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.

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.

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.

Evolution of bidirectional costly mutualism from byproduct consumption.

Mutualisms are essential for life, yet it is unclear how they arise. A two-stage process has been proposed for the evolution of mutualisms that involve exchanges of two costly resources. First, costly provisioning by one species may be selected for if that species gains a benefit from costless byproducts generated by a second species, and cooperators get disproportionate access to byproducts. Selection could then drive the second species to provide costly resources in return. Previously, a synthetic consortium evolved the first stage of this scenario: Salmonella enterica evolved costly production of methionine in exchange for costless carbon byproducts generated by an auxotrophic Escherichia coli Growth on agar plates localized the benefits of cooperation around methionine-secreting S. enterica Here, we report that further evolution of these partners on plates led to hypercooperative E. coli that secrete the sugar galactose. Sugar secretion arose repeatedly across replicate communities and is costly to E. coli producers, but enhances the growth of S. enterica The tradeoff between individual costs and group benefits led to maintenance of both cooperative and efficient E. coli genotypes in this spatially structured environment. This study provides an experimental example of de novo, bidirectional costly mutualism evolving from byproduct consumption. The results validate the plausibility of costly cooperation emerging from initially costless exchange, a scenario widely used to explain the origin of the mutualistic species interactions that are central to life on Earth.

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.

A shared limiting resource leads to competitive exclusion in a cross-feeding system.

Species interactions and coexistence are often dependent upon environmental conditions. When two cross-feeding bacteria exchange essential nutrients, the addition of a cross-fed nutrient to the environment can release one species from its dependence on the other. Previous studies suggest that continued coexistence depends on relative growth rates: coexistence is maintained if the slower-growing species is released from its dependence on the other, but if the faster-growing species is released, the slower-growing species will be lost (a hypothesis that we call 'feed the faster grower' or FFG). Using invasion-from-rare experiments with two reciprocally cross-feeding bacteria, genome-scale metabolic modelling and classical ecological models, we explored the potential for coexistence when one cross-feeder became independent. We found that whether nutrient addition shifted an interaction from mutualism to commensalism or parasitism depended on whether the nutrient that limited total growth was required by one or both species. Parasitism resulted when both species required the growth-limiting resource. Importantly, coexistence was only lost when the interaction became parasitism, and the obligate species had a slower growth rate. Under these restricted conditions, the FFG hypothesis applied. Our results contribute to a mechanistic understanding of how resources can be manipulated to alter interactions and coexistence in microbial communities.

Competitive resource allocation to metabolic pathways contributes to overflow metabolisms and emergent properties in cross-feeding microbial consortia.

Resource scarcity is a common stress in nature and has a major impact on microbial physiology. This review highlights microbial acclimations to resource scarcity, focusing on resource investment strategies for chemoheterotrophs from the molecular level to the pathway level. Competitive resource allocation strategies often lead to a phenotype known as overflow metabolism; the resulting overflow byproducts can stabilize cooperative interactions in microbial communities and can lead to cross-feeding consortia. These consortia can exhibit emergent properties such as enhanced resource usage and biomass productivity. The literature distilled here draws parallels between in silico and laboratory studies and ties them together with ecological theories to better understand microbial stress responses and mutualistic consortia functioning.

The ability of flux balance analysis to predict evolution of central metabolism scales with the initial distance to the optimum.

The most powerful genome-scale framework to model metabolism, flux balance analysis (FBA), is an evolutionary optimality model. It hypothesizes selection upon a proposed optimality criterion in order to predict the set of internal fluxes that would maximize fitness. Here we present a direct test of the optimality assumption underlying FBA by comparing the central metabolic fluxes predicted by multiple criteria to changes measurable by a (13)C-labeling method for experimentally-evolved strains. We considered datasets for three Escherichia coli evolution experiments that varied in their length, consistency of environment, and initial optimality. For ten populations that were evolved for 50,000 generations in glucose minimal medium, we observed modest changes in relative fluxes that led to small, but significant decreases in optimality and increased the distance to the predicted optimal flux distribution. In contrast, seven populations evolved on the poor substrate lactate for 900 generations collectively became more optimal and had flux distributions that moved toward predictions. For three pairs of central metabolic knockouts evolved on glucose for 600-800 generations, there was a balance between cases where optimality and flux patterns moved toward or away from FBA predictions. Despite this variation in predictability of changes in central metabolism, two generalities emerged. First, improved growth largely derived from evolved increases in the rate of substrate use. Second, FBA predictions bore out well for the two experiments initiated with ancestors with relatively sub-optimal yield, whereas those begun already quite optimal tended to move somewhat away from predictions. These findings suggest that the tradeoff between rate and yield is surprisingly modest. The observed positive correlation between rate and yield when adaptation initiated further from the optimum resulted in the ability of FBA to use stoichiometric constraints to predict the evolution of metabolism despite selection for rate.

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.

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy.

A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V). This increase in membrane protein is hypothesized to be essential to competitive Escherichia coli phenotypes. The presented membrane-centric theory uses biophysical properties and metabolic systems analysis to successfully predict the phenotypes of E. coli K-12 strains, MG1655 and NCM3722, which are genetically similar but have SA:V ratios that differ up to 30%, maximum growth rates on glucose media that differ by 40%, and overflow phenotypes that start at growth rates that differ by 80%. These analyses did not consider cytosolic macromolecular crowding, highlighting the distinct properties of the presented theory. Cell geometry and membrane protein crowding are significant biophysical constraints on phenotype and provide a theoretical framework for improved understanding and control of cell biology.

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.

Multiple long-term, experimentally-evolved populations of Escherichia coli acquire dependence upon citrate as an iron chelator for optimal growth on glucose.

BACKGROUND: Specialization for ecological niches is a balance of evolutionary adaptation and its accompanying tradeoffs. Here we focus on the Lenski Long-Term Evolution Experiment, which has maintained cultures of Escherichia coli in the same defined seasonal environment for 50,000 generations. Over this time, much adaptation and specialization to the environment has occurred. The presence of citrate in the growth media selected one lineage to gain the novel ability to utilize citrate as a carbon source after 31,000 generations. Here we test whether other strains have specialized to rely on citrate after 50,000 generations. RESULTS: We show that in addition to the citrate-catabolizing strain, three other lineages evolving in parallel have acquired a dependence on citrate for optimal growth on glucose. None of these strains were stimulated indirectly by the sodium present in disodium citrate, nor exhibited even partial utilization of citrate as a carbon source. Instead, all three of these citrate-stimulated populations appear to rely on it as a chelator of iron. CONCLUSIONS: The strains we examine here have evolved specialization to their environment through apparent loss of function. Our results are most consistent with the accumulation of mutations in iron transport genes that were obviated by abundant citrate. The results present another example where a subtle decision in the design of an evolution experiment led to unexpected evolutionary outcomes.

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.

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.

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.

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.