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.     

COMMIT: Consideration of metabolite leakage and community composition improves microbial community reconstructions

Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.     

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host''s nutrient-based mechanism for bacterial partner selection.     

A trait-based approach for modelling microbial litter decomposition

Trait-based models are an emerging tool in ecology with the potential to link community dynamics, environmental responses and ecosystem processes. These models represent complex communities by defining taxa with trait combinations derived from prior distributions that may be constrained by trade-offs. Herein I develop a model that links microbial community composition with physiological and enzymatic traits to predict litter decomposition rates. This approach allows for trade-offs among traits that represent alternative microbial strategies for resource acquisition. The model predicts that optimal strategies depend on the level of enzyme production in the whole community, which determines resource availability and decomposition rates. There is also evidence for facilitation and competition among microbial taxa that co-occur on decomposing litter. These interactions vary with community investment in extracellular enzyme production and the magnitude of trade-offs affecting enzyme biochemical traits. The model accounted for 69% of the variation in decomposition rates of 15 Hawaiian litter types and up to 26% of the variation in enzyme activities. By explicitly representing diversity, trait-based models can predict ecosystem processes based on functional trait distributions in a community. The model developed herein illustrates that traits influencing microbial enzyme production are some of the key controls on litter decomposition rates.     

Fungal-Fungal Associations Affect the Assembly of Endophyte Communities in Maize (Zea mays)

Many factors can affect the assembly of communities, ranging from species pools to habitat effects to interspecific interactions. In microbial communities, the predominant focus has been on the well-touted ability of microbes to disperse and the environment acting as a selective filter to determine which species are present. In this study, we investigated the role of biotic interactions (e.g., competition, facilitation) in fungal endophyte community assembly by examining endophyte species co-occurrences within communities using null models. We used recombinant inbred lines (genotypes) of maize (Zea mays) to examine community assembly at multiple habitat levels, at the individual plant and host genotype levels. Both culture-dependent and culture-independent approaches were used to assess endophyte communities. Communities were analyzed using the complete fungal operational taxonomic unit (OTU) dataset or only the dominant (most abundant) OTUs in order to ascertain whether species co-occurrences were different for dominant members compared to when all members were included. In the culture-dependent approach, we found that for both datasets, OTUs co-occurred on maize genotypes more frequently than expected under the null model of random species co-occurrences. In the culture-independent approach, we found that OTUs negatively co-occurred at the individual plant level but were not significantly different from random at the genotype level for either the dominant or complete datasets. Our results showed that interspecific interactions can affect endophyte community assembly, but the effects can be complex and depend on host habitat level. To our knowledge, this is the first study to examine endophyte community assembly in the same host species at multiple habitat levels. Understanding the processes and mechanisms that shape microbial communities will provide important insights into microbial community structure and the maintenance of microbial biodiversity.     

Unraveling negative biotic interactions determining soil microbial community assembly and functioning

Microbial communities play important roles in all ecosystems and yet a comprehensive understanding of the ecological processes governing the assembly of these communities is missing. To address the role of biotic interactions between microorganisms in assembly and for functioning of the soil microbiota, we used a top-down manipulation approach based on the removal of various populations in a natural soil microbial community. We hypothesized that removal of certain microbial groups will strongly affect the relative fitness of many others, therefore unraveling the contribution of biotic interactions in shaping the soil microbiome. Here we show that 39% of the dominant bacterial taxa across treatments were subjected to competitive interactions during soil recolonization, highlighting the importance of biotic interactions in the assembly of microbial communities in soil. Moreover, our approach allowed the identification of microbial community assembly rule as exemplified by the competitive exclusion between members of Bacillales and Proteobacteriales. Modified biotic interactions resulted in greater changes in activities related to N- than to C-cycling. Our approach can provide a new and promising avenue to study microbial interactions in complex ecosystems as well as the links between microbial community composition and ecosystem function.Subject terms: Soil microbiology, Ecology     

Integration and scaling of UV-B radiation effects on plants: the relative sensitivity of growth forms and interspecies interactions

Aims The relative plant type sensitivity and selected community interactions under increased UV-B radiation where examined. Specifically, we investigated: (i) if there are differences among growth forms in regard to their sensitivity to UV-B radiation, (ii) if increased UV-B radiation influences the plant competitive balance in plant communities and (iii) the response mechanisms of the UV-B radiation-sensitive species that might increase their fitness.Methods To answer our research questions, we used a mechanistic model that, for the first time, integrated the effects of increased UV-B radiation from molecular level processes, whole plant growth and development, and community interactions.Important findings In the model simulations, species types exhibited different levels of sensitivity to increased UV-B radiation. Summer C₃ and C₄ annuals showed similar growth inhibition rates, while biennials and winter C₃ annuals were the most sensitive. Perennials exhibited inhibitions in growth only if increased UV-B radiation results in increases in metabolic rates. In communities, species sensitive to UV-B radiation may have a competitive disadvantage compared to resistant plant species. But, sensitive species may have a wide array of responses that can increase their fitness and reproductive success in the community, such as, increased secondary metabolites production, changes in timing of emergence and reproduction, and changes in seed size. While individual plants may exhibit significant inhibitions in growth and development, in communities, these inhibitions can be mitigated by small morphological and physiological adaptations. Infrequent or occasional increased UV-B radiation events should not have any lasting effect on the structure of the community, unless other environmental factors are perturbing the dynamic equilibrium.     

A trait-based framework for predicting when and where microbial adaptation to climate change will affect ecosystem functioning

As the earth system changes in response to human activities, a critical objective is to predict how biogeochemical process rates (e.g. nitrification, decomposition) and ecosystem function (e.g. net ecosystem productivity) will change under future conditions. A particular challenge is that the microbial communities that drive many of these processes are capable of adapting to environmental change in ways that alter ecosystem functioning. Despite evidence that microbes can adapt to temperature, precipitation regimes, and redox fluctuations, microbial communities are typically not optimally adapted to their local environment. For example, temperature optima for growth and enzyme activity are often greater than in situ temperatures in their environment. Here we discuss fundamental constraints on microbial adaptation and suggest specific environments where microbial adaptation to climate change (or lack thereof) is most likely to alter ecosystem functioning. Our framework is based on two principal assumptions. First, there are fundamental ecological trade-offs in microbial community traits that occur across environmental gradients (in time and space). These trade-offs result in shifting of microbial function (e.g. ability to take up resources at low temperature) in response to adaptation of another trait (e.g. limiting maintenance respiration at high temperature). Second, the mechanism and level of microbial community adaptation to changing environmental parameters is a function of the potential rate of change in community composition relative to the rate of environmental change. Together, this framework provides a basis for developing testable predictions about how the rate and degree of microbial adaptation to climate change will alter biogeochemical processes in aquatic and terrestrial ecosystems across the planet.     

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.     

Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities

The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i) Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.     

Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity

Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing energy use in response to interspecific competition and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By utilizing an oxidation–reduction (redox) reaction network model that represents microbial redox metabolic interactions, we investigated multiple species-level competition and cooperation within the network. Our results suggest that microbial functional diversity allows for metabolic handoffs, which in turn leads to increased energy use efficiency. Furthermore, the mutualistic division of labour and the resulting complexity of redox pathways actively drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and the co-evolution of life and Earth.     

All together now: experimental multispecies biofilm model systems

Studies of microorganisms have traditionally focused on single species populations, which have greatly facilitated our understanding of the genetics and physiology that underpin microbial growth, adaptation and biofilm development. However, given that most microorganisms exist as multispecies consortia, the field is increasingly exploring microbial communities using a range of technologies traditionally limited to populations, including meta‐omics based approaches and high resolution imaging. The experimental communities currently being explored range from relatively low diversity, for example, two to four species, to significantly more complex systems, comprised of several hundred species. Results from both defined and undefined communities have revealed a number of emergent properties, including improved stress tolerance, increased biomass production, community level signalling and metabolic cooperation. Based on results published to date, we submit that community‐based studies are timely and increasingly reveal new properties associated with multispecies consortia that could not be predicted by studies of the individual component species. Here, we review a range of defined and undefined experimental systems used to study microbial community interactions.     

Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016. © 2016 The Authors. Journal of Cellular Physiology Published by Wiley Periodicals, Inc.     

Comparing classical community models: theoretical consequences for patterns of diversity

Abstract: Mechanisms proposed to explain the maintenance of species diversity within ecological communities of sessile organisms include niche differentiation mediated by competitive trade-offs, frequency dependence resulting from species-specific pests, recruitment limitation due to local dispersal, and a speciation-extinction dynamic equilibrium mediated by stochasticity (drift). While each of these processes, and more, have been shown to act in particular communities, much remains to be learned about their relative importance in shaping community-level patterns. We used a spatially-explicit, individual-based model to assess the effects of each of these processes on species richness, relative abundance, and spatial patterns such as the species-area curve. Our model communities had an order-of-magnitude more individuals than any previous such study, and we also developed a finite-size scaling analysis to infer the large-scale properties of these systems in order to establish the generality of our conclusions across system sizes. As expected, each mechanism can promote diversity. We found some qualitative differences in community patterns across communities in which different combinations of these mechanisms operate. Species-area curves follow a power law with short-range dispersal and a logarithmic law with global dispersal. Relative-abundance distributions are more even for systems with competitive differences and trade-offs than for those in which all species are competitively equivalent, and they are most even when frequency dependence (even if weak) is present. Overall, however, communities in which different processes operated showed surprisingly similar patterns, which suggests that the form of community-level patterns cannot in general be used to distinguish among mechanisms maintaining diversity there. Nevertheless, parameterization of models such as these from field data on the strengths of the different mechanisms could yield insight into their relative roles in diversity maintenance in any given community.     

Community heritability measures the evolutionary consequences of indirect genetic effects on community structure

The evolutionary analysis of community organization is considered a major frontier in biology. Nevertheless, current explanations for community structure exclude the effects of genes and selection at levels above the individual. Here, we demonstrate a genetic basis for community structure, arising from the fitness consequences of genetic interactions among species (i.e., interspecific indirect genetic effects or IIGEs). Using simulated and natural communities of arthropods inhabiting North American cottonwoods (Populus), we show that when species comprising ecological communities are summarized using a multivariate statistical method, nonmetric multidimensional scaling (NMDS), the resulting univariate scores can be analyzed using standard techniques for estimating the heritability of quantitative traits. Our estimates of the broad-sense heritability of arthropod communities on known genotypes of cottonwood trees in common gardens explained 56-63% of the total variation in community phenotype. To justify and help interpret our empirical approach, we modeled synthetic communities in which the number, intensity, and fitness consequences of the genetic interactions among species comprising the community were explicitly known. Results from the model suggest that our empirical estimates of broad-sense community heritability arise from heritable variation in a host tree trait and the fitness consequences of IGEs that extend from tree trait to arthropods. When arthropod traits are heritable, interspecific IGEs cause species interactions to change, and community evolution occurs. Our results have implications for establishing the genetic foundations of communities and ecosystems.     

Soil microbial communities adapt to genetic variation in leaf litter inputs

Plant genotypes can have important community‐ and ecosystem‐level effects. However, whether the extended phenotypes of plants feed back to influence the fitness of causal genotypes through soil processes remains unknown. We investigated whether aspen genotypes create distinct soil microbial communities that could potentially affect plant fitness. Using naturally occurring aspen stands in an old‐field system, we set up reciprocal litter transplants among ten genetically distinct aspen clones and tracked decomposition and changes in belowground nutrients and microbial communities for three years. We found that belowground microbial communities became adapted to process specific genotypes of aspen litter to the extent allowable by environment and litter chemistry. Belowground processes were driven by a combination of little quality and prior exposure to specific genotypes of litter. In general, litter from aspen genotypes native to the soil community decomposed more rapidly than did litter from foreign aspen genotypes (i.e. a home‐field advantage existed). While home‐field advantages have been documented to occur among litters of different species, we show that intraspecific variation can elicit similar, albeit weak, effects within a single species. Because rapid decomposition and nutrient cycling is likely to benefit fast‐growing, early‐successional species such as aspen, genotype‐mediated selection for soil microbial communities may feed back to positively affect plant fitness. In addition, belowground communities exhibited significant shifts in response to leaf litter inputs. When exposed to foreign litter, microbial communities changed to become more similar to the microbial community beneath the foreign litter's origin, indicating that belowground microbial communities are predictable given the genotype of the aboveground aspen clone.     

Convergent evolution of modularity in metabolic networks through different community structures

ABSTRACT: BACKGROUND: It has been reported that the modularity of metabolic networks of bacteria is closely relatedto the variability of their living habitats. However, given the dependency of the modularityscore on the community structure, it remains unknown whether organisms achieve certainmodularity via similar or different community structures. RESULTS: In this work, we studied the relationship between similarities in modularity scores andsimilarities in community structures of the metabolic networks of 1021 species. Bothsimilarities are then compared against the genetic distances. We revisited the associationbetween modularity and variability of the microbial living environments and extended theanalysis to other aspects of their life style such as temperature and oxygen requirements. Wealso tested both topological and biological intuition of the community structures identifiedand investigated the extent of their conservation with respect to the taxomony. CONCLUSIONS: We find that similar modularities are realized by different community structures. We findthat such convergent evolution of modularity is closely associated with the number of(distinct) enzymes in the organism's metabolome, a consequence of different life styles ofthe species. We find that the order of modularity is the same as the order of the number ofthe enzymes under the classification based on the temperature preference but not on theoxygen requirement. Besides, inspection of modularity-based communities reveals thatthese communities are graph-theoretically meaningful yet not reflective of specificbiological functions. From an evolutionary perspective, we find that the communitystructures are conserved only at the level of kingdoms. Our results call for moreinvestigation into the interplay between evolution and modularity: how evolution shapesmodularity, and how modularity affects evolution (mainly in terms of fitness andevolvability). Further, our results call for exploring new measures of modularity andnetwork communities that better correspond to functional categorizations.     

Metabolic and Demographic Feedbacks Shape the Emergent Spatial Structure and Function of Microbial Communities

Microbes are predominantly found in surface-attached and spatially structured polymicrobial communities. Within these communities, microbial cells excrete a wide range of metabolites, setting the stage for interspecific metabolic interactions. The links, however, between metabolic and ecological interactions (functional relationships), and species spatial organization (structural relationships) are still poorly understood. Here, we use an individual-based modelling framework to simulate the growth of a two-species surface-attached community where food (resource) is traded for detoxification (service) and investigate how metabolic constraints of individual species shape the emergent structural and functional relationships of the community. We show that strong metabolic interdependence drives the emergence of mutualism, robust interspecific mixing, and increased community productivity. Specifically, we observed a striking and highly stable emergent lineage branching pattern, generating a persistent lineage mixing that was absent when the metabolic exchange was removed. These emergent community properties are driven by demographic feedbacks, such that aid from neighbouring cells directly enhances focal cell growth, which in turn feeds back to neighbour fecundity. In contrast, weak metabolic interdependence drives conflict (exploitation or competition), and in turn greater interspecific segregation. Together, these results support the idea that species structural and functional relationships represent the net balance of metabolic interdependencies.     

The Pareto principle: To what extent does it apply to resource acquisition in stable microbial communities and thereby steer their geno−/ecotype compositions and interactions between their members?

The Pareto principle, or 20:80 rule, describes resource distribution in stable communities whereby 20% of community members acquire 80% of a key resource. In this Burning Question, we ask to what extent the Pareto principle applies to the acquisition of limiting resources in stable microbial communities; how it may contribute to our understanding of microbial interactions, microbial community exploration of evolutionary space, and microbial community dysbiosis; and whether it can serve as a benchmark of microbial community stability and functional optimality?