Emergent antibiotic persistence in a spatially structured synthetic microbial mutualism.

Antibiotic persistence (heterotolerance) allows a sub-population 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.

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.

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, does the type of ecological interaction, such as mutualism or competition, change the average effect of a mutation (i.e., the mean of the distribution of fitness effects)? Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, we discovered that in the mutualism S. enterica obtained vitamins and more amino acids than previously known. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, it would be useful to know if the type of ecological interaction, such as mutualism or competition, changes the average effect of a mutation (i.e., the mean of the distribution of fitness effects). Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, the fitness effects of several knockouts in the mutualistic communities were surprising. We discovered that S. enterica is obtaining a different source of carbon and more vitamins and amino acids than we had expected. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

Disentangling a metabolic cross-feeding in a halophilic archaea-bacteria consortium.

Microbial syntrophy, a cooperative metabolic interaction among prokaryotes, serves a critical role in shaping communities, due to the auxotrophic nature of many microorganisms. Syntrophy played a key role in the evolution of life, including the hypothesized origin of eukaryotes. In a recent exploration of the microbial mats within the exceptional and uniquely extreme Cuatro Cienegas Basin (CCB), a halophilic isolate, designated as AD140, emerged as a standout due to its distinct growth pattern. Subsequent genome sequencing revealed AD140 to be a co-culture of a halophilic archaeon from the Halorubrum genus and a marine halophilic bacterium, Marinococcus luteus, both occupying the same ecological niche. This intriguing coexistence hints at an early-stage symbiotic relationship that thrives on adaptability. By delving into their metabolic interdependence through genomic analysis, this study aims to uncover shared characteristics that enhance their symbiotic association, offering insights into the evolution of halophilic microorganisms and their remarkable adaptations to high-salinity environments.

Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions.

Fitness benefits from division of labor are well documented in microbial consortia, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a "push" interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a "pull" interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild-type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of by-product accumulation all relative to the WT. However, the LAE consortium had the trade-off of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered performance metric. Performance advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the performance advantages compared to WT. IMPORTANCE Most naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence performance properties of consortia. Environmental context ultimately controlled the division of labor performance as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning, which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.

Harnessing intercellular signals to engineer the soil microbiome.

Covering: Focus on 2015 to 2020Plant and soil microbiomes consist of diverse communities of organisms from across kingdoms and can profoundly affect plant growth and health. Natural product-based intercellular signals govern important interactions between microbiome members that ultimately regulate their beneficial or harmful impacts on the plant. Exploiting these evolved signalling circuits to engineer microbiomes towards beneficial interactions with crops is an attractive goal. There are few reports thus far of engineering the intercellular signalling of microbiomes, but this article argues that it represents a tremendous opportunity for advancing the field of microbiome engineering. This could be achieved through the selection of synergistic consortia in combination with genetic engineering of signal pathways to realise an optimised microbiome.

A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS).

Genome-scale stoichiometric modeling of metabolism has become a standard systems biology tool for modeling cellular physiology and growth. Extensions of this approach are emerging as a valuable avenue for predicting, understanding and designing microbial communities. Computation of microbial ecosystems in time and space (COMETS) extends dynamic flux balance analysis to generate simulations of multiple microbial species in molecularly complex and spatially structured environments. Here we describe how to best use and apply the most recent version of COMETS, which incorporates a more accurate biophysical model of microbial biomass expansion upon growth, evolutionary dynamics and extracellular enzyme activity modules. In addition to a command-line option, COMETS includes user-friendly Python and MATLAB interfaces compatible with the well-established COBRA models and methods, as well as comprehensive documentation and tutorials. This protocol provides a detailed guideline for installing, testing and applying COMETS to different scenarios, generating simulations that take from a few minutes to several days to run, with broad applicability to microbial communities across biomes and scales.

Conformational rearrangements enable iterative backbone N-methylation in RiPP biosynthesis.

Peptide backbone α-N-methylations change the physicochemical properties of amide bonds to provide structural constraints and other favorable characteristics including biological membrane permeability to peptides. Borosin natural product pathways are the only known ribosomally encoded and posttranslationally modified peptides (RiPPs) pathways to incorporate backbone α-N-methylations on translated peptides. Here we report the discovery of type IV borosin natural product pathways (termed 'split borosins'), featuring an iteratively acting α-N-methyltransferase and separate precursor peptide substrate from the metal-respiring bacterium Shewanella oneidensis. A series of enzyme-precursor complexes reveal multiple conformational states for both α-N-methyltransferase and substrate. Along with mutational and kinetic analyses, our results give rare context into potential strategies for iterative maturation of RiPPs.

Limitation by a shared mutualist promotes coexistence of multiple competing partners.

Although mutualisms are often studied as simple pairwise interactions, they typically involve complex networks of interacting species. How multiple mutualistic partners that provide the same service and compete for resources are maintained in mutualistic networks is an open question. We use a model bacterial community in which multiple 'partner strains' of Escherichia coli compete for a carbon source and exchange resources with a 'shared mutualist' strain of Salmonella enterica. In laboratory experiments, competing E. coli strains readily coexist in the presence of S. enterica, despite differences in their competitive abilities. We use ecological modeling to demonstrate that a shared mutualist can create temporary resource niche partitioning by limiting growth rates, even if yield is set by a resource external to a mutualism. This mechanism can extend to maintain multiple competing partner species. Our results improve our understanding of complex mutualistic communities and aid efforts to design stable microbial communities.

Weakest-Link Dynamics Predict Apparent Antibiotic Interactions in a Model Cross-Feeding Community.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, in which multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding coculture. Using our previously described weakest-link hypothesis, we hypothesized antibiotic interactions in coculture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in coculture. We tested the interactions between 10 previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict coculture patterns based solely on the weakest-link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

Phage cocktail strategies for the suppression of a pathogen in a cross-feeding coculture.

Cocktail combinations of bacteria-infecting viruses (bacteriophages) can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phages targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually exclusive mechanisms could explain these results: (i) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phages, and (ii) S. enterica set the growth rate of the coculture, and therefore, targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed.

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.

Disruption of Cross-Feeding Inhibits Pathogen Growth in the Sputa of Patients with Cystic Fibrosis.

A critical limitation in the management of chronic polymicrobial infections is the lack of correlation between antibiotic susceptibility testing (AST) and patient responses to therapy. Underlying this disconnect is our inability to accurately recapitulate the in vivo environment and complex polymicrobial communities in vitro However, emerging evidence suggests that, if modeled and tested accurately, interspecies relationships can be exploited by conventional antibiotics predicted to be ineffective by standard AST. As an example, under conditions where Pseudomonas aeruginosa relies on cocolonizing organisms for nutrients (i.e., cross-feeding), multidrug-resistant P. aeruginosa may be indirectly targeted by inhibiting the growth of its metabolic partners. While this has been shown in vitro using synthetic bacterial communities, the efficacy of a "weakest-link" approach to controlling host-associated polymicrobial infections has not yet been demonstrated. To test whether cross-feeding inhibition can be leveraged in clinically relevant contexts, we collected sputa from cystic fibrosis (CF) subjects and used enrichment culturing to isolate both P. aeruginosa and anaerobic bacteria from each sample. Predictably, both subpopulations showed various antibiotic susceptibilities when grown independently. However, when P. aeruginosa was cultured and treated under cooperative conditions in which it was dependent on anaerobic bacteria for nutrients, the growth of both the pathogen and the anaerobe was constrained despite their intrinsic antibiotic resistance profiles. These data demonstrate that the control of complex polymicrobial infections may be achieved by exploiting obligate or facultative interspecies relationships. Toward this end, in vitro susceptibility testing should evolve to more accurately reflect in vivo growth environments and microbial interactions found within them.IMPORTANCE Antibiotic efficacy achieved in vitro correlates poorly with clinical outcomes after treatment of chronic polymicrobial diseases; if a pathogen demonstrates susceptibility to a given antibiotic in the lab, that compound is often ineffective when administered clinically. Conversely, if a pathogen is resistant in vitro, patient treatment with that same compound can elicit a positive response. This discordance suggests that the in vivo growth environment impacts pathogen antibiotic susceptibility. Indeed, here we demonstrate that interspecies relationships among microbiotas in the sputa of cystic fibrosis patients can be targeted to indirectly inhibit the growth of Pseudomonas aeruginosa The therapeutic implication is that control of chronic lung infections may be achieved by exploiting obligate or facultative relationships among airway bacterial community members. This strategy is particularly relevant for pathogens harboring intrinsic multidrug resistance and is broadly applicable to chronic polymicrobial airway, wound, and intra-abdominal infections.

Increasing growth rate slows adaptation when genotypes compete for diffusing resources.

The rate at which a species responds to natural selection is a central predictor of the species' ability to adapt to environmental change. It is well-known that spatially-structured environments slow the rate of adaptation due to increased intra-genotype competition. Here, we show that this effect magnifies over time as a species becomes better adapted and grows faster. Using a reaction-diffusion model, we demonstrate that growth rates are inextricably coupled with effective spatial scales, such that higher growth rates cause more localized competition. This has two effects: selection requires more generations for beneficial mutations to fix, and spatially-caused genetic drift increases. Together, these effects diminish the value of additional growth rate mutations in structured environments.

Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community.

Bacteriophage shape the composition and function of microbial communities. Yet it remains difficult to predict the effect of phage on microbial interactions. Specifically, little is known about how phage influence mutualisms in networks of cross-feeding bacteria. We mathematically modeled the impacts of phage in a synthetic microbial community in which Escherichia coli and Salmonella enterica exchange essential metabolites. In this model, independent phage attack of either species was sufficient to temporarily inhibit both members of the mutualism; however, the evolution of phage resistance facilitated yields similar to those observed in the absence of phage. In laboratory experiments, attack of S. enterica with P22vir phage followed these modeling expectations of delayed community growth with little change in the final yield of bacteria. In contrast, when E. coli was attacked with T7 phage, S. enterica, the nonhost species, reached higher yields compared with no-phage controls. T7 infection increased nonhost yield by releasing consumable cell debris, and by driving evolution of partially resistant E. coli that secreted more carbon. Our results demonstrate that phage can have extensive indirect effects in microbial communities, that the nature of these indirect effects depends on metabolic and evolutionary mechanisms, and that knowing the degree of evolved resistance leads to qualitatively different predictions of bacterial community dynamics in response to phage attack.

Common principles and best practices for engineering microbiomes.

Despite broad scientific interest in harnessing the power of Earth's microbiomes, knowledge gaps hinder their efficient use for addressing urgent societal and environmental challenges. We argue that structuring research and technology developments around a design-build-test-learn (DBTL) cycle will advance microbiome engineering and spur new discoveries of the basic scientific principles governing microbiome function. In this Review, we present key elements of an iterative DBTL cycle for microbiome engineering, focusing on generalizable approaches, including top-down and bottom-up design processes, synthetic and self-assembled construction methods, and emerging tools to analyse microbiome function. These approaches can be used to harness microbiomes for broad applications related to medicine, agriculture, energy and the environment. We also discuss key challenges and opportunities of each approach and synthesize them into best practice guidelines for engineering microbiomes. We anticipate that adoption of a DBTL framework will rapidly advance microbiome-based biotechnologies aimed at improving human and animal health, agriculture and enabling the bioeconomy.

Genomewide Assessment of Mycobacterium tuberculosis Conditionally Essential Metabolic Pathways.

A better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditionally essential genes by Tn-seq, we used media with different nutrient compositions. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolite availability. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex, including some existing antitubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions, highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis IMPORTANCE Mycobacterium tuberculosis causes 10 million cases of tuberculosis (TB), resulting in over 1 million deaths each year. TB therapy is challenging because it requires a minimum of 6 months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug-resistant M. tuberculosis necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genomewide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential antitubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

The Power of Metabolism for Predicting Microbial Community Dynamics.

Quantitative understanding and prediction of microbial community dynamics are an outstanding challenge. We test the hypothesis that metabolic mechanisms provide a foundation for accurate prediction of dynamics in microbial systems. In our research, metabolic models have been able to accurately predict species interactions, evolutionary trajectories, and response to perturbation in simple synthetic consortia. However, metabolic models have many constraints and often serve best as null models to identify additional processes at play. We anticipate that major advances in metabolic systems biology will involve scaling bottom-up approaches to complex communities and expanding the processes that are incorporated in a metabolic perspective. Ultimately, cellular metabolism will inform predictive ecology that enables precision management of microbial systems.