Evolutionary paths to antibiotic resistance under dynamically sustained drug selection

DHFR Mutants Modulate Their Synchronized Dynamics with the Substrate by Shifting Hydrogen Bond Occupancies

Antibiotic resistance is a global health problem in which mutations occurring in functional proteins render drugs ineffective. The working mechanisms of the arising mutants are seldom apparent; a methodology to decipher these mechanisms systematically would render devising therapies to control the arising mutational pathways possible. Here we utilize C_α-C_β bond vector relaxations obtained from moderate length MD trajectories to determine conduits for functionality of the resistance conferring mutants of Escherichia coli dihydrofolate reductase. We find that the whole enzyme is synchronized to the motions of the substrate, irrespective of the mutation introducing gain-of-function or loss-of function. The total coordination of the motions suggests changes in the hydrogen bond dynamics with respect to the wild type as a possible route to determine and classify the mode-of-action of individual mutants. As a result, nine trimethoprim-resistant point mutations arising frequently in evolution experiments are categorized. One group of mutants that display the largest occurrence (L28R, W30G) work directly by modifying the dihydrofolate binding region. Conversely, W30R works indirectly by the formation of the E139-R30 salt bridge which releases energy resulting from tight binding by distorting the binding cavity. A third group (D27E, F153S, I94L) arising as single, resistance invoking mutants in evolution experiment trajectories allosterically and dynamically affects a hydrogen bonding motif formed at residues 59-69-71 which in turn modifies the binding site dynamics. The final group (I5F, A26T, R98P) consists of those mutants that have properties most similar to the wild type; these only appear after one of the other mutants is fixed on the protein structure and therefore display clear epistasis. Thus, we show that the binding event is governed by the entire enzyme dynamics while the binding site residues play gating roles. The adjustments made in the total enzyme in response to point mutations are what make quantifying and pinpointing their effect a hard problem. Here, we show that hydrogen bond dynamics recorded on sub-μs time scales provide the necessary fingerprints to decipher the various mechanisms at play.

Understanding Functional Redundancy and Promiscuity of Multidrug Transporters in E. coli under Lipophilic Cation Stress

Multidrug transporters (MDTs) are major contributors to microbial drug resistance and are further utilized for improving host phenotypes in biotechnological applications. Therefore, the identification of these MDTs and the understanding of their mechanisms of action in vivo are of great importance. However, their promiscuity and functional redundancy represent a major challenge towards their identification. Here, a multistep tolerance adaptive laboratory evolution (TALE) approach was leveraged to achieve this goal. Specifically, a wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB were evolved separately under increasing concentrations of two lipophilic cations, tetraphenylphosphonium (TPP⁺), and methyltriphenylphosphonium (MTPP⁺). The evolved strains showed a significant increase in MIC values of both cations and an apparent cross-cation resistance. Sequencing of all evolved mutants highlighted diverse mutational mechanisms that affect the activity of nine MDTs including acrB, mdtK, mdfA, acrE, emrD, tolC, acrA, mdtL, and mdtP. Besides regulatory mutations, several structural mutations were recognized in the proximal binding domain of acrB and the permeation pathways of both mdtK and mdfA. These details can aid in the rational design of MDT inhibitors to efficiently combat efflux-based drug resistance. Additionally, the TALE approach can be scaled to different microbes and molecules of medical and biotechnological relevance.

Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape

The fitness landscape represents the complex relationship between genotype or phenotype and fitness under a given environment, the structure of which allows the explanation and prediction of evolutionary trajectories. Although previous studies have constructed fitness landscapes by comprehensively studying the mutations in specific genes, the high dimensionality of genotypic changes prevents us from developing a fitness landscape capable of predicting evolution for the whole cell. Herein, we address this problem by inferring the phenotype-based fitness landscape for antibiotic resistance evolution by quantifying the multidimensional phenotypic changes, i.e., time-series data of resistance for eight different drugs. We show that different peaks of the landscape correspond to different drug resistance mechanisms, thus supporting the validity of the inferred phenotype-fitness landscape. We further discuss how inferred phenotype-fitness landscapes could contribute to the prediction and control of evolution. This approach bridges the gap between phenotypic/genotypic changes and fitness while contributing to a better understanding of drug resistance evolution.

Antibiotic Cycling Affects Resistance Evolution Independently of Collateral Sensitivity

Antibiotic cycling has been proposed as a promising approach to slow down resistance evolution against currently employed antibiotics. It remains unclear, however, to which extent the decreased resistance evolution is the result of collateral sensitivity, an evolutionary trade-off where resistance to one antibiotic enhances the sensitivity to the second, or due to additional effects of the evolved genetic background, in which mutations accumulated during treatment with a first antibiotic alter the emergence and spread of resistance against a second antibiotic via other mechanisms. Also, the influence of antibiotic exposure patterns on the outcome of drug cycling is unknown. Here, we systematically assessed the effects of the evolved genetic background by focusing on the first switch between two antibiotics against Salmonella Typhimurium, with cefotaxime fixed as the first and a broad variety of other drugs as the second antibiotic. By normalizing the antibiotic concentrations to eliminate the effects of collateral sensitivity, we demonstrated a clear contribution of the evolved genetic background beyond collateral sensitivity, which either enhanced or reduced the adaptive potential depending on the specific drug combination. We further demonstrated that the gradient strength with which cefotaxime was applied affected both cefotaxime resistance evolution and adaptation to second antibiotics, an effect that was associated with higher levels of clonal interference and reduced cost of resistance in populations evolved under weaker cefotaxime gradients. Overall, our work highlights that drug cycling can affect resistance evolution independently of collateral sensitivity, in a manner that is contingent on the antibiotic exposure pattern.

Adaptation in Outbred Sexual Yeast is Repeatable, Polygenic and Favors Rare Haplotypes

We carried out a 200 generation Evolve and Resequence (E&R) experiment initiated from an outbred diploid recombined 18-way synthetic base population. Replicate populations were evolved at large effective population sizes (>105 individuals), exposed to several different chemical challenges over 12 weeks of evolution, and whole-genome resequenced. Weekly forced outcrossing resulted in an average between adjacent-gene per cell division recombination rate of ∼0.0008. Despite attempts to force weekly sex, roughly half of our populations evolved cheaters and appear to be evolving asexually. Focusing on seven chemical stressors and 55 total evolved populations that remained sexual we observed large fitness gains and highly repeatable patterns of genome-wide haplotype change within chemical challenges, with limited levels of repeatability across chemical treatments. Adaptation appears highly polygenic with almost the entire genome showing significant and consistent patterns of haplotype change with little evidence for long-range linkage disequilibrium in a subset of populations for which we sequenced haploid clones. That is, almost the entire genome is under selection or drafting with selected sites. At any given locus adaptation was almost always dominated by one of the 18 founder's alleles, with that allele varying spatially and between treatments, suggesting that selection acts primarily on rare variants private to a founder or haplotype blocks harboring multiple mutations.

Gene flow and introgression are pervasive forces shaping the evolution of bacterial species

BACKGROUND

Although originally thought to evolve clonally, studies have revealed that most bacteria exchange DNA. However, it remains unclear to what extent gene flow shapes the evolution of bacterial genomes and maintains the cohesion of species.

RESULTS

Here, we analyze the patterns of gene flow within and between >2600 bacterial species. Our results show that fewer than 10% of bacterial species are truly clonal, indicating that purely asexual species are rare in nature. We further demonstrate that the taxonomic criterion of ~95% genome sequence identity routinely used to define bacterial species does not accurately represent a level of divergence that imposes an effective barrier to gene flow across bacterial species. Interruption of gene flow can occur at various sequence identities across lineages, generally from 90 to 98% genome identity. This likely explains why a ~95% genome sequence identity threshold has empirically been judged as a good approximation to define bacterial species. Our results support a universal mechanism where the availability of identical genomic DNA segments required to initiate homologous recombination is the primary determinant of gene flow and species boundaries in bacteria. We show that these barriers of gene flow remain porous since many distinct species maintain some level of gene flow, similar to introgression in sexual organisms.

CONCLUSIONS

Overall, bacterial evolution and speciation are likely shaped by similar forces driving the evolution of sexual organisms. Our findings support a model where the interruption of gene flow-although not necessarily the initial cause of speciation-leads to the establishment of permanent and irreversible species borders.

Detecting bacterial adaptation within individual microbiomes

The human microbiome harbours a large capacity for within-person adaptive mutations. Commensal bacterial strains can stably colonize a person for decades, and billions of mutations are generated daily within each person's microbiome. Adaptive mutations emerging during health might be driven by selective forces that vary across individuals, vary within an individual, or are completely novel to the human population. Mutations emerging within individual microbiomes might impact the immune system, the metabolism of nutrients or drugs, and the stability of the community to perturbations. Despite this potential, relatively little attention has been paid to the possibility of adaptive evolution within complex human-associated microbiomes. This review discusses the promise of studying within-microbiome adaptation, the conceptual and technical limitations that may have contributed to an underappreciation of adaptive de novo mutations occurring within microbiomes to date, and methods for detecting recent adaptive evolution. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

The red thread between methylation and mutation in bacterial antibiotic resistance: How third-generation sequencing can help to unravel this relationship

DNA methylation is an important mechanism involved in bacteria limiting foreign DNA acquisition, maintenance of mobile genetic elements, DNA mismatch repair, and gene expression. Changes in DNA methylation pattern are observed in bacteria under stress conditions, including exposure to antimicrobial compounds. These changes can result in transient and fast-appearing adaptive antibiotic resistance (AdR) phenotypes, e.g., strain overexpressing efflux pumps. DNA methylation can be related to DNA mutation rate, because it is involved in DNA mismatch repair systems and because methylated bases are well-known mutational hotspots. The AdR process can be the first important step in the selection of antibiotic-resistant strains, allowing the survival of the bacterial population until more efficient resistant mutants emerge. Epigenetic modifications can be investigated by third-generation sequencing platforms that allow us to simultaneously detect all the methylated bases along with the DNA sequencing. In this scenario, this sequencing technology enables the study of epigenetic modifications in link with antibiotic resistance and will help to investigate the relationship between methylation and mutation in the development of stable mechanisms of resistance.

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

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

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole‐cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth‐mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth‐mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs.

Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective.

Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations.

In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications.

While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.

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

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

Lineage dynamics in growing biofilms: Spatial patterns of standing vs. de novo diversity

Microbial biofilms show high phenotypic and genetic diversity, yet the mechanisms underlying diversity generation and maintenance remain unclear. Here, we investigate how spatial patterns of growth activity within a biofilm lead to spatial patterns of genetic diversity. Using individual-based computer simulations, we show that the active layer of growing cells at the biofilm interface controls the distribution of lineages within the biofilm, and therefore the patterns of standing and de novo diversity. Comparing biofilms of equal size, those with a thick active layer retain more standing diversity, while de novo diversity is more evenly distributed within the biofilm. In contrast, equal-sized biofilms with a thin active layer retain less standing diversity, and their de novo diversity is concentrated at the top of the biofilm, and in fewer lineages. In the context of antimicrobial resistance, biofilms with a thin active layer may be more prone to generate lineages with multiple resistance mutations, and to seed new resistant biofilms via sloughing of resistant cells from the upper layers. Our study reveals fundamental "baseline" mechanisms underlying the patterning of diversity within biofilms.

Evolution of Habitat-Dependent Antibiotic Resistance in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic human pathogen that usually causes difficult-to-treat infections due to its low intrinsic antibiotic susceptibility and outstanding capacity for becoming resistant to antibiotics. In addition, it has a remarkable metabolic versatility, being able to grow in different habitats, from natural niches to different and changing inpatient environments. Study of the environmental conditions that shape genetic and phenotypic changes of P. aeruginosa toward antibiotic resistance supposes a novelty, since experimental evolution assays are usually performed with well-defined antibiotics in regular laboratory growth media. Therefore, in this work we address the extent to which the nutrients’ availability may constrain the evolution of antibiotic resistance. We determined that P. aeruginosa genetic trajectories toward resistance to tobramycin, ceftazidime, and ceftazidime-avibactam are different when evolving in laboratory rich medium, urine, or synthetic sputum. Furthermore, our study, linking genotype with phenotype, showed a clear impact of each analyzed environment on both the fitness and resistance level associated with particular resistance mutations. This indicates that the phenotype associated with specific resistance mutations is variable and dependent on the bacterial metabolic state in each particular habitat. Our results support that the design of evolution-based strategies to tackle P. aeruginosa infections should be based on robust patterns of evolution identified within each particular infection and body location.

IMPORTANCE Predicting evolution toward antibiotic resistance (AR) and its associated trade-offs, such as collateral sensitivity, is important to design evolution-based strategies to tackle AR. However, the effect of nutrients' availability on such evolution, particularly those that can be found under in vivo infection conditions, has been barely addressed. We analyzed the evolutionary patterns of P. aeruginosa in the presence of antibiotics in different media, including urine and synthetic sputum, whose compositions are similar to the ones in infections, finding that AR evolution differs, depending on growth conditions. Furthermore, the representative mutants isolated under each condition tested render different AR levels and fitness costs, depending on nutrients’ availability, supporting the idea that environmental constraints shape the phenotypes associated with specific AR mutations. Consequently, the selection of AR mutations that render similar phenotypes is environment dependent. The analysis of evolution patterns toward AR requires studying growth conditions mimicking those that bacteria face during in vivo evolution.

Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight

Small-scale, low-cost bioreactors provide exquisite control of environmental parameters of microbial cultures over long durations. Their use is gaining popularity in quantitative systems and synthetic biology. However, existing setups are limited in their measurement capabilities. Here, we present ReacSight, a strategy to enhance bioreactor arrays for automated measurements and reactive experiment control. ReacSight leverages low-cost pipetting robots for sample collection, handling and loading, and provides a flexible instrument control architecture. We showcase ReacSight capabilities on three applications in yeast. First, we demonstrate real-time optogenetic control of gene expression. Second, we explore the impact of nutrient scarcity on fitness and cellular stress using competition assays. Third, we perform dynamic control of the composition of a two-strain consortium. We combine custom or chi.bio reactors with automated cytometry. To further illustrate ReacSight's genericity, we use it to enhance plate-readers with pipetting capabilities and perform repeated antibiotic treatments on a bacterial clinical isolate.

Microfluidic platform for spatially segregated experimental evolution studies with E. coli

Microdroplet emulsions allow investigators to build controllable microenvironments for applications in experimental evolution and synthetic ecology. We designed a microfluidic platform that uses highly homogenous microdroplets to enable these experiments. We also present a step-by-step protocol for the rapid production of highly homogeneous microdroplets suitable for experimental evolution. We also describe protocols for the propagation and serial passage of microbial populations across a range of selection schemes and potential spatial structures.

For complete details on the use and execution of this protocol, please refer to Seo et al. (2021).

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Microfluidics for the study of microbial evolution and biomarker discovery

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Highly homogenous microdroplets as spatially segregated microenvironments

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Platform to identify evolutionary trajectories leading to antimicrobial resistance

Evolved resistance to a novel cationic peptide antibiotic requires high mutation supply

Background and Objectives

A key strategy for resolving the antibiotic resistance crisis is the development of new drugs with antimicrobial properties. The engineered cationic antimicrobial peptide WLBU2 (also known as PLG0206) is a promising broad-spectrum antimicrobial compound that has completed Phase I clinical studies. It has activity against Gram-negative and Gram-positive bacteria including infections associated with biofilm. No definitive mechanisms of resistance to WLBU2 have been identified.

Methodology

Here, we used experimental evolution under different levels of mutation supply and whole genome sequencing (WGS) to detect the genetic pathways and probable mechanisms of resistance to this peptide. We propagated populations of wild-type and hypermutator Pseudomonas aeruginosa in the presence of WLBU2 and performed WGS of evolved populations and clones.

Results

Populations that survived WLBU2 treatment acquired a minimum of two mutations, making the acquisition of resistance more difficult than for most antibiotics, which can be tolerated by mutation of a single target. Major targets of resistance to WLBU2 included the orfN and pmrB genes, previously described to confer resistance to other cationic peptides. More surprisingly, mutations that increase aggregation such as the wsp pathway were also selected despite the ability of WLBU2 to kill cells growing in a biofilm.

Conclusions and implications

The results show how experimental evolution and WGS can identify genetic targets and actions of new antimicrobial compounds and predict pathways to resistance of new antibiotics in clinical practice.

N-methyl Benzimidazole Tethered Cholic Acid Amphiphiles Can Eradicate S. aureus-Mediated Biofilms and Wound Infections

Infections associated with Gram-positive bacteria like S. aureus pose a major threat as these bacteria can develop resistance and thereby limit the applications of antibiotics. Therefore, there is a need for new antibacterials to mitigate these infections. Bacterial membranes present an attractive therapeutic target as these membranes are anionic in nature and have a low chance of developing modifications in their physicochemical features. Antimicrobial peptides (AMPs) can disrupt the microbial membranes via electrostatic interactions, but the poor stability of AMPs halts their clinical translation. Here, we present the synthesis of eight N-methyl benzimidazole substituted cholic acid amphiphiles as antibacterial agents. We screened these novel heterocyclic cholic acid amphiphiles against different pathogens. Among the series, CABI-6 outperformed the other amphiphiles in terms of bactericidal activity against S. aureus. The membrane disruptive property of CABI-6 using a fluorescence-based assay has also been investigated, and it was inferred that CABI-6 can enhance the production of reactive oxygen species. We further demonstrated that CABI-6 can clear the pre-formed biofilms and can mitigate wound infection in murine models.

Microbial Interspecies Interactions and Their Impact on the Emergence and Spread of Antimicrobial Resistance

Bacteria are social organisms that commonly live in dense communities surrounded by a multitude of other species. The competitive and cooperative interactions between these species not only shape the bacterial communities but also influence their susceptibility to antimicrobials. While several studies have shown that mixed-species communities are more tolerant toward antimicrobials than their monospecies counterparts, only limited empirical data are currently available on how interspecies interactions influence resistance development. We here propose a theoretic framework outlining the potential impact of interspecies social behavior on different aspects of resistance development. We identify factors by which interspecies interactions might influence resistance evolution and distinguish between their effect on (a) the emergence of a resistant mutant and (b) the spread of this resistance throughout the population. Our analysis indicates that considering the social life of bacteria is imperative to the rational design of more effective antibiotic treatment strategies with a minimal hazard for resistance development. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genomics of diversification of Pseudomonas aeruginosa in cystic fibrosis lung-like conditions

Pseudomonas aeruginosa is among the most problematic opportunistic pathogens for adults with cystic fibrosis (CF), causing repeated and resilient infections in the lung and surrounding airways. Evidence suggests that long-term infections are associated with diversification into specialized types but the underlying cause of that diversification and the effect it has on the persistence of infections remains poorly understood. Here, we use evolve-and-resequence experiments to investigate the genetic changes accompanying rapid, de novo phenotypic diversification in lab environments designed to mimic two aspects of human lung ecology: spatial structure and complex nutritional content. After ∼220 generations of evolution, we find extensive genetic variation present in all environments, including those that most closely resemble the CF lung. We use the abundance and frequency of nonsynonymous and synonymous mutations to estimate the ratio of mutations that are selectively neutral (hitchhikers) to those that are under positive selection (drivers). A significantly lower proportion of driver mutations in spatially structured populations suggests that reduced dispersal generates subpopulations with reduced effective population size, decreasing the supply of beneficial mutations and causing more divergent evolutionary trajectories. In addition, we find mutations in a handful of genes typically associated with chronic infection in the CF lung, including one gene associated with antibiotic resistance. This demonstrates that many of the genetic changes considered to be hallmarks of CF lung adaptation can arise as a result of adaptation to a novel environment and do not necessarily require antimicrobial treatment, immune system suppression, or competition from other microbial species to occur.

Methods for the directed evolution of biomolecular interactions

Noncovalent interactions between biomolecules such as proteins and nucleic acids coordinate all cellular processes through changes in proximity. Tools that perturb these interactions are and will continue to be highly valuable for basic and translational scientific endeavors. By taking cues from natural systems, such as the adaptive immune system, we can design directed evolution platforms that can generate proteins that bind to biomolecules of interest. In recent years, the platforms used to direct the evolution of biomolecular binders have greatly expanded the range of types of interactions one can evolve. Herein, we review recent advances in methods to evolve protein-protein, protein-RNA, and protein-DNA interactions.

Decreased thermal niche breadth as a trade-off of antibiotic resistance

Evolutionary theory predicts that adaptations, including antibiotic resistance, should come with associated fitness costs; yet, many resistance mutations seemingly contradict this prediction by inducing no growth rate deficit. However, most growth assays comparing sensitive and resistant strains have been performed under a narrow range of environmental conditions, which do not reflect the variety of contexts that a pathogenic bacterium might encounter when causing infection. We hypothesized that reduced niche breadth, defined as diminished growth across a diversity of environments, can be a cost of antibiotic resistance. Specifically, we test whether chloramphenicol-resistant Escherichia coli incur disproportionate growth deficits in novel thermal conditions. Here we show that chloramphenicol-resistant bacteria have greater fitness costs at novel temperatures than their antibiotic-sensitive ancestors. In several cases, we observed no resistance cost in growth rate at the historic temperature but saw diminished growth at warmer and colder temperatures. These results were consistent across various genetic mechanisms of resistance. Thus, we propose that decreased thermal niche breadth is an under-documented fitness cost of antibiotic resistance. Furthermore, these results demonstrate that the cost of antibiotic resistance shifts rapidly as the environment changes; these context-dependent resistance costs should select for the rapid gain and loss of resistance as an evolutionary strategy.

Recombination resolves the cost of horizontal gene transfer in experimental populations of Helicobacter pylori

Horizontal gene transfer (HGT)—the transfer of DNA between lineages—is responsible for a large proportion of the genetic variation that contributes to evolution in microbial populations. While HGT can bring beneficial genetic innovation, the transfer of DNA from other species or strains can also have deleterious effects. In this study, we evolve populations of the bacteria Helicobacter pylori and use DNA sequencing to identify over 40,000 genetic variants transferred by HGT. We measure the cost of many of these and find that both strongly beneficial mutations and deleterious mutations are genetic variants transferred by natural transformation. Importantly, we also show how recombination that separates linked beneficial and deleterious mutations resolves the cost of HGT.

Horizontal gene transfer (HGT) is important for microbial evolution, yet we know little about the fitness effects and dynamics of horizontally transferred genetic variants. In this study, we evolve laboratory populations of Helicobacter pylori, which take up DNA from their environment by natural transformation, and measure the fitness effects of thousands of transferred genetic variants. We find that natural transformation increases the rate of adaptation but comes at the cost of significant genetic load. We show that this cost is circumvented by recombination, which increases the efficiency of selection by decoupling deleterious and beneficial genetic variants. Our results show that adaptation with HGT, pervasive in natural microbial populations, is shaped by a combination of selection, recombination, and genetic drift not accounted for in existing models of evolution.

Metagenomic insights into the profile of antibiotic resistomes in sediments of aquaculture wastewater treatment system

To meet the rapidly growing global demand for aquaculture products, large amounts of antibiotics were used in aquaculture, which might accelerate the evolution of antibiotic-resistant bacteria (ARB) and the propagation of antibiotic genes (ARGs). In our research, we revealed the ARGs profiles, their co-occurrence with mobile genetic elements (MGEs), and potential hosts in sediments of a crab pond wastewater purification system based on metagenomic analysis. The residual antibiotic seems to increase the propagation of ARGs in the crab pond, but there was no clear relationship between a given antibiotic type and the corresponding resistance genes. The effect of aquaculture on sediment was not as profound as that of other anthropogentic activities, but increased the relative abundance of sulfonamide resistance gene. A higher abundance of MGEs, especially plasmid, increased the potential ARGs dissemination risk in crab and purification ponds. Multidrug and sulfonamide resistance genes had greater potential to transfer because they were more frequently carried by MGEs. The horizontal gene transfer was likely to occur among a variety of microorganisms, and various ARGs hosts including Pseudomonas, Acinetobacter, Escherichia, and Klebsiella were identified. Bacterial community influenced the composition of ARG hosts, and Proteobacteria was the predominant hosts. Overall, our study provides novel insights into the environmental risk of ARGs in sediments of aquaculture wastewater treatment system.

Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections

Currently, treatment of bacterial infections focuses on choosing an antibiotic which matches a pathogen’s susceptibility, with less attention to the risk that even susceptibility-matched treatments can fail due to resistance emerging in response to treatment. Here, combining whole-genome sequencing of 1,113 pre- and post- treatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections (UTIs) and 7,365 wound infections, we find that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common, yet driven not by de novo resistance evolution, but rather by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from the patient’s own microbiota, these resistance-gaining recurrences can be predicted based on the patient’s past infection history, and their expected risk minimized by machine learning personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.

Adaptive laboratory evolution in S. cerevisiae highlights role of transcription factors in fungal xenobiotic resistance

In vitro evolution and whole genome analysis were used to comprehensively identify the genetic determinants of chemical resistance in Saccharomyces cerevisiae. Sequence analysis identified many genes contributing to the resistance phenotype as well as numerous amino acids in potential targets that may play a role in compound binding. Our work shows that compound-target pairs can be conserved across multiple species. The set of 25 most frequently mutated genes was enriched for transcription factors, and for almost 25 percent of the compounds, resistance was mediated by one of 100 independently derived, gain-of-function SNVs found in a 170 amino acid domain in the two Zn₂C₆ transcription factors YRR1 and YRM1 (p < 1 × 10⁻¹⁰⁰). This remarkable enrichment for transcription factors as drug resistance genes highlights their important role in the evolution of antifungal xenobiotic resistance and underscores the challenge to develop antifungal treatments that maintain potency.

Ottilie et al. employ an experimental evolution approach to investigate the role of transcription factors in yeast chemical resistance. Most emergent mutations in resistant strains were enriched in transcription factor coding genes, highlighting their importance in drug resistance.

Genome Recombination-Mediated tRNA Up-Regulation Conducts General Antibiotic Resistance of Bacteria at Early Stage

Bacterial antibiotic resistance sets a great challenge to human health. It seems that the bacteria can spontaneously evolve resistance against any antibiotic within a short time without the horizontal transfer of heterologous genes and before accumulating drug-resistant mutations. We have shown that the tRNA-mediated translational regulation counteracts the reactive oxygen species (ROS) in bacteria. In this study, we demonstrated that isolated and subcultured Escherichia coli elevated its tRNAs under antibiotic stress to rapidly provide antibiotic resistance, especially at the early stage, before upregulating the efflux pump and evolving resistance mutations. The DNA recombination system repaired the antibiotic-induced DNA breakage in the genome, causing numerous structural variations. These structural variations are overrepresented near the tRNA genes, which indicated the cause of tRNA up-regulation. Knocking out the recombination system abolished the up-regulation of tRNAs, and coincidently, they could hardly evolve antibiotic resistance in multiple antibiotics, respectively. With these results, we proposed a multi-stage model of bacterial antibiotic resistance in an isolated scenario: the early stage (recombination—tRNA up-regulation—translational regulation); the medium stage (up-regulation of efflux pump); the late stage (resistant mutations). These results also indicated that the bacterial DNA recombination system and tRNA could be targeted to retard the bacterial spontaneous drug resistance.

Identification of Evolutionary Trajectories Associated with Antimicrobial Resistance Using Microfluidics

In vitro experimental evolution of pathogens to antibiotics is commonly used for the identification of clinical biomarkers associated with antibiotic resistance. Microdroplet emulsions allow exquisite control of spatial structure, species complexity, and selection microenvironments for such studies. We investigated the use of monodisperse microdroplets in experimental evolution. Using Escherichia coli adaptation to doxycycline, we examined how changes in environmental conditions such as droplet size, starting lambda value, selection strength, and incubation method affected evolutionary outcomes. We also examined the extent to which emulsions could reveal potentially new evolutionary trajectories and dynamics associated with antimicrobial resistance. Interestingly, we identified both expected and unexpected evolutionary trajectories including large-scale chromosomal rearrangements and amplification that were not observed in suspension culture methods. As microdroplet emulsions are well-suited for automation and provide exceptional control of conditions, they can provide a high-throughput approach for biomarker identification as well as preclinical evaluation of lead compounds.

Silent Genes: Antimicrobial Resistance and Antibiotic Production

Silent genes are DNA sequences that are generally not expressed or expressed at a very low level. These genes become active as a result of mutation, recombination, or insertion. Silent genes can also be activated in laboratory conditions using pleiotropic, targeted genome-wide, or biosynthetic gene cluster approaches. Like every other gene, silent genes can spread through horizontal gene transfer. Most studies have focused on strains with phenotypic resistance, which is the most common subject. However, to fully understand the mechanism behind the spreading of antibiotic resistance, it is reasonable to study the whole resistome, including silent genes.

Analyzing rare mutations in metagenomes assembled using long and accurate reads

The advent of long and accurate "HiFi" reads has greatly improved our ability to generate complete metagenome-assembled genomes (MAGs), enabling "complete metagenomics" studies that were nearly impossible to conduct with short reads. In particular, HiFi reads simplify the identification and phasing of mutations in MAGs: It is increasingly feasible to distinguish between positions that are prone to mutations and positions that rarely ever mutate, and to identify co-occurring groups of mutations. However, the problems of identifying rare mutations in MAGs, estimating the false-discovery rate (FDR) of these identifications, and phasing identified mutations remain open in the context of HiFi data. We present strainFlye, a pipeline for the FDR-controlled identification and analysis of rare mutations in MAGs assembled using HiFi reads. We show that deep HiFi sequencing has the potential to reveal and phase tens of thousands of rare mutations in a single MAG, identify hotspots and coldspots of these mutations, and detail MAGs' growth dynamics.

Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?

Accurate prediction of tumor progression is key for adaptive therapy and precision medicine. Cancer progression models (CPMs) can be used to infer dependencies in mutation accumulation from cross-sectional data and provide predictions of tumor progression paths. However, their performance when predicting complete evolutionary trajectories is limited by violations of assumptions and the size of available data sets. Instead of predicting full tumor progression paths, here we focus on short-term predictions, more relevant for diagnostic and therapeutic purposes. We examine whether five distinct CPMs can be used to answer the question "Given that a genotype with n mutations has been observed, what genotype with n + 1 mutations is next in the path of tumor progression?" or, shortly, "What genotype comes next?". Using simulated data we find that under specific combinations of genotype and fitness landscape characteristics CPMs can provide predictions of short-term evolution that closely match the true probabilities, and that some genotype characteristics can be much more relevant than global features. Application of these methods to 25 cancer data sets shows that their use is hampered by a lack of information needed to make principled decisions about method choice. Fruitful use of these methods for short-term predictions requires adapting method's use to local genotype characteristics and obtaining reliable indicators of performance; it will also be necessary to clarify the interpretation of the method's results when key assumptions do not hold.

Multiple novel traits without immediate benefits originate in bacteria evolving on single antibiotics

How new traits originate in evolution is a fundamental question of evolutionary biology. When such traits arise, they can either be immediately beneficial in their environment of origin, or they may become beneficial only in a future environment. Compared to immediately beneficial novel traits, novel traits without immediate benefits remain poorly studied. Here we use experimental evolution to study novel traits that are not immediately beneficial but that allow bacteria to survive in new environments. Specifically, we evolved multiple E. coli populations in five antibiotics with different mechanisms of action, and then determined their ability to grow in more than 200 environments that are different from the environment in which they evolved. Our populations evolved viability in multiple environments that contain not just clinically relevant antibiotics, but a broad range of antimicrobial molecules, such as surfactants, organic and inorganic salts, nucleotide analogues and pyridine derivatives. Genome sequencing of multiple evolved clones shows that pleiotropic mutations are important for the origin of these novel traits. Our experiments, which lasted fewer than 250 generations, demonstrate that evolution can readily create an enormous reservoir of latent traits in microbial populations. These traits can facilitate adaptive evolution in a changing world.

Advantages of laboratory natural selection in the applied sciences

In the past three decades, laboratory natural selection has become a widely used technique in biological research. Most studies which have utilized this technique are in the realm of basic science, often testing hypotheses related to mechanisms of evolutionary change or ecological dynamics. While laboratory natural selection is currently utilized heavily in this setting, there is a significant gap with its usage in applied studies, especially when compared to the other selection experiment methodologies like artificial selection and directed evolution. This is despite avenues of research in the applied sciences which seem well suited to laboratory natural selection. In this review, we place laboratory natural selection in context with other selection experiments, identify the characteristics which make it well suited for particular kinds of applied research and briefly cover key examples of the usefulness of selection experiments within applied science. Finally, we identify three promising areas of inquiry for laboratory natural selection in the applied sciences: bioremediation technology, identifying mechanisms of drug resistance and optimizing biofuel production. Although laboratory natural selection is currently less utilized in applied science when compared to basic research, the method has immense promise in the field moving forward.

Antibacterial pathway of cefquinome against Staphylococcus aureus based on label-free quantitative proteomics analysis

Cefquinome (CEQ) is a novel β-lactam antibiotic that exhibits excellent antibacterial activity against Staphylococcus aureus. However, the bacterial protein targets of CEQ are unclear. To evaluate the relationship between the pharmacokinetic/pharmacodynamic (PK/PD) parameters of CEQ and strains with varying degrees of resistance and to elucidate bacterial protein responses to CEQ treatment, label-free quantitative proteomics analysis was conducted. The sensitive S. aureus ATCC6538 and the resistant 2MIC and 8MIC were tested for differentially expressed proteins. An in vitro model was treated with different concentrations of CEQ (3, 5, or 10 µg/ml) with different terminal half-lives (2.5 or 5 h) at different intervals (12 or 24 h). Differentially expressed proteins were evaluated using Gene Ontology analysis followed by KEGG pathway enrichment analysis and STRING network analysis. RT-qPCR was performed to validate the differentially expressed proteins at the molecular level. The results showed that the degree of resistance increased in a cumulative manner and increased gradually with the extension of administration time. The resistant strain would not have appeared in the model only if %T > mutant prevention concentration ≥ 50%. The expression of 45 proteins significantly changed following CEQ treatment, among which 42 proteins were obviously upregulated and 3 were downregulated. GO analysis revealed that the differentially expressed proteins were mainly present on cells and the cell membrane, participated in metabolic and intracellular processes, and had catalytic and binding activities. The RPSO, SDHB, CITZ, ADK, and SAOUHSC 00113 genes in S. aureus may play important roles in the development of resistance to CEQ. These results provided important reference candidate proteins as targets for overcoming S. aureus resistance to CEQ.

Switching an active site helix in dihydrofolate reductase reveals limits to subdomain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study subdomain modularity by creating 20 chimeras of an enzyme, Escherichia coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue α-helical sequence is replaced by α-helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this α-helix are under selection pressure to maintain catalytic activity in DHFR. Reversion to phenylalanine at key position 31 was found to partially restore catalytic activity, which could be explained by evolutionary coupling values. We performed molecular dynamics simulations using replica exchange with solute tempering. Chimeras with low catalytic activity exhibit nonhelical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate-binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although α-helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment.

Quantitative biology of survival under antibiotic treatments

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

The Use of Experimental Evolution to Study the Response of Pseudomonas aeruginosa to Single or Double Antibiotic Treatment

The widespread use of antibiotics promotes the evolution and dissemination of drug resistance and tolerance. Both mechanisms promote survival during antibiotic exposure and their role and development can be studied in vitro with different assays to document the gradual adaptation through the selective enrichment of resistant or tolerant mutant variants. Here, we describe the use of experimental evolution in combination with time-resolved genome analysis as a powerful tool to study the interaction of antibiotic tolerance and resistance in the human pathogen Pseudomonas aeruginosa . This method guides the identification of components involved in alleviating antibiotic stress and helps to unravel specific molecular pathways leading to drug tolerance or resistance. We discuss the influence of single or double drug treatment regimens and environmental aspects on the evolution of antibiotic resilience mechanisms.

Ciprofloxacin induced antibiotic resistance in Salmonella Typhimurium mutants and genome analysis

Antibiotic resistance of Salmonella species is well reported. Ciprofloxacin is the frontline antibiotic for salmonellosis. The repeated exposure to ciprofloxacin leads to resistant strains. After 20 cycles of antibiotic exposure, resistant bacterial clones were evaluated. The colony size of the mutants was small and had an extended lag phase compared to parent strain. The whole genome sequencing showed 40,513 mutations across the genome. Small percentage (5.2%) of mutations was non-synonymous. Four-fold more transitions were observed than transversions. Ratio of < 1 transition vs transversion showed a positive selection for antibiotic resistant trait. Mutation distribution across the genome was uniform. The native plasmid was an exception and 2 mutations were observed on 90 kb plasmid. The important genes like dnaE, gyrA, iroC, metH and rpoB involved in antibiotic resistance had point mutations. The genome analysis revealed most of the metabolic pathways were affected.

Adaptation and compensation in a bacterial gene regulatory network evolving under antibiotic selection

Gene regulatory networks allow organisms to generate coordinated responses to environmental challenges. In bacteria, regulatory networks are re-wired and re-purposed during evolution, though the relationship between selection pressures and evolutionary change is poorly understood. In this study, we discover that the early evolutionary response of Escherichia coli to the antibiotic trimethoprim involves derepression of PhoPQ signaling, an Mg²⁺-sensitive two-component system, by inactivation of the MgrB feedback-regulatory protein. We report that derepression of PhoPQ confers trimethoprim-tolerance to E. coli by hitherto unrecognized transcriptional upregulation of dihydrofolate reductase (DHFR), target of trimethoprim. As a result, mutations in mgrB precede and facilitate the evolution of drug resistance. Using laboratory evolution, genome sequencing, and mutation re-construction, we show that populations of E. coli challenged with trimethoprim are faced with the evolutionary ‘choice’ of transitioning from tolerant to resistant by mutations in DHFR, or compensating for the fitness costs of PhoPQ derepression by inactivating the RpoS sigma factor, itself a PhoPQ-target. Outcomes at this evolutionary branch-point are determined by the strength of antibiotic selection, such that high pressures favor resistance, while low pressures favor cost compensation. Our results relate evolutionary changes in bacterial gene regulatory networks to strength of selection and provide mechanistic evidence to substantiate this link.

Joint effects of genes underlying a temperature specialization tradeoff in yeast

A central goal of evolutionary genetics is to understand, at the molecular level, how organisms adapt to their environments. For a given trait, the answer often involves the acquisition of variants at unlinked sites across the genome. Genomic methods have achieved landmark successes in pinpointing these adaptive loci. To figure out how a suite of adaptive alleles work together, and to what extent they can reconstitute the phenotype of interest, requires their transfer into an exogenous background. We studied the joint effect of adaptive, gain-of-function thermotolerance alleles at eight unlinked genes from Saccharomyces cerevisiae, when introduced into a thermosensitive sister species, S. paradoxus. Although the loci damped each other’s beneficial impact (that is, they were subject to negative epistasis), most boosted high-temperature growth alone and in combination, and none was deleterious. The complete set of eight genes was sufficient to confer ~15% of the S. cerevisiae thermotolerance phenotype in the S. paradoxus background. The same loci also contributed to a heretofore unknown advantage in cold growth by S. paradoxus. Together, our data establish temperature resistance in yeasts as a model case of a genetically complex evolutionary tradeoff, which can be partly reconstituted from the sequential assembly of unlinked underlying loci.

Organisms adapt to threats in the environment by acquiring DNA sequence variants that tweak traits to improve fitness. Experimental studies of this process have proven to be a particular challenge when they involve manipulation of a suite of genes, all on different chromosomes. We set out to understand how so many loci could work together to confer a trait. We used as a model system eight genes that govern the ability of the unicellular yeast Saccharomyces cerevisiae to grow at high temperature. We introduced these variant loci stepwise into a non-thermotolerant sister species, and found that the more S. cerevisiae alleles we added, the better the phenotype. We saw no evidence for toxic interactions between the genes as they were combined. We also used the eight-fold transgenic to dissect the biological mechanism of thermotolerance. And we discovered a tradeoff: the same alleles that boosted growth at high temperature eroded the organism’s ability to deal with cold conditions. These results serve as a case study of modular construction of a trait from nature, by assembling the genes together in one genome.

Antibiotic susceptibility test under a linear concentration gradient using travelling surface acoustic waves

An efficient and accurate antibiotic susceptibility test (AST) is indispensable for measuring the antimicrobial resistance of pathogenic bacteria. A minimal inhibitory concentration (MIC) can be obtained without performing repeated dilutions of the antibiotic by forming a linear antibiotic concentration gradient in a microfluidic channel. We demonstrated a device designed to use travelling surface acoustic waves (TSAWs) to enable a rapid formation of an antibiotic gradient in a few seconds. The TSAWs produced by a focused interdigital transducer deposited on the surface of a piezoelectric (LiNbO₃) substrate generated an acoustic streaming flow inside a microfluidic channel, which mixed confluent streams of antibiotics in a controlled fashion. The growth of bacteria exposed to the antibiotic gradient was determined by measuring the MIC, which was used as an indicator of the effectiveness of the AST. The concentration gradient produced using our device was linear, a feature that enhanced the reliability of measurements throughout the microchannel. Two ASTs, namely Pseudomonas aeruginosa against gentamicin and levofloxacin were chosen for the case of slowly proliferating bacteria, and one AST, namely Escherichia coli against gentamicin, were chosen for the rapidly proliferating case. Appropriate antibiotic doses for Pseudomonas aeruginosa and Escherichia coli were each obtained in an efficient manner.

Contributions of meat waste decomposition to the abundance and diversity of pathogens and antibiotic-resistance genes in the atmosphere

Airborne transmission of antibiotic-resistance genes (ARGs) in landfill and acquisition of antibiotic resistance by pathogenic bacteria are posing potential threat to human and environmental health. However, little is known about contribution of waste decomposition to airborne ARGs and pathogens during landfilling of household waste. Herein, the dynamic changes of microbial communities and ARGs were comparatively investigated in leachate and bioaerosol during the decomposition of chicken, fish, and pork wastes. Results found that chicken and pork decomposition could result in emitting high abundance of bioaerosol and pathogen, while fish fermentation will lead to high airborne microbial activity. The main pathogens were Bacilli, Burkholderia-Paraburkholderia and Mycobacterium in bioaerosols, but were Wohlfahrtiimonas, Peptoniphilus and Fusobacterium in leachate, suggesting that the ability of aerosolization of bacteria in leachate was independent of their abundance and diversity. Whereas, diversity and relative abundance of ARGs in leachate were significantly higher than bioaerosol. Moreover, the relative abundance of ARGs in leachate and bioaerosols was not completely relevant. The changes of pathogenic community contributed significantly to the prevalence of ARGs in bioaerosol and leachate. The results will define the contribution of household waste decomposition to airborne pathogen and ARG distribution and provide foundation for airborne bacterial exposure risk and control in landfill.

Vibrio splendidus persister cells induced by host coelomic fluids show a similar phenotype to antibiotic-induced counterparts

Persister cells are dormant variants of regular cells that are multidrug tolerant and have heterogeneous phenotypes; these cells are a potential threat to hosts because they can escape the immune system or antibiotic treatments and reconstitute infectious. Skin ulcer syndrome (SUS) frequently occurs in the sea cucumber (Apostichopus japonicus), and Vibrio splendidus is one of the main bacterial pathogens of SUS. This study found that the active cells of V. splendidus became persister cells more readily in the presence of A. japonicus coelomic fluids. We showed that the A. japonicus coelomic fluids plus antibiotics induce 100-fold more persister cells in V. splendidus compared with antibiotics alone via nine sets of experiments including assays for antibiotic resistance, metabolic activity, and single-cell phenotypes. Furthermore, the coelomic fluids-induced persister cells showed similar phenotypes as the antibiotic-induced persister cells. Further investigation showed that guanosine pentaphosphate/tetraphosphate (henceforth ppGpp) and SOS response pathway involved in the formation of persister cells as determined using real-time RT-PCR. In addition, single-cell observations showed that, similar to the antibiotic-induced V. splendidus persister cells, the coelomic fluids-induced persister cells have five resuscitation phenotypes: no growth, expansion, elongation, elongation and then division, and elongation followed by death/disappearance. In addition, dark foci formed in the majority of persister cells for both the antibiotic-induced and coelomic fluids-induced persister cells. Our results highlight that the pathogen V. splendidus might escape from the host immune system by entering the persister state during the process of infection due to exposure to coelomic fluids.

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

Impact of Antibiotic Resistance Genes in Gut Microbiome of Patients With Cirrhosis

BACKGROUND AND AIMS

Cirrhosis is associated with changes in intestinal microbiota that can lead to hepatic encephalopathy (HE) and infections, especially with antibiotic-resistant organisms. However, the impact of gut microbial antibiotic resistance gene (ARG) burden on clinical outcomes is unclear. The aims of the study were to determine the impact of ARGs in cirrhosis-related gut metagenome on outcomes and disease progression, study the effect of rifaximin on ARG burden, and compare ARGs in cirrhosis with chronic kidney disease (CKD) and diabetes.

METHODS

In outpatients with cirrhosis who underwent metagenomics, we evaluated change in ARG abundances with progression and their multivariable impact on 90-day hospitalizations and deaths over 1 year. We also studied ARGs pre- and 8 weeks post-rifaximin in patients with compensated cirrhosis in an open-label trial. Finally, ARGs from CKD and diabetes studies were compared with cirrhosis on machine learning.

RESULTS

A total of 163 patients with cirrhosis (43 compensated, 20 ascites-only, 30 HE-only, 70 both) and 40 controls were included. ARG abundances were higher in cirrhosis versus controls and worsened with advancing cirrhosis severity; 44 patients were hospitalized and 14 died. ARG abundances were associated with hospitalizations and mortality while controlling for cirrhosis complications, medications, and demographics. Rifaximin trial: ARG abundance patterns were minimally affected in 19 patients post-rifaximin. CKD/diabetes comparison: ARG abundance patterns in cirrhosis are distinguishable on machine learning and include more gram-positive ARGs.

CONCLUSIONS

Cirrhosis is associated with high gut microbial ARG gene burden compared with controls, which worsens with disease progression and may be different from CKD and diabetes. ARGs are not affected by rifaximin and are associated with hospitalizations and death.

Growth strategies of a model picoplankter depend on social milieu and pCO2

Phytoplankton exist in genetically diverse populations, but are often studied as single lineages (single strains), so that interpreting single-lineage studies relies critically on understanding how microbial growth differs with social milieu, defined as the presence or absence of conspecifics. The properties of lineages grown alone often fail to predict the growth of these same lineages in the presence of conspecifics, and this discrepancy points towards an opportunity to improve our understanding of the factors that affect lineage growth rates. We demonstrate that different lineages of a marine picoplankter modulate their maximum lineage growth rate in response to the presence of non-self conspecifics, even when resource competition is effectively absent. This explains why growth rates of lineages in isolation do not reliably predict their growth rates in mixed culture, or the lineage composition of assemblages under conditions of rapid growth. The diversity of growth strategies observed here are consistent with lineage-specific energy allocation that depends on social milieu. Since lineage growth is only one of many traits determining fitness in natural assemblages, we hypothesize that intraspecific variation in growth strategies should be common, with more strategies possible in ameliorated environments that support higher maximum growth rates, such as high CO2 for many marine picoplankton.

Development of antibacterial compounds that constrain evolutionary pathways to resistance

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

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

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