Experimentally evolved Staphylococcus aureus shows increased survival in the presence of Pseudomonas aeruginosa by acquiring mutations in the amino acid transporter, GltT.

When cultured together under standard laboratory conditions Pseudomonas aeruginosa has been shown to be an effective inhibitor of Staphylococcus aureus. However, P. aeruginosa and S. aureus are commonly observed in coinfections of individuals with cystic fibrosis (CF) and in chronic wounds. Previous work from our group revealed that S. aureus isolates from CF infections are able to persist in the presence of P. aeruginosa strain PAO1 with a range of tolerances with some isolates being eliminated entirely and others maintaining large populations. In this study, we designed a serial transfer, evolution experiment to identify mutations that allow S. aureus to survive in the presence of P. aeruginosa. Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa PAO1. After eight coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT, that were unique to evolved P. aeruginosa-tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompeted wild-type S. aureus when glutamate was absent from chemically defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two are cultured together.

Global dynamics of microbial communities emerge from local interaction rules.

Most microbes live in spatially structured communities (e.g., biofilms) in which they interact with their neighbors through the local exchange of diffusible molecules. To understand the functioning of these communities, it is essential to uncover how these local interactions shape community-level properties, such as the community composition, spatial arrangement, and growth rate. Here, we present a mathematical framework to derive community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our framework consists of two parts: a biophysical model to derive the local interaction rules (i.e. interaction range and strength) from the molecular parameters underlying the cell-cell interactions and a graph based model to derive the equilibrium properties of the community (i.e. composition, spatial arrangement, and growth rate) from these local interaction rules. Our framework shows that key molecular parameters underlying the cell-cell interactions (e.g., the uptake and leakage rates of molecules) determine community-level properties. We apply our model to mutualistic cross-feeding communities and show that spatial structure can be detrimental for these communities. Moreover, our model can qualitatively recapitulate the properties of an experimental microbial community. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level properties emerge from microscopic interactions between cells.

Multilevel selection favors fragmentation modes that maintain cooperative interactions in multispecies communities.

Reproduction is one of the requirements for evolution and a defining feature of life. Yet, across the tree of life, organisms reproduce in many different ways. Groups of cells (e.g., multicellular organisms, colonial microbes, or multispecies biofilms) divide by releasing propagules that can be single-celled or multicellular. What conditions determine the number and size of reproductive propagules? In multicellular organisms, existing theory suggests that single-cell propagules prevent the accumulation of deleterious mutations (e.g., cheaters). However, groups of cells, such as biofilms, sometimes contain multiple metabolically interdependent species. This creates a reproductive dilemma: small daughter groups, which prevent the accumulation of cheaters, are also unlikely to contain the species diversity that is required for ecological success. Here, we developed an individual-based, multilevel selection model to investigate how such multi-species groups can resolve this dilemma. By tracking the dynamics of groups of cells that reproduce by fragmenting into smaller groups, we identified fragmentation modes that can maintain cooperative interactions. We systematically varied the fragmentation mode and calculated the maximum mutation rate that communities can withstand before being driven to extinction by the accumulation of cheaters. We find that for groups consisting of a single species, the optimal fragmentation mode consists of releasing single-cell propagules. For multi-species groups we find various optimal strategies. With migration between groups, single-cell propagules are favored. Without migration, larger propagules sizes are optimal; in this case, group-size dependent fissioning rates can prevent the accumulation of cheaters. Our work shows that multi-species groups can evolve reproductive strategies that allow them to maintain cooperative interactions.

The role of multilevel selection in host microbiome evolution.

Animals are associated with a microbiome that can affect their reproductive success. It is, therefore, important to understand how a host and its microbiome coevolve. According to the hologenome concept, hosts and their microbiome form an integrated evolutionary entity, a holobiont, on which selection can potentially act directly. However, this view is controversial, and there is an active debate on whether the association between hosts and their microbiomes is strong enough to allow for selection at the holobiont level. Much of this debate is based on verbal arguments, but a quantitative framework is needed to investigate the conditions under which selection can act at the holobiont level. Here, we use multilevel selection theory to develop such a framework. We found that selection at the holobiont level can in principle favor a trait that is costly to the microbes but that provides a benefit to the host. However, such scenarios require rather stringent conditions. The degree to which microbiome composition is heritable decays with time, and selection can only act at the holobiont level when this decay is slow enough, which occurs when vertical transmission is stronger than horizontal transmission. Moreover, the host generation time has to be short enough compared with the timescale of the evolutionary dynamics at the microbe level. Our framework thus allows us to quantitatively predict for what kind of systems selection could act at the holobiont level.

Generality of associations between biological richness and the rates of metabolic processes across microbial communities.

Biological richness is positively associated with the rates of some metabolic processes performed by microbial communities. It remains unclear, however, whether these positive associations are a general feature of the metabolic processes performed by microbial communities or whether they are specific to certain types of metabolic processes. For example, it was hypothesized that the strength of any particular positive association depends on how many different genotypes within a microbial community perform the metabolic process of interest (i.e. the 'rarity hypothesis'). We tested the generality of these positive associations by measuring the taxonomic richness, functional gene richness and rate constants for 71 different metabolic processes across 30 independent microbial communities. We found that both taxonomic and functional gene richness do indeed tend to positively associate with the rates of metabolic processes. In addition, we found that positive associations occur across a wide range of different environmental conditions. Counter to the 'rarity hypothesis', however, we did not detect a relationship between the strengths of the positive associations and the rarity of each metabolic process. Together, our data provide empirical evidence that positive associations with biological richness may indeed be a general feature of the metabolic processes performed by microbial communities.

Bacterial Ventures into Multicellularity: Collectivism through Individuality.

Multicellular eukaryotes can perform functions that exceed the possibilities of an individual cell. These functions emerge through interactions between differentiated cells that are precisely arranged in space. Bacteria also form multicellular collectives that consist of differentiated but genetically identical cells. How does the functionality of these collectives depend on the spatial arrangement of the differentiated bacteria? In a previous issue of PLOS Biology, van Gestel and colleagues reported an elegant example of how the spatial arrangement of differentiated cells gives rise to collective behavior in Bacillus subtilus colonies, further demonstrating the similarity of bacterial collectives to higher multicellular organisms.

The effects of chemical interactions and culture history on the colonization of structured habitats by competing bacterial populations.

BACKGROUND: Bacterial habitats, such as soil and the gut, are structured at the micrometer scale. Important aspects of microbial life in such spatial ecosystems are migration and colonization. Here we explore the colonization of a structured ecosystem by two neutrally labeled strains of Escherichia coli. Using time-lapse microscopy we studied the colonization of one-dimensional arrays of habitat patches linked by connectors, which were invaded by the two E. coli strains from opposite sides. RESULTS: The two strains colonize a habitat from opposite sides by a series of traveling waves followed by an expansion front. When population waves collide, they branch into a continuing traveling wave, a reflected wave and a stationary population. When the two strains invade the landscape from opposite sides, they remain segregated in space and often one population will displace the other from most of the habitat. However, when the strains are co-cultured before entering the habitats, they colonize the habitat together and do not separate spatially. Using physically separated, but diffusionally coupled, habitats we show that colonization waves and expansion fronts interact trough diffusible molecules, and not by direct competition for space. Furthermore, we found that colonization outcome is influenced by a culture's history, as the culture with the longest doubling time in bulk conditions tends to take over the largest fraction of the habitat. Finally, we observed that population distributions in parallel habitats located on the same device and inoculated with cells from the same overnight culture are significantly more similar to each other than to patterns in identical habitats located on different devices inoculated with cells from different overnight cultures, even tough all cultures were started from the same -80°C frozen stock. CONCLUSIONS: We found that the colonization of spatially structure habitats by two interacting populations can lead to the formation of complex, but reproducible, spatiotemporal patterns. Furthermore, we showed that chemical interactions between two populations cause them to remain spatially segregated while they compete for habitat space. Finally, we observed that growth properties in bulk conditions correlate with the outcome of habitat colonization. Together, our data show the crucial roles of chemical interactions between populations and a culture's history in determining the outcome of habitat colonization.

A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution.

Monitoring changes of signaling molecules and metabolites with high temporal resolution is key to understanding dynamic biological systems. Here, we use directed evolution to develop a genetically encoded ratiometric biosensor for c-di-GMP, a ubiquitous bacterial second messenger regulating important biological processes like motility, surface attachment, virulence and persistence. The resulting biosensor, cdGreen2, faithfully tracks c-di-GMP in single cells and with high temporal resolution over extended imaging times, making it possible to resolve regulatory networks driving bimodal developmental programs in different bacterial model organisms. We further adopt cdGreen2 as a simple tool for in vitro studies, facilitating high-throughput screens for compounds interfering with c-di-GMP signaling and biofilm formation. The sensitivity and versatility of cdGreen2 could help reveal c-di-GMP dynamics in a broad range of microorganisms with high temporal resolution. Its design principles could also serve as a blueprint for the development of similar, orthogonal biosensors for other signaling molecules, metabolites and antibiotics.

Spatial self-organization of metabolism in microbial systems: A matter of enzymes and chemicals.

Most bacteria live in dense, spatially structured communities such as biofilms. The high density allows cells to alter the local microenvironment, whereas the limited mobility can cause species to become spatially organized. Together, these factors can spatially organize metabolic processes within microbial communities so that cells in different locations perform different metabolic reactions. The overall metabolic activity of a community depends both on how metabolic reactions are arranged in space and on how they are coupled, i.e., how cells in different regions exchange metabolites. Here, we review mechanisms that lead to the spatial organization of metabolic processes in microbial systems. We discuss factors that determine the length scales over which metabolic activities are arranged in space and highlight how the spatial organization of metabolic processes affects the ecology and evolution of microbial communities. Finally, we define key open questions that we believe should be the main focus of future research.

Changes in interactions over ecological time scales influence single-cell growth dynamics in a metabolically coupled marine microbial community.

Microbial communities thrive in almost all habitats on earth. Within these communities, cells interact through the release and uptake of metabolites. These interactions can have synergistic or antagonistic effects on individual community members. The collective metabolic activity of microbial communities leads to changes in their local environment. As the environment changes over time, the nature of the interactions between cells can change. We currently lack understanding of how such dynamic feedbacks affect the growth dynamics of individual microbes and of the community as a whole. Here we study how interactions mediated by the exchange of metabolites through the environment change over time within a simple marine microbial community. We used a microfluidic-based approach that allows us to disentangle the effect cells have on their environment from how they respond to their environment. We found that the interactions between two species-a degrader of chitin and a cross-feeder that consumes metabolic by-products-changes dynamically over time as cells modify their environment. Cells initially interact positively and then start to compete at later stages of growth. Our results demonstrate that interactions between microorganisms are not static and depend on the state of the environment, emphasizing the importance of disentangling how modifications of the environment affects species interactions. This experimental approach can shed new light on how interspecies interactions scale up to community level processes in natural environments.

Experimentally Evolved Staphylococcus aureus Survives in the Presence of Pseudomonas aeruginosa by Acquiring Mutations in the Amino Acid Transporter, GltT.

Staphylococcus aureus and Pseudomonas aeruginosa are the most common bacterial pathogens isolated from cystic fibrosis (CF) related lung infections. When both of these opportunistic pathogens are found in a coinfection, CF patients tend to have higher rates of pulmonary exacerbations and experience a more rapid decrease in lung function. When cultured together under standard laboratory conditions, it is often observed that P. aeruginosa effectively inhibits S. aureus growth. Previous work from our group revealed that S. aureus from CF infections have isolate-specific survival capabilities when cocultured with P. aeruginosa. In this study, we designed a serial transfer evolution experiment to identify mutations that allow S. aureus to adapt to the presence of P. aeruginosa. Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa strain, PAO1. After 8 coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT, that were unique to evolved P. aeruginosa-tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompete wild-type S. aureus when glutamate is absent from chemically-defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two bacteria are cultured together.

Frequency modulation of a bacterial quorum sensing response.

In quorum sensing, bacteria secrete or release small molecules into the environment that, once they reach a certain threshold, trigger a behavioural change in the population. As the concentration of these so-called autoinducers is supposed to reflect population density, they were originally assumed to be continuously produced by all cells in a population. However, here we show that in the α-proteobacterium Sinorhizobium meliloti expression of the autoinducer synthase gene is realized in asynchronous stochastic pulses that result from scarcity and, presumably, low binding affinity of the key activator. Physiological cues modulate pulse frequency, and pulse frequency in turn modulates the velocity with which autoinducer levels in the environment reach the threshold to trigger the quorum sensing response. We therefore propose that frequency-modulated pulsing in S. meliloti represents the molecular mechanism for a collective decision-making process in which each cell's physiological state and need for behavioural adaptation is encoded in the pulse frequency with which it expresses the autoinducer synthase gene; the pulse frequencies of all members of the population are then integrated in the common pool of autoinducers, and only once this vote crosses the threshold, the response behaviour is initiated.

Microfluidics for Single-Cell Study of Antibiotic Tolerance and Persistence Induced by Nutrient Limitation.

Nutrient limitation is one of the most common triggers of antibiotic tolerance and persistence. Here, we present two microfluidic setups to study how spatial and temporal variation in nutrient availability lead to increased survival of bacteria to antibiotics. The first setup is designed to mimic the growth dynamics of bacteria in spatially structured populations (e.g., biofilms) and can be used to study how spatial gradients in nutrient availability, created by the collective metabolic activity of a population, increase antibiotic tolerance. The second setup captures the dynamics of feast-and-famine cycles that bacteria recurrently encounter in nature, and can be used to study how phenotypic heterogeneity in growth resumption after starvation increases survival of clonal bacterial populations. In both setups, the growth rates and metabolic activity of bacteria can be measured at the single-cell level. This is useful to build a mechanistic understanding of how spatiotemporal variation in nutrient availability triggers bacteria to enter phenotypic states that increase their tolerance to antibiotics.

Publisher Correction: Short-range interactions govern the dynamics and functions of microbial communities.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Short-range interactions govern the dynamics and functions of microbial communities.

Communities of interacting microorganisms play important roles across all habitats on Earth. These communities typically consist of a large number of species that perform different metabolic processes. The functions of microbial communities ultimately emerge from interactions between these different microorganisms. To understand the dynamics and functions of microbial communities, we thus need to know the nature and strength of these interactions. Here, we quantified the interaction strength between individual cells in microbial communities. We worked with synthetic communities of Escherichia coli bacteria that exchange metabolites to grow. We combined single-cell growth rate measurements with mathematical modelling to quantify metabolic interactions between individual cells and to map the spatial interaction network in these communities. We found that cells only interact with other cells in their immediate neighbourhood. This short interaction range limits the coupling between different species and reduces their ability to perform metabolic processes collectively. Our experiments and models demonstrate that the spatial scale of biotic interaction plays a fundamental role in shaping the ecological dynamics of communities and the functioning of ecosystems.

Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations.

Bacteria often live in spatially structured groups such as biofilms. In these groups, cells can collectively generate gradients through the uptake and release of compounds. In turn, individual cells adapt their activities to the environment shaped by the whole group. Here, we studied how these processes can generate phenotypic variation in clonal populations and how this variation contributes to the resilience of the population to antibiotics. We grew two-dimensional populations of Escherichia coli in microfluidic chambers where limiting amounts of glucose were supplied from one side. We found that the collective metabolic activity of cells created microscale gradients where nutrient concentration varied over a few cell lengths. As a result, growth rates and gene expression levels varied strongly between neighbouring cells. Furthermore, we found evidence for a metabolic cross-feeding interaction between glucose-fermenting and acetate-respiring subpopulations. Finally, we found that subpopulations of cells were able to survive an antibiotic pulse that was lethal in well-mixed conditions, likely due to the presence of a slow-growing subpopulation. Our work shows that emergent metabolic gradients can have important consequences for the functionality of bacterial populations as they create opportunities for metabolic interactions and increase the populations' tolerance to environmental stressors. This article is part of a discussion meeting issue 'Single cell ecology'.

Metabolic activity affects the response of single cells to a nutrient switch in structured populations.

Microbes live in ever-changing environments where they need to adapt their metabolism to different nutrient conditions. Many studies have characterized the response of genetically identical cells to nutrient switches in homogeneous cultures; however, in nature, microbes often live in spatially structured groups such as biofilms where cells can create metabolic gradients by consuming and releasing nutrients. Consequently, cells experience different local microenvironments and vary in their phenotype. How does this phenotypic variation affect the ability of cells to cope with nutrient switches? Here, we address this question by growing dense populations of Escherichia coli in microfluidic chambers and studying a switch from glucose to acetate at the single-cell level. Before the switch, cells vary in their metabolic activity: some grow on glucose, while others cross-feed on acetate. After the switch, only few cells can resume growth after a period of lag. The probability to resume growth depends on a cells' phenotype prior to the switch: it is highest for cells cross-feeding on acetate, while it depends in a non-monotonic way on the growth rate for cells growing on glucose. Our results suggest that the strong phenotypic variation in spatially structured populations might enhance their ability to cope with fluctuating environments.