Environmental modulators of algae-bacteria interactions at scale.

Interactions between photosynthetic and heterotrophic microbes play a key role in global primary production. Understanding phototroph-heterotroph interactions remains challenging because these microbes reside in chemically complex environments. Here, we leverage a massively parallel droplet microfluidic platform that enables us to interrogate interactions between photosynthetic algae and heterotrophic bacteria in >100,000 communities across ∼525 environmental conditions with varying pH, carbon availability, and phosphorus availability. By developing a statistical framework to dissect interactions in this complex dataset, we reveal that the dependence of algae-bacteria interactions on nutrient availability is strongly modulated by pH and buffering capacity. Furthermore, we show that the chemical identity of the available organic carbon source controls how pH, buffering capacity, and nutrient availability modulate algae-bacteria interactions. Our study reveals the previously underappreciated role of pH in modulating phototroph-heterotroph interactions and provides a framework for thinking about interactions between phototrophs and heterotrophs in more natural contexts.

Environmentally dependent interactions shape patterns in gene content across natural microbiomes.

Sequencing surveys of microbial communities in hosts, oceans and soils have revealed ubiquitous patterns linking community composition to environmental conditions. While metabolic capabilities restrict the environments suitable for growth, the influence of ecological interactions on patterns observed in natural microbiomes remains uncertain. Here we use denitrification as a model system to demonstrate how metagenomic patterns in soil microbiomes can emerge from pH-dependent interactions. In an analysis of a global soil sequencing survey, we find that the abundances of two genotypes trade off with pH; nar gene abundances increase while nap abundances decrease with declining pH. We then show that in acidic conditions strains possessing nar fail to grow in isolation but are enriched in the community due to an ecological interaction with nap genotypes. Our study provides a road map for dissecting how associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

Functional regimes define the response of the soil microbiome to environmental change.

The metabolic activity of soil microbiomes plays a central role in carbon and nitrogen cycling. Given the changing climate, it is important to understand how the metabolism of natural communities responds to environmental change. However, the ecological, spatial, and chemical complexity of soils makes understanding the mechanisms governing the response of these communities to perturbations challenging. Here, we overcome this complexity by using dynamic measurements of metabolism in microcosms and modeling to reveal regimes where a few key mechanisms govern the response of soils to environmental change. We sample soils along a natural pH gradient, construct >1500 microcosms to perturb the pH, and quantify the dynamics of respiratory nitrate utilization, a key process in the nitrogen cycle. Despite the complexity of the soil microbiome, a minimal mathematical model with two variables, the quantity of active biomass in the community and the availability of a growth-limiting nutrient, quantifies observed nitrate utilization dynamics across soils and pH perturbations. Across environmental perturbations, changes in these two variables give rise to three functional regimes each with qualitatively distinct dynamics of nitrate utilization over time: a regime where acidic perturbations induce cell death that limits metabolic activity, a nutrient-limiting regime where nitrate uptake is performed by dominant taxa that utilize nutrients released from the soil matrix, and a resurgent growth regime in basic conditions, where excess nutrients enable growth of initially rare taxa. The underlying mechanism of each regime is predicted by our interpretable model and tested via amendment experiments, nutrient measurements, and sequencing. Further, our data suggest that the long-term history of environmental variation in the wild influences the transitions between functional regimes. Therefore, quantitative measurements and a mathematical model reveal the existence of qualitative regimes that capture the mechanisms and dynamics of a community responding to environmental change.

Statistical prediction of microbial metabolic traits from genomes.

The metabolic activity of microbial communities is central to their role in biogeochemical cycles, human health, and biotechnology. Despite the abundance of sequencing data characterizing these consortia, it remains a serious challenge to predict microbial metabolic traits from sequencing data alone. Here we culture 96 bacterial isolates individually and assay their ability to grow on 10 distinct compounds as a sole carbon source. Using these data as well as two existing datasets, we show that statistical approaches can accurately predict bacterial carbon utilization traits from genomes. First, we show that classifiers trained on gene content can accurately predict bacterial carbon utilization phenotypes by encoding phylogenetic information. These models substantially outperform predictions made by constraint-based metabolic models automatically constructed from genomes. This result solidifies our current knowledge about the strong connection between phylogeny and metabolic traits. However, phylogeny-based predictions fail to predict traits for taxa that are phylogenetically distant from any strains in the training set. To overcome this we train improved models on gene presence/absence to predict carbon utilization traits from gene content. We show that models that predict carbon utilization traits from gene presence/absence can generalize to taxa that are phylogenetically distant from the training set either by exploiting biochemical information for feature selection or by having sufficiently large datasets. In the latter case, we provide evidence that a statistical approach can identify putatively mechanistic genes involved in metabolic traits. Our study demonstrates the potential power for predicting microbial phenotypes from genotypes using statistical approaches.

Global patterns in gene content of soil microbiomes emerge from microbial interactions.

Microbial metabolism sustains life on Earth. Sequencing surveys of communities in hosts, oceans, and soils have revealed ubiquitous patterns linking the microbes present, the genes they possess, and local environmental conditions. One prominent explanation for these patterns is environmental filtering: local conditions select strains with particular traits. However, filtering assumes ecological interactions do not influence patterns, despite the fact that interactions can and do play an important role in structuring communities. Here, we demonstrate the insufficiency of the environmental filtering hypothesis for explaining global patterns in topsoil microbiomes. Using denitrification as a model system, we find that the abundances of two characteristic genotypes trade-off with pH; nar gene abundances increase while nap abundances decrease with declining pH. Contradicting the filtering hypothesis, we show that strains possessing the Nar genotype are enriched in low pH conditions but fail to grow alone. Instead, the dominance of Nar genotypes at low pH arises from an ecological interaction with Nap genotypes that alleviates nitrite toxicity. Our study provides a roadmap for dissecting how global associations between environmental variables and gene abundances arise from environmentally modulated community interactions.

Statistically learning the functional landscape of microbial communities.

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes-analogues to fitness landscapes-that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.

Algae drive convergent bacterial community assembly at low dilution frequency.

Microbial community assembly is a complex dynamical process that determines community structure and function. The interdependence of inter-species interactions and nutrient availability presents a challenge for understanding community assembly. We sought to understand how external nutrient supply rate modulated interactions to affect the assembly process. A statistical decomposition of taxonomic structures of bacterial communities assembled with and without algae and at varying dilution frequencies allowed the separation of the effects of biotic (presence of algae) and abiotic (dilution frequency) factors on community assembly. For infrequent dilutions, the algae strongly impact community assembly, driving initially diverse bacterial consortia to converge to a common structure. Analyzing sequencing data revealed that this convergence is largely mediated by a decline in the relative abundance of specific taxa in the presence of algae. This study shows that complex phototroph-heterotroph communities can be powerful model systems for understanding assembly processes relevant to the global ecosystem functioning.

The community-function landscape of microbial consortia.

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

Microbial biofilms: An ecological tale of Jekyll and Hyde.

The molecules of life can be double-edged, performing both beneficial and detrimental roles depending on the environmental context. New work reveals how the Jekyll and Hyde nature of nitric oxide shapes complexity in microbial biofilms, from ecological interactions to spatial structure.

An ensemble approach to the structure-function problem in microbial communities.

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Genomic structure predicts metabolite dynamics in microbial communities.

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

Closed microbial communities self-organize to persistently cycle carbon.

Cycles of nutrients (N, P, etc.) and resources (C) are a defining emergent feature of ecosystems. Cycling plays a critical role in determining ecosystem structure at all scales, from microbial communities to the entire biosphere. Stable cycles are essential for ecosystem persistence because they allow resources and nutrients to be regenerated. Therefore, a central problem in ecology is understanding how ecosystems are organized to sustain robust cycles. Addressing this problem quantitatively has proved challenging because of the difficulties associated with manipulating ecosystem structure while measuring cycling. We address this problem using closed microbial ecosystems (CES), hermetically sealed microbial consortia provided with only light. We develop a technique for quantifying carbon cycling in hermetically sealed microbial communities and show that CES composed of an alga and diverse bacterial consortia self-organize to robustly cycle carbon for months. Comparing replicates of diverse CES, we find that carbon cycling does not depend strongly on the taxonomy of the bacteria present. Moreover, despite strong taxonomic differences, self-organized CES exhibit a conserved set of metabolic capabilities. Therefore, an emergent carbon cycle enforces metabolic but not taxonomic constraints on ecosystem organization. Our study helps establish closed microbial communities as model ecosystems to study emergent function and persistence in replicate systems while controlling community composition and the environment.

Evolution of Generalists by Phenotypic Plasticity.

Adapting organisms face a tension between specializing their phenotypes for certain ecological tasks and developing generalist strategies that permit persistence in multiple environmental conditions. Understanding when and how generalists or specialists evolve is an important question in evolutionary dynamics. Here, we study the evolution of bacterial range expansions by selecting Escherichia coli for faster migration through porous media containing one of four different sugars supporting growth and chemotaxis. We find that selection in any one sugar drives the evolution of faster migration in all sugars. Measurements of growth and motility of all evolved lineages in all nutrient conditions reveal that the ubiquitous evolution of fast migration arises via phenotypic plasticity. Phenotypic plasticity permits evolved strains to exploit distinct strategies to achieve fast migration in each environment, irrespective of the environment in which they were evolved. Therefore, selection in a homogeneous environment drives phenotypic plasticity that improves performance in other environments.

Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations.

Natural bacterial populations are subjected to constant predation pressure by bacteriophages. Bacteria use a variety of molecular mechanisms to defend themselves from phage predation. However, since phages are nonmotile, perhaps the simplest defense against phage is for bacteria to move faster than phages. In particular, chemotaxis, the active migration of bacteria up attractant gradients, may help the bacteria escape slowly diffusing phages. Here we study phage infection dynamics in migrating bacterial populations driven by chemotaxis through low viscosity agar plates. We find that expanding phage-bacteria populations supports two moving fronts, an outermost bacterial front driven by nutrient uptake and chemotaxis and an inner phage front at which the bacterial population collapses due to phage predation. We show that with increasing adsorption rate and initial phage population, the speed of the moving phage front increases, eventually overtaking the bacterial front and driving the system across a transition from a regime where bacterial front speed exceeds that of the phage front to one where bacteria must evolve phage resistance to survive. Our data support the claim that this process requires phage to hitchhike with moving bacteria. A deterministic model recapitulates the transition under the assumption that phage virulence declines with host growth rate which we confirm experimentally. Finally, near the transition between regimes we observe macroscopic fluctuations in bacterial densities at the phage front. Our work opens a new, spatio-temporal, line of investigation into the eco-evolutionary struggle between bacteria and phage.

Higher-Order Interaction between Species Inhibits Bacterial Invasion of a Phototroph-Predator Microbial Community.

The composition of an ecosystem is thought to be important for determining its resistance to invasion. Studies of natural ecosystems, from plant to microbial communities, have found that more diverse communities are more resistant to invasion. In some cases, more diverse communities resist invasion by more completely consuming the resources necessary for the invader. We show that Escherichia coli can successfully invade cultures of the alga Chlamydomonas reinhardtii (phototroph) or the ciliate Tetrahymena thermophila (predator) but cannot invade a community where both are present. The invasion resistance of the algae-ciliate community arises from a higher-order interaction between species (interaction modification) that is unrelated to resource consumption. We show that the mode of this interaction is the algal inhibition of bacterial aggregation, which leaves bacteria vulnerable to predation. This mode requires both the algae and the ciliate to be present and provides an example of invasion resistance through an interaction modification.

Massively parallel screening of synthetic microbial communities.

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.

Frequency- and Amplitude-Dependent Microbial Population Dynamics during Cycles of Feast and Famine.

In nature microbial populations are subject to fluctuating nutrient levels. Nutrient fluctuations are important for evolutionary and ecological dynamics in microbial communities since they impact growth rates, population sizes, and biofilm formation. Here we use automated continuous-culture devices and high-throughput imaging to show that when populations of Escherichia coli are subjected to cycles of nutrient excess (feasts) and scarcity (famine) their abundance dynamics during famines depend on the frequency and amplitude of feasts. We show that frequency and amplitude dependent dynamics in planktonic populations arise from nutrient and history dependent rates of aggregation and dispersal. A phenomenological model recapitulates our experimental observations. Our results show that the statistical properties of environmental fluctuations have substantial impacts on spatial structure in bacterial populations driving large changes in abundance dynamics.

Biophysical constraints determine the selection of phenotypic fluctuations during directed evolution.

Phenotypes of individuals in a population of organisms are not fixed. Phenotypic fluctuations, which describe temporal variation of the phenotype of an individual or individual-to-individual variation across a population, are present in populations from microbes to higher animals. Phenotypic fluctuations can provide a basis for adaptation and be the target of selection. Here we present a theoretical and experimental investigation of the fate of phenotypic fluctuations in directed evolution experiments where phenotypes are subject to constraints. We show that selecting bacterial populations for fast migration through a porous environment drives a reduction in cell-to-cell variation across the population. Using sequencing and genetic engineering we study the genetic basis for this reduction in phenotypic fluctuations. We study the generality of this reduction by developing a simple, abstracted, numerical simulation model of the evolution of phenotypic fluctuations subject to constraints. Using this model we find that strong and weak selection generally lead respectively to increasing or decreasing cell-to-cell variation as a result of a bound on the selected phenotype under a wide range of parameters. However, other behaviors are also possible, and we describe the outcome of selection simulations for different model parameters and suggest future experiments. We analyze the mechanism of the observed reduction of phenotypic fluctuations in our experimental system, discuss the relevance of our abstract model to the experiment and explore its broader implications for evolution.