Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism

Predicting the First Steps of Evolution in Randomly Assembled Communities

Microbial communities can self-assemble into highly diverse states with predictable statistical properties. However, these initial states can be disrupted by rapid evolution of the resident strains. When a new mutation arises, it competes for resources with its parent strain and with the other species in the community. This interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a mathematical framework for predicting the first steps of evolution in large randomly assembled communities that compete for substitutable resources. We show how the fitness effects of new mutations and the probability that they coexist with their parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. At the same time, these invading mutants often cause extinctions of metabolically distant species. Our results suggest that even small amounts of evolution can produce distinct genetic signatures in natural microbial communities.

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.

Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

Biodiversity is enhanced by sequential resource utilization and environmental fluctuations via emergent temporal niches

How natural communities maintain their remarkable biodiversity and which species survive in complex communities are central questions in ecology. Resource competition models successfully explain many phenomena but typically predict only as many species as resources can coexist. Here, we demonstrate that sequential resource utilization, or diauxie, with periodic growth cycles can support many more species than resources. We explore how communities modify their own environments by sequentially depleting resources to form sequences of temporal niches, or intermediately depleted environments. Biodiversity is enhanced when community-driven or environmental fluctuations modulate the resource depletion order and produce different temporal niches on each growth cycle. Community-driven fluctuations under constant environmental conditions are rare, but exploring them illuminates the temporal niche structure that emerges from sequential resource utilization. With environmental fluctuations, we find most communities have more stably coexisting species than resources with survivors accurately predicted by the same temporal niche structure and each following a distinct optimal strategy. Our results thus present a new niche-based approach to understanding highly diverse fluctuating communities.

Resource competition predicts assembly of gut bacterial communities in vitro

Microbial community dynamics arise through interspecies interactions, including resource competition, cross-feeding and pH modulation. The individual contributions of these mechanisms to community structure are challenging to untangle. Here we develop a framework to estimate multispecies niche overlaps by combining metabolomics data of individual species, growth measurements in spent media and mathematical models. We applied our framework to an in vitro model system comprising 15 human gut commensals in complex media and showed that a simple model of resource competition accounted for most pairwise interactions. Next, we built a coarse-grained consumer-resource model by grouping metabolomic features depleted by the same set of species and showed that this model predicted the composition of 2-member to 15-member communities with reasonable accuracy. Furthermore, we found that incorporation of cross-feeding and pH-mediated interactions improved model predictions of species coexistence. Our theoretical model and experimental framework can be applied to characterize interspecies interactions in bacterial communities in vitro.

Challenges in quantifying functional redundancy and selection in microbial communities

Microbiomes can exhibit large variations in species abundances but high reproducibility in abundances of functional units, an observation often considered evidence for functional redundancy. Based on such reduction in functional variability, selection is hypothesized to act on functional units in these ecosystems. However, the link between functional redundancy and selection remains unclear. Here, we show that reduction in functional variability does not always imply selection on functional profiles. We propose empirical null models to account for the confounding effects of statistical averaging and bias toward environment-independent beneficial functions. We apply our models to existing data sets, and find that the abundances of metabolic groups within microbial communities from bromeliad foliage do not exhibit any evidence of the previously hypothesized functional selection. By contrast, communities of soil bacteria or human gut commensals grown in vitro are selected for metabolic capabilities. By separating the effects of averaging and functional bias on functional variability, we find that the appearance of functional selection in gut microbiome samples from the Human Microbiome Project is artifactual, and that there is no evidence of selection for any molecular function represented by KEGG orthology. These concepts articulate a basic framework for quantifying functional redundancy and selection, advancing our understanding of the mapping between microbiome taxonomy and function.

Genome-scale community modelling reveals conserved metabolic cross-feedings in epipelagic bacterioplankton communities

Marine microorganisms form complex communities of interacting organisms that influence central ecosystem functions in the ocean such as primary production and nutrient cycling. Identifying the mechanisms controlling their assembly and activities is a major challenge in microbial ecology. Here, we integrated Tara Oceans meta-omics data to predict genome-scale community interactions within prokaryotic assemblages in the euphotic ocean. A global genome-resolved co-activity network revealed a significant number of inter-lineage associations across diverse phylogenetic distances. Identified co-active communities include species displaying smaller genomes but encoding a higher potential for quorum sensing, biofilm formation, and secondary metabolism. Community metabolic modelling reveals a higher potential for interaction within co-active communities and points towards conserved metabolic cross-feedings, in particular of specific amino acids and group B vitamins. Our integrated ecological and metabolic modelling approach suggests that genome streamlining and metabolic auxotrophies may act as joint mechanisms shaping bacterioplankton community assembly in the global ocean surface.

Phase Transition to Chaos in Complex Ecosystems with Nonreciprocal Species-Resource Interactions

Nonreciprocal interactions between microscopic constituents can profoundly shape the large-scale properties of complex systems. Here, we investigate the effects of nonreciprocity in the context of theoretical ecology by analyzing a generalization of MacArthur's consumer-resource model with asymmetric interactions between species and resources. Using a mixture of analytic cavity calculations and numerical simulations, we show that such ecosystems generically undergo a phase transition to chaotic dynamics as the amount of nonreciprocity is increased. We analytically construct the phase diagram for this model and show that the emergence of chaos is controlled by a single quantity: the ratio of surviving species to surviving resources. We also numerically calculate the Lyapunov exponents in the chaotic phase and carefully analyze finite-size effects. Our findings show how nonreciprocal interactions can give rise to complex and unpredictable dynamical behaviors even in the simplest ecological consumer-resource models.

Community-forming traits play role in effective colonization of plant-growth-promoting bacteria and improved plant growth

Community-forming traits (CFts) play an important role in the effective colonization of plant-growth-promoting bacterial communities that influence host plants positively by modulating their adaptive functions. In this study, by considering plant-growth-promoting traits (PGPts) and community-forming traits (CFts), three communities were constructed, viz., SM1 (PGPts), SM2 (CFts), and SM3 (PGPts+CFts). Each category isolates were picked up on the basis of their catabolic diversity of different carbon sources. Results revealed a distinctive pattern in the colonization of the communities possessed with CF traits. It was observed that the community with CFts colonized inside the plant in groups or in large aggregations, whereas the community with only PGPts colonized as separate individual and small colonies inside the plant root and leaf. The effect of SM3 in the microcosm experiment was more significant than the uninoculated control by 22.12%, 27.19%, and 9.11% improvement in germination percentage, chlorophyll content, and plant biomass, respectively. The significant difference shown by the microbial community SM3 clearly demonstrates the integrated effect of CFts and PGPts on effective colonization vis-à-vis positive influence on the host plant. Further detailed characterization of the interaction will take this technology ahead in sustainable agriculture.

A universal niche geometry governs the response of ecosystems to environmental perturbations

How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment. Here, we introduce a theoretical framework for calculating the linear response of ecosystems to environmental perturbations in generalized consumer-resource models. Our construction is applicable to a wide class of systems, including models with non-reciprocal interactions, cross-feeding, and non-linear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces and ecological interactions are represented by geometric transformations between these spaces. We show that near a steady state, such geometric transformations directly map environmental perturbations - in resource availability and mortality rates - to shifts in niche structure. We illustrate these ideas in a variety of settings including a minimal model for pH-induced toxicity in bacterial denitrification.

Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth

The structure and diversity of microbial communities are intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g. genus vs. family), an empirical pattern previously evaluated in the context of the Diversity Begets Diversity (DBD) hypothesis (Madi et al., 2020). We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members, demonstrating that the DBD can be sufficiently explained using the SLM as a null model of ecology. Contrastingly, only by including correlations between the abundances of community members (e.g. as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.

Emergent antibiotic persistence in a spatially structured synthetic microbial mutualism

Antibiotic persistence (heterotolerance) allows a subpopulation of bacteria to survive antibiotic-induced killing and contributes to the evolution of antibiotic resistance. Although bacteria typically live in microbial communities with complex ecological interactions, little is known about how microbial ecology affects antibiotic persistence. Here, we demonstrated within a synthetic two-species microbial mutualism of Escherichia coli and Salmonella enterica that the combination of cross-feeding and community spatial structure can emergently cause high antibiotic persistence in bacteria by increasing the cell-to-cell heterogeneity. Tracking ampicillin-induced death for bacteria on agar surfaces, we found that E. coli forms up to 55 times more antibiotic persisters in the cross-feeding coculture than in monoculture. This high persistence could not be explained solely by the presence of S. enterica, the presence of cross-feeding, average nutrient starvation, or spontaneous resistant mutations. Time-series fluorescent microscopy revealed increased cell-to-cell variation in E. coli lag time in the mutualistic co-culture. Furthermore, we discovered that an E. coli cell can survive antibiotic killing if the nearby S. enterica cells on which it relies die first. In conclusion, we showed that the high antibiotic persistence phenotype can be an emergent phenomenon caused by a combination of cross-feeding and spatial structure. Our work highlights the importance of considering spatially structured interactions during antibiotic treatment and understanding microbial community resilience more broadly.

Deterministic and stochastic processes generating alternative states of microbiomes

The structure of microbiomes is often classified into discrete or semi-discrete types potentially differing in community-scale functional profiles. Elucidating the mechanisms that generate such "alternative states" of microbiome compositions has been one of the major challenges in ecology and microbiology. In a time-series analysis of experimental microbiomes, we here show that both deterministic and stochastic ecological processes drive divergence of alternative microbiome states. We introduced species-rich soil-derived microbiomes into eight types of culture media with 48 replicates, monitoring shifts in community compositions at six time points (8 media × 48 replicates × 6 time points = 2304 community samples). We then confirmed that microbial community structure diverged into a few state types in each of the eight medium conditions as predicted in the presence of both deterministic and stochastic community processes. In other words, microbiome structure was differentiated into a small number of reproducible compositions under the same environment. This fact indicates not only the presence of selective forces leading to specific equilibria of community-scale resource use but also the influence of demographic drift (fluctuations) on the microbiome assembly. A reference-genome-based analysis further suggested that the observed alternative states differed in ecosystem-level functions. These findings will help us examine how microbiome structure and functions can be controlled by changing the "stability landscapes" of ecological community compositions.

Searching for Principles of Microbial Ecology Across Levels of Biological Organization

Microbial communities play pivotal roles in ecosystems across different scales, from global elemental cycles to household food fermentations. These complex assemblies comprise hundreds or thousands of microbial species whose abundances vary over time and space. Unraveling the principles that guide their dynamics at different levels of biological organization, from individual species, their interactions, to complex microbial communities, is a major challenge. To what extent are these different levels of organization governed by separate principles, and how can we connect these levels to develop predictive models for the dynamics and function of microbial communities? Here, we will discuss recent advances that point towards principles of microbial communities, rooted in various disciplines from physics, biochemistry, and dynamical systems. By considering the marine carbon cycle as a concrete example, we demonstrate how the integration of levels of biological organization can offer deeper insights into the impact of increasing temperatures, such as those associated with climate change, on ecosystem-scale processes. We argue that by focusing on principles that transcend specific microbiomes, we can pave the way for a comprehensive understanding of microbial community dynamics and the development of predictive models for diverse ecosystems.

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.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Metabolic similarity and the predictability of microbial community assembly

When microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's R^{*} and species coexistence cones. Here, we extend these arguments by constructing a geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.

Synthesizing microbial biodiversity

The diversity of microbial ecosystems is linked to crucial ecological processes and functions. Despite its significance, the ecological mechanisms responsible for the initiation and maintenance of microbiome diversity are still not fully understood. The primary challenge lies in the difficulty of isolating, monitoring, and manipulating the complex and interrelated ecological processes that modulate the diversity of microbial communities in their natural habitats. Synthetic ecology experiments provide a suitable alternative for investigating the mechanisms behind microbial biodiversity in controlled laboratory settings, as the environment can be systematically and modularly manipulated by adding and removing components. This enables the testing of hypotheses and the advancement of predictive theories. In this review, we present an overview of recent progress toward achieving this goal.

Natural and anthropogenic carbon input affect microbial activity in salt marsh sediment

Salt marshes are dynamic, highly productive ecosystems positioned at the interface between terrestrial and marine systems. They are exposed to large quantities of both natural and anthropogenic carbon input, and their diverse sediment-hosted microbial communities play key roles in carbon cycling and remineralization. To better understand the effects of natural and anthropogenic carbon on sediment microbial ecology, several sediment cores were collected from Little Sippewissett Salt Marsh (LSSM) on Cape Cod, MA, USA and incubated with either Spartina alterniflora cordgrass or diesel fuel. Resulting shifts in microbial diversity and activity were assessed via bioorthogonal non-canonical amino acid tagging (BONCAT) combined with fluorescence-activated cell sorting (FACS) and 16S rRNA gene amplicon sequencing. Both Spartina and diesel amendments resulted in initial decreases of microbial diversity as well as clear, community-wide shifts in metabolic activity. Multi-stage degradative frameworks shaped by fermentation were inferred based on anabolically active lineages. In particular, the metabolically versatile Marinifilaceae were prominent under both treatments, as were the sulfate-reducing Desulfovibrionaceae, which may be attributable to their ability to utilize diverse forms of carbon under nutrient limited conditions. By identifying lineages most directly involved in the early stages of carbon processing, we offer potential targets for indicator species to assess ecosystem health and highlight key players for selective promotion of bioremediation or carbon sequestration pathways.

Carbon source shaped microbial ecology, metabolism and performance in denitrification systems

The limited information on microbial interactions and metabolic patterns in denitrification systems, especially those fed with different carbon sources, has hindered the establishment of ecological linkages between microscale connections and macroscopic reactor performance. In this work, denitrification performance, metabolic patterns, and ecological structure were investigated in parallel well-controlled bioreactors with four representative carbon sources, i.e., methanol, glycerol, acetate, and glucose. After long-term acclimation, significant differences were observed among the four bioreactors in terms of denitrification rates, organic utilization, and heterotrophic bacterial yields. Different carbon sources induced the succession of denitrifying microbiota toward different ecological structures and exhibited distinct metabolic patterns. Methanol-fed reactors showed distinctive microbial carbon utilization pathways and a more intricate microbial interaction network, leading to significant variations in organic utilization and metabolite production compared to other carbon sources. Three keystone taxa belonging to the Verrucomicrobiota phylum, SJA-15 order and the Kineosphaera genus appeared as network hubs in the methanol, glycerol, and acetate-fed systems, playing essential roles in their ecological functions. Several highly connected species were also identified within the glucose-fed system. The close relationship between microbial metabolites, ecological structures, and system performances suggests that this complex network relationship may greatly contribute to the efficient operation of bioreactors.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by the metabolites available in their environment, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. To this end, we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the diverse assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively smaller ones, is necessary and sufficient to recapitulate all of our experimental observations. In addition to pointing to a fundamental principle of community assembly, the divergence-complexity effect has important implications for microbiome engineering applications, as it can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions.

Stress-induced metabolic exchanges between complementary bacterial types underly a dynamic mechanism of inter-species stress resistance

Metabolic cross-feeding plays vital roles in promoting ecological diversity. While some microbes depend on exchanges of essential nutrients for growth, the forces driving the extensive cross-feeding needed to support the coexistence of free-living microbes are poorly understood. Here we characterize bacterial physiology under self-acidification and establish that extensive excretion of key metabolites following growth arrest provides a collaborative, inter-species mechanism of stress resistance. This collaboration occurs not only between species isolated from the same community, but also between unrelated species with complementary (glycolytic vs. gluconeogenic) modes of metabolism. Cultures of such communities progress through distinct phases of growth-dilution cycles, comprising of exponential growth, acidification-triggered growth arrest, collaborative deacidification, and growth recovery, with each phase involving different combinations of physiological states of individual species. Our findings challenge the steady-state view of ecosystems commonly portrayed in ecological models, offering an alternative dynamical view based on growth advantages of complementary species in different phases.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze Consumer Resource Models (CRMs) using geometric arguments. This has led to broadly applicable principles such as Tilman's R* and species coexistence cones. Here, we extend these arguments by constructing a novel geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species coexistence and enumerate ecologically-stable steady states and transitions between them. Collectively, these results constitute a qualitatively new way of understanding the role of species traits in shaping ecosystems within niche theory.

A social niche breadth score reveals niche range strategies of generalists and specialists

Generalists can survive in many environments, whereas specialists are restricted to a single environment. Although a classical concept in ecology, niche breadth has remained challenging to quantify for microorganisms because it depends on an objective definition of the environment. Here, by defining the environment of a microorganism as the community it resides in, we integrated information from over 22,000 environmental sequencing samples to derive a quantitative measure of the niche, which we call social niche breadth. At the level of genera, we explored niche range strategies throughout the prokaryotic tree of life. We found that social generalists include opportunists that stochastically dominate local communities, whereas social specialists are stable but low in abundance. Social generalists have a more diverse and open pan-genome than social specialists, but we found no global correlation between social niche breadth and genome size. Instead, we observed two distinct evolutionary strategies, whereby specialists have relatively small genomes in habitats with low local diversity, but relatively large genomes in habitats with high local diversity. Together, our analysis shines data-driven light on microbial niche range strategies.

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.

High carbon resource diversity enhances the certainty of successful plant pathogen and disease control

The host-associated microbiome highly determines plant health. Available organic resources, such as food for microbes, are important in shaping microbial community structure and multifunctionality. However, how using organic resources precisely manipulates the soil microbiome and makes it supportive of plant health remains unclear. Here, we experimentally tested the influence of carbon resource diversity on the microbial trophic network and pathogen invasion success in a microcosm study. We further explored how resource diversity affects microbial evenness, community functions, and plant disease outcomes in systems involving tomato plants and the in vivo soil microbiome. Increasing available resource diversity altered trophic network architecture, increased microbial evenness, and thus increased the certainty of successful pathogen control. By contrast, the invasion resistance effects of low resource diversity were less effective and highly varied. Accordingly, increases in the evenness and connection of dominant species induced by high resource diversity significantly contributed to plant disease suppression. Furthermore, high carbohydrate diversity upregulated plant immune system regulation-related microbial functions. Our results deepen the biodiversity-invasion resistance theory and provide practical guidance for the control of plant pathogens and diseases by using organic resource-mediated approaches, such as crop rotation, intercropping, and organic amendments.

Highly efficient reduction of ammonia emissions from livestock waste by the synergy of novel manure acidification and inhibition of ureolytic bacteria

The global livestock system is one of the largest sources of ammonia emissions and there is an urgent need for ammonia mitigation. Here, we designed and constructed a novel strategy to abate ammonia emissions via livestock manure acidification based on a synthetic lactic acid bacteria community (LAB SynCom). The LAB SynCom possessed a wide carbon source spectrum and pH profile, high adaptability to the manure environment, and a high capability of generating lactic acid. The mitigation strategy was optimized based on the test and performance by adjusting the LAB SynCom inoculation ratio and the adding frequency of carbon source, which contributed to a total ammonia reduction efficiency of 95.5 %. Furthermore, 16S rDNA amplicon sequencing analysis revealed that the LAB SynCom treatment reshaped the manure microbial community structure. Importantly, 22 manure ureolytic microbial genera and urea hydrolysis were notably inhibited by the LAB SynCom treatment during the treatment process. These findings provide new insight into manure acidification that the conversion from ammonia to ammonium ions and the inhibition of ureolytic bacteria exerted a synergistic effect on ammonia mitigation. This work systematically developed a novel strategy to mitigate ammonia emissions from livestock waste, which is a crucial step forward from traditional manure acidification to novel and environmental-friendly acidification.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Carbohydrate complexity limits microbial growth and reduces the sensitivity of human gut communities to perturbations

Dietary fibre impacts the growth dynamics of human gut microbiota, yet we lack a detailed and quantitative understanding of how these nutrients shape microbial interaction networks and responses to perturbations. By building human gut communities coupled with computational modelling, we dissect the effects of fibres that vary in chemical complexity and each of their constituent sugars on community assembly and response to perturbations. We demonstrate that the degree of chemical complexity across different fibres limits microbial growth and the number of species that can utilize these nutrients. The prevalence of negative interspecies interactions is reduced in the presence of fibres compared with their constituent sugars. Carbohydrate chemical complexity enhances the reproducibility of community assembly and resistance of the community to invasion. We demonstrate that maximizing or minimizing carbohydrate competition between resident and invader species enhances resistance to invasion. In sum, the quantitative effects of carbohydrate chemical complexity on microbial interaction networks could be exploited to inform dietary and bacterial interventions to modulate community resistance to perturbations.

Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium

Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent medium assays based on the predictions of a mathematical model to decipher the interactions among four bacterial species: Agrobacterium tumefaciens, Comamonas testosteroni, Microbacterium saperdae, and Ochrobactrum anthropi. While most experimental results matched model predictions, the behavior of C. testosteroni did not: its lag phase was reduced in the pure spent media of A. tumefaciens and M. saperdae but prolonged again when we replenished our growth medium. Further experiments showed that the growth medium actually delayed the growth of C. testosteroni, leading us to suspect that A. tumefaciens and M. saperdae could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification," and instead, we identified metabolites secreted by A. tumefaciens and M. saperdae that were then consumed or "cross-fed" by C. testosteroni, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types such as cross-detoxification and cross-feeding. IMPORTANCE Communities of microbes colonize virtually every place on earth. Ultimately, we strive to predict and control how these communities behave, for example, if they reside in our guts and make us sick. But precise control is impossible unless we can identify exactly how their member species interact with one another. To find a systematic way to measure interactions, we started very simply with a small community of four bacterial species and carefully designed experiments based on a mathematical model. This first attempt accurately mapped out interactions for all species except one. By digging deeper, we understood that our method failed for that species as it was suffering in the growth medium that we chose. A revised model that considered that growth media can be harmful could then make more accurate predictions. What we have learned with these four species can now be applied to decipher interactions in larger communities.

Community instability in the microbial world

Miniature ecosystems provide insights into general ecological principles.

Cross-feeding niches among commensal leaf bacteria are shaped by the interaction of strain-level diversity and resource availability

Leaf microbiomes play crucial roles in plant health, making it important to understand the origins and functional relevance of their diversity. High strain-level leaf bacterial genetic diversity is known to be relevant for interactions with hosts, but little is known about its relevance for interactions with the multitude of diverse co-colonizing microorganisms. In leaves, nutrients like amino acids are major regulators of microbial growth and activity. Using metabolomics of leaf apoplast fluid, we found that different species of the plant genus Flaveria considerably differ in the concentrations of high-cost amino acids. We investigated how these differences affect bacterial community diversity and assembly by enriching leaf bacteria in vitro with only sucrose or sucrose + amino acids as possible carbon sources. Enrichments from F. robusta were dominated by Pantoea sp. and Pseudomonas sp., regardless of carbon source. The latter was unable to grow on sucrose alone but persisted in the sucrose-only enrichment thanks to exchange of diverse metabolites from Pantoea sp. Individual Pseudomonas strains in the enrichments had high genetic similarity but still displayed clear niche partitioning, enabling distinct strains to cross-feed in parallel. Pantoea strains were also closely related, but individuals enriched from F. trinervia fed Pseudomonas more poorly than those from F. robusta. This can be explained in part by the plant environment, since some cross-feeding interactions were selected for, when experimentally evolved in a poor (sucrose-only) environment but selected against in a rich (sucrose + amino acids) one. Together, our work shows that leaf bacterial diversity is functionally relevant in cross-feeding interactions and strongly suggests that the leaf resource environment can shape these interactions and thereby indirectly drive bacterial diversity.

Competition-cooperation in the chemoautotrophic ecosystem of Movile Cave: first metagenomic approach on sediments

BACKGROUND

Movile Cave (SE Romania) is a chemoautotrophically-based ecosystem fed by hydrogen sulfide-rich groundwater serving as a primary energy source analogous to the deep-sea hydrothermal ecosystems. Our current understanding of Movile Cave microbiology has been confined to the sulfidic water and its proximity, as most studies focused on the water-floating microbial mat and planktonic accumulations likely acting as the primary production powerhouse of this unique subterranean ecosystem. By employing comprehensive genomic-resolved metagenomics, we questioned the spatial variation, chemoautotrophic abilities, ecological interactions and trophic roles of Movile Cave's microbiome thriving beyond the sulfidic-rich water.

RESULTS

A customized bioinformatics pipeline led to the recovery of 106 high-quality metagenome-assembled genomes from 7 cave sediment metagenomes. Assemblies' taxonomy spanned 19 bacterial and three archaeal phyla with Acidobacteriota, Chloroflexota, Proteobacteria, Planctomycetota, Ca. Patescibacteria, Thermoproteota, Methylomirabilota, and Ca. Zixibacteria as prevalent phyla. Functional gene analyses predicted the presence of CO₂ fixation, methanotrophy, sulfur and ammonia oxidation in the explored sediments. Species Metabolic Coupling Analysis of metagenome-scale metabolic models revealed the highest competition-cooperation interactions in the sediments collected away from the water. Simulated metabolic interactions indicated autotrophs and methanotrophs as major donors of metabolites in the sediment communities. Cross-feeding dependencies were assumed only towards 'currency' molecules and inorganic compounds (O₂, PO₄^3-, H⁺, Fe²⁺, Cu²⁺) in the water proximity sediment, whereas hydrogen sulfide and methanol were assumedly traded exclusively among distant gallery communities.

CONCLUSIONS

These findings suggest that the primary production potential of Movile Cave expands way beyond its hydrothermal waters, enhancing our understanding of the functioning and ecological interactions within chemolithoautotrophically-based subterranean ecosystems.

Substrate Utilization and Competitive Interactions Among Soil Bacteria Vary With Life-History Strategies

Microorganisms have evolved various life-history strategies to survive fluctuating resource conditions in soils. However, it remains elusive how the life-history strategies of microorganisms influence their processing of organic carbon, which may affect microbial interactions and carbon cycling in soils. Here, we characterized the genomic traits, exometabolite profiles, and interactions of soil bacteria representing copiotrophic and oligotrophic strategists. Isolates were selected based on differences in ribosomal RNA operon (rrn) copy number, as a proxy for life-history strategies, with pairs of “high” and “low” rrn copy number isolates represented within the Micrococcales, Corynebacteriales, and Bacillales. We found that high rrn isolates consumed a greater diversity and amount of substrates than low rrn isolates in a defined growth medium containing common soil metabolites. We estimated overlap in substrate utilization profiles to predict the potential for resource competition and found that high rrn isolates tended to have a greater potential for competitive interactions. The predicted interactions positively correlated with the measured interactions that were dominated by negative interactions as determined through sequential growth experiments. This suggests that resource competition was a major force governing interactions among isolates, while cross-feeding of metabolic secretion likely contributed to the relatively rare positive interactions observed. By connecting bacterial life-history strategies, genomic features, and metabolism, our study advances the understanding of the links between bacterial community composition and the transformation of carbon in soils.

Horizontal gene transfer drives the evolution of dependencies in bacteria

Many naturally occurring bacteria lead a lifestyle of metabolic dependency for crucial resources. We do not understand what factors drive bacteria toward this lifestyle and how. Here, we systematically show the crucial role of horizontal gene transfer (HGT) in dependency evolution in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree and assess the impact of HGT and gene loss on metabolic networks. Our analyses suggest that HGT-enabled gene gains can affect which genes are later lost. HGT typically adds new catabolic routes to bacterial metabolic networks, leading to new metabolic interactions between bacteria. We also find that gaining new routes can promote the loss of ancestral routes (”coupled gains and losses”, CGLs). Phylogenetic patterns indicate that both dependencies—mediated by CGLs and those purely by gene loss—are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.

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Metabolic dependencies are widespread across bacterial genomes

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New genes expand bacterial catabolism via the process of horizontal gene transfer

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During evolution, efficient pathways are gained, whereas redundant pathways are lost

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Gained pathways often depend on the metabolic byproducts of the surrounding community

Diauxic lags explain unexpected coexistence in multi-resource environments

How the coexistence of species is affected by the presence of multiple resources is a major question in microbial ecology. We experimentally demonstrate that differences in diauxic lags, which occur as species deplete their own environments and adapt their metabolisms, allow slow-growing microbes to stably coexist with faster-growing species in multi-resource environments despite being excluded in single-resource environments. In our focal example, an Acinetobacter species (Aci2) competitively excludes Pseudomonas aurantiaca (Pa) on alanine and on glutamate. However, they coexist on the combination of both resources. Experiments reveal that Aci2 grows faster but Pa has shorter diauxic lags. We establish a tradeoff between Aci2's fast growth and Pa's short lags as their mechanism for coexistence. We model this tradeoff to accurately predict how environmental changes affect community composition. We extend our work by surveying a large set of competitions and observe coexistence nearly four times as frequently when the slow-grower is the fast-switcher. Our work illustrates a simple mechanism, based entirely on supplied-resource growth dynamics, for the emergence of multi-resource coexistence.

Soil Aggregates and Fertilizer Treatments Drive Bacterial Interactions via Interspecies Niche Overlap

Bacterial interactions play significant roles in ecological functions in responding to anthropogenic interference and soil structure changes. However, it remains largely unknown how fertilizer regimes and soil particle sizes drive bacterial interactions. To evaluate bacterial interaction patterns in soil aggregates under long-term fertilizer treatments, we sampled nine bacterial co-occurrence communities and compared the difference between interspecies resource consumption patterns and network structure. Despite the differences between fertilizer treatments, the negative correlation ratios of interaction networks in soil aggregates were macroaggregates > microaggregates > silt + clays. Likewise, NPK-supplement (chemical fertilizer) had also decreased the number of positive correlations of the interaction network than M-supplement (organic fertilizer), regardless of the size of soil aggregates. Linear model analysis revealed that interspecies trophic patterns, including niche overlap and nestedness, drove bacterial competition in the interaction networks. Most importantly, interspecies niche overlap may be the intrinsic factor in the effects of fertilizer treatments and soil aggregates on bacterial interactions. This study enhances our understanding of the potential for changes in species trophic patterns and might guide the promotion of land management. IMPORTANCE Despite that the influence of soil structure and fertilizer treatments on the bacterial community has been widely studied, how they drive interspecies interactions has not been largely explored. Connectance and nestedness were significantly correlated with bacterial interactions, but no differences were found in different soil aggregates and fertilizer treatments. However, interspecies niche overlap could respond to soil aggregates and fertilizer treatments and ultimately drive the bacterial interactions. This study enhances our understanding of the mechanism of microbial interactions and highlights the importance of trophic patterns in the bacterial community. Our findings extend knowledge for nutrient availability on interspecific interactions.

Particle foraging strategies promote microbial diversity in marine environments

Microbial foraging in patchy environments, where resources are fragmented into particles or pockets embedded in a large matrix, plays a key role in natural environments. In the oceans and freshwater systems, particle-associated bacteria can interact with particle surfaces in different ways: some colonize only during short transients, while others form long-lived, stable colonies. We do not yet understand the ecological mechanisms by which both short- and long-term colonizers can coexist. Here, we address this problem with a mathematical model that explains how marine populations with different detachment rates from particles can stably coexist. In our model, populations grow only while on particles, but also face the increased risk of mortality by predation and sinking. Key to coexistence is the idea that detachment from particles modulates both net growth and mortality, but in opposite directions, creating a trade-off between them. While slow-detaching populations show the highest growth return (i.e., produce more net offspring), they are more susceptible to suffer higher rates of mortality than fast-detaching populations. Surprisingly, fluctuating environments, manifesting as blooms of particles (favoring growth) and predators (favoring mortality) significantly expand the likelihood that populations with different detachment rates can coexist. Our study shows how the spatial ecology of microbes in the ocean can lead to a predictable diversification of foraging strategies and the coexistence of multiple taxa on a single growth-limiting resource.

Putting microbial interactions back into community contexts

Microbial interactions are key aspects of the biology of microbiomes. Recently, there has been a shift in the field towards studying interactions in more representative contexts, whether using multispecies model microbial communities or by looking at interactions in situ. Across diverse microbial systems, these studies have begun to identify common interaction mechanisms. These mechanisms include interactions related to toxic molecules, nutrient competition and cross-feeding, access to metals, signaling pathways, pH changes, and interactions within biofilms. Leveraging technological innovations, many of these studies have used an interdisciplinary approach combining genetic, metabolomic, imaging, and/or microfluidic techniques to gain insight into mechanisms of microbial interactions and into the impact of these interactions on microbiomes.