Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

Exploring the role of gut microbiota in antibiotic resistance and prevention

BACKGROUND/INTRODUCTION

Antimicrobial resistance (AMR) and the evolution of multiple drug-resistant (MDR) bacteria is of grave public health concern. To combat the pandemic of AMR, it is necessary to focus on novel alternatives for drug development. Within the host, the interaction of the pathogen with the microbiome plays a pivotal role in determining the outcome of pathogenesis. Therefore, microbiome-pathogen interaction is one of the potential targets to be explored for novel antimicrobials.

MAIN BODY

This review focuses on how the gut microbiome has evolved as a significant component of the resistome as a source of antibiotic resistance genes (ARGs). Antibiotics alter the composition of the native microbiota of the host by favouring resistant bacteria that can manifest as opportunistic infections. Furthermore, gut dysbiosis has also been linked to low-dosage antibiotic ingestion or subtherapeutic antibiotic treatment (STAT) from food and the environment.

DISCUSSION

Colonization by MDR bacteria is potentially acquired and maintained in the gut microbiota. Therefore, it is pivotal to understand microbial diversity and its role in adapting pathogens to AMR. Implementing several strategies to prevent or treat dysbiosis is necessary, including faecal microbiota transplantation, probiotics and prebiotics, phage therapy, drug delivery models, and antimicrobial stewardship regulation.

Pharmacodynamics of interspecies interactions in polymicrobial infections

The pharmacodynamic response of bacterial pathogens to antibiotics can be influenced by interactions with other bacterial species in polymicrobial infections (PMIs). Understanding the complex eco-evolutionary dynamics of PMIs and their impact on antimicrobial treatment response represents a step towards developing improved treatment strategies for PMIs. Here, we investigated how interspecies interactions in a multi-species bacterial community affect the pharmacodynamic response to antimicrobial treatment. To this end, we developed an in silico model which combined agent-based modeling with ordinary differential equations. Our analyses suggest that both interspecies interactions, modifying either drug sensitivity or bacterial growth rate, and drug-specific pharmacological properties drive the bacterial pharmacodynamic response. Furthermore, lifestyle of the bacterial population and the range of interactions can influence the impact of species interactions. In conclusion, this study provides a foundation for the design of antimicrobial treatment strategies for PMIs which leverage the effects of interspecies interactions.

Antibiotic exposure enriches streptococci carrying resistance genes in periodontitis plaque biofilms

Background

Periodontitis is not always satisfactorily treated with conventional scaling and root planing, and adjunctive use of antibiotics is required in clinical practice. Therefore, it is important for clinicians to understand the diversity and the antibiotic resistance of subgingival microbiota when exposed to different antibiotics.

Materials and Methods

In this study, subgingival plaques were collected from 10 periodontitis patients and 11 periodontally healthy volunteers, and their microbiota response to selective pressure of four antibiotics (amoxicillin, metronidazole, clindamycin, and tetracycline) were evaluated through 16S rRNA gene amplicon and metagenomic sequencing analysis. Additionally, sensitive and resistant strains were isolated and cultured in vitro for resistance evaluation.

Results

Cultivation of subgingival microbiota revealed the oral microbiota from periodontitis patients were more resistant to antibiotics than that of healthy. Significant differences were also observed for the microbial community between with and without antibiotics (especially amoxicillin and tetracycline) treated in periodontitis group.

Conclusion

Overall, after the two antibiotics (amoxicillin and tetracycline) exposed, the oral subgingival microbiota in periodontitis patients exhibited different diversity and composition. Streptococcus may account for oral biofilm-specific antibiotic resistance in periodontitis. This provides information for personalized treatment of periodontitis.

An ecological and stochastic perspective on persisters resuscitation

Resistance, tolerance, and persistence to antibiotics have mainly been studied at the level of a single microbial isolate. However, in recent years it has become evident that microbial interactions play a role in determining the success of antibiotic treatments, in particular by influencing the occurrence of persistence and tolerance within a population. Additionally, the challenge of resuscitation (the capability of a population to revive after antibiotic exposure) and pathogen clearance are strongly linked to the small size of the surviving population and to the presence of fluctuations in cell counts. Indeed, while large population dynamics can be considered deterministic, small populations are influenced by stochastic processes, making their behaviour less predictable. Our study argues that microbe-microbe interactions within a community affect the mode, tempo, and success of persister resuscitation and that these are further influenced by noise. To this aim, we developed a theoretical model of a three-member microbial community and analysed the role of cell-to-cell interactions on pathogen clearance, using both deterministic and stochastic simulations. Our findings highlight the importance of ecological interactions and population size fluctuations (and hence the underlying cellular mechanisms) in determining the resilience of microbial populations following antibiotic treatment.

Intraspecific Diversity of Staphylococcus aureus Populations Isolated from Cystic Fibrosis Respiratory Infections

Chronic bacterial infections are often polymicrobial, comprising multiple bacterial species or variants of the same species. Because chronic infections may last for decades, they have the potential to generate high levels of intraspecific variation through within-host diversification over time, and the potential for superinfections to occur through the introduction of multiple pathogen populations to the ongoing infection. Traditional methods for identifying infective agents generally involve isolating one single colony from a given sample, usually after selecting for a specific pathogen or antibiotic resistance profile. Isolating a recognized virulent or difficult to treat pathogen is an important part of informing clinical treatment and correlative research; however, these reductive methods alone, do not provide researchers or healthcare providers with the potentially important perspective on the true pathogen population structure and dynamics over time. To begin to address this limitation, in this study, we compare findings on Staphylococcus aureus single colonies versus and pools of colonies taken from fresh sputum samples from three patients with cystic fibrosis to isolates collected from the same sputum samples and processed by the clinical microbiology laboratory. Phenotypic and genotypic analysis of isolated S. aureus populations revealed coexisting lineages in two of three sputum samples as well as population structures that were not reflected in the single colony isolates. Altogether, our observations presented here demonstrate that clinically relevant diversity can be missed with standard sampling methods when assessing chronic infections. More broadly, this work outlines the potential impact that comprehensive population-level sampling may have for both research efforts and more effective treatment practices.

Nutrient competition predicts gut microbiome restructuring under drug perturbations

Human gut commensal bacteria are routinely exposed to various stresses, including therapeutic drugs, and collateral effects are difficult to predict. To systematically interrogate community-level effects of drug perturbations, we screened stool-derived in vitro communities with 707 clinically relevant small molecules. Across ∼5,000 community-drug interaction conditions, compositional and metabolomic responses were predictably impacted by nutrient competition, with certain species exhibiting improved growth due to adverse impacts on competitors. Changes to community composition were generally reversed by reseeding with the original community, although occasionally species promotion was long-lasting, due to higher-order interactions, even when the competitor was reseeded. Despite strong selection pressures, emergence of resistance within communities was infrequent. Finally, while qualitative species responses to drug perturbations were conserved across community contexts, nutrient competition quantitatively affected their abundances, consistent with predictions of consumer-resource models. Our study reveals that quantitative understanding of the interaction landscape, particularly nutrient competition, can be used to anticipate and potentially mitigate side effects of drug treatment on the gut microbiota.

Bacteriophage specificity is impacted by interactions between bacteria

Predators play a central role in shaping community structure, function, and stability. The degree to which bacteriophage predators (viruses that infect bacteria) evolve to be specialists with a single bacterial prey species versus generalists able to consume multiple types of prey has implications for their effect on microbial communities. The presence and abundance of multiple bacterial prey types can alter selection for phage generalists, but less is known about how interactions between prey shape predator specificity in microbial systems. Using a phenomenological mathematical model of phage and bacterial populations, we find that the dominant phage strategy depends on prey ecology. Given a fitness cost for generalism, generalist predators maintain an advantage when prey species compete, while specialists dominate when prey are obligately engaged in cross-feeding interactions. We test these predictions in a synthetic microbial community with interacting strains of Escherichia coli and Salmonella enterica by competing a generalist T5-like phage able to infect both prey against P22vir, an S. enterica-specific phage. Our experimental data conform to our modeling expectations when prey species are competing or obligately mutualistic, although our results suggest that the in vitro cost of generalism is caused by a combination of biological mechanisms not anticipated in our model. Our work demonstrates that interactions between bacteria play a role in shaping ecological selection on predator specificity in obligately lytic bacteriophages and emphasizes the diversity of ways in which fitness trade-offs can manifest.

IMPORTANCE

There is significant natural diversity in how many different types of bacteria a bacteriophage can infect, but the mechanisms driving this diversity are unclear. This study uses a combination of mathematical modeling and an in vitro system consisting of Escherichia coli, Salmonella enterica, a T5-like generalist phage, and the specialist phage P22vir to highlight the connection between bacteriophage specificity and interactions between their potential microbial prey. Mathematical modeling suggests that competing bacteria tend to favor generalist bacteriophage, while bacteria that benefit each other tend to favor specialist bacteriophage. Experimental results support this general finding. The experiments also show that the optimal phage strategy is impacted by phage degradation and bacterial physiology. These findings enhance our understanding of how complex microbial communities shape selection on bacteriophage specificity, which may improve our ability to use phage to manage antibiotic-resistant microbial infections.

Dynamic Effect of β-Lactam Antibiotic Inactivation Due to the Inter- and Intraspecies Interaction of Drug-Resistant Microbes

The presence of β-lactamase positive microorganisms imparts a pharmacological effect on a variety of organisms that can impact drug efficacy by influencing the function or composition of bacteria. Although studies to assess dynamic intra- and interspecies communication with bacterial communities exist, the efficacy of drug treatment and quantitative assessment of multiorganism response is not well understood due to the lack of technological advances that can be used to study coculture interactions in a dynamic format. In this study, we investigate how β-lactamase positive microorganisms can neutralize the effect of β-lactam antibiotics in a dynamic format at the inter- and intraspecies level using microbial bead technology. Three interactive models for the biological compartmentalization of organisms were demonstrated to evaluate the effect of β-lactam antibiotics on coculture systems. Our model at the intraspecies level attempts to mimic the biofilm matrix more closely as a community-level feature of microorganisms, which acknowledges the impact of nondrug-resistant species in shaping the dynamic response. In particular, the results of intraspecies studies are highly supportive of the biofilm mode of bacterial growth, which can provide structural support and protect the bacteria from an assault on host or environmental factors. Our findings also indicate that β-lactamase positive bacteria can neutralize the cytotoxic effect of β-lactam antibiotics at the interspecies level when cocultured with cancer cells. Results were validated using β-lactamase positive bacteria isolated from environmental niches, which can trigger phenotypical alteration of β-lactams when cocultured with other organisms. Our compartmentalization strategy acts as an independent ecosystem and provides a new avenue for multiscale studies to assess intra- and interspecies interactions.

Screening and selection of essential oils for an intranasal spray against bovine respiratory pathogens based on antimicrobial, antiviral, immunomodulatory, and antibiofilm activities

Introduction

The rise in antibiotic resistant pathogens associated with bovine respiratory disease (BRD) poses a serious challenge, particularly to the beef feedlot industry, as they currently depend on antibiotics to prevent BRD to mitigate the financial burden (approx. $1 billion annual loss) inflicted by BRD-associated high mortality and morbidity in feedlot cattle. Thus, there is an impetus need for the development of antimicrobial alternative strategies against BRD. This study aimed to screen and select candidate essential oils (EOs) for the development of an intranasal EO spray that can inhibit BRD pathogens and promote microbiota-mediated respiratory health.

Methods

The effects of selected EOs (ajowan, cinnamon leaf, citronella, grapefruit, fennel, and thyme) on a bovine nasopharyngeal microbiota culture were evaluated using 16S rRNA gene sequencing. The microbiota culture was enriched by incubating nasopharyngeal swabs obtained from finishing beef heifers in brain heart infusion broth with and without EOs (0.025%, v/v). These EOs were then also evaluated for their immunomodulatory effects on bovine turbinate (BT) cells by analyzing the concentrations of 15 cytokines and chemokines in cell culture after 24 h incubation. The crystal violet assay was done to assess the antibiofilm activity of EOs against Escherichia coli UMN026 strain. Finally, 15 EOs were screened for their antiviral activity against the bovine viral diarrhea virus 1 (BVDV-1) using BT cells and a fluorescence-based method.

Results

Ajowan, fennel, and thyme resulted in a moderate reduction of overall nasopharyngeal microbiota growth with significant alterations of both alpha and beta diversity, and the relative abundance of predominant bacterial families (e.g., increasing Enterobacteriaceae and decreasing Moraxellaceae) compared to the control (p < 0.05). Co-incubation of BT cells with selected EOs resulted in minimal alterations in cytokine and chemokine levels (p > 0.05). Ajowan, thyme, fennel, and cinnamon leaf exhibited antibiofilm activity at concentrations of 0.025 and 0.05%. Reduction of BVDV-1 replication in BT cells was observed with thyme (strong), and ajowan and citronella (moderate) at 0.0125% concentration.

Discussion

Accordingly, ajowan, thyme, fennel, cinnamon leaf, and citronella EOs were selected for further development as an intranasal EO spray to prevent and control of BRD pathogens in feedlot cattle.

Emergent antibiotic persistence in a spatially structured synthetic microbial mutualism

Antibiotic persistence (heterotolerance) allows a subpopulation of bacteria to survive antibiotic-induced killing and contributes to the evolution of antibiotic resistance. Although bacteria typically live in microbial communities with complex ecological interactions, little is known about how microbial ecology affects antibiotic persistence. Here, we demonstrated within a synthetic two-species microbial mutualism of Escherichia coli and Salmonella enterica that the combination of cross-feeding and community spatial structure can emergently cause high antibiotic persistence in bacteria by increasing the cell-to-cell heterogeneity. Tracking ampicillin-induced death for bacteria on agar surfaces, we found that E. coli forms up to 55 times more antibiotic persisters in the cross-feeding coculture than in monoculture. This high persistence could not be explained solely by the presence of S. enterica, the presence of cross-feeding, average nutrient starvation, or spontaneous resistant mutations. Time-series fluorescent microscopy revealed increased cell-to-cell variation in E. coli lag time in the mutualistic co-culture. Furthermore, we discovered that an E. coli cell can survive antibiotic killing if the nearby S. enterica cells on which it relies die first. In conclusion, we showed that the high antibiotic persistence phenotype can be an emergent phenomenon caused by a combination of cross-feeding and spatial structure. Our work highlights the importance of considering spatially structured interactions during antibiotic treatment and understanding microbial community resilience more broadly.

Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China

Sharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.

Antibiotic perturbations to the gut microbiome

Antibiotic-mediated perturbation of the gut microbiome is associated with numerous infectious and autoimmune diseases of the gastrointestinal tract. Yet, as the gut microbiome is a complex ecological network of microorganisms, the effects of antibiotics can be highly variable. With the advent of multi-omic approaches for systems-level profiling of microbial communities, we are beginning to identify microbiome-intrinsic and microbiome-extrinsic factors that affect microbiome dynamics during antibiotic exposure and subsequent recovery. In this Review, we discuss factors that influence restructuring of the gut microbiome on antibiotic exposure. We present an overview of the currently complex picture of treatment-induced changes to the microbial community and highlight essential considerations for future investigations of antibiotic-specific outcomes. Finally, we provide a synopsis of available strategies to minimize antibiotic-induced damage or to restore the pretreatment architectures of the gut microbial community.

A comprehensive profiling of quorum quenching by bacterial pigments identifies quorum sensing inhibition and antibiofilm action of prodigiosin against Acinetobacter baumannii

Bacterial pigments represent a diverse group of secondary metabolites, which confer fitness advantages to the producers while residing in communities. The bioactive potential of such metabolites, including antimicrobial, anticancer, and immunomodulation, are being explored. Reckoning that a majority of such pigments are produced in response to quorum sensing (QS) mediated expression of biosynthetic gene clusters and, in turn, influence cell-cell communication, systemic profiling of the pigments for possible impact on QS appears crucial. A systemic screening of bacterial pigments for QS-inhibition combined with exploration of antibiofilm and antimicrobial action against Acinetobacter baumannii might offer viable alternatives to combat the priority pathogen. Major bacterial pigments are classified (clustered) based on their physicochemical properties, and representatives of the clusters are screened for QS inhibition. The screen highlighted prodigiosin as a potent quorum quencher, although its production from Serratia marcescens appeared to be QS-independent. In silico analysis indicated potential interactions between AbaI and AbaR, two major QS regulators in A. baumannii, and prodigiosin, which impaired biofilm formation, a major QS-dependent process in the bacteria. Prodigiosin augmented antibiotic action of ciprofloxacin against A. baumannii biofilms. Cell viability analysis revealed prodigiosin to be modestly cytotoxic against HEK293, a non-cancer human cell line. While developing dual-species biofilm, prodigiosin producer S. marcescens significantly impaired the fitness of A. baumannii. Enhanced susceptibility of A. baumannii toward colistin was also noted while growing in co-culture with S. marcescens. Antibiotic resistant isolates demonstrated varied responsiveness against prodigiosin, with two resistant strains demonstrating possible collateral sensitivity. Collectively, the results underpin the prospect of a prodigiosin-based therapeutic strategy in combating A. baumannii infection.

Phenotypic and Genotypic Comparison of Antimicrobial-Resistant Variants of Escherichia coli and Salmonella Typhimurium Isolated from Evolution Assays with Antibiotics or Commercial Products Based on Essential Oils

On account of the widespread development and propagation of antimicrobial-resistant (AMR) bacteria, essential oils (EOs) have emerged as potential alternatives to antibiotics. However, as already observed for antibiotics, recent studies have raised concerns regarding the potential emergence of resistant variants (RVs) to EOs. In this study, we assessed the emergence of RVs in Escherichia coli and Salmonella enterica Typhimurium after evolution assays under extended exposure to subinhibitory doses of two commercial EOs (AEN and COLIFIT) as well as to two antibiotics (amoxicillin and colistin). Phenotypic characterization of RVs from evolution assays with commercial EOs yielded no relevant increases in the minimum inhibitory concentration (MIC) of E. coli and did not even modify MIC values in S. Typhimurium. Conversely, RVs of E. coli and S. Typhimurium isolated from evolution assays with antibiotics showed increased resistance. Genotypic analysis demonstrated that resistance to commercial EOs was associated with enhanced protection against oxidative stress and redirection of cell energy toward efflux activity, while resistance to antibiotics was primarily linked to modifications in the cell binding sites of antibiotics. These findings suggest that AEN and COLIFIT could serve as safe alternatives to antibiotics in combating the emergence and dissemination of antimicrobial resistance within the agrifood system.

Translating eco-evolutionary biology into therapy to tackle antibiotic resistance

Antibiotic resistance is currently one of the most important public health problems. The golden age of antibiotic discovery ended decades ago, and new approaches are urgently needed. Therefore, preserving the efficacy of the antibiotics currently in use and developing compounds and strategies that specifically target antibiotic-resistant pathogens is critical. The identification of robust trends of antibiotic resistance evolution and of its associated trade-offs, such as collateral sensitivity or fitness costs, is invaluable for the design of rational evolution-based, ecology-based treatment approaches. In this Review, we discuss these evolutionary trade-offs and how such knowledge can aid in informing combination or alternating antibiotic therapies against bacterial infections. In addition, we discuss how targeting bacterial metabolism can enhance drug activity and impair antibiotic resistance evolution. Finally, we explore how an improved understanding of the original physiological function of antibiotic resistance determinants, which have evolved to reach clinical resistance after a process of historical contingency, may help to tackle antibiotic resistance.

Antibiotic resistance in bacterial communities

Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem.

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'.

Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics

In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile's antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile's response to antibiotics, which could inform therapeutic interventions.

Community interactions drive the evolution of antibiotic tolerance in bacteria

The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in the growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in the death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental, and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified two genetic hot spots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied.

Mutualism-enhancing mutations dominate early adaptation in a two-species microbial community

Species interactions drive evolution while evolution shapes these interactions. The resulting eco-evolutionary dynamics and their repeatability depend on how adaptive mutations available to community members affect fitness and ecologically relevant traits. However, the diversity of adaptive mutations is not well characterized, and we do not know how this diversity is affected by the ecological milieu. Here we use barcode lineage tracking to address this question in a community of yeast Saccharomyces cerevisiae and alga Chlamydomonas reinhardtii that have a net commensal relationship that results from a balance between competitive and mutualistic interactions. We find that yeast has access to many adaptive mutations with diverse ecological consequences, in particular those that increase and reduce the yields of both species. The presence of the alga does not change which mutations are adaptive in yeast (that is, there is no fitness trade-off for yeast between growing alone or with alga), but rather shifts selection to favour yeast mutants that increase the yields of both species and make the mutualism stronger. Thus, in the presence of the alga, adaptative mutations contending for fixation in yeast are more likely to enhance the mutualism, even though cooperativity is not directly favoured by natural selection in our system. Our results demonstrate that ecological interactions not only alter the trajectory of evolution but also dictate its repeatability; in particular, weak mutualisms can repeatably evolve to become stronger.

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.

Resistance evolution can disrupt antibiotic exposure protection through competitive exclusion of the protective species

Antibiotic degrading bacteria can reduce the efficacy of drug treatments by providing antibiotic exposure protection to pathogens. While this has been demonstrated at the ecological timescale, it is unclear how exposure protection might alter and be affected by pathogen antibiotic resistance evolution. Here, we utilised a two-species model cystic fibrosis (CF) community where we evolved the bacterial pathogen Pseudomonas aeruginosa in a range of imipenem concentrations in the absence or presence of Stenotrophomonas maltophilia, which can detoxify the environment by hydrolysing β-lactam antibiotics. We found that P. aeruginosa quickly evolved resistance to imipenem via parallel loss of function mutations in the oprD porin gene. While the level of resistance did not differ between mono- and co-culture treatments, the presence of S. maltophilia increased the rate of imipenem resistance evolution in the four μg/ml imipenem concentration. Unexpectedly, imipenem resistance evolution coincided with the extinction of S. maltophilia due to increased production of pyocyanin, which was cytotoxic to S. maltophilia. Together, our results show that pathogen resistance evolution can disrupt antibiotic exposure protection due to competitive exclusion of the protective species. Such eco-evolutionary feedbacks may help explain changes in the relative abundance of bacterial species within CF communities despite intrinsic resistance to anti-pseudomonal drugs.

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.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.

Putting microbial interactions back into community contexts

Microbial interactions are key aspects of the biology of microbiomes. Recently, there has been a shift in the field towards studying interactions in more representative contexts, whether using multispecies model microbial communities or by looking at interactions in situ. Across diverse microbial systems, these studies have begun to identify common interaction mechanisms. These mechanisms include interactions related to toxic molecules, nutrient competition and cross-feeding, access to metals, signaling pathways, pH changes, and interactions within biofilms. Leveraging technological innovations, many of these studies have used an interdisciplinary approach combining genetic, metabolomic, imaging, and/or microfluidic techniques to gain insight into mechanisms of microbial interactions and into the impact of these interactions on microbiomes.

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs - and in turn, the adaptation of growth rate - is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Positive interactions within and between populations decrease the likelihood of evolutionary rescue

Positive interactions, including intraspecies cooperation and interspecies mutualisms, play crucial roles in shaping the structure and function of many ecosystems, ranging from plant communities to the human microbiome. While the evolutionary forces that form and maintain positive interactions have been investigated extensively, the influence of positive interactions on the ability of species to adapt to new environments is still poorly understood. Here, we use numerical simulations and theoretical analyses to study how positive interactions impact the likelihood that populations survive after an environment deteriorates, such that survival in the new environment requires quick adaptation via the rise of new mutants-a scenario known as evolutionary rescue. We find that the probability of evolutionary rescue in populations engaged in positive interactions is reduced significantly. In cooperating populations, this reduction is largely due to the fact that survival may require at least a minimal number of individuals, meaning that adapted mutants must arise and spread before the population declines below this threshold. In mutualistic populations, the rescue probability is decreased further due to two additional effects-the need for both mutualistic partners to adapt to the new environment, and competition between the two species. Finally, we show that the presence of cheaters reduces the likelihood of evolutionary rescue even further, making it extremely unlikely. These results indicate that while positive interactions may be beneficial in stable environments, they can hinder adaptation to changing environments and thereby elevate the risk of population collapse. Furthermore, these results may hint at the selective pressures that drove co-dependent unicellular species to form more adaptable organisms able to differentiate into multiple phenotypes, including multicellular life.

Ignoring social distancing: advances in understanding multi-species bacterial interactions

Almost every ecosystem on this planet is teeming with microbial communities made of diverse bacterial species. At a reductionist view, many of these bacteria form pairwise interactions, but, as the field of view expands, the neighboring organisms and the abiotic environment can play a crucial role in shaping the interactions between species. Over the years, a strong foundation of knowledge has been built on isolated pairwise interactions between bacteria, but now the field is advancing toward understanding how cohabitating bacteria and natural surroundings affect these interactions. Use of bottom-up approaches, piecing communities together, and top-down approaches that deconstruct communities are providing insight on how different species interact. In this review, we highlight how studies are incorporating more complex communities, mimicking the natural environment, and recurring findings such as the importance of cooperation for stability in harsh environments and the impact of bacteria-induced environmental pH shifts. Additionally, we will discuss how omics are being used as a top-down approach to identify previously unknown interspecies bacterial interactions and the challenges of these types of studies for microbial ecology.

Microbial model communities: To understand complexity, harness the power of simplicity

Natural microbial communities are complex ecosystems with myriads of interactions. To deal with this complexity, we can apply lessons learned from the study of model organisms and try to find simpler systems that can shed light on the same questions. Here, microbial model communities are essential, as they can allow us to learn about the metabolic interactions, genetic mechanisms and ecological principles governing and structuring communities. A variety of microbial model communities of varying complexity have already been developed, representing different purposes, environments and phenomena. However, choosing a suitable model community for one's research question is no easy task. This review aims to be a guide in the selection process, which can help the researcher to select a sufficiently well-studied model community that also fulfills other relevant criteria. For example, a good model community should consist of species that are easy to grow, have been evaluated for community behaviors, provide simple readouts and - in some cases - be of relevance for natural ecosystems. Finally, there is a need to standardize growth conditions for microbial model communities and agree on definitions of community-specific phenomena and frameworks for community interactions. Such developments would be the key to harnessing the power of simplicity to start disentangling complex community interactions.

Competition delays multi-drug resistance evolution during combination therapy

Combination therapies have shown remarkable success in preventing the evolution of resistance to multiple drugs, including HIV, tuberculosis, and cancer. Nevertheless, the rise in drug resistance still remains an important challenge. The capability to accurately predict the emergence of resistance, either to one or multiple drugs, may help to improve treatment options. Existing theoretical approaches often focus on exponential growth laws, which may not be realistic when scarce resources and competition limit growth. In this work, we study the emergence of single and double drug resistance in a model of combination therapy of two drugs. The model describes a sensitive strain, two types of single-resistant strains, and a double-resistant strain. We compare the probability that resistance emerges for three growth laws: exponential growth, logistic growth without competition between strains, and logistic growth with competition between strains. Using mathematical estimates and numerical simulations, we show that between-strain competition only affects the emergence of single resistance when resources are scarce. In contrast, the probability of double resistance is affected by between-strain competition over a wider space of resource availability. This indicates that competition between different resistant strains may be pertinent to identifying strategies for suppressing drug resistance, and that exponential models may overestimate the emergence of resistance to multiple drugs. A by-product of our work is an efficient strategy to evaluate probabilities of single and double resistance in models with multiple sequential mutations. This may be useful for a range of other problems in which the probability of resistance is of interest.

Tracking Antimicrobial Resistance Determinants in Diarrheal Pathogens: A Cross-Institutional Pilot Study

Infectious diarrhea affects over four billion individuals annually and causes over a million deaths each year. Though not typically prescribed for treatment of uncomplicated diarrheal disease, antimicrobials serve as a critical part of the armamentarium used to treat severe or persistent cases. Due to widespread over- and misuse of antimicrobials, there has been an alarming increase in global resistance, for which a standardized methodology for geographic surveillance would be highly beneficial. To demonstrate that a standardized methodology could be used to provide molecular surveillance of antimicrobial resistance (AMR) genes, we initiated a pilot study to test 130 diarrheal pathogens (Campylobacter spp., Escherichia coli, Salmonella, and Shigella spp.) from the USA, Peru, Egypt, Cambodia, and Kenya for the presence/absence of over 200 AMR determinants. We detected a total of 55 different determinants conferring resistance to ten different categories of antimicrobials: genes detected in ≥ 25 samples included bla_TEM, tet(A), tet(B), mac(A), mac(B), aadA1/A2, strA, strB, sul1, sul2, qacEΔ1, cmr, and dfrA1. The number of determinants per strain ranged from none (several Campylobacter spp. strains) to sixteen, with isolates from Egypt harboring a wider variety and greater number of genes per isolate than other sites. Two samples harbored carbapenemase genes, bla_OXA-48 or bla_NDM. Genes conferring resistance to azithromycin (ere(A), mph(A)/mph(K), erm(B)), a first-line therapeutic for severe diarrhea, were detected in over 10% of all Enterobacteriaceae tested: these included >25% of the Enterobacteriaceae from Egypt and Kenya. Forty-six percent of the Egyptian Enterobacteriaceae harbored genes encoding CTX-M-1 or CTX-M-9 families of extended-spectrum β-lactamases. Overall, the data provide cross-comparable resistome information to establish regional trends in support of international surveillance activities and potentially guide geospatially informed medical care.