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

Dynamic coexistence driven by physiological transitions in microbial communities

Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes in exponential growth often modify their environments so strongly that they are forced out of the growth state into stressed, nongrowing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State Model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states during cyclic succession. Our model specifies the growth preference of each species along a global ecological coordinate, taken to be the biomass density of the community, but is otherwise agnostic to specific interactions (e.g., nutrient starvation, stress, aggregation), in order to focus on self-consistency conditions on combinations of physiological states, "community states," in a stable ecosystem. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: enhanced community stability through staggered dominance of different species in different community states, increased tolerance of community diversity to fast growing species dominating distinct community states, and increased requirement of growth dominance by late-growing species. These features, derived explicitly for simplified models, are proposed here as principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on fixed, idealized interspecies interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics.

Exploring the role of carbon source types in trace-level sulfamethoxazole removal and greenhouse gas emissions in AnMBRs

The efficient removal of trace-level sulfamethoxazole (SMX) from wastewater remains a significant challenge. Different carbon sources can enrich distinct microbiomes, leading to variations in the functional capacity of the community. This makes it possible to select appropriate carbon sources that are conducive to enhancing SMX removal, thereby improving the overall SMX removal efficiency in WWTPs. In this study, acetate, citrate, and glucose were tested as carbon sources in anaerobic membrane bioreactors (AnMBRs) to investigate their effects on trace-level SMX removal. Glucose, as a carbon source, achieved the highest SMX removal efficiency, reduced the risk of resistance gene transmission, and maintained stable nutrient removal performance. The higher abundance of SMX-degrading bacteria and the higher content of extracellular polymeric substances in glucose-fed cultures are the reasons for the higher SMX removal rate. Additionally, GHG emissions, primarily methane, increase with the increase of SMX concentration within the range of 10-250 μg L-1. Methane production is predominantly driven by the acetate-to-methane pathway (M00357 KEGG). Higher SMX concentrations led to an increase in the abundance of SMX-resistant bacteria, causing a large amount of CH4 emissions. These findings provide valuable insights into optimizing carbon source selection and deepen our understanding of the relationship between trace-level SMX removal and GHG emissions in wastewater treatment processes.

Aerospace Mutagenized Tea Tree Increases Rhizospheric Microorganisms, Enhances Nutrient Conversion Capacity and Promotes Growth

The utilization of aerospace mutagenesis in plant breeding is a novel, efficient technology. This study investigates the effects of aerospace mutagenesis on tea tree growth, soil nutrient conversion, and soil microbial community structure and function. The results showed that aerospace mutagenized tea trees showed increased leaf area, 100-bud weight, and yield. The rhizosphere soil of mutagenized tea tree displayed an increase in microorganisms, enhanced carbon and nitrogen cycling capacity, and significant increases in nutrient conversion and antioxidant enzyme activities. In addition, the content of available nutrients was also increased. Aerospace mutagenesis showed an increase in the abundance of soil-characteristic microorganisms (Solirubrobacterales bacterium, Capillimicrobium parvum, Mycobacterium colombiense, Mycobacterium rhizamassiliense, and Conexibacter woesei), and enhancement of the intensity of metabolic pathways, glyoxylate and dicarboxylate metabolism, biosynthesis of secondary metabolites, microbial metabolism in diverse environments, carbon metabolism, fatty acid metabolism, carbon metabolism, biosynthesis of amino acids, and biosynthesis of cofactors of soil microorganisms. Interaction network and partial least squares structural equation modeling (PLS-SEM) equation analysis showed that after aerospace mutagenesis, soil-characteristic microorganisms positively affected soil microbial functions, soil microbial biomass carbon and nitrogen, respiration intensity, and soil enzyme activities; furthermore, it improved available nutrient content and tea tree growth. This study provides an important reference for the cultivation and management of aerospace mutagenized tea trees and microbial regulation of tea tree growth.

The impact of ecological pressure caused by toxic lignin metabolites on microbial function transformation during composting

Environmental stress coexists with microbial interactions during composting, temperature was thought to be the main stress, the toxic stress caused by aromatic compounds was often overlooked. Herein, lignin and lignin degradation products (LDP) were used alone or in combination to culture the compost microbial community, which manually delineated 32 niches to explore the effects of toxic stress on microbe and their responses. Results showed that the toxicity of LDP significantly changed the distribution of microbial community. Then, microorganisms were divided into three groups: specialist showed high diversity and low abundance, responsible for the metabolism of toxic macromolecules, with detoxification effect. Generalist exhibited high abundance and diversity, which mainly changed metabolic byproducts to cellular energy and sustaining the survival of microorganisms. Accordingly, these characteristics were the survival strategies of microbial communities to harmonize the toxic stress in different ecological niche. While the results of the lignin metabolic catalog have confirmed the metabolic tendency of different microorganism in the metabolic module. Finally, this study help to further understand microbe survival strategies in aromatic pollutants composting, which may be possible to accelerate or alter the composting process by manipulating the microbial community.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Hydrogen sulfide mediates the interaction between C. elegans and Actinobacteria from its natural microbial environment

Caenorhabditis elegans proliferates poorly in the presence of abundant Actinobacteria from its natural ecology, but it is unknown why. Here, we show how perturbed levels of hydrogen sulfide modulate the growth rate of both C. elegans and Actinobacteria. From a forward genetic selection, we find C. elegans mutants with faster growth on an Actinobacteria Microbacterium species and mutant alleles of conserved cystathionine gamma-lyase (cth-2/CTH) that improve growth rate. Conversely, null alleles of cth-2 cause developmental arrest of animals grown on Actinobacteria, but not on Proteobacteria, which can be rescued by exogenous H2S. We also find mutations in a leucine-rich-repeat gene that regulates cysteine and H2S production, lrr-2/LRRC58. We place lrr-2 in the animal sulfur metabolism pathway by demonstrating its role in post-translationally regulating levels of cysteine dioxygenase (cdo-1/CDO1). Exogenously supplied H2S inhibits the growth of Actinobacteria but not Proteobacteria. Thus, we conclude that the C. elegans-Actinobacteria interaction is mediated by H2S.

Designing host-associated microbiomes using the consumer/resource model

A key step toward rational microbiome engineering is in silico sampling of realistic microbial communities that correspond to desired host phenotypes, and vice versa. This remains challenging due to a lack of generative models that simultaneously capture compositions of host-associated microbiomes and host phenotypes. To that end, we present a generative model based on the mechanistic consumer/resource (C/R) framework. In the model, variation in microbial ecosystem composition arises due to differences in the availability of effective resources (inferred latent variables), while species' resource preferences remain conserved. Simultaneously, the latent variables are used to model phenotypic states of hosts. In silico microbiomes generated by our model accurately reproduce universal and dataset-specific statistics of bacterial communities. The model allows us to address three salient questions in host-associated microbial ecologies: (i) which host phenotypes maximally constrain the composition of the host-associated microbiomes? (ii) how context-specific are phenotype/microbiome associations, and (iii) what are plausible microbiome compositions that correspond to desired host phenotypes? Our approach aids the analysis and design of microbial communities associated with host phenotypes of interest.

IMPORTANCE

Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

From nature to urbanity: exploring phyllosphere microbiome and functional gene responses to the Anthropocene

The Anthropocene exerts various pressures and influences on the stability and function of the Earth's ecosystems. However, our understanding of how the microbiome responds in form and function to these disturbances is still limited, particularly when considering the phyllosphere, which represents one of the largest microbial reservoirs in the terrestrial ecosystem. In this study, we comprehensively characterized tree phyllosphere bacteria and associated nutrient-cycling genes in natural, rural, suburban, and urban habitats in China. Results revealed that phyllosphere bacterial community diversity, richness, stability, and composition heterogeneity were greatest at the most disturbed sites. Stochastic processes primarily governed the assembly of phyllosphere bacterial communities, although the role of deterministic processes (environmental selection) in shaping these communities gradually increased as we moved from rural to urban sites. Our findings also suggest that human disturbance is associated with the reduced influence of drift as increasingly layered environmental filters deterministically constrain phyllosphere bacterial communities. The intensification of human activity was mirrored in changes in functional gene expression within the phyllosphere microbiome, resulting in enhanced gene abundance, diversity, and compositional variation in highly human-driven disturbed environments. Furthermore, we found that while the relative proportion of core microbial taxa decreased in disturbed habitats, a core set of microbial taxa shaped the distributional characteristics of both microbiomes and functional genes at all levels of disturbance. In sum, this study offers valuable insights into how anthropogenic disturbance may influence phyllosphere microbial dynamics and improves our understanding of the intricate relationship between environmental stressors, microbial communities, and plant function within the Anthropocene.

Assembly Graph as the Rosetta Stone of Ecological Assembly

Ecological assembly-the process of ecological community formation through species introductions-has recently seen exciting theoretical advancements across dynamical, informational, and probabilistic approaches. However, these theories often remain inaccessible to non-theoreticians, and they lack a unifying lens. Here, I introduce the assembly graph as an integrative tool to connect these emerging theories. The assembly graph visually represents assembly dynamics, where nodes symbolise species combinations and edges represent transitions driven by species introductions. Through the lens of assembly graphs, I review how ecological processes reduce uncertainty in random species arrivals (informational approach), identify graphical properties that guarantee species coexistence and examine how the class of dynamical models constrain the topology of assembly graphs (dynamical approach), and quantify transition probabilities with incomplete information (probabilistic approach). To facilitate empirical testing, I also review methods to decompose complex assembly graphs into smaller, measurable components, as well as computational tools for deriving empirical assembly graphs. In sum, this math-light review of theoretical progress aims to catalyse empirical research towards a predictive understanding of ecological assembly.

Ecosystem stability relies on diversity difference between trophic levels

The stability of ecological communities has a profound impact on humans, ranging from individual health influenced by the microbiome to ecosystem services provided by fisheries. A long-standing goal of ecology is the elucidation of the interplay between biodiversity and ecosystem stability, with some ecologists warning of instability due to loss of species diversity while others arguing that greater diversity will instead lead to instability. Here, by considering a minimal two-level ecosystem with multiple predator and prey species, we show that stability does not depend on absolute diversity but rather on diversity differences between levels. We found that increasing diversity in either level first destabilizes but then stabilizes the community (i.e., a reentrant stability transition). We therefore find that it is the diversity difference between levels that is the key to stability, with the least stable communities having similar diversities in different levels. An analytical stability criterion is derived, demonstrating quantitatively that the critical diversity difference is determined by the correlation between how one level affects another and how it is affected in turn. Our stability criterion also applies to consumer-resource models with other forms of interaction such as cross-feeding. Finally, we show that stability depends on diversity differences in ecosystems with three trophic levels. Our finding of a nonmonotonic dependence of stability on diversity provides a natural explanation for the variety of diversity-stability relationships reported in the literature, and emphasizes the significance of level structure in predicting complex community behaviors.

Microplastics stimulated soil bacterial alpha diversity and nitrogen cycle: A global hierarchical meta-analysis

Microplastics (MPs) pollution is recognized as a global emerging threat with serious potential impacts on ecosystems. Our meta-analysis was conducted based on 117 carefully selected publications, from which 2160 datasets were extracted. These publications described experiments in which MPs were added to soil (in laboratory or greenhouse experiments or in the field) after which the soil microbial community was analyzed and compared to a control group. From these publications, we extracted 1315 observations on soil bacterial alpha diversity and richness indices and 845 datasets on gene abundance of bacterial genes related to the soil nitrogen cycle. These data were analyzed using a multiple hierarchical mixed effects meta-analysis. The mean effect of microplastic exposure was a significant decrease of soil bacterial community diversity and richness. We explored these responses for different regulators, namely MPs addition rates, particle size and plastic type, soil texture and land use, and study type. Of the bacterial processes involved in the soil nitrogen cycle, MPs addition significantly promoted assimilation of ammonium, nitrogen fixation and urea decomposition, but significantly inhibited nitrification. These results suggest that MPs contamination may have considerable impacts on soil bacterial community structure and function as well as on the soil nitrogen cycle.

Geographic Distribution Pattern Determines Soil Microbial Community Assembly Process in Acanthopanax senticosus Rhizosphere Soil

The geographic distribution patterns of soil microbial communities associated with cultivated Acanthopanax senticosus plants in Northeast China were investigated. High-throughput sequencing revealed that the diversity and community assembly of bacterial and fungal communities in the inter-root soil varied significantly with geographic location. The study found that bacterial communities were predominantly assembled through stochastic processes at most sites, while fungal communities showed greater variation, with both stochastic and deterministic processes involved. The complexity of bacterial-fungal co-occurrence networks also varied with longitude and latitude, demonstrating both positive and negative interactions. PICRUSt 2.0 and FUNGuild were used to predict the potential functions of soil bacterial and fungal microbiota, respectively, during different land use patterns. The average taxonomic distinctness (AVD) index indicated varying degrees of community stability across sites. Key microbial taxa contributing to community variability were identified through Random Forest modeling, with Bacteriap25 and Sutterellaceae standing out among bacteria, and Archaeorhizomyces and Clavaria among fungi. Soil chemical properties, including pH, TN, TP, EC, and SOC, significantly correlated with microbial diversity, composition, and co-occurrence networks. Structural equation modeling revealed that geographic distribution patterns directly and indirectly influenced soil chemical properties and microbial communities. Overall, the study provides insights into the geographic distribution patterns of soil microbial communities associated with A. senticosus and highlights the need for further research into the underlying mechanisms shaping these patterns.

Lake ecological restoration of vegetation removal mitigates algal blooms and alters landscape patterns of water and sediment bacteria

Elucidating the influences of ecological restoration measure of lakeshore vegetation removal on water quality and biological community is an important but underestimated subject. We adopted molecular and statistical tools to estimate ecological restoration performance in a plateau lake receiving vegetation removal and simultaneously investigated variabilities of bacterial communities in water and sediment. Significant decreases in lake trophic level and algal bloom degree followed notable decreases in water total nitrogen and total phosphorus after vegetation removal. Non-significant changes in sediment nutrients accompanied remarkable variabilities of abundance and composition of nutrient-cycling functional genes (NCFGs) of sediment bacteria. Taxonomic and phylogenetic α-diversities, functional redundancies, and dispersal potentials of bacteria in water and sediment decreased after vegetation removal, and community successions of water and sediment bacteria were separately significant and non-significant. There were opposite changes in ecological attributes of bacteria in water and sediment in response to vegetation removal, including niche breadth, species replacement, richness difference, community complexity, and community stability. Species replacement rather than richness difference affected more on taxonomic β-diversities of bacteria in water and sediment before and after vegetation removal, and determinism rather than stochasticity dominated bacterial community assemblage. Our results highlighted vegetation removal mitigated algal bloom and affected differently on landscapes of water and sediment bacteria. These findings point to dominant ecological mechanisms underlying landscape shifts in water and sediment bacteria in a disturbed lake receiving vegetation removal and have the potential to guide lake ecological restoration.

At what cost? The impact of bacteriophage resistance on the growth kinetics and protein synthesis of Escherichia coli

Cost of bacteriophage resistance (COR) is important in explaining processes of diversification and coexistence in microbial communities. COR can be expressed in different traits, and the lack of universally applicable methods to measure fitness trade-offs makes COR challenging to study. Due to its fundamental role in growth, we explored protein synthesis as a target for quantifying COR. In this study, the growth kinetics of three genome-sequenced strains of phage-resistant Escherichia coli, along with the phage-susceptible wild-type, were characterized over a range of glucose concentrations. Bioorthogonal non-canonical amino acid tagging (BONCAT) was used to track differences in protein synthetic activity between the wild-type and phage-resistant E. coli. Two of the resistant strains, with different levels of phage susceptibility, showed mucoid phenotypes corresponding with mutations in genes associated with the Rcs phosphorelay. These mucoid isolates, however, had reduced growth rates and potentially lower protein synthetic activity. Another resistant isolate with a different mutational profile maintained the same growth rate as the wild-type and showed increased BONCAT fluorescence, but its yield was lower. Together, these findings present different patterns of trade-offs resulting from the phage-induced mutations and demonstrate the potential applicability of BONCAT as a tool for measuring COR.

Quinolone-mediated metabolic cross-feeding develops aluminium tolerance in soil microbial consortia

Aluminium (Al)-tolerant beneficial bacteria confer resistance to Al toxicity to crops in widely distributed acidic soils. However, the mechanism by which microbial consortia maintain Al tolerance under acid and Al toxicity stress remains unknown. Here, we demonstrate that a soil bacterial consortium composed of Rhodococcus erythropolis and Pseudomonas aeruginosa exhibit greater Al tolerance than either bacterium alone. P. aeruginosa releases the quorum sensing molecule 2-heptyl-1H-quinolin-4-one (HHQ), which is efficiently degraded by R. erythropolis. This degradation reduces population density limitations and further enhances the metabolic activity of P. aeruginosa under Al stress. Moreover, R. erythropolis converts HHQ into tryptophan, promoting the synthesis of peptidoglycan, a key component for cell wall stability, thereby improving the Al tolerance of R. erythropolis. This study reveals a metabolic cross-feeding mechanism that maintains microbial Al tolerance, offering insights for designing synthetic microbial consortia to sustain food security and sustainable agriculture in acidic soil regions.

From grasslands to genes: exploring the major microbial drivers of antibiotic-resistance in microhabitats under persistent overgrazing

BACKGROUND

The extensive use of antibiotics in the global livestock industry in recent decades has accelerated the accumulation and dissemination of antibiotic-resistance genes (ARGs) within terrestrial ecosystems. This occurs due to the limited absorption of most antibiotics, leading to their release into the environment through feces and urine. This poses a significant threat to both the environment and human health. However, the response of antibiotic-resistant microorganisms and their ARGs in grasslands to prolonged grazing, as well as the primary microbial taxa driving the ARG distribution, remain poorly understood, especially within various microhabitats. In this study, we characterized ARGs in the phyllosphere, litter, and soil after decades of livestock grazing in a meadow steppe. We particularly focused on identifying the major members of the microbial community influencing ARGs and the distinction between microbial generalists and specialists.

RESULTS

Our findings indicate that a core set of ARGs accounted for 90% of the abundance in this plant-soil ecosystem. While the soil exhibited the highest ARG abundance, the phyllosphere, and litter displayed higher ARG diversity and diverse distribution patterns after overgrazing. Grazing increased ARG abundance by elevating the proportion of core ARGs and suppressing stochastic ARGs in the phyllosphere and litter, while it had little effect on the ARGs in the soil. Additionally, microbial generalist abundance increased, but specialist abundance decreased in the phyllosphere and litter, with no effect in the soil, under grazed conditions. Ultimately, microbial microhabitats and grazing influenced ARG community characteristics through direct (i.e., feces and other exogenous ARG input) and indirect (i.e., trampling and selective feeding) effects on nutrient availability, microbial community composition, and mobile genetic elements. The generalist community, with its broad ecological niches and phylogenetic composition, made the most significant contribution to the ARG characteristics.

CONCLUSIONS

This study underscores the impact of environmental disturbances on the distributional patterns of ARGs in ecosystems, mediated by the regulation of microbial generalized species. These insights enhance our understanding of microbial control over ARGs and facilitate predictions regarding the dynamics and risk of ARGs in diverse ecological niches subjected to anthropogenic disturbances. Video Abstract.

Phosphorus resource partitioning underpins diversity patterns and assembly processes of microbial communities in plateau karst lakes

Eutrophication triggered by internal phosphorus (P) poses a substantial threat to the biodiversity of organisms in freshwater ecosystems. However, little is known about the linkages between P resource partitioning and microbial succession, especially in karst sediments. Here, we studied the diversity patterns and assembly processes of bacterial and archaeal communities in sediment cores from two historically hyper-eutrophicated karst lakes, Hongfeng Lake and Aha Lake, and investigated the relative contribution of P fractions to them. Our null and neutral models consistently indicated that bacterial and archaeal community assembly was judged to be deterministic rather than stochastic. We found a monotonically decreasing pattern for bacterial Shannon diversity toward deep sediments in Aha Lake, but U- or hump-shaped patterns for archaea in Hongfeng and Aha Lakes. Intriguingly, the community dissimilarity Bray-Curtis of bacteria and archaea consistently increased with increasing depth distance, with slopes of 0.0080 and 0.0069 in Hongfeng Lake and 0.0078 and 0.0087 in Aha Lake, respectively. Such cross-taxon congruence was well-supported by equivalent ecological processes (i.e., environmental selection). For bacteria and archaea, Shannon diversity was primarily affected by the total P (TP) fractions such as the loosely adsorbed TP or calcium-bound TP and sediment TP. Their community composition was significantly (P < 0.05) affected by calcium-bound inorganic P (Pi), loosely adsorbed Pi and reductant-soluble Pi. Although sediment properties were important, bacterial and archaeal diversity or community composition were well-explained by the Pi fractions, with high direct or indirect effects. In particular, Pi fractions exhibited stronger effects on bacterial and archaeal characteristics than organic P fractions. Taken together, our study provides novel insights into the ecological importance of P resource partitioning to microbial succession, which has crucial implications for disentangling the biogeochemical processes of P cycling in aquatic ecosystems.

Metabolic Trade-Offs Can Reverse the Resource-Diversity Relationship

AbstractFor species that partition resources, the classic expectation is that increasing resource diversity allows for increased species diversity. On the other hand, for neutral species, such as those competing equally for a single resource, diversity reflects a balance between the rate of introduction of novelty (e.g., by immigration or speciation) and the rate of extinction. Recent models of microbial metabolism have identified scenarios where metabolic trade-offs among species partitioning multiple resources can produce emergent neutral-like dynamics. In this hybrid scenario, one might expect that both resource diversity and immigration will act to boost species diversity. We show, however, that the reverse may be true: when metabolic trade-offs hold and population sizes are sufficiently large, increasing resource diversity can act to reduce species diversity, sometimes drastically. This reversal is explained by a generic transition between neutral- and niche-like dynamics, driven by the diversity of resources. The inverted resource-diversity relationship that results may be a signature of consumer-resource systems with strong metabolic trade-offs.

Inferring bacterial interspecific interactions from microcolony growth expansion

Bacterial species interactions significantly shape growth and behavior in communities, determining the emergence of community functions. Typically, these interactions are studied through bulk population measurements, overlooking the role of cell-to-cell variability and spatial context. This study uses real-time surface growth measurements of thousands of sparsely positioned microcolonies to investigate interactions and kinetic variations in monocultures and cocultures of Pseudomonas putida and P. veronii under substrate competition (succinate) or substrate independence (d-mannitol and putrescine). In monoculture, microcolonies exhibited expected substrate-dependent expansion rates, but individual colony sizes were affected by founder cell density, spatial positioning, growth rates, and lag times. In coculture, substrate competition favored P. putida, but unexpectedly, reduced the maximum growth rates of both species. In contrast, 10% of P. veronii microcolonies under competition grew larger than expected, likely due to founder cell phenotypic variation and stochastic spatial positioning. These effects were alleviated under substrate independence. A linear relationship between founder cell ratios and final colony area ratios in local neighborhoods (6.5-65 µm radius) was observed in coculture, with its slope reflecting interaction type and strength. Measured slopes in the P. putida to P. veronii biomass ratio under competition were one-third reduced compared to kinetic predictions using a cell-agent growth model, which exometabolite analysis and simulations suggested may be due to metabolite cross-feeding or inhibitory compound production. This indicates additional factors beyond inherent monoculture growth kinetics driving spatial interactions. Overall, the study demonstrates how microcolony growth experiments offer valuable insights into bacterial interactions, from local to community-level dynamics.

Extremely acidic proteomes and metabolic flexibility in bacteria and highly diversified archaea thriving in geothermal chaotropic brines

Few described archaeal, and fewer bacterial, lineages thrive under salt-saturating conditions, such as solar saltern crystallizers (salinity above 30% w/v). They accumulate molar K+ cytoplasmic concentrations to maintain osmotic balance ('salt-in' strategy) and have proteins adaptively enriched in negatively charged acidic amino acids. Here we analysed metagenomes and metagenome-assembled genomes from geothermally influenced hypersaline ecosystems with increasing chaotropicity in the Danakil Depression. Normalized abundances of universal single-copy genes confirmed that haloarchaea and Nanohaloarchaeota encompass 99% of microbial communities in the near-life-limiting conditions of the Western-Canyon Lakes. Danakil metagenome- and metagenome-assembled-genome-inferred proteomes, compared with those of freshwater, seawater and solar saltern ponds up to saturation (6-14-32% salinity), showed that Western-Canyon Lake archaea encode the most acidic proteomes ever observed (median protein isoelectric points ≤4.4). We identified previously undescribed haloarchaeal families as well as an Aenigmatarchaeota family and a bacterial phylum independently adapted to extreme halophily. Despite phylum-level diversity decreasing with increasing salinity-chaotropicity, and unlike in solar salterns, adapted archaea exceedingly diversified in Danakil ecosystems, challenging the notion of decreasing diversity under extreme conditions. Metabolic flexibility to utilize multiple energy and carbon resources generated by local hydrothermalism along feast-and-famine strategies seemingly shapes microbial diversity in these ecosystems near life limits.

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.

Ecological associations distribution modelling of marine plankton at a global scale

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, 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. Here 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 they 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 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 simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Connecting microbial community assembly and function

Microbial ecology is moving away from purely descriptive analyses to experiments that can determine the underlying mechanisms driving changes in community assembly and function. More species-rich microbial communities generally have higher functional capabilities depending on if there is positive selection of certain species or complementarity among different species. When building synthetic communities or laboratory enrichment cultures, there are specific choices that can increase the number of species able to coexist. Higher resource complexity or the addition of physical niches are two of the many factors leading to greater biodiversity and associated increases in functional capabilities. We can use principles from community ecology and knowledge of microbial physiology to generate improved microbiomes for use in medicine, agriculture, or environmental management.

Substrate complexity buffers negative interactions in a synthetic community of leaf litter degraders

Leaf litter microbes collectively degrade plant polysaccharides, influencing land-atmosphere carbon exchange. An open question is how substrate complexity-defined as the structure of the saccharide and the amount of external processing by extracellular enzymes-influences species interactions. We tested the hypothesis that monosaccharides (i.e. xylose) promote negative interactions through resource competition, and polysaccharides (i.e. xylan) promote neutral or positive interactions through resource partitioning or synergism among extracellular enzymes. We assembled a three-species community of leaf litter-degrading bacteria isolated from a grassland site in Southern California. In the polysaccharide xylan, pairs of species stably coexisted and grew equally in coculture and in monoculture. Conversely, in the monosaccharide xylose, competitive exclusion and negative interactions prevailed. These pairwise dynamics remained consistent in a three-species community: all three species coexisted in xylan, while only two species coexisted in xylose, with one species capable of using peptone. A mathematical model showed that in xylose these dynamics could be explained by resource competition. Instead, the model could not predict the coexistence patterns in xylan, suggesting other interactions exist during biopolymer degradation. Overall, our study shows that substrate complexity influences species interactions and patterns of coexistence in a synthetic microbial community of leaf litter degraders.

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.