the DNA gyrase target; and (ii) various mutations and genomic rearrangements leading to upregulation of efflux pumps, species-specific (AcrAB/TolC in E. coli and AdeIJK in A. baumannii) and shared by both species (MdtK). A comparison with the experimental evolution of resistance to ciprofloxacin (CIP), previously performed using the same workflow and strains, revealed fundamental differences between these two distinct classes of compounds. Most notable were non-overlapping spectra of target mutations and distinct evolutionary trajectories that, in the case of GP6, were dominated by upregulation of efflux machinery prior to (or even in lieu) of target modification. Most of efflux-driven GP6-resistant isolates of both species displayed a robust cross-resistance to CIP, while CIP-resistant clones showed no appreciable increase in GP6-resistance.

Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications

The term "druggability" describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant's sensitivity across a breadth of drugs in a panel, or a given drug's range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16 β-lactamase alleles and seven β-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel ("variant vulnerability"), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target ("drug applicability"). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G × G × E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Intrahost evolution of the gut microbiota

A massive number of microorganisms, belonging to different species, continuously divide inside the guts of animals and humans. The large size of these communities and their rapid division times imply that we should be able to watch microbial evolution in the gut in real time, in a similar manner to what has been done in vitro. Here, we review recent findings on how natural selection shapes intrahost evolution (also known as within-host evolution), with a focus on the intestines of mice and humans. The microbiota of a healthy host is not as static as initially thought from the information measured at only one genomic marker. Rather, the genomes of each gut-colonizing species can be highly dynamic, and such dynamism seems to be related to the microbiota species diversity. Genetic and bioinformatic tools, and analysis of time series data, allow quantification of the selection strength on emerging mutations and horizontal transfer events in gut ecosystems. The drivers and functional consequences of gut evolution can now begin to be grasped. The rules of this intrahost microbiota evolution, and how they depend on the biology of each species, need to be understood for more effective development of microbiota therapies to help maintain or restore host health.

Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.

Kinetic Barrier to Enzyme Inhibition Is Manipulated by Dynamical Local Interactions in E. coli DHFR

Dihydrofolate reductase (DHFR) is an important drug target and a highly studied model protein for understanding enzyme dynamics. DHFR's crucial role in folate synthesis renders it an ideal candidate to understand protein function and protein evolution mechanisms. In this study, to understand how a newly proposed DHFR inhibitor, 4'-deoxy methyl trimethoprim (4'-DTMP), alters evolutionary trajectories, we studied interactions that lead to its superior performance over that of trimethoprim (TMP). To elucidate the inhibition mechanism of 4'-DTMP, we first confirmed, both computationally and experimentally, that the relative binding free energy cost for the mutation of TMP and 4'-DTMP is the same, pointing the origin of the characteristic differences to be kinetic rather than thermodynamic. We then employed an interaction-based analysis by focusing first on the active site and then on the whole enzyme. We confirmed that the polar modification in 4'-DTMP induces additional local interactions with the enzyme, particularly, the M20 loop. These changes are propagated to the whole enzyme as shifts in the hydrogen bond networks. To shed light on the allosteric interactions, we support our analysis with network-based community analysis and show that segmentation of the loop domain of inhibitor-bound DHFR must be avoided by a successful inhibitor.

Combatting persister cells: The daunting task in post-antibiotics era

Over the years, much attention has been drawn to antibiotic resistance bacteria, but drug inefficacy caused by a subgroup of special phenotypic variants - persisters - has been largely neglected in both scientific and clinical field. Interestingly, this subgroup of phenotypic variants displayed their power of withstanding sufficient antibiotics exposure in a mechanism different from antibiotic resistance. In this review, we summarized the clinical importance of bacterial persisters, the evolutionary link between resistance, tolerance, and persistence, redundant mechanisms of persister formation as well as methods of studying persister cells. In the light of our recent findings of membrane-less organelle aggresome and its important roles in regulating bacterial dormancy depth, we propose an alternative approach for anti-persister therapy. That is, to force a persister into a deeper dormancy state to become a VBNC (viable but non-culturable) cell that is incapable of regrowth. We hope to provide the latest insights on persister studies and call upon more research interest into this field.

A Review of Carbapenem Resistance in Enterobacterales and Its Detection Techniques

Infectious disease outbreaks have caused thousands of deaths and hospitalizations, along with severe negative global economic impacts. Among these, infections caused by antimicrobial-resistant microorganisms are a major growing concern. The misuse and overuse of antimicrobials have resulted in the emergence of antimicrobial resistance (AMR) worldwide. Carbapenem-resistant Enterobacterales (CRE) are among the bacteria that need urgent attention globally. The emergence and spread of carbapenem-resistant bacteria are mainly due to the rapid dissemination of genes that encode carbapenemases through horizontal gene transfer (HGT). The rapid dissemination enables the development of host colonization and infection cases in humans who do not use the antibiotic (carbapenem) or those who are hospitalized but interacting with environments and hosts colonized with carbapenemase-producing (CP) bacteria. There are continuing efforts to characterize and differentiate carbapenem-resistant bacteria from susceptible bacteria to allow for the appropriate diagnosis, treatment, prevention, and control of infections. This review presents an overview of the factors that cause the emergence of AMR, particularly CRE, where they have been reported, and then, it outlines carbapenemases and how they are disseminated through humans, the environment, and food systems. Then, current and emerging techniques for the detection and surveillance of AMR, primarily CRE, and gaps in detection technologies are presented. This review can assist in developing prevention and control measures to minimize the spread of carbapenem resistance in the human ecosystem, including hospitals, food supply chains, and water treatment facilities. Furthermore, the development of rapid and affordable detection techniques is helpful in controlling the negative impact of infections caused by AMR/CRE. Since delays in diagnostics and appropriate antibiotic treatment for such infections lead to increased mortality rates and hospital costs, it is, therefore, imperative that rapid tests be a priority.

Embracing Complexity: Yeast Evolution Experiments Featuring Standing Genetic Variation

The yeast Saccharomyces cerevisiae has a long and esteemed history as a model system for laboratory selection experiments. The majority of yeast evolution experiments begin with an isogenic ancestor, impose selection as cells divide asexually, and track mutations that arise and accumulate over time. Within the last decade, the popularity of S. cerevisiae as a model system for exploring the evolution of standing genetic variation has grown considerably. As a facultatively sexual microbe, it is possible to initiate experiments with populations that harbor diversity and also to maintain that diversity by promoting sexual recombination as the experiment progresses. These experimental choices expand the scope of evolutionary hypotheses that can be tested with yeast. And, in this review, I argue that yeast is one of the best model systems for testing such hypotheses relevant to eukaryotic species. Here, I compile a list of yeast evolution experiments that involve standing genetic variation, initially and/or by implementing protocols that induce sexual recombination in evolving populations. I also provide an overview of experimental methods required to set up such an experiment and discuss the unique challenges that arise in this type of research. Throughout the article, I emphasize the best practices emerging from this small but growing niche of the literature.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

The evolution of colistin resistance increases bacterial resistance to host antimicrobial peptides and virulence

Antimicrobial peptides (AMPs) offer a promising solution to the antibiotic resistance crisis. However, an unresolved serious concern is that the evolution of resistance to therapeutic AMPs may generate cross-resistance to host AMPs, compromising a cornerstone of the innate immune response. We systematically tested this hypothesis using globally disseminated mobile colistin resistance (MCR) that has been selected by the use of colistin in agriculture and medicine. Here, we show that MCR provides a selective advantage to Escherichia coli in the presence of key AMPs from humans and agricultural animals by increasing AMP resistance. Moreover, MCR promotes bacterial growth in human serum and increases virulence in a Galleria mellonella infection model. Our study shows how the anthropogenic use of AMPs can drive the accidental evolution of resistance to the innate immune system of humans and animals. These findings have major implications for the design and use of therapeutic AMPs and suggest that MCR may be difficult to eradicate, even if colistin use is withdrawn.

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis-describing the nonlinear interaction between mutations and their phenotypic consequences-manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

Time-lapse proteomics unveil constant high exposure of non-antibiotic drug induces synthetic susceptibility towards regular antibiotics

Antibiotic resistance is a significant threat to the human race, as regular consumption of antibiotics may lead to antibiotic-resistant bacterial strains. Non-antibiotic drugs also have an extensive impact on bacterial strains, where persistent uptake alters the survival mechanisms of bacteria that could lead to cross-resistance towards other antibiotics. Here, we use time-lapse proteomics shift assays to examine Gram-negative (E. coli. O157:H7 and P. aeruginosa) and Gram-positive (E. faecalis and S. aureus) strains of bacteria for short and continuous exposure to the non-antibiotic drug Hydroxychloroquine (HCQ). Proteomic transitions from wild type to HCQ-exposed strains revealed bacterial transitions and their survival adaptabilities, which were different across all strains. In addition to their structural differences, some shared pathways were enriched among Gram-negative and positive strains. We also validated the cross-resistance and sensitivity towards 24 regularly prescribed antibiotics, indicating that long-term exposure to non-antibiotic drugs may induce general proteomics alterations in the bacterial strains, promoting antibiotic resistance. We validated that HCQ exposure renders Gram-negative strains resistant to Β-lactam and susceptible to macrolides and folic acid. In contrast, Gram-positive strains become susceptible to Β-lactam and resistant to aminoglycosides. Exposure to non-antibiotic drugs causes resistance or susceptibility toward other antibiotics, providing clinicians a reason to overcome antibiotic resistance.

Analytical methods for assessing antimicrobial activity of nanomaterials in complex media: advances, challenges, and perspectives

Assessing the antimicrobial activity of engineered nanomaterials (ENMs), especially in realistic scenarios, is of great significance for both basic research and applications. Multiple analytical methods are available for analysis via off-line or on-line measurements. Real-world samples are often complex with inorganic and organic components, which complicates the measurements of microbial viability and/or metabolic activity. This article highlights the recent advances achieved in analytical methods including typical applications and specifics regarding their accuracy, cost, efficiency, and user-friendliness. Methodological drawbacks, technique gaps, and future perspectives are also discussed. This review aims to help researchers select suitable methods for gaining insight into antimicrobial activities of targeted ENMs in artificial and natural complex matrices.

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

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

Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions

Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.

Multiple phase transitions shape biodiversity of a migrating population

In a wide variety of natural systems, closely related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms affect the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise-this may provide an experimental means for detecting and altering impending extinction.

Plasticity and Stereotypic Rewiring of the Transcriptome Upon Bacterial Evolution of Antibiotic Resistance

Bacterial evolution of antibiotic resistance frequently has deleterious side effects on microbial growth, virulence, and susceptibility to other antimicrobial agents. However, it is unclear how these trade-offs could be utilized for manipulating antibiotic resistance in the clinic, not least because the underlying molecular mechanisms are poorly understood. Using laboratory evolution, we demonstrate that clinically relevant resistance mutations in Escherichia coli constitutively rewire a large fraction of the transcriptome in a repeatable and stereotypic manner. Strikingly, lineages adapted to functionally distinct antibiotics and having no resistance mutations in common show a wide range of parallel gene expression changes that alter oxidative stress response, iron homeostasis, and the composition of the bacterial outer membrane and cell surface. These common physiological alterations are associated with changes in cell morphology and enhanced sensitivity to antimicrobial peptides. Finally, the constitutive transcriptomic changes induced by resistance mutations are largely distinct from those induced by antibiotic stresses in the wild type. This indicates a limited role for genetic assimilation of the induced antibiotic stress response during resistance evolution. Our work suggests that diverse resistance mutations converge on similar global transcriptomic states that shape genetic susceptibility to antimicrobial compounds.

Conversion of Escherichia coli into Mixotrophic CO2 Assimilation with Malate and Hydrogen Based on Recombinant Expression of 2-Oxoglutarate:Ferredoxin Oxidoreductase Using Adaptive Laboratory Evolution

We report the mixotrophic growth of Escherichia coli based on recombinant 2-oxoglutarate:ferredoxin oxidoreductase (OGOR) to assimilate CO₂ using malate as an auxiliary carbon source and hydrogen as an energy source. We employ a long-term (~184 days) two-stage adaptive evolution to convert heterotrophic E. coli into mixotrophic E. coli. In the first stage of evolution with serine, diauxic growth emerges as a prominent feature. At the end of the second stage of evolution with malate, the strain exhibits mixotrophy with CO₂ as an essential substrate for growth. We expect this work will open new possibilities in the utilization of OGOR for microbial CO₂ assimilation and future hydrogen-based electro-microbial conversion.

Susceptible bacteria survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance

In vitro systems have provided great insight into the mechanisms of antibiotic resistance. Yet, in vitro approaches cannot reflect the full complexity of what transpires within a host. As the mammalian gut is host to trillions of resident bacteria and thus a potential breeding ground for antibiotic resistance, we sought to better understand how gut bacteria respond to antibiotic treatment in vivo . Here, we colonized germ-free mice with a genetically barcoded antibiotic pan-susceptible Escherichia coli clinical isolate and then administered the antibiotic cefepime via programmable subcutaneous pumps which allowed for closer emulation of human parenteral antibiotic pharmacokinetics/dynamics. After seven days of antibiotics, we were unable to culture E. coli from feces. We were, however, able to recover barcoded E. coli from harvested gastrointestinal (GI) tissue, despite high GI tract and plasma cefepime concentrations. Strikingly, these E. coli isolates were not resistant to cefepime but had acquired mutations â€" most notably in the wbaP gene, which encodes an enzyme required for the initiation of the synthesis of the polysaccharide capsule and lipopolysaccharide O antigen - that increased their ability to invade and survive within intestinal cells, including cultured human colonocytes. Further, these E. coli mutants exhibited a persister phenotype when exposed to cefepime, allowing for greater survival to pulses of cefepime treatment when compared to the wildtype strain. Our findings highlight a mechanism by which bacteria in the gastrointestinal tract can adapt to antibiotic treatment by increasing their ability to persist during antibiotic treatment and invade intestinal epithelial cells where antibiotic concentrations are substantially reduced.

Thiostrepton, a resurging drug inhibiting the stringent response to counteract antibiotic-resistance and expression of virulence determinants in Neisseria gonorrhoeae

Due to the increased resistance to all available antibiotics and the lack of vaccines, Neisseria gonorrhoeae (the gonococcus) poses an urgent threat. Although the mechanisms of virulence and antibiotic resistance have been largely investigated in this bacterium, very few studies have addressed the stringent response (SR) that in pathogenic bacteria controls the expression of genes involved in host-pathogen interaction and tolerance and persistence toward antibiotics. In this study, the results of the transcriptome analysis of a clinical isolate of N. gonorrhoeae, after induction of the SR by serine hydroxamate, provided us with an accurate list of genes that are transcriptionally modulated during the SR. The list includes genes associated with metabolism, cellular machine functions, host-pathogen interaction, genome plasticity, and antibiotic tolerance and persistence. Moreover, we found that the artificial induction of the SR in N. gonorrhoeae by serine hydroxamate is prevented by thiostrepton, a thiopeptide antibiotic that is known to interact with ribosomal protein L11, thereby inhibiting functions of EF-Tu and EF-G, and binding of pppGpp synthase I (RelA) to ribosome upon entry of uncharged tRNA. We found that N. gonorrhoeae is highly sensitive to thiostrepton under in vitro conditions, and that thiostrepton, in contrast to other antibiotics, does not induce tolerance or persistence. Finally, we observed that thiostrepton attenuated the expression of key genes involved in the host-pathogen interaction. These properties make thiostrepton a good drug candidate for dampening bacterial virulence and preventing antibiotic tolerance and persistence. The ongoing challenge is to increase the bioavailability of thiostrepton through the use of chemistry and nanotechnology.