Range expansion promotes cooperation in an experimental microbial metapopulation

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.

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.

Fungal hyphae regulate bacterial diversity and plasmid-mediated functional novelty during range expansion

The amount of bacterial diversity present on many surfaces is enormous; however, how these levels of diversity persist in the face of the purifying processes that occur as bacterial communities expand across space (referred to here as range expansion) remains enigmatic. We shed light on this apparent paradox by providing mechanistic evidence for a strong role of fungal hyphae-mediated dispersal on regulating bacterial diversity during range expansion. Using pairs of fluorescently labeled bacterial strains and a hyphae-forming fungal strain that expand together across a nutrient-amended surface, we show that a hyphal network increases the spatial intermixing and extent of range expansion of the bacterial strains. This is true regardless of the type of interaction (competition or resource cross-feeding) imposed between the bacterial strains. We further show that the underlying cause is that flagellar motility drives bacterial dispersal along the hyphal network, which counteracts the purifying effects of ecological drift at the expansion frontier. We finally demonstrate that hyphae-mediated spatial intermixing increases the conjugation-mediated spread of plasmid-encoded antibiotic resistance. In conclusion, fungal hyphae are important regulators of bacterial diversity and promote plasmid-mediated functional novelty during range expansion in an interaction-independent manner.

Rare and localized events stabilize microbial community composition and patterns of spatial self-organization in a fluctuating environment

Spatial self-organization is a hallmark of surface-associated microbial communities that is governed by local environmental conditions and further modified by interspecific interactions. Here, we hypothesize that spatial patterns of microbial cell-types can stabilize the composition of cross-feeding microbial communities under fluctuating environmental conditions. We tested this hypothesis by studying the growth and spatial self-organization of microbial co-cultures consisting of two metabolically interacting strains of the bacterium Pseudomonas stutzeri. We inoculated the co-cultures onto agar surfaces and allowed them to expand (i.e. range expansion) while fluctuating environmental conditions that alter the dependency between the two strains. We alternated between anoxic conditions that induce a mutualistic interaction and oxic conditions that induce a competitive interaction. We observed co-occurrence of both strains in rare and highly localized clusters (referred to as “spatial jackpot events”) that persist during environmental fluctuations. To resolve the underlying mechanisms for the emergence of spatial jackpot events, we used a mechanistic agent-based mathematical model that resolves growth and dispersal at the scale relevant to individual cells. While co-culture composition varied with the strength of the mutualistic interaction and across environmental fluctuations, the model provides insights into the formation of spatially resolved substrate landscapes with localized niches that support the co-occurrence of the two strains and secure co-culture function. This study highlights that in addition to spatial patterns that emerge in response to environmental fluctuations, localized spatial jackpot events ensure persistence of strains across dynamic conditions.

Disease-Induced Cooperation Mitigates Populations Against Decline: The Cascade Effect of Cooperation Evolution

Due to density-dependent selection, the ecological factors impacting population dynamics can play an important role in promoting cooperation, and accordingly, benefit a population from the eco-evolutionary feedback. This implies that cooperation between individuals could help resist the attack of infectious diseases. Yet, little is known about how cooperation evolves in response to infections. We here examined theoretically the impact of disease infections with various transmission types on cooperation evolution and its feedback to population dynamics. Results show that infected populations can evolve to be more cooperative, and the level of cooperation increases with the transmission rate, which can protect the population against decline due to infection and prevent population extinction driven by defection. A high transmission rate can stabilize population fluctuation, while a relatively low transmission rate could destabilize population dynamics. We argue that the mechanism underlying such stress-induced cooperation is analogous to the cascade effect of trophic interactions in food webs: reduction in selfishness from environmental stress indirectly relaxes the exploitation of cooperators by defectors. These findings emphasize the role of eco-evolutionary feedback in evolving cooperation and the ecological significance of cooperation evolution for populations withstanding disease infection.

Slow expanders invade by forming dented fronts in microbial colonies

Living organisms never cease to evolve, so there is a significant interest in predicting and controlling evolution in all branches of life sciences. The most basic question is whether a trait should increase or decrease in a given environment. The answer seems to be trivial for traits such as the growth rate in a bioreactor or the expansion rate of a tumor. Yet, it has been suggested that such traits can decrease, rather than increase, during evolution. Here, we report a mutant that outcompeted the ancestor despite having a slower expansion velocity when in isolation. To explain this observation, we developed and validated a theory that describes spatial competition between organisms with different expansion rates and arbitrary competitive interactions.

Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that mutants that grow slower in isolation nevertheless win in competition, not only when the two types are intermixed, but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities, but overlooked the local competitions at sector boundaries. Local competition, however, can enhance the velocity of either type at the sector boundary and thus alter expansion dynamics. We developed a theory that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower on its own, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally, and their shapes matched quantitatively with the theory. In simulations, we further explored several mechanisms that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations.

Role of bacterial persistence in spatial population expansion

Bacterial persistence, tolerance to antibiotics via stochastic phenotype switching, provides a survival strategy and a fitness advantage in temporally fluctuating environments. Here we study its possible benefit in spatially varying environments using a Fisher wave approach. We study the spatial expansion of a population with stochastic switching between two phenotypes in spatially homogeneous conditions and in the presence of an antibiotic barrier. Our analytical results show that the expansion speed in growth-supporting conditions depends on the fraction of persister cells at the leading edge of the population wave. The leading edge contains a small fraction of persister cells, keeping the effect on the expansion speed minimal. The fraction of persisters increases gradually in the interior of the wave. This persister pool benefits the population when it is stalled by an antibiotic environment. In that case, the presence of persister enables the population to spread deeper into the antibiotic region and to cross an antibiotic region more rapidly. Further we observe that optimal switching rates maximize the expansion speed of the population in spatially varying environments with alternating regions of growth permitting conditions and antibiotics. Overall, our results show that stochastic switching can promote population expansion in the presence of antibiotic barriers or other stressful environments.

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.

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.

Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

The spatial organization of microbial communities during range expansion

Microbes in nature often live in dense and diverse communities exhibiting a variety of spatial structures. Microbial range expansion is a universal ecological process that enables populations to form spatial patterns. It can be driven by both passive and active processes, for example, mechanical forces from cell growth and bacterial motility. In this review, we provide a taste of recent creative and sophisticated efforts being made to address basic questions in spatial ecology and pattern formation during range expansion. We especially highlight the role of motility to shape community structures, and discuss the research challenges and future directions.

Quorum sensing provides a molecular mechanism for evolution to tune and maintain investment in cooperation

As selection frequently favors noncooperating defectors in mixed populations with cooperators, mechanisms that promote cooperation stability clearly exist. One potential mechanism is bacterial cell-to-cell communication, quorum sensing (QS), which can allow cooperators to prevent invasion by defectors. However, the impact of QS on widespread maintenance of cooperation in well-mixed conditions has not been experimentally demonstrated over extended evolutionary timescales. Here, we use wild-type (WT) Vibrio campbellii that regulates cooperation with QS and an unconditional cooperating (UC) mutant to examine the evolutionary origins and dynamics of novel defectors during a long-term evolution experiment. We found that UC lineages were completely outcompeted by defectors, whereas functioning QS enabled the maintenance of cooperative variants in most WT populations. Sequencing evolved populations revealed multiple luxR mutations that swept the UC lineages. However, the evolution of mutant lineages with reduced levels of bioluminescence (dims) occurred in many WT lineages. These dim variants also decreased other cooperative phenotypes regulated by QS, including protease production, indicating they result from changes to QS regulation. This diminished investment phenotype optimizes a tradeoff between cooperative input and growth output and suggests that decreasing the cost of QS could be a favorable strategy for maintaining the cooperative behaviors it regulates.

The Community Simulator: A Python package for microbial ecology

Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.

Evolutionary Rescue from a Wave of Biological Invasion

Evolution can potentially rescue populations from being driven extinct by biological invasions, but predictions for this occurrence are generally lacking. Here I derive theoretical predictions for evolutionary rescue of a resident population experiencing invasion from an introduced competitor that spreads over its introduced range as a traveling spatial wave that displaces residents. I compare the likelihood of evolutionary rescue from invasion for two modes of competition: exploitation and interference competition. I find that, all else equal, evolutionary rescue is less effective at preventing extinction caused by interference-driven invasions than by exploitation-driven invasions. Rescue from interference-driven invasions is, surprisingly, independent of invader dispersal rate or the speed of invasion and is more weakly dependent on range size than in the exploitation-driven case. In contrast, rescue from exploitation-driven invasions strongly depends on range size and is less likely during fast invasions. The results presented here have potential applications for conserving endemic species from nonnative invaders and for ensuring extinction of pests using intentionally introduced biocontrol agents.

Cooperation in Microbial Populations: Theory and Experimental Model Systems

Cooperative behavior, the costly provision of benefits to others, is common across all domains of life. This review article discusses cooperative behavior in the microbial world, mediated by the exchange of extracellular products called public goods. We focus on model species for which the production of a public good and the related growth disadvantage for the producing cells are well described. To unveil the biological and ecological factors promoting the emergence and stability of cooperative traits we take an interdisciplinary perspective and review insights gained from both mathematical models and well-controlled experimental model systems. Ecologically, we include crucial aspects of the microbial life cycle into our analysis and particularly consider population structures where ensembles of local communities (subpopulations) continuously emerge, grow, and disappear again. Biologically, we explicitly consider the synthesis and regulation of public good production. The discussion of the theoretical approaches includes general evolutionary concepts, population dynamics, and evolutionary game theory. As a specific but generic biological example, we consider populations of Pseudomonas putida and its regulation and use of pyoverdines, iron scavenging molecules, as public goods. The review closes with an overview on cooperation in spatially extended systems and also provides a critical assessment of the insights gained from the experimental and theoretical studies discussed. Current challenges and important new research opportunities are discussed, including the biochemical regulation of public goods, more realistic ecological scenarios resembling native environments, cell-to-cell signaling, and multispecies communities.

Cooperation mitigates diversity loss in a spatially expanding microbial population

Spatially expanding populations lose genetic diversity rapidly because of repeated bottlenecks formed at the colonization front. However, the rate of diversity loss depends on the specifics of the expanding population, such as its growth and dispersal dynamics. We have previously demonstrated that changing the amount of within-species cooperation leads to a qualitative transition in the nature of expansion from pulled (driven by growth at the low-density tip) to pushed (driven by migration from the high-density region at the front, but behind the tip). Here we demonstrate experimentally that pushed waves result in strongly reduced genetic drift during range expansions, thus preserving genetic diversity in the newly colonized region.

The evolution and potentially even the survival of a spatially expanding population depends on its genetic diversity, which can decrease rapidly due to a serial founder effect. The strength of the founder effect is predicted to depend strongly on the details of the growth dynamics. Here, we probe this dependence experimentally using a single microbial species, Saccharomyces cerevisiae, expanding in multiple environments that induce varying levels of cooperativity during growth. We observe a drastic reduction in diversity during expansions when yeast grows noncooperatively on simple sugars, but almost no loss of diversity when cooperation is required to digest complex metabolites. These results are consistent with theoretical expectations: When cells grow independently from each other, the expansion proceeds as a pulled wave driven by growth at the low-density tip of the expansion front. Such populations lose diversity rapidly because of the strong genetic drift at the expansion edge. In contrast, diversity loss is substantially reduced in pushed waves that arise due to cooperative growth. In such expansions, the low-density tip of the front grows much more slowly and is often reseeded from the genetically diverse population core. Additionally, in both pulled and pushed expansions, we observe a few instances of abrupt changes in allele fractions due to rare fluctuations of the expansion front and show how to distinguish such rapid genetic drift from selective sweeps.

Bottom-Up Approaches to Synthetic Cooperation in Microbial Communities

Microbial cooperation pervades ecological scales, from single-species populations to host-associated microbiomes. Understanding the mechanisms promoting the stability of cooperation against potential threats by cheaters is a major question that only recently has been approached experimentally. Synthetic biology has helped to uncover some of these basic mechanisms, which were to some extent anticipated by theoretical predictions. Moreover, synthetic cooperation is a promising lead towards the engineering of novel functions and enhanced productivity of microbial communities. Here, we review recent progress on engineered cooperation in microbial ecosystems. We focus on bottom-up approaches that help to better understand cooperation at the population level, progressively addressing the challenges of tackling higher degrees of complexity: spatial structure, multispecies communities, and host-associated microbiomes. We envisage cooperation as a key ingredient in engineering complex microbial ecosystems.

Spatial interference scale as a determinant of microbial range expansion

In microbial communities, social interactions such as competition occur ubiquitously across multiple spatial scales from local proximity to remote distance. However, it remains unclear how such a spatial variation of interaction contributes to the structural development of microbial populations. Here, we developed synthetic consortia, biophysical theory, and simulations to elucidate the role of spatial interference scale in governing ecosystem organization during range expansion. For consortia with unidirectional interference, we discovered that, at growing fronts, the extinction time of toxin-sensitive species is reciprocal to the spatial interference scale. In contrast, for communities with bidirectional interference, their structures diverge into distinct monoculture colonies under different initial conditions, with the corresponding separatrix set by the spatial scale of interference. Near the separatrix, ecosystem development becomes noise-driven and yields opposite structures. Our results establish spatial interaction scale as a key determinant for microbial range expansion, providing insights into microbial spatial organization and synthetic ecosystem engineering.

Maximizing Growth Yield and Dispersal via Quorum Sensing Promotes Cooperation in Vibrio Bacteria

Quorum sensing (QS) is a form of bacterial chemical communication that regulates cellular phenotypes, including certain cooperative behaviors, in response to environmental and demographic changes. Despite the existence of proposed mechanisms that stabilize QS against defector exploitation, it is unclear if or how QS cooperators can proliferate in some model systems in populations mostly consisting of defectors. We predicted that growth in fragmented subpopulations could allow QS cooperators to invade a QS defector population. This could occur despite cooperators having lower relative fitnesses than defectors due to favored weighting of genotypes that produce larger populations of bacteria. Mixed metapopulations of Vibrio QS-proficient or unconditional cooperators and QS defectors were diluted and fragmented into isolated subpopulations in an environment that requires QS-regulated public good production to achieve larger population yields. Under these conditions, we observed global invasions of both cooperator genotypes into populations composed of primarily defectors. This spatially dependent increase in cooperator frequency was replicated for QS cooperators when mixed populations were competed in soft agar motility plates under conditions that allowed cooperators to disperse and outcompete defectors at the population edge, despite being less motile in isolation than defectors. These competition results show that the coordinated growth and dispersal of QS cooperators to additional resources is heavily favored in comparison to unconditional cooperation, and that dispersal of cooperators by motility into new environments, examined here in laboratory populations, constitutes a key mechanism for maintaining QS-regulated cooperation in the face of defection.IMPORTANCE Behaviors that are cooperative in nature are at risk of exploitation by cheating and are thus difficult to maintain by natural selection alone. While bacterial cell-cell communication, known as quorum sensing (QS), can stabilize microbial cooperative behaviors and is widespread in Vibrio species, it is unclear how QS can increase the frequency of cooperative strains in the presence of defectors without additional mechanisms. In this study, we demonstrate under multiple conditions that QS-mediated cooperation can increase in populations of Vibrio strains when cells experience narrow population bottlenecks or disperse from defectors. This occurred for both conditional cooperation mediated by QS and for unconditional cooperation, although conditional cooperators were better able to stabilize cooperation over a much wider range of conditions. Thus, we observed that population structuring allowed for assortment of competing genotypes and promoted cooperation via kin selection in microbes in a QS-dependent manner.

Rate of recovery from perturbations as a means to forecast future stability of living systems

Anticipating critical transitions in complex ecological and living systems is an important need because it is often difficult to restore a system to its pre-transition state once the transition occurs. Recent studies demonstrate that several indicators based on changes in ecological time series can indicate that the system is approaching an impending transition. An exciting question is, however, whether we can predict more characteristics of the future system stability using measurements taken away from the transition. We address this question by introducing a model-less forecasting method to forecast catastrophic transition of an experimental ecological system. The experiment is based on the dynamics of a yeast population, which is known to exhibit a catastrophic transition as the environment deteriorates. By measuring the system's response to perturbations prior to transition, we forecast the distance to the upcoming transition, the type of the transition (i.e., catastrophic/non-catastrophic) and the future equilibrium points within a range near the transition. Experimental results suggest a strong potential for practical applicability of this approach for ecological systems which are at risk of catastrophic transitions, where there is a pressing need for information about upcoming thresholds.

Ecology and evolution of metabolic cross-feeding interactions in bacteria

Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

When increasing population density can promote the evolution of metabolic cooperation

Microbial cooperation drives ecological and epidemiological processes and is affected by the ecology and demography of populations. Population density influences the selection for cooperation, with spatial structure and the type of social dilemma, namely public-goods production or self-restraint, shaping the outcome. While existing theories predict that in spatially structured environments increasing population density can select either for or against cooperation, experimental studies with both public-goods production and self-restraint systems have only ever shown that increasing population density favours cheats. We suggest that the disparity between theory and empirical studies results from experimental procedures not capturing environmental conditions predicted by existing theories to influence the outcome. Our study resolves this issue and provides the first experimental evidence that high population density can favour cooperation in spatially structured environments for both self-restraint and public-goods production systems. Moreover, using a multi-trait mathematical model supported by laboratory experiments we extend this result to systems where the self-restraint and public-goods social dilemmas interact. We thus provide a systematic understanding of how the strength of interaction between the two social dilemmas and the degree of spatial structure within an environment affect selection for cooperation. These findings help to close the current gap between theory and experiments.

Synthetic microbial ecology and the dynamic interplay between microbial genotypes

Assemblages of microbial genotypes growing together can display surprisingly complex and unexpected dynamics and result in community-level functions and behaviors that are not readily expected from analyzing each genotype in isolation. This complexity has, at least in part, inspired a discipline of synthetic microbial ecology. Synthetic microbial ecology focuses on designing, building and analyzing the dynamic behavior of ‘ecological circuits’ (i.e. a set of interacting microbial genotypes) and understanding how community-level properties emerge as a consequence of those interactions. In this review, we discuss typical objectives of synthetic microbial ecology and the main advantages and rationales of using synthetic microbial assemblages. We then summarize recent findings of current synthetic microbial ecology investigations. In particular, we focus on the causes and consequences of the interplay between different microbial genotypes and illustrate how simple interactions can create complex dynamics and promote unexpected community-level properties. We finally propose that distinguishing between active and passive interactions and accounting for the pervasiveness of competition can improve existing frameworks for designing and predicting the dynamics of microbial assemblages.

Microbial expansion-collision dynamics promote cooperation and coexistence on surfaces

Microbes colonizing a surface often experience colony growth dynamics characterized by an initial phase of spatial clonal expansion followed by collision between neighboring colonies to form potentially genetically heterogeneous boundaries. For species with life cycles consisting of repeated surface colonization and dispersal, these spatially explicit "expansion-collision dynamics" generate periodic transitions between two distinct selective regimes, "expansion competition" and "boundary competition," each one favoring a different growth strategy. We hypothesized that this dynamic could promote stable coexistence of expansion- and boundary-competition specialists by generating time-varying, negative frequency-dependent selection that insulates both types from extinction. We tested this experimentally in budding yeast by competing an exoenzyme secreting "cooperator" strain (expansion-competition specialists) against nonsecreting "defectors" (boundary-competition specialists). As predicted, we observed cooperator-defector coexistence or cooperator dominance with expansion-collision dynamics, but only defector dominance otherwise. Also as predicted, the steady-state frequency of cooperators was determined by colonization density (the average initial cell-cell distance) and cost of cooperation. Lattice-based spatial simulations give good qualitative agreement with experiments, supporting our hypothesis that expansion-collision dynamics with costly public goods production is sufficient to generate stable cooperator-defector coexistence. This mechanism may be important for maintaining public-goods cooperation and conflict in microbial pioneer species living on surfaces.

Modeling Microbial Communities: A Call for Collaboration between Experimentalists and Theorists

With our growing understanding of the impact of microbial communities, understanding how such communities function has become a priority. The influence of microbial communities is widespread. Human-associated microbiota impacts health, environmental microbes determine ecosystem sustainability, and microbe-driven industrial processes are expanding. This broad range of applications has led to a wide range of approaches to analyze and describe microbial communities. In particular, theoretical work based on mathematical modeling has been a steady source of inspiration for explaining and predicting microbial community processes. Here, we survey some of the modeling approaches used in different contexts. We promote classifying different approaches using a unified platform, and encourage cataloging the findings in a database. We believe that the synergy emerging from a coherent collection facilitates a better understanding of important processes that determine microbial community functions. We emphasize the importance of close collaboration between theoreticians and experimentalists in formulating, classifying, and improving models of microbial communities.

Cooperation in microbial communities and their biotechnological applications

Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross-feeding, which can be understood in the general framework of ecological and evolutionary (eco-evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of 'cheating' cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

Eco-evolutionary feedbacks can rescue cooperation in microbial populations

Bacterial populations whose growth depends on the cooperative production of public goods are usually threatened by the rise of cheaters that do not contribute but just consume the common resource. Minimizing cheater invasions appears then as a necessary mechanism to maintain these populations. However, that invasions result instead in the persistence of cooperation is a prospect that has yet remained largely unexplored. Here, we show that the demographic collapse induced by cheaters in the population can actually contribute to the rescue of cooperation, in a clear illustration of how ecology and evolution can influence each other. The effect is made possible by the interplay between spatial constraints and the essentiality of the shared resource. We validate this result by carefully combining theory and experiments, with the engineering of a synthetic bacterial community in which the public compound allows survival to a lethal stress. The characterization of the experimental system identifies additional factors that can matter, like the impact of the lag phase on the tolerance to stress, or the appearance of spontaneous mutants. Our work explains the unanticipated dynamics that eco-evolutionary feedbacks can generate in microbial communities, feedbacks that reveal fundamental for the adaptive change of ecosystems at all scales.

A continuous ideal free distribution approach to the dynamics of selfish, cooperative and kleptoparasitic populations

Population distributions depend upon the aggregate behavioural responses of individuals to a range of environmental factors. We extend a model of ideally motivated populations to describe the local and regional consequences of interactions between three populations distinguished by their levels of cooperation and exploitation. Inspired by the classic prisoner's dilemma game, stereotypical fitness functions describe a baseline non-cooperative population whose per capita fitness decreases with density, obligate co-operators who initially benefit from the presence of conspecifics, and kleptoparasites who require heterospecifics to extract resources from the environment. We examine these populations in multiple combinations, determine where both local and regional coexistence is permitted, and investigate conditions under which one population will invade another. When they invade co-operators in resource-rich areas, kleptoparasites initiate a dynamic instability that leads to the loss of both populations; however, selfish hosts, who can persist at low densities, are immune to this risk. Furthermore, adaptive movement may delay the onset of instability as dispersal relieves dynamic stress. Selfish and cooperative populations default to mutual exclusion, but asymmetric variations in interference strength may relax this condition and permit limited sympatry within the environment. Distinct sub-communities characterize the overall spatial structure.

Range expansions transition from pulled to pushed waves as growth becomes more cooperative in an experimental microbial population

Range expansions are becoming more frequent due to environmental changes and rare long-distance dispersal, often facilitated by anthropogenic activities. Simple models in theoretical ecology explain many emergent properties of range expansions, such as a constant expansion velocity, in terms of organism-level properties such as growth and dispersal rates. Testing these quantitative predictions in natural populations is difficult because of large environmental variability. Here, we used a controlled microbial model system to study range expansions of populations with and without intraspecific cooperativity. For noncooperative growth, the expansion dynamics were dominated by population growth at the low-density front, which pulled the expansion forward. We found these expansions to be in close quantitative agreement with the classical theory of pulled waves by Fisher [Fisher RA (1937) Ann Eugen 7(4):355-369] and Skellam [Skellam JG (1951) Biometrika 38(1-2):196-218], suitably adapted to our experimental system. However, as cooperativity increased, the expansions transitioned to being pushed, that is, controlled by growth and dispersal in the bulk as well as in the front. Given the prevalence of cooperative growth in nature, understanding the effects of cooperativity is essential to managing invading species and understanding their evolution.

The Evolution of Quorum Sensing as a Mechanism to Infer Kinship

Bacteria regulate many phenotypes via quorum sensing systems. Quorum sensing is typically thought to evolve because the regulated cooperative phenotypes are only beneficial at certain cell densities. However, quorum sensing systems are also threatened by non-cooperative "cheaters" that may exploit quorum-sensing regulated cooperation, which begs the question of how quorum sensing systems are maintained in nature. Here we study the evolution of quorum sensing using an individual-based model that captures the natural ecology and population structuring of microbial communities. We first recapitulate the two existing observations on quorum sensing evolution: density-dependent benefits favor quorum sensing but competition and cheating will destabilize it. We then model quorum sensing in a dense community like a biofilm, which reveals a novel benefit to quorum sensing that is intrinsically evolutionarily stable. In these communities, competing microbial genotypes gradually segregate over time leading to positive correlation between density and genetic similarity between neighboring cells (relatedness). This enables quorum sensing to track genetic relatedness and ensures that costly cooperative traits are only activated once a cell is safely surrounded by clonemates. We hypothesize that under similar natural conditions, the benefits of quorum sensing will not result from an assessment of density but from the ability to infer kinship.

Build to understand: synthetic approaches to biology

In this review we discuss how synthetic biology facilitates the task of investigating genetic circuits that are observed in naturally occurring biological systems. Specifically, we give examples where experimentation with synthetic gene circuits has been used to understand four fundamental mechanisms intrinsic to development and disease: multistability, stochastic gene expression, oscillations, and cell-cell communication. Within each area, we also discuss how mathematical modeling has been employed as an essential tool to guide the design of novel gene circuits and as a theoretical basis for exploring circuit topologies exhibiting robust behaviors in the presence of noise.

Expansion load and the evolutionary dynamics of a species range

Expanding populations incur a mutation burden, the so-called expansion load. Using a mixture of individual-based simulations and analytical modeling, we study the expansion load process in models where population growth depends on the population's fitness (i.e., hard selection). We show that expansion load can severely slow down expansions and limit a species' range, even in the absence of environmental variation. We also study the effect of recombination on the dynamics of a species range and on the evolution of mean fitness on the wave front. If recombination is strong, mean fitness on front approaches an equilibrium value at which the effects of fixed mutations cancel each other out. The equilibrium rate at which new demes are colonized is similar to the rate at which beneficial mutations spread through the core. Without recombination, the dynamics is more complex, and beneficial mutations from the core of the range can invade the front of the expansion, which results in irregular and episodic expansion. Although the rate of adaptation is generally higher in recombining organisms, the mean fitness on the front may be larger in the absence of recombination because high-fitness individuals from the core have a higher chance to invade the front. Our findings have important consequences for the evolutionary dynamics of species ranges as well as on the role and the evolution of recombination during range expansions.

High cost enhances cooperation through the interplay between evolution and self-organisation

Background

Cooperation is ubiquitous in biological systems, yet its evolution is a long lasting evolutionary problem. A general and intuitive result from theoretical models of cooperative behaviour is that cooperation decreases when its costs are higher, because selfish individuals gain selective advantage.

Results

Contrary to this intuition, we show that cooperation can increase with higher costs. We analyse a minimal model where individuals live on a lattice and evolve the degree of cooperation. We find that a feedback establishes between the evolutionary dynamics of public good production and the spatial self-organisation of the population. The evolutionary dynamics lead to the speciation of a cooperative and a selfish lineage. The ensuing spatial self-organisation automatically diversifies the selection pressure on the two lineages. This enables selfish individuals to successfully invade cooperators at the expenses of their autonomous replication, and cooperators to increase public good production while expanding in the empty space left behind by cheaters. We show that this emergent feedback leads to higher degrees of cooperation when costs are higher.

Conclusions

An emergent feedback between evolution and self-organisation leads to high degrees of cooperation at high costs, under simple and general conditions. We propose this as a general explanation for the evolution of cooperative behaviours under seemingly prohibitive conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0600-9) contains supplementary material, which is available to authorized users.

Acknowledging selection at sub-organismal levels resolves controversy on pro-cooperation mechanisms

Cooperators who pay a cost to produce publically-available benefits can be exploited by cheaters who do not contribute fairly. How might cooperation persist against cheaters? Two classes of mechanisms are known to promote cooperation: 'partner choice', where a cooperator preferentially interacts with cooperative over cheating partners; and 'partner fidelity feedback', where repeated interactions between individuals ensure that cheaters suffer as their cooperative partners languish (see, for example, Momeni et al., 2013). However when both mechanisms can act, differentiating them has generated controversy. Here, I resolve this controversy by noting that selection can operate on organismal and sub-organismal 'entities' such that partner fidelity feedback at sub-organismal level can appear as partner choice at organismal level. I also show that cooperation between multicellular eukaryotes and mitochondria is promoted by partner fidelity feedback and partner choice between sub-organismal entities, in addition to being promoted by partner fidelity feedback between hosts and symbionts, as was previously known.

Bulk Segregant Analysis Reveals the Genetic Basis of a Natural Trait Variation in Fission Yeast

Although the fission yeast Schizosaccharomyces pombe is a well-established model organism, studies of natural trait variations in this species remain limited. To assess the feasibility of segregant-pool-based mapping of phenotype-causing genes in natural strains of fission yeast, we investigated the cause of a maltose utilization defect (Mal(-)) of the S. pombe strain CBS5557 (originally known as Schizosaccharomyces malidevorans). Analyzing the genome sequence of CBS5557 revealed 955 nonconservative missense substitutions, and 61 potential loss-of-function variants including 47 frameshift indels, 13 early stop codons, and 1 splice site mutation. As a side benefit, our analysis confirmed 146 sequence errors in the reference genome and improved annotations of 27 genes. We applied bulk segregant analysis to map the causal locus of the Mal(-) phenotype. Through sequencing the segregant pools derived from a cross between CBS5557 and the laboratory strain, we located the locus to within a 2.23-Mb chromosome I inversion found in most S. pombe isolates including CBS5557. To map genes within the inversion region that occupies 18% of the genome, we created a laboratory strain containing the same inversion. Analyzing segregants from a cross between CBS5557 and the inversion-containing laboratory strain narrowed down the locus to a 200-kb interval and led us to identify agl1, which suffers a 5-bp deletion in CBS5557, as the causal gene. Interestingly, loss of agl1 through a 34-kb deletion underlies the Mal(-) phenotype of another S. pombe strain CGMCC2.1628. This work adapts and validates the bulk segregant analysis method for uncovering trait-gene relationship in natural fission yeast strains.

Synthetic Ecology of Microbes: Mathematical Models and Applications

As the indispensable role of natural microbial communities in many aspects of life on Earth is uncovered, the bottom-up engineering of synthetic microbial consortia with novel functions is becoming an attractive alternative to engineering single-species systems. Here, we summarize recent work on synthetic microbial communities with a particular emphasis on open challenges and opportunities in environmental sustainability and human health. We next provide a critical overview of mathematical approaches, ranging from phenomenological to mechanistic, to decipher the principles that govern the function, dynamics and evolution of microbial ecosystems. Finally, we present our outlook on key aspects of microbial ecosystems and synthetic ecology that require further developments, including the need for more efficient computational algorithms, a better integration of empirical methods and model-driven analysis, the importance of improving gene function annotation, and the value of a standardized library of well-characterized organisms to be used as building blocks of synthetic communities.

Making pathogens sociable: the [corrected] emergence of high relatedness through limited host invasibility

Cooperation depends upon high relatedness, the high genetic similarity of interacting partners relative to the wider population. For pathogenic bacteria, which show diverse cooperative traits, the population processes that determine relatedness are poorly understood. Here, we explore whether within-host dynamics can produce high relatedness in the insect pathogen Bacillus thuringiensis. We study the effects of host/pathogen interactions on relatedness via a model of host invasion and fit parameters to competition experiments with marked strains. We show that invasibility is a key parameter for determining relatedness and experimentally demonstrate the emergence of high relatedness from well-mixed inocula. We find that a single infection cycle results in a bottleneck with a similar level of relatedness to those previously reported in the field. The bottlenecks that are a product of widespread barriers to infection can therefore produce the population structure required for the evolution of cooperative virulence.

The idiosyncrasy of spatial structure in bacterial competition

Background

The spatial structure of a habitat can have a strong impact on community dynamics. Different experimental approaches exist to explore the effect of spatial structure on bacterial communities. To investigate the effect of ‘space’, a single implementation of spatial structure is often contrasted to bacterial community dynamics in well-mixed cultures. While such comparisons are useful, it is likely that the observed dynamics will be particular to the specific experimental implementation of spatial structure. In order to address this question, we track the community dynamics of a two-strain Escherichia coli community in various spatial habitats and relate the observed dynamics to the structure of a habitat.

Results

By tracking the community dynamics of rpoS wild-type and mutant E. coli in radially expanding colonies on solid and semi-solid agar plates, we find that the mutant strain outcompetes the wild-type on semi-solid agar plates, whereas the two strains coexist on solid agar. We compare these results to previous studies in which the same two strains were shown to coexist in habitats spatially structured by microfabrication, while the mutant outcompeted the wild-type in well-mixed batch cultures. Together, these observations show that different implementations of space may result in qualitatively different community dynamics. Furthermore, we argue that the same competitive outcome (e.g. coexistence) may arise from distinct underlying dynamics in different experimental implementations of spatial structure.

Conclusions

Our observations demonstrate that different experimental implementations of spatial structure may not only lead to quantitatively different communities (changes in the relative abundance of types) but can also lead to qualitatively different outcomes of long-term community dynamics (coexistence versus extinction and loss of biodiversity).

Bacterial swarms recruit cargo bacteria to pave the way in toxic environments

Swarming bacteria are challenged by the need to invade hostile environments. Swarms of the flagellated bacterium Paenibacillus vortex can collectively transport other microorganisms. Here we show that P. vortex can invade toxic environments by carrying antibiotic-degrading bacteria; this transport is mediated by a specialized, phenotypic subpopulation utilizing a process not dependent on cargo motility. Swarms of beta-lactam antibiotic (BLA)-sensitive P. vortex used beta-lactamase-producing, resistant, cargo bacteria to detoxify BLAs in their path. In the presence of BLAs, both transporter and cargo bacteria gained from this temporary cooperation; there was a positive correlation between BLA resistance and dispersal. P. vortex transported only the most beneficial antibiotic-resistant cargo (including environmental and clinical isolates) in a sustained way. P. vortex displayed a bet-hedging strategy that promoted the colonization of nontoxic niches by P. vortex alone; when detoxifying cargo bacteria were not needed, they were lost. This work has relevance for the dispersal of antibiotic-resistant microorganisms and for strategies for asymmetric cooperation with agricultural and medical implications.

Antibiotic resistance is a major health threat. We show a novel mechanism for the local spread of antibiotic resistance. This involves interactions between different bacteria: one species provides an enzyme that detoxifies the antibiotic (a sessile cargo bacterium carrying a resistance gene), while the other (Paenibacillus vortex) moves itself and transports the cargo. P. vortex used a bet-hedging strategy, colonizing new environments alone when the cargo added no benefit, but cooperating when the cargo was needed. This work is of interest in an evolutionary context and sheds light on fundamental questions, such as how environmental antibiotic resistance may lead to clinical resistance and also microbial social organization, as well as the costs, benefits, and risks of dispersal in the environment.

Evolutionary limits to cooperation in microbial communities

Microbes produce many compounds that are costly to a focal cell but promote the survival and reproduction of neighboring cells. This observation has led to the suggestion that microbial strains and species will commonly cooperate by exchanging compounds. Here, we examine this idea with an ecoevolutionary model where microbes make multiple secretions, which can be exchanged among genotypes. We show that cooperation between genotypes only evolves under specific demographic regimes characterized by intermediate genetic mixing. The key constraint on cooperative exchanges is a loss of autonomy: strains become reliant on complementary genotypes that may not be reliably encountered. Moreover, the form of cooperation that we observe arises through mutual exploitation that is related to cheating and "Black Queen" evolution for a single secretion. A major corollary is that the evolution of cooperative exchanges reduces community productivity relative to an autonomous strain that makes everything it needs. This prediction finds support in recent work from synthetic communities. Overall, our work suggests that natural selection will often limit cooperative exchanges in microbial communities and that, when exchanges do occur, they can be an inefficient solution to group living.