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.

Metabolite toxicity slows local diversity loss during expansion of a microbial cross-feeding community.

Metabolic interactions between populations can influence patterns of spatial organization and diversity within microbial communities. Cross-feeding is one type of metabolic interaction that is pervasive within microbial communities, where one genotype consumes a resource into a metabolite while another genotype then consumes the metabolite. A typical feature of cross-feeding is that the metabolite may impose toxicity if it accumulates to sufficient concentrations. However, little is known about the effect of metabolite toxicity on spatial organization and local diversity within microbial communities. We addressed this knowledge gap by experimentally varying the toxicity of a single cross-fed metabolite and measuring the consequences on a synthetic microbial cross-feeding community. Our results demonstrate that metabolite toxicity slows demixing and thus slows local diversity loss of the metabolite-producing population. Using mathematical modeling, we show that this is because toxicity slows growth, which enables more cells to emigrate from the founding region and contribute towards population expansion. Our results show that metabolite toxicity is an important factor affecting local diversity within microbial communities and that spatial organization can be affected by non-intuitive mechanisms.

Successive range expansion promotes diversity and accelerates evolution in spatially structured microbial populations.

Successive range expansions occur within all domains of life, where one population expands first (primary expansion) and one or more secondary populations then follow (secondary expansion). In general, genetic drift reduces diversity during range expansion. However, it is not clear whether the same effect applies during successive range expansion, mainly because the secondary population must expand into space occupied by the primary population. Here we used an experimental microbial model system to show that, in contrast to primary range expansion, successive range expansion promotes local population diversity. Because of mechanical constraints imposed by the presence of the primary population, the secondary population forms fractal-like dendritic structures. This divides the advancing secondary population into many small sub-populations and promotes intermixing between the primary and secondary populations. We further developed a mathematical model to simulate the formation of dendritic structures in the secondary population during succession. By introducing mutations in the primary or dendritic secondary populations, we found that mutations are more likely to accumulate in the dendritic secondary populations. Our results thus show that successive range expansion can promote intermixing over the short term and increase genetic diversity over the long term. Our results therefore have potentially important implications for predicting the ecological processes and evolutionary trajectories of microbial communities.

A passive mutualistic interaction promotes the evolution of spatial structure within microbial populations.

BACKGROUND: While mutualistic interactions between different genotypes are pervasive in nature, their evolutionary origin is not clear. The dilemma is that, for mutualistic interactions to emerge and persist, an investment into the partner genotype must pay off: individuals of a first genotype that invest resources to promote the growth of a second genotype must receive a benefit that is not equally accessible to individuals that do not invest. One way for exclusive benefits to emerge is through spatial structure (i.e., physical barriers to the movement of individuals and resources). RESULTS: Here we propose that organisms can evolve their own spatial structure based on physical attachment between individuals, and we hypothesize that attachment evolves when spatial proximity to members of another species is advantageous. We tested this hypothesis using experimental evolution with combinations of E. coli strains that depend on each other to grow. We found that attachment between cells repeatedly evolved within 8 weeks of evolution and observed that many different types of mutations potentially contributed to increased attachment. CONCLUSIONS: We postulate a general principle by which passive beneficial interactions between organisms select for attachment, and attachment then provides spatial structure that could be conducive for the evolution of active mutualistic interactions.

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.

Metabolic specialization and the assembly of microbial communities.

Metabolic specialization is a general biological principle that shapes the assembly of microbial communities. Individual cell types rarely metabolize a wide range of substrates within their environment. Instead, different cell types often specialize at metabolizing only subsets of the available substrates. What is the advantage of metabolizing subsets of the available substrates rather than all of them? In this perspective piece, we argue that biochemical conflicts between different metabolic processes can promote metabolic specialization and that a better understanding of these conflicts is therefore important for revealing the general principles and rules that govern the assembly of microbial communities. We first discuss three types of biochemical conflicts that could promote metabolic specialization. Next, we demonstrate how knowledge about the consequences of biochemical conflicts can be used to predict whether different metabolic processes are likely to be performed by the same cell type or by different cell types. We then discuss the major challenges in identifying and assessing biochemical conflicts between different metabolic processes and propose several approaches for their measurement. Finally, we argue that a deeper understanding of the biochemical causes of metabolic specialization could serve as a foundation for the field of synthetic ecology, where the objective would be to rationally engineer the assembly of a microbial community to perform a desired biotransformation.

Measurement and interpretation of microbial adenosine tri-phosphate (ATP) in aquatic environments.

There is a widespread need for cultivation-free methods to quantify viability of natural microbial communities in aquatic environments. Adenosine tri-phosphate (ATP) is the energy currency of all living cells, and therefore a useful indicator of viability. A luminescence-based ATP kit/protocol was optimised in order to detect ATP concentrations as low as 0.0001 nM with a standard deviation of <5%. Using this method, more than 100 water samples from a variety of aquatic environments (drinking water, groundwater, bottled water, river water, lake water and wastewater effluent) were analysed for extracellular ATP and microbial ATP in comparison with flow-cytometric (FCM) parameters. Microbial ATP concentrations ranged between 3% and 97% of total ATP concentrations, and correlated well (R(2)=0.8) with the concentrations of intact microbial cells (after staining with propidium iodide). From this correlation, we calculated an average ATP-per-cell value of 1.75x10(-10)nmol/cell. An even better correlation (R(2)=0.88) was observed between intact biovolume (derived from FCM scatter data) and microbial ATP concentrations, and an average ATP-per-biovolume value of 2.95x10(-9)nmol/microm(3) was calculated. These results support the use of ATP analysis for both routine monitoring and research purposes, and contribute towards a better interpretation of ATP data.