Community interactions and spatial structure shape selection on antibiotic resistant lineages

Resource limitation and population fluctuation drive spatiotemporal order in microbial communities

Microbial communities display complex spatiotemporal behaviors leading to spatially-structured and ordered organization driven by species interactions and environmental factors. Resource availability plays a pivotal role in shaping the dynamics of bacterial colonies. In this study, we delve into the intricate interplay between resource limitation and the emergent properties of a growing colony of two visually distinct bacterial strains having similar growth and mechanical properties. Employing an agent-based modeling and computer simulations, we analyze the resource-driven effect on segregation and sectoring, cell length regulation and nematic ordering within a growing colony. We introduce a dimensionless parameter referred to as the active layer thickness, derived from nutrient diffusion equations, indicating effective population participation due to local resource availability. Our results reveal that lower values of active layer thickness arising from decreased resource abundance lead to rougher colony fronts, fostering heightened population fluctuations within the colony and faster spatial genetic diversity loss. Our temporal analyses unveil the dynamics of mean cell length and fluctuations, showcasing how initial disturbances evolve as colonies are exposed to nutrients and subsequently settle. Furthermore, examining microscopic details, we find that lower resource levels yield diverse cell lengths and enhanced nematic ordering, driven by the increased prevalence of longer rod-shaped cells. Our investigation sheds light on the multifaceted relationship between resource constraints and bacterial colony dynamics, revealing insights into their spatiotemporal organization.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Cooperation between autotrophic and heterotrophic denitrifiers under low C/N ratios revealed by individual-based modelling

Denitrifying biofilms, in which autotrophic denitrifiers (AD) and heterotrophic denitrifiers (HD) coexist, play a crucial role in removing nitrate from water or wastewater. However, it is difficult to elucidate the interactions between HD and AD through sequencing-based experimental methods. Here, we developed an individual-based model to describe the interspecies dynamics and priority effects between sulfur-based AD (Thiobacillus denitrificans) and HD (Thauera phenylcarboxya) under different C/N ratios. In test I (coexistence simulation), AD and HD were initially inoculated at a ratio of 1:1. The simulation results showed excellent denitrification performance and a coaggregation pattern of denitrifiers, indicating that cooperation was the predominant interaction at a C/N ratio of 0.25 to 1.5. In test II (invasion simulation), in which only one type of denitrifier was initially inoculated and the other was added at the invasion time, denitrifiers exhibited a stratification pattern in biofilms. When HD invaded AD, the final HD abundance decreased with increasing invasion time, indicating an enhanced priority effect. When AD invaded HD, insufficient organic carbon sources weakened the priority effect by limiting the growth of HD populations. This study reveals the interaction between autotrophic and heterotrophic denitrifiers, providing guidance for optimizing wastewater treatment process.

Salmonella-induced microbiome profile in response to sanitation by quaternary ammonium chloride

Salmonella enterica is a prominent cause of foodborne disease in the United States. However, the mechanism and route of pathogen transmission that leads to Salmonella infection in commercial processing plants are poorly understood. This study aimed to investigate the effect of mixed-species biofilms on S. enterica survival and persistence under sanitizer stress [Quaternary ammonium compounds (QACs)] by analyzing 78 floor drain samples from a meat processing facility and three S. enterica strains (serovars Cerro, Montevideo, and Typhimurium) isolated from that facility and an unrelated source. The four test groups were as follows: control, QAC treatment, Salmonella addition, and QAC treatment with Salmonella addition. DNAs were extracted, and 16S rRNA gene based on the variable region V4 amplicon sequencing was performed to analyze the relative abundance, core microbiome, and Alpha and Beta diversity using the qiime2 pipeline. At the genus level, the Brochothrix (45.56%), Pseudomonas (38.94%), Carnobacterium (6.18%), Lactococcus (4.68%), Serratia (3.14%), and Staphylococcus (0.82%) were shown to be the most prevalent in all drain samples. The results demonstrate that the relative abundance of different bacterial genera was affected by both QAC treatment and Salmonella addition, with some genera showing increases or decreases in abundance. Notably, the correlation network was constructed to understand the relationships between the different bacteria. Nitrospira had the greatest number of connections in the floor drain environment network, with two negative and eight positive correlations. The results suggest that Nitrospira in the mixed-species biofilm community may play a role in converting ammonium in the QAC sanitizer into nitrites. Thus, Nitrospira could be a potentially important genus in providing sanitizer resistance to pathogen-encompassed mixed-species biofilms.IMPORTANCESalmonella contamination in meat processing facilities can lead to foodborne illness outbreaks. Our study characterized the microbiome dynamics in beef facility drains and their response to Salmonella addition and common sanitizer (QAC). Nitrospira could be an important genus in providing sanitizer resistance to pathogen-encompassed mixed-species biofilms. The results provide insight into the impact of mixed-species biofilms on Salmonella survival and persistence under sanitizer stress in meat processing facilities. The results highlight the need to consider mixed-species biofilm effects when developing targeted interventions to enhance food safety.

Antibiotic resistance genes in integrated surface ice, cryoconite, and glacier-fed stream in a mountain glacier in Central Asia

Glacier ice, cryoconite, and glacier-fed streams are interconnected features that have important implications for the dynamics and distribution of abiotic and biotic materials. However, the presence and behavior of antibiotic resistance genes (ARGs) within these glacial environments remained largely unexplored. Addressing this gap, we hypothesized that ARGs are widely distributed and exhibit distinct yet interconnected patterns of diversity and dynamics in these glacial environments. Here, we investigated ARGs in a mountain glacier in Central Asia. A total of 944 ARGs, spanning 22 antibiotic classes, were identified, with 633 ARGs shared across all three environments. Cryoconite exhibited the highest ARG richness, followed by ice, while stream biofilm displayed the lowest value. Exploring ARG profiles, we observed a consistent pattern in terms of antibiotic class and resistance mechanism across all three environments. Beta-lactam resistance genes exhibited the highest diversity, followed by multidrug, glycopeptide, and MLS. The predominant mechanisms were antibiotic inactivation, antibiotic efflux, and target alteration. The most prevalent ARG is cls, followed by mdfA, ropB, fabI, and macB. The similarity in ARG profiles between surface ice and cryoconite samples was more pronounced than their resemblance to stream biofilm samples. The variations of ARG profiles between any pair of environments were largely contributed by turnover component. Further insights into microbial interactions revealed 2328 significant associations between 80 OTUs and 356 ARGs, indicating complex relationships. Certain OTUs, including those from the genera Polaromonas, Ferruginibacter, Hymenobacter, Phormidesmis, Novosphingobium, and Polymorphobacter, were speculated as potential hosts for a variety of ARGs. Our findings underscore the intricate dynamics of antibiotic resistance in glacial ecosystems, emphasizing the need for a holistic understanding of ARG distribution, diversity, and associations across diverse environmental compartments. This research contributes valuable insights into the potential ecological implications of antibiotic resistance dissemination in cold environments, particularly as influenced by increasing climate change.

Flagellar interference with plasmid uptake in biofilms: a joint experimental and modeling study

Plasmid conjugation is a key facilitator of horizontal gene transfer (HGT), and plasmids encoding antibiotic resistance drive the increasing prevalence of antibiotic resistance. In natural, engineered, and clinical environments, bacteria often grow in protective biofilms. Therefore, a better understanding of plasmid transfer in biofilms is needed. Our aim was to investigate plasmid transfer in a biofilm-adapted wrinkly colony mutant of Xanthomonas retroflexus (XRw) with enhanced matrix production and reduced motility. We found that XRw biofilms had an increased uptake of the broad host-range IncP-1ϵ plasmid pKJK5 compared to the wild type (WT). Proteomics revealed fewer flagellar-associated proteins in XRw, suggesting that flagella were responsible for reducing plasmid uptake. This was confirmed by the higher plasmid uptake of non-flagellated fliM mutants of the X. retroflexus wrinkly mutant as well as the wild type. Moreover, testing several flagellar mutants of Pseudomonas putida suggested that the flagellar effect was more general. We identified seven mechanisms with the potential to explain the flagellar effect and simulated them in an individual-based model. Two mechanisms could thus be eliminated (increased distances between cells and increased lag times due to flagella). Another mechanism identified as viable in the modeling was eliminated by further experiments. The possibility of steric hindrance of pilus movement and binding by flagella, reducing the frequency of contact and thus plasmid uptake, proved viable, and the three other viable mechanisms had a reduced probability of plasmid transfer in common. Our findings highlight the important yet complex effects of flagella during bacterial conjugation in biofilms.IMPORTANCEBiofilms are the dominant form of microbial life and bacteria living in biofilms are markedly different from their planktonic counterparts, yet the impact of the biofilm lifestyle on horizontal gene transfer (HGT) is still poorly understood. Horizontal gene transfer by conjugative plasmids is a major driver in bacterial evolution and adaptation, as exemplified by the troubling spread of antibiotic resistance. To either limit or promote plasmid prevalence and dissemination, we need a better understanding of plasmid transfer between bacterial cells, especially in biofilms. Here, we identified a new factor impacting the transfer of plasmids, flagella, which are required for many types of bacterial motility. We show that their absence or altered activity can lead to enhanced plasmid uptake in two bacterial species, Xanthomonas retroflexus and Pseudomonas putida. Moreover, we demonstrate the utility of mathematical modeling to eliminate hypothetical mechanisms.

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.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Multispecies coinfections and presence of antibiotics shape resistance and fitness costs in a pathogenic bacterium

Increasing antimicrobial resistance (AMR) poses a challenge for treatment of bacterial diseases. In real life, bacterial infections are typically embedded within complex multispecies communities and influenced by the environment, which can shape costs and benefits of AMR. However, knowledge of such interactions and their implications for AMR in vivo is limited. To address this knowledge gap, we investigated fitness-related traits of a pathogenic bacterium (Flavobacterium columnare) in its fish host, capturing the effects of bacterial antibiotic resistance, coinfections between bacterial strains and metazoan parasites (fluke Diplostomum pseudospathaceum) and antibiotic exposure. We quantified real-time replication and virulence of sensitive and resistant bacteria and demonstrate that both bacteria can benefit from coinfection in terms of persistence and replication, depending on the coinfecting partner and antibiotic presence. We also show that antibiotics can benefit resistant bacteria by increasing bacterial replication under coinfection with flukes. These results emphasize the importance of diverse, inter-kingdom coinfection interactions and antibiotic exposure in shaping costs and benefits of AMR, supporting their role as significant contributors to spread and long-term persistence of 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.

Evaluation of vaginal microbiome equilibrium states identifies microbial parameters linked to resilience after menses and antibiotic therapy

The vaginal microbiome (VMB) is a complex microbial community that is closely tied to reproductive health. Optimal VMB communities have compositions that are commonly defined by the dominance of certain Lactobacillus spp. and can remain stable over time or transition to non-optimal states dominated by anaerobic bacteria and associated with bacterial vaginosis (BV). The ability to remain stable or undergo transitions suggests a system with either single (mono-stable) or multiple (multi-stable) equilibrium states, though factors that contribute to stability have been difficult to determine due to heterogeneity in microbial growth characteristics and inter-species interactions. Here, we use a computational model to determine whether differences in microbial growth and interaction parameters could alter equilibrium state accessibility and account for variability in community composition after menses and antibiotic therapies. Using a global uncertainty and sensitivity analysis that captures parameter sets sampled from a physiologically relevant range, model simulations predicted that 79.7% of microbial communities were mono-stable (gravitate to one composition type) and 20.3% were predicted to be multi-stable (can gravitate to more than one composition type, given external perturbations), which was not significantly different from observations in two clinical cohorts (HMP cohort, 75.2% and 24.8%; Gajer cohort, 78.1% and 21.9%, respectively). The model identified key microbial parameters that governed equilibrium state accessibility, such as the importance of non-optimal anaerobic bacteria interactions with Lactobacillus spp., which is largely understudied. Model predictions for composition changes after menses and antibiotics were not significantly different from those observed in clinical cohorts. Lastly, simulations were performed to illustrate how this quantitative framework can be used to gain insight into the development of new combinatorial therapies involving altered prebiotic and antibiotic dosing strategies. Altogether, dynamical models could guide development of more precise therapeutic strategies to manage BV.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

Glacier-Fed Stream Biofilms Harbor Diverse Resistomes and Biosynthetic Gene Clusters

Antimicrobial resistance (AMR) is a universal phenomenon the origins of which lay in natural ecological interactions such as competition within niches, within and between micro- to higher-order organisms. To study these phenomena, it is crucial to examine the origins of AMR in pristine environments, i.e., limited anthropogenic influences. In this context, epilithic biofilms residing in glacier-fed streams (GFSs) are an excellent model system to study diverse, intra- and inter-domain, ecological crosstalk. We assessed the resistomes of epilithic biofilms from GFSs across the Southern Alps (New Zealand) and the Caucasus (Russia) and observed that both bacteria and eukaryotes encoded twenty-nine distinct AMR categories. Of these, beta-lactam, aminoglycoside, and multidrug resistance were both abundant and taxonomically distributed in most of the bacterial and eukaryotic phyla. AMR-encoding phyla included Bacteroidota and Proteobacteria among the bacteria, alongside Ochrophyta (algae) among the eukaryotes. Additionally, biosynthetic gene clusters (BGCs) involved in the production of antibacterial compounds were identified across all phyla in the epilithic biofilms. Furthermore, we found that several bacterial genera (Flavobacterium, Polaromonas, Superphylum Patescibacteria) encode both atimicrobial resistance genes (ARGs) and BGCs within close proximity of each other, demonstrating their capacity to simultaneously influence and compete within the microbial community. Our findings help unravel how naturally occurring BGCs and AMR contribute to the epilithic biofilms mode of life in GFSs. Additionally, we report that eukaryotes may serve as AMR reservoirs owing to their potential for encoding ARGs. Importantly, these observations may be generalizable and potentially extended to other environments that may be more or less impacted by human activity. IMPORTANCE Antimicrobial resistance is an omnipresent phenomenon in the anthropogenically influenced ecosystems. However, its role in shaping microbial community dynamics in pristine environments is relatively unknown. Using metagenomics, we report the presence of antimicrobial resistance genes and their associated pathways in epilithic biofilms within glacier-fed streams. Importantly, we observe biosynthetic gene clusters associated with antimicrobial resistance in both pro- and eukaryotes in these biofilms. Understanding the role of resistance in the context of this pristine environment and complex biodiversity may shed light on previously uncharacterized mechanisms of cross-domain interactions.

Mechanisms driving spatial distribution of residents in colony biofilms: an interdisciplinary perspective

Biofilms are consortia of microorganisms that form collectives through the excretion of extracellular matrix compounds. The importance of biofilms in biological, industrial and medical settings has long been recognized due to their emergent properties and impact on surrounding environments. In laboratory situations, one commonly used approach to study biofilm formation mechanisms is the colony biofilm assay, in which cell communities grow on solid-gas interfaces on agar plates after the deposition of a population of founder cells. The residents of a colony biofilm can self-organize to form intricate spatial distributions. The assay is ideally suited to coupling with mathematical modelling due to the ability to extract a wide range of metrics. In this review, we highlight how interdisciplinary approaches have provided deep insights into mechanisms causing the emergence of these spatial distributions from well-mixed inocula.

Calibrating spatiotemporal models of microbial communities to microscopy data: A review

Spatiotemporal models that account for heterogeneity within microbial communities rely on single-cell data for calibration and validation. Such data, commonly collected via microscopy and flow cytometry, have been made more accessible by recent advances in microfluidics platforms and data processing pipelines. However, validating models against such data poses significant challenges. Validation practices vary widely between modelling studies; systematic and rigorous methods have not been widely adopted. Similar challenges are faced by the (macrobial) ecology community, in which systematic calibration approaches are often employed to improve quantitative predictions from computational models. Here, we review single-cell observation techniques that are being applied to study microbial communities and the calibration strategies that are being employed for accompanying spatiotemporal models. To facilitate future calibration efforts, we have compiled a list of summary statistics relevant for quantifying spatiotemporal patterns in microbial communities. Finally, we highlight some recently developed techniques that hold promise for improved model calibration, including algorithmic guidance of summary statistic selection and machine learning approaches for efficient model simulation.

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.

Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies.

IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

Social evolution of shared biofilm matrix components

Biofilm formation is an important and ubiquitous mode of growth among bacteria. Central to the evolutionary advantage of biofilm formation is cell-cell and cell-surface adhesion achieved by a variety of factors, some of which are diffusible compounds that may operate as classical public goods-factors that are costly to produce but may benefit other cells. An outstanding question is how diffusible matrix production, in general, can be stable over evolutionary timescales. In this work, using Vibrio cholerae as a model, we show that shared diffusible biofilm matrix proteins are indeed susceptible to cheater exploitation and that the evolutionary stability of producing these matrix components fundamentally depends on biofilm spatial structure, intrinsic sharing mechanisms of these components, and flow conditions in the environment. We further show that exploitation of diffusible adhesion proteins is localized within a well-defined spatial range around cell clusters that produce them. Based on this exploitation range and the spatial distribution of cell clusters, we constructed a model of costly diffusible matrix production and related these length scales to the relatedness coefficient in social evolution theory. Our results show that production of diffusible biofilm matrix components is evolutionarily stable under conditions consistent with natural biofilm habitats and host environments. We expect the mechanisms revealed in this study to be relevant to other secreted factors that operate as cooperative public goods in bacterial communities and the concept of exploitation range and the associated analysis tools to be generally applicable.

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.

Inter-species interactions alter antibiotic efficacy in bacterial communities

The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional in vitro susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members' susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against the pathogen Pseudomonas aeruginosa in model cystic fibrosis (CF) lung communities derived from clinical samples. We demonstrate that multi-drug resistant Stenotrophomonas maltophilia can provide high levels of antibiotic protection to otherwise sensitive P. aeruginosa. Exposure protection to imipenem was provided by chromosomally encoded metallo-β-lactamase that detoxified the environment; protection was dependent upon S. maltophilia cell density and was provided by S. maltophilia strains isolated from CF sputum, increasing the MIC of P. aeruginosa by up to 16-fold. In contrast, the presence of S. maltophilia provided no protection against meropenem, another routinely used carbapenem. Mathematical ordinary differential equation modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multispecies communities.

Environmental structure impacts microbial composition and secondary metabolism

Determining the drivers of microbial community assembly is a central theme of microbial ecology, and chemical ecologists seek to characterize how secondary metabolites mediate these assembly patterns. Environmental structure affects how communities assemble and what metabolic pathways aid in that assembly. Here, we bridged these two perspectives by addressing the chemical drivers of community assembly within a spatially structured landscape with varying oxygen availability. We hypothesized that structured environments would favor higher microbial diversity and metabolite diversity. We anticipated that the production of a compound would be more advantageous in a structured environment (less mixing) compared to an unstructured environment (more mixing), where the molecule would have a diminished local effect. We observed this to be partially true in our experiments: structured environments had similar microbial diversity compared to unstructured environments but differed significantly in the metabolites produced. We also found that structured environments selected for communities with higher evenness, rather than communities with higher richness. This supports the idea that when characterizing the drivers of community assembly, it matters less about who is there and more about what they are doing. Overall, these data contribute to a growing effort to approach microbial community assembly with interdisciplinary tools and perspectives.

Competition dynamics in long-term propagations of Schizosaccharomyces pombe strain communities

Experimental evolution studies with microorganisms such as bacteria and yeast have been an increasingly important and powerful tool to draw long-term inferences of how microbes interact. However, while several strains of the same species often exist in natural environments, many ecology and evolution studies in microbes are typically performed with isogenic populations of bacteria or yeast. In the present study, we firstly perform a genotypic and phenotypic characterization of two laboratory and eight natural strains of the yeast Schizosaccharomyces pombe. We then propagated, in a rich resource environment, yeast communities of 2, 3, 4, and 5 strains for hundreds of generations and asked which fitness-related phenotypes-maximum growth rate or relative competitive fitness-would better predict the outcome of a focal strain during the propagations. While the strain's growth rates would wrongly predict long-term coexistence, pairwise competitive fitness with a focal strain qualitatively predicted the success or extinction of the focal strain by a simple multigenotype population genetics model, given the initial community composition. Interestingly, we have also measured the competitive fitness of the ancestral and evolved communities by the end of the experiment (≈370 generations) and observed frequent maladaptation to the abiotic environment in communities with more than three members. Overall, our results aid establishing pairwise competitive fitness as good qualitative measurement of long-term community composition but also reveal a complex adaptive scenario when trying to predict the evolutionary outcome of those communities.

Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Antibiotic resistance in microbial communities reflects a combination of processes operating at different scales. In this work, we investigate the spatiotemporal dynamics of bacterial colonies comprised of drug-resistant and drug-sensitive cells undergoing range expansion under antibiotic stress. Using the opportunistic pathogen Enterococcus faecalis with plasmid-encoded β-lactamase, we track colony expansion dynamics and visualize spatial patterns in fluorescently labeled populations exposed to antibiotics. We find that the radial expansion rate of mixed communities is approximately constant over a wide range of drug concentrations and initial population compositions. Imaging of the final populations shows that resistance to ampicillin is cooperative, with sensitive cells surviving in the presence of resistant cells at otherwise lethal concentrations. The populations exhibit a diverse range of spatial segregation patterns that depend on drug concentration and initial conditions. Mathematical models indicate that the observed dynamics are consistent with global cooperation, despite the fact that β-lactamase remains cell-associated. Experiments confirm that resistant colonies provide a protective effect to sensitive cells on length scales multiple times the size of a single colony, and populations seeded with (on average) no more than a single resistant cell can produce mixed communities in the presence of the drug. While biophysical models of drug degradation suggest that individual resistant cells offer only short-range protection to neighboring cells, we show that long-range protection may arise from synergistic effects of multiple resistant cells, providing surprisingly large protection zones even at small population fractions.

Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control

The human microbiome can protect against colonization with pathogenic antibiotic-resistant bacteria (ARB), but its impacts on the spread of antibiotic resistance are poorly understood. We propose a mathematical modeling framework for ARB epidemiology formalizing within-host ARB-microbiome competition, and impacts of antibiotic consumption on microbiome function. Applied to the healthcare setting, we demonstrate a trade-off whereby antibiotics simultaneously clear bacterial pathogens and increase host susceptibility to their colonization, and compare this framework with a traditional strain-based approach. At the population level, microbiome interactions drive ARB incidence, but not resistance rates, reflecting distinct epidemiological relevance of different forces of competition. Simulating a range of public health interventions (contact precautions, antibiotic stewardship, microbiome recovery therapy) and pathogens (Clostridioides difficile, methicillin-resistant Staphylococcus aureus, multidrug-resistant Enterobacteriaceae) highlights how species-specific within-host ecological interactions drive intervention efficacy. We find limited impact of contact precautions for Enterobacteriaceae prevention, and a promising role for microbiome-targeted interventions to limit ARB spread.

Species interactions drive the spread of ampicillin resistance in human-associated gut microbiota

Background and objectives

Slowing the spread of antimicrobial resistance is urgent if we are to continue treating infectious diseases successfully. There is increasing evidence microbial interactions between and within species are significant drivers of resistance. On one hand, cross-protection by resistant genotypes can shelter susceptible microbes from the adverse effects of antibiotics, reducing the advantage of resistance. On the other hand, antibiotic-mediated killing of susceptible genotypes can alleviate competition and allow resistant strains to thrive (competitive release). Here, by observing interactions both within and between species in microbial communities sampled from humans, we investigate the potential role for cross-protection and competitive release in driving the spread of ampicillin resistance in the ubiquitous gut commensal and opportunistic pathogen Escherichia coli.

Methodology

Using anaerobic gut microcosms comprising E.coli embedded within gut microbiota sampled from humans, we tested for cross-protection and competitive release both within and between species in response to the clinically important beta-lactam antibiotic ampicillin.

Results

While cross-protection gave an advantage to antibiotic-susceptible E.coli in standard laboratory conditions (well-mixed LB medium), competitive release instead drove the spread of antibiotic-resistant E.coli in gut microcosms (ampicillin boosted growth of resistant bacteria in the presence of susceptible strains).

Conclusions and implications

Competition between resistant strains and other members of the gut microbiota can restrict the spread of ampicillin resistance. If antibiotic therapy alleviates competition with resident microbes by killing susceptible strains, as here, microbiota-based interventions that restore competition could be a key for slowing the spread of resistance.

Lay Summary

Slowing the spread of global antibiotic resistance is an urgent task. In this paper, we ask how interactions between microbial species drive the spread of resistance. We show that antibiotic killing of susceptible microbes can free up resources for resistant microbes and allow them to thrive. Therefore, we should consider microbes in light of their social interactions to understand the spread of resistance.

Causes and consequences of pattern diversification in a spatially self-organizing microbial community

Surface-attached microbial communities constitute a vast amount of life on our planet. They contribute to all major biogeochemical cycles, provide essential services to our society and environment, and have important effects on human health and disease. They typically consist of different interacting genotypes that arrange themselves non-randomly across space (referred to hereafter as spatial self-organization). While spatial self-organization is important for the functioning, ecology, and evolution of these communities, the underlying determinants of spatial self-organization remain unclear. Here, we performed a combination of experiments, statistical modeling, and mathematical simulations with a synthetic cross-feeding microbial community consisting of two isogenic strains. We found that two different patterns of spatial self-organization emerged at the same length and time scales, thus demonstrating pattern diversification. This pattern diversification was not caused by initial environmental heterogeneity or by genetic heterogeneity within populations. Instead, it was caused by nongenetic heterogeneity within populations, and we provide evidence that the source of this nongenetic heterogeneity is local differences in the initial spatial positionings of individuals. We further demonstrate that the different patterns exhibit different community-level properties; namely, they have different expansion speeds. Together, our results demonstrate that pattern diversification can emerge in the absence of initial environmental heterogeneity or genetic heterogeneity within populations and can affect community-level properties, thus providing novel insights into the causes and consequences of microbial spatial self-organization.

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.

Assessment of cooperative antibiotic resistance of Salmonella Typhimurium within heterogeneous population

This study was designed to investigate the cooperative resistance in the mixed culture of antibiotic-sensitive and antibiotic-resistant Salmonella Typhimurium. Strains of S. Typhimurium ATCC 19585 (ST^S) and clinically isolated antibiotic-resistant S. Typhimurium CCARM 8009 (ST^R) grown in single and mixture with 1 × MIC ceftriaxone (CEF) were used to determine the viability, β-lactamase activity, and gene expression. The MIC₅₀ values of ST^R to CEF was increased by more than 5-fold with increasing inoculum densities from 10² to 10⁷ CFU/mL. ST^S was resistant to 1 × MIC CEF in the mixed culture of ST^S and ST^R, showing the more than 10⁸ CFU/mL after 20 h of incubation at 37 °C. The highest β-lactamase activity was 18 μmol/min/mL in the mixed culture, corresponding to the highest relative expression of β-lactamase-related genes (bla_TEM). These results shed new light on the cooperative resistance of antibiotic-sensitive bacteria within a heterogeneous population including β-lactamase-producing bacteria.

Ecology and evolution of antimicrobial resistance in bacterial communities

Accumulating evidence suggests that the response of bacteria to antibiotics is significantly affected by the presence of other interacting microbes. These interactions are not typically accounted for when determining pathogen sensitivity to antibiotics. In this perspective, we argue that resistance and evolutionary responses to antibiotic treatments should not be considered only a trait of an individual bacteria species but also an emergent property of the microbial community in which pathogens are embedded. We outline how interspecies interactions can affect the responses of individual species and communities to antibiotic treatment, and how these responses could affect the strength of selection, potentially changing the trajectory of resistance evolution. Finally, we identify key areas of future research which will allow for a more complete understanding of antibiotic resistance in bacterial communities. We emphasise that acknowledging the ecological context, i.e. the interactions that occur between pathogens and within communities, could help the development of more efficient and effective antibiotic treatments.

Using ecological coexistence theory to understand antibiotic resistance and microbial competition

Tackling antibiotic resistance necessitates deep understanding of how resource competition within and between species modulates the fitness of resistant microbes. Recent advances in ecological coexistence theory offer a powerful framework to probe the mechanisms regulating intra- and interspecific competition, but the significance of this body of theory to the problem of antibiotic resistance has been largely overlooked. In this Perspective, we draw on emerging ecological theory to illustrate how changes in resource niche overlap can be equally important as changes in competitive ability for understanding costs of resistance and the persistence of resistant pathogens in microbial communities. We then show how different temporal patterns of resource and antibiotic supply, alongside trade-offs in competitive ability at high and low resource concentrations, can have diametrically opposing consequences for the coexistence and exclusion of resistant and susceptible strains. These insights highlight numerous opportunities for innovative experimental and theoretical research into the ecological dimensions of antibiotic resistance.

2-Aminoimidazoles as potent inhibitors of contaminating brewery biofilms

Cleaning and disinfection protocols are not always able to remove biofilm microbes present in breweries, indicating that novel anti-biofilm strategies are needed. The preventive activities of three in-house synthesized members of the 2-aminoimidazole class of anti-biofilm molecules were studied against 17 natural brewery biofilms and benchmarked against 18 known inhibitors. Two 2-aminoimidazoles belonged to the top six inhibitors, which were retested against 12 defined brewery biofilm models. For the three best inhibitors, tannic acid (n° 1), 2-aminoimidazole imi-AAC-5 (n° 2), and baicalein (n° 3), the effect on the microbial metabolic activity was evaluated. Here, the top three inhibitors showed similar effectiveness, with baicalein possessing a slightly higher efficacy. Even though the 2-aminoimidazole was the second-best inhibitor, it showed a lower biocidal activity than tannic acid, making it less prone to resistance evolution. Overall, this study supports the potential of 2-aminoimidazoles as a preventive anti-biofilm strategy.

The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling

Many antibiotic resistance genes are associated with plasmids. The ecological success of these mobile genetic elements within microbial communities depends on varying mechanisms to secure their own propagation, not only on environmental selection. Among the most important are the cost of plasmids and their ability to be transferred to new hosts through mechanisms such as conjugation. These are regulated by dynamic control systems of the conjugation machinery and genetic adaptations that plasmid-host pairs can acquire in coevolution. However, in complex communities, these processes and mechanisms are subject to a variety of interactions with other bacterial species and other plasmid types. This article summarizes basic plasmid properties and ecological principles particularly important for understanding the persistence of plasmid-coded antibiotic resistance in aquatic environments. Through selected examples, it further introduces to the features of different types of simulation models such as systems of ordinary differential equations and individual-based models, which are considered to be important tools to understand these complex systems. This ecological perspective aims to improve the way we study and understand the dynamics, diversity and persistence of plasmids and associated antibiotic resistance genes.

Evolutionary Approaches to Combat Antibiotic Resistance: Opportunities and Challenges for Precision Medicine

The rise of antimicrobial resistance (AMR) in bacterial pathogens is acknowledged by the WHO as a major global health crisis. It is estimated that in 2050 annually up to 10 million people will die from infections with drug resistant pathogens if no efficient countermeasures are implemented. Evolution of pathogens lies at the core of this crisis, which enables rapid adaptation to the selective pressures imposed by antimicrobial usage in both medical treatment and agriculture, consequently promoting the spread of resistance genes or alleles in bacterial populations. Approaches developed in the field of Evolutionary Medicine attempt to exploit evolutionary insight into these adaptive processes, with the aim to improve diagnostics and the sustainability of antimicrobial therapy. Here, we review the concept of evolutionary trade-offs in the development of AMR as well as new therapeutic approaches and their impact on host-microbiome-pathogen interactions. We further discuss the possible translation of evolution-informed treatments into clinical practice, considering both the rapid cure of the individual patients and the prevention of AMR.

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

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. In obligate mutualisms, the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners. This sets the community antibiotic sensitivity at that of the 'weakest link' species. In this study, we tested the hypothesis that weakest link dynamics in an obligate cross-feeding relationship would limit the extent and mechanisms of antibiotic resistance evolution. We experimentally evolved an obligate co-culture and monoculture controls along gradients of two different antibiotics. We measured the rate at which each treatment increased antibiotic resistance, and sequenced terminal populations to question whether mutations differed between mono- and co-cultures. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.

Observing Nutrient Gradients, Gene Expression and Growth Variation Using the "Yeast Machine" Microfluidic Device

The natural environment of microbial cells like bacteria and yeast is often a complex community in which growth and internal organization reflect morphogenetic processes and interactions that are dependent on spatial position and time. While most of research is performed in simple homogeneous environments (e.g., bulk liquid cultures), which cannot capture full spatiotemporal community dynamics, studying biofilms or colonies is complex and usually does not give access to the spatiotemporal dynamics at single cell level. Here, we detail a protocol for generation of a microfluidic device, the "yeast machine", with arrays of long monolayers of yeast colonies to advance the global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined and customizable spatial dimensions. With Saccharomyces cerevisiae as a model yeast system we used the "yeast machine" to demonstrate the emergence of glucose gradients by following expression of fluorescently labelled hexose transporters. We further quantified the expression spatial patterns with intra-colony growth rates and expression of other genes regulated by glucose availability. In addition to this, we showed that gradients of amino acids also form within a colony, potentially opening similar approaches to study spatiotemporal formation of gradients of many other nutrients and metabolic waste products. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development, and morphogenesis in other same species or more complex multi-species systems at single-cell resolution and timescales relevant to ecology and evolution.

A survey of within-host and between-hosts modelling for antibiotic resistance

Antibiotic resistance is a global public health problem which has the attention of many stakeholders including clinicians, the pharmaceutical industry, researchers and policy makers. Despite the existence of many studies, control of resistance transmission has become a rather daunting task as the mechanisms underlying resistance evolution and development are not fully known. Here, we discuss the mechanisms underlying antibiotic resistance development, explore some treatment strategies used in the fight against antibiotic resistance and consider recent findings on collateral susceptibilities amongst antibiotic classes. Mathematical models have proved valuable for unravelling complex mechanisms in biology and such models have been used in the quest of understanding the development and spread of antibiotic resistance. While assessing the importance of such mathematical models, previous systematic reviews were interested in investigating whether these models follow good modelling practice. We focus on theoretical approaches used for resistance modelling considering both within and between host models as well as some pharmacodynamic and pharmakokinetic approaches and further examine the interaction between drugs and host immune response during treatment with antibiotics. Finally, we provide an outlook for future research aimed at modelling approaches for combating antibiotic resistance.

Bacterial Interactions as Detected by Pooled Antibiotic Susceptibility Testing (P-AST) in Polymicrobial Urine Specimens

INTRODUCTION

Antimicrobial susceptibility is well characterized in monomicrobial infections, but bacterial species often coexist with other bacterial species. Antimicrobial susceptibility is often tested against single bacterial isolates; this approach ignores interactions between cohabiting bacteria that could impact susceptibility. Here, we use Pooled Antibiotic Susceptibility Testing to compare antimicrobial susceptibility patterns exhibited by polymicrobial and monomicrobial urine specimens obtained from patients with urinary tract infection symptoms.

METHODS

Urine samples were collected from patients who had symptoms consistent with a urinary tract infection. Multiplex polymerase chain reaction testing was performed to identify and quantify 31 bacterial species. Antibiotic susceptibility was determined using a novel Pooled Antibiotic Susceptibility Testing method. Antibiotic resistance rates in polymicrobial specimens were compared with those in monomicrobial infections. Using a logistic model, resistance rates were estimated when specific bacterial species were present. To assess interactions between pairs of bacteria, the predicted resistance rates were compared when a pair of bacterial species were present versus when just one bacterial species was present.

RESULTS

Urine specimens were collected from 3,124 patients with symptoms of urinary tract infection. Of these, multiplex polymerase chain reaction testing detected bacteria in 61.1% (1910) of specimens. Pooled Antibiotic Susceptibility Testing results were available for 70.8% (1352) of these positive specimens. Of these positive specimens, 43.9% (594) were monomicrobial, while 56.1% (758) were polymicrobial. The odds of resistance to ampicillin (p = 0.005), amoxicillin/clavulanate (p = 0.008), five different cephalosporins, vancomycin (p = <0.0001), and tetracycline (p = 0.010) increased with each additional species present in a polymicrobial specimen. In contrast, the odds of resistance to piperacillin/tazobactam decreased by 75% for each additional species present (95% CI 0.61, 0.94, p = 0.010). For one or more antibiotics tested, thirteen pairs of bacterial species exhibited statistically significant interactions compared with the expected resistance rate obtained with the Highest Single Agent Principle and Union Principle.

CONCLUSION

Bacterial interactions in polymicrobial specimens can result in antimicrobial susceptibility patterns that are not detected when bacterial isolates are tested by themselves. Optimizing an effective treatment regimen for patients with polymicrobial infections may depend on accurate identification of the constituent species, as well as results obtained by Pooled Antibiotic Susceptibility Testing.

Community coalescence: an eco-evolutionary perspective

Community coalescence, the mixing of different communities, is widespread throughout microbial ecology. Coalescence can result in approximately equal contributions from the founding communities or dominance of one community over another. These different outcomes have ramifications for community structure and function in natural communities, and the use of microbial communities in biotechnology and medicine. However, we have little understanding of when a particular outcome might be expected. Here, we integrate existing theory and data to speculate on how a crucial characteristic of microbial communities-the type of species interaction that dominates the community-might affect the outcome of microbial community coalescence. Given the often comparable timescales of microbial ecology and microevolution, we explicitly consider ecological and evolutionary dynamics, and their interplay, in determining coalescence outcomes. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

Fitness and Productivity Increase with Ecotypic Diversity among Escherichia coli Strains That Coevolved in a Simple, Constant Environment

Polymicrobial consortia occur in both environmental and clinical settings. In many cases, diversity and productivity correlate in these consortia, especially when sustained by positive, density-dependent interactions. However, the evolutionary history of such entities is typically obscure, making it difficult to establish the relative fitness of consortium partners and to use those data to illuminate the diversity-productivity relationship. Here, we dissect an Escherichia coli consortium that evolved under continuous glucose limitation in the laboratory from a single common ancestor. We show that a partnership consisting of cross-feeding ecotypes is better able to secure primary and secondary resources and to convert those resources to offspring than the ancestral clone. Such interactions may be a prelude to a special form of syntrophy and are likely determinants of microbial community structure in nature, including those having clinical significance such as chronic infections.

The productivity of a biological community often correlates with its diversity. In the microbial world this phenomenon can sometimes be explained by positive, density-dependent interactions such as cross-feeding and syntrophy. These metabolic interactions help account for the astonishing variety of microbial life and drive many of the biogeochemical cycles without which life as we know it could not exist. While it is difficult to recapitulate experimentally how these interactions evolved among multiple taxa, we can explore in the laboratory how they arise within one. These experiments provide insight into how different bacterial ecotypes evolve and from these, possibly new “species.” We have previously shown that in a simple, constant environment a single clone of Escherichia coli can give rise to a consortium of genetically and phenotypically differentiated strains, in effect, a set of ecotypes, that coexist by cross-feeding. We marked these different ecotypes and their shared ancestor by integrating fluorescent protein into their genomes and then used flow cytometry to show that each evolved strain is more fit than the shared ancestor, that pairs of evolved strains are fitter still, and that the entire consortium is the fittest of all. We further demonstrate that the rank order of fitness values agrees with estimates of yield, indicating that an experimentally evolved consortium more efficiently converts primary and secondary resources to offspring than its ancestor or any member acting in isolation.

IMPORTANCE Polymicrobial consortia occur in both environmental and clinical settings. In many cases, diversity and productivity correlate in these consortia, especially when sustained by positive, density-dependent interactions. However, the evolutionary history of such entities is typically obscure, making it difficult to establish the relative fitness of consortium partners and to use those data to illuminate the diversity-productivity relationship. Here, we dissect an Escherichia coli consortium that evolved under continuous glucose limitation in the laboratory from a single common ancestor. We show that a partnership consisting of cross-feeding ecotypes is better able to secure primary and secondary resources and to convert those resources to offspring than the ancestral clone. Such interactions may be a prelude to a special form of syntrophy and are likely determinants of microbial community structure in nature, including those having clinical significance such as chronic infections.

Interaction-dependent effects of surface structure on microbial spatial self-organization

Surface-attached microbial communities consist of different cell types that, at least to some degree, organize themselves non-randomly across space (referred to as spatial self-organization). While spatial self-organization can have important effects on the functioning, ecology and evolution of communities, the underlying determinants of spatial self-organization remain unclear. Here, we hypothesize that the presence of physical objects across a surface can have important effects on spatial self-organization. Using pairs of isogenic strains of Pseudomonas stutzeri, we performed range expansion experiments in the absence or presence of physical objects and quantified the effects on spatial self-organization. We demonstrate that physical objects create local deformities along the expansion frontier, and these deformities increase in magnitude during range expansion. The deformities affect the densities of interspecific boundaries and diversity along the expansion frontier, and thus affect spatial self-organization, but the effects are interaction-dependent. For competitive interactions that promote sectorized patterns of spatial self-organization, physical objects increase the density of interspecific boundaries and diversity. By contrast, for cross-feeding interactions that promote dendritic patterns, they decrease the density of interspecific boundaries and diversity. These qualitatively different outcomes are probably caused by fundamental differences in the orientations of the interspecific boundaries. Thus, in order to predict the effects of physical objects on spatial self-organization, information is needed regarding the interactions present within a community and the general geometric shapes of spatial self-organization that emerge from those interactions. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.

Implications of indoor microbial ecology and evolution on antibiotic resistance

The indoor environment is an important source of microbial exposures for its human occupants. While we naturally want to favor positive health outcomes, built environment design and operation may counter-intuitively favor negative health outcomes, particularly with regard to antibiotic resistance. Indoor environments contain microbes from both human and non-human origins, providing a unique venue for microbial interactions, including horizontal gene transfer. Furthermore, stressors present in the built environment could favor the exchange of genetic material in general and the retention of antibiotic resistance genes in particular. Intrinsic and acquired antibiotic resistance both pose a potential threat to human health; these phenomena need to be considered and controlled separately. The presence of both environmental and human-associated microbes, along with their associated antibiotic resistance genes, in the face of stressors, including antimicrobial chemicals, creates a unique opportunity for the undesirable spread of antibiotic resistance. In this review, we summarize studies and findings related to various interactions between human-associated bacteria, environmental bacteria, and built environment conditions, and particularly their relation to antibiotic resistance, aiming to guide "healthy" building design.

Diffusion-driven enhancement of the antibiotic resistance selection window

The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled Escherichia coli in 3D-printed devices that allow us to control the rate of diffusion of antibiotics between neighbouring compartments and quantify the spatio-temporal distribution of resistant and susceptible bacterial cells.

Cellular heterogeneity results from indirect effects under metabolic tradeoffs

The emergence and maintenance of multicellularity requires the coexistence of diverse cellular populations displaying cooperative relationships. This enables long-term persistence of cellular consortia, particularly under environmental constraints that challenge cell survival. Toxic environments are known to trigger the formation of multicellular consortia capable of dealing with waste while promoting cell diversity as a way to overcome selection barriers. In this context, recent theoretical studies suggest that an environment containing both resources and toxic waste can promote the emergence of complex, spatially distributed proto-organisms exhibiting division of labour and higher-scale features beyond the cell-cell pairwise interactions. Some previous non-spatial models suggest that the presence of a growth inhibitor can trigger the coexistence of competitive species in an antibiotic-resistance context. In this paper, we further explore this idea using both mathematical and computational models taking the most fundamental features of the proto-organisms model interactions. It is shown that this resource-waste environmental context, in which both species are lethally affected by the toxic waste and metabolic tradeoffs are present, favours the maintenance of diverse populations. A spatial, stochastic extension confirms our basic results. The evolutionary and ecological implications of these results are outlined.

Antibiotic resistance in Pseudomonas aeruginosa - Mechanisms, epidemiology and evolution

Antibiotics are powerful drugs used in the treatment of bacterial infections. The inappropriate use of these medicines has driven the dissemination of antibiotic resistance (AR) in most bacteria. Pseudomonas aeruginosa is an opportunistic pathogen commonly involved in environmental- and difficult-to-treat hospital-acquired infections. This species is frequently resistant to several antibiotics, being in the "critical" category of the WHO's priority pathogens list for research and development of new antibiotics. In addition to a remarkable intrinsic resistance to several antibiotics, P. aeruginosa can acquire resistance through chromosomal mutations and acquisition of AR genes. P. aeruginosa has one of the largest bacterial genomes and possesses a significant assortment of genes acquired by horizontal gene transfer (HGT), which are frequently localized within integrons and mobile genetic elements (MGEs), such as transposons, insertion sequences, genomic islands, phages, plasmids and integrative and conjugative elements (ICEs). This genomic diversity results in a non-clonal population structure, punctuated by specific clones that are associated with significant morbidity and mortality worldwide, the so-called high-risk clones. Acquisition of MGEs produces a fitness cost in the host, that can be eased over time by compensatory mutations during MGE-host coevolution. Even though plasmids and ICEs are important drivers of AR, the underlying evolutionary traits that promote this dissemination are poorly understood. In this review, we provide a comprehensive description of the main strategies involved in AR in P. aeruginosa and the leading drivers of HGT in this species. The most recently developed genomic tools that allowed a better understanding of the features contributing for the success of P. aeruginosa are discussed.

Bystander Selection for Antimicrobial Resistance: Implications for Patient Health

Antimicrobial therapy promotes resistance emergence in target infections and in off-target microbiota. Off-target resistance emergence threatens patient health when off-target populations are a source of future infections, as they are for many important drug-resistant pathogens. However, the health risks of antimicrobial exposure in off-target populations remain largely unquantified, making rational antibiotic stewardship challenging. Here, we discuss the contribution of bystander antimicrobial exposure to the resistance crisis, the implications for antimicrobial stewardship, and some novel opportunities to limit resistance evolution while treating target pathogens.

A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies

Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labeled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, for example amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.