Multistability and regime shifts in microbial communities explained by competition for essential nutrients

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.

Sediment DNA Records the Critical Transition of Bacterial Communities in the Arid Lake

It is necessary to predict the critical transition of lake ecosystems due to their abrupt, non-linear effects on social-economic systems. Given the promising application of paleolimnological archives to tracking the historical changes of lake ecosystems, it is speculated that they can also record the lake's critical transition. We studied Lake Dali-Nor in the arid region of Inner Mongolia because of the profound shrinking the lake experienced between the 1300 s and the 1600 s. We reconstructed the succession of bacterial communities from a 140-cm-long sediment core at 4-cm intervals and detected the critical transition. Our results showed that the historical trajectory of bacterial communities from the 1200 s to the 2010s was divided into two alternative states: state1 from 1200 to 1300 s and state2 from 1400 to 2010s. Furthermore, in the late 1300 s, the appearance of a tipping point and critical slowing down implied the existence of a critical transition. By using a multi-decadal time series from the sedimentary core, with general Lotka-Volterra model simulations, local stability analysis found that bacterial communities were the most unstable as they approached the critical transition, suggesting that the collapse of stability triggers the community shift from an equilibrium state to another state. Furthermore, the most unstable community harbored the strongest antagonistic and mutualistic interactions, which may imply the detrimental role of interaction strength on community stability. Collectively, our study showed that sediment DNA can be used to detect the critical transition of lake ecosystems.

A Mapping Review of the Pathogenesis of Peri-Implantitis: The Biofilm-Mediated Inflammation and Bone Dysregulation (BIND) Hypothesis

This mapping review highlights the need for a new paradigm in the understanding of peri-implantitis pathogenesis. The biofilm-mediated inflammation and bone dysregulation (BIND) hypothesis is proposed, focusing on the relationship between biofilm, inflammation, and bone biology. The close interactions between immune and bone cells are discussed, with multiple stable states likely existing between clinically observable definitions of peri-implant health and peri-implantitis. The framework presented aims to explain the transition from health to disease as a staged and incremental process, where multiple factors contribute to distinct steps towards a tipping point where disease is manifested clinically. These steps might be reached in different ways in different patients and may constitute highly individualised paths. Notably, factors affecting the underlying biology are identified in the pathogenesis of peri-implantitis, highlighting that disruptions to the host-microbe homeostasis at the implant-mucosa interface may not be the sole factor. An improved understanding of disease pathogenesis will allow for intervention on multiple levels and a personalised treatment approach. Further research areas are identified, such as the use of novel biomarkers to detect changes in macrophage polarisation and activation status, and bone turnover.

Nutrient status changes bacterial interactions in a synthetic community

Microbial interactions affect community stability and niche spaces in all ecosystems. However, it is not clear what factors influence these interactions, leading to changes in species fitness and ecological niches. Here, we utilized 16 monocultures and their corresponding pairwise co-cultures to measure niche changes among 16 cultivable bacterial species in a wide range of carbon sources, and we used resource availability as a parameter to alter the interactions of the synthetic bacterial community. Our results suggest that metabolic similarity drives niche deformation between bacterial species. We further found that resource limitation resulted in increased microbial inhibition and more negative interactions. At high resource availability, bacteria exhibited little inhibitory potential and stronger facilitation (in 71% of cases), promoting niche expansion. Overall, our results show that metabolic similarity induces different degrees of resource competition, altering pairwise interactions within the synthetic community and potentially modulating bacterial niches. This framework may lay the basis for understanding complex niche deformation and microbial interactions as modulated by metabolic similarity and resource availability.IMPORTANCEUnderstanding the intricate dynamics of microbial interactions is crucial for unraveling the stability and ecological roles of diverse ecosystems. However, the factors driving these interactions, leading to shifts in species fitness and ecological niches, remain inadequately explored. We demonstrate that metabolic similarity serves as a key driver of niche deformation between bacterial species. Resource availability emerges as a pivotal parameter, affecting interactions within the community. Our findings reveal heightened microbial inhibition and more negative interactions under resource-limited conditions. The prevalent facilitation is observed under conditions of high resource availability, underscoring the potential for niche expansion in such contexts. These findings emphasize that metabolic similarity induces varying degrees of resource competition, thereby altering pairwise interactions within the synthetic community and potentially modulating bacterial niches. Our workflow has broad implications for understanding the roles of metabolic similarity and resource availability in microbial interactions and for designing synthetic microbial communities.

From microbiome composition to functional engineering, one step at a time

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms. While nucleic acid sequencing technologies can chart microbiota composition with high precision, we mostly lack information about the functional roles and interactions of each strain present in a given microbiome. This limits our ability to predict microbiome function in natural habitats and, in the case of dysfunction or dysbiosis, to redirect microbiomes onto stable paths. Here, we will discuss a systematic approach (dubbed the N+1/N-1 concept) to enable step-by-step dissection of microbiome assembly and functioning, as well as intervention procedures to introduce or eliminate one particular microbial strain at a time. The N+1/N-1 concept is informed by natural invasion events and selects culturable, genetically accessible microbes with well-annotated genomes to chart their proliferation or decline within defined synthetic and/or complex natural microbiota. This approach enables harnessing classical microbiological and diversity approaches, as well as omics tools and mathematical modeling to decipher the mechanisms underlying N+1/N-1 microbiota outcomes. Application of this concept further provides stepping stones and benchmarks for microbiome structure and function analyses and more complex microbiome intervention strategies.

Statistically learning the functional landscape of microbial communities

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

Functional convergence in slow-growing microbial communities arises from thermodynamic constraints

The dynamics of microbial communities is complex, determined by competition for metabolic substrates and cross-feeding of byproducts. Species in the community grow by harvesting energy from chemical reactions that transform substrates to products. In many anoxic environments, these reactions are close to thermodynamic equilibrium and growth is slow. To understand the community structure in these energy-limited environments, we developed a microbial community consumer-resource model incorporating energetic and thermodynamic constraints on an interconnected metabolic network. The central element of the model is product inhibition, meaning that microbial growth may be limited not only by depletion of metabolic substrates but also by accumulation of products. We demonstrate that these additional constraints on microbial growth cause a convergence in the structure and function of the community metabolic network-independent of species composition and biochemical details-providing a possible explanation for convergence of community function despite taxonomic variation observed in many natural and industrial environments. Furthermore, we discovered that the structure of community metabolic network is governed by the thermodynamic principle of maximum free energy dissipation. Our results predict the decrease of functional convergence in faster growing communities, which we validate by analyzing experimental data from anaerobic digesters. Overall, the work demonstrates how universal thermodynamic principles may constrain community metabolism and explain observed functional convergence in microbial communities.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

Evaluation of vaginal microbiome equilibrium states identifies microbial parameters linked to resilience after menses and antibiotic therapy

The vaginal microbiome (VMB) is a complex microbial community that is closely tied to reproductive health. Optimal VMB communities have compositions that are commonly defined by the dominance of certain Lactobacillus spp. and can remain stable over time or transition to non-optimal states dominated by anaerobic bacteria and associated with bacterial vaginosis (BV). The ability to remain stable or undergo transitions suggests a system with either single (mono-stable) or multiple (multi-stable) equilibrium states, though factors that contribute to stability have been difficult to determine due to heterogeneity in microbial growth characteristics and inter-species interactions. Here, we use a computational model to determine whether differences in microbial growth and interaction parameters could alter equilibrium state accessibility and account for variability in community composition after menses and antibiotic therapies. Using a global uncertainty and sensitivity analysis that captures parameter sets sampled from a physiologically relevant range, model simulations predicted that 79.7% of microbial communities were mono-stable (gravitate to one composition type) and 20.3% were predicted to be multi-stable (can gravitate to more than one composition type, given external perturbations), which was not significantly different from observations in two clinical cohorts (HMP cohort, 75.2% and 24.8%; Gajer cohort, 78.1% and 21.9%, respectively). The model identified key microbial parameters that governed equilibrium state accessibility, such as the importance of non-optimal anaerobic bacteria interactions with Lactobacillus spp., which is largely understudied. Model predictions for composition changes after menses and antibiotics were not significantly different from those observed in clinical cohorts. Lastly, simulations were performed to illustrate how this quantitative framework can be used to gain insight into the development of new combinatorial therapies involving altered prebiotic and antibiotic dosing strategies. Altogether, dynamical models could guide development of more precise therapeutic strategies to manage BV.

Metabolic interaction models recapitulate leaf microbiota ecology

Resource allocation affects the structure of microbiomes, including those associated with living hosts. Understanding the degree to which this dependency determines interspecies interactions may advance efforts to control host-microbiome relationships. We combined synthetic community experiments with computational models to predict interaction outcomes between plant-associated bacteria. We mapped the metabolic capabilities of 224 leaf isolates from Arabidopsis thaliana by assessing the growth of each strain on 45 environmentally relevant carbon sources in vitro. We used these data to build curated genome-scale metabolic models for all strains, which we combined to simulate >17,500 interactions. The models recapitulated outcomes observed in planta with >89% accuracy, highlighting the role of carbon utilization and the contributions of niche partitioning and cross-feeding in the assembly of leaf microbiomes.

Quorum sensing-mediated microbial interactions: Mechanisms, applications, challenges and perspectives

Microbial community in natural or artificial environments playes critical roles in substance cycles, products synthesis and species evolution. Although microbial community structures have been revealed via culture-dependent and culture-independent approaches, the hidden forces driving the microbial community are rarely systematically discussed. As a mode of cell-to-cell communication that modifies microbial interactions, quorum sensing can regulate biofilm formation, public goods secretion, and antimicrobial substances synthesis, directly or indirectly influencing microbial community to adapt to the changing environment. Therefore, the current review focuses on microbial community in the different habitats from the quorum sensing perspective. Firstly, the definition and classification of quorum sensing were simply introduced. Subsequently, the relationships between quorum sensing and microbial interactions were deeply explored. The latest progressives regarding the applications of quorum sensing in wastewater treatment, human health, food fermentation, and synthetic biology were summarized in detail. Finally, the bottlenecks and outlooks of quorum sensing driving microbial community were adequately discussed. To our knowledge, this current review is the first to reveal the driving force of microbial community from the quorum sensing perspective. Hopefully, this review provides a theoretical basis for developing effective and convenient approaches to control the microbial community with quorum sensing approaches.

Drivers of stability and transience in composition-functioning links during serial propagation of litter-decomposing microbial communities

Biotic factors that influence the temporal stability of microbial community functioning are an emerging research focus for control of natural and engineered systems. Discovery of common features within community ensembles that differ in functional stability over time is a starting point to explore biotic factors. We serially propagated a suite of soil microbial communities through five generations of 28 d microcosm incubations to examine microbial community compositional and functional stability during plant-litter decomposition. Using DOC abundance as a target function, we hypothesized that microbial diversity, compositional stability, and associated changes in interactions would explain the relative stability of the ecosystem function between generations. Communities with initially high DOC abundance tended to converge towards a "low DOC" phenotype within two generations, but across all microcosms, functional stability between generations was highly variable. By splitting communities into two cohorts based on their relative DOC functional stability, we found that compositional shifts, diversity, and interaction network complexity were associated with the stability of DOC abundance between generations. Further, our results showed that legacy effects were important in determining compositional and functional outcomes, and we identified taxa associated with high DOC abundance. In the context of litter decomposition, achieving functionally stable communities is required to utilize soil microbiomes to increase DOC abundance and long-term terrestrial DOC sequestration as 1 solution to reduce atmospheric carbon dioxide concentrations. Identifying factors that stabilize function for a community of interest may improve the success of microbiome engineering applications. Importance Microbial community functioning can be highly dynamic over time. Identifying and understanding biotic factors that control functional stability is of significant interest for natural and engineered communities alike. Using plant litter decomposing communities as a model system, this study examined the stability of ecosystem function over time following repeated community transfers. By identifying microbial community features that are associated with stable ecosystem functions, microbial communities can be manipulated in ways that promote the consistency and reliability of the desired function, improving outcomes and increasing the utility of microorganisms.

Modulation of antibiotic effects on microbial communities by resource competition

Antibiotic treatment significantly impacts the human gut microbiota, but quantitative understanding of how antibiotics affect community diversity is lacking. Here, we build on classical ecological models of resource competition to investigate community responses to species-specific death rates, as induced by antibiotic activity or other growth-inhibiting factors such as bacteriophages. Our analyses highlight the complex dependence of species coexistence that can arise from the interplay of resource competition and antibiotic activity, independent of other biological mechanisms. In particular, we identify resource competition structures that cause richness to depend on the order of sequential application of antibiotics (non-transitivity), and the emergence of synergistic and antagonistic effects under simultaneous application of multiple antibiotics (non-additivity). These complex behaviors can be prevalent, especially when generalist consumers are targeted. Communities can be prone to either synergism or antagonism, but typically not both, and antagonism is more common. Furthermore, we identify a striking overlap in competition structures that lead to non-transitivity during antibiotic sequences and those that lead to non-additivity during antibiotic combination. In sum, our results establish a broadly applicable framework for predicting microbial community dynamics under deleterious perturbations.

Drivers of stability and transience in composition-functioning links during serial propagation of litter-decomposing microbial communities

IMPORTANCE

Microbial community functioning can be highly dynamic over time. Identifying and understanding biotic factors that control functional stability is of significant interest for natural and engineered communities alike. Using plant litter decomposing communities as a model system, this study examined the stability of ecosystem function over time following repeated community transfers. By identifying microbial community features that are associated with stable ecosystem functions, microbial communities can be manipulated in ways that promote the consistency and reliability of the desired function, improving outcomes and increasing the utility of microorganisms.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Ecological landscapes guide the assembly of optimal microbial communities

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.

Changes in microbial community structure and functioning with elevation are linked to local soil characteristics as well as climatic variables

Mountain forests are important carbon stocks and biodiversity hotspots but are threatened by increased insect outbreaks and climate-driven forest conversion. Soil microorganisms play an eminent role in nutrient cycling in forest habitats and form the basis of soil food webs. Uncovering the driving factors shaping microbial communities and functioning at mountainsides across the world is of eminent importance to better understand their dynamics at local and global scales. We investigated microbial communities and their climatic and local soil-related drivers along an elevational gradient (800-1700 m asl) of primary forests at Changbai Mountain, China. We analyzed substrate-induced respiration and phospholipid fatty acids (PLFA) in litter and two soil layers at seven sites. Microbial biomass (Cmic) peaked in the litter layer and increased towards higher elevations. In the litter layer, the increase in Cmic and in stress indicator ratios was negatively correlated with Ca concentrations indicating increased nutritional stress in high microbial biomass communities at sites with lower Ca availability. PLFA profiles in the litter layer separated low and high elevations, but this was less pronounced in soil, suggesting that the litter layer functions as a buffer for soil microbial communities. Annual variations in temperature correlated with PLFA profiles in all three layers, while annual variations in precipitation correlated with PLFA profiles in upper soil only. Furthermore, the availability of resources, soil moisture, Ca concentrations, and pH structured the microbial communities. Pronounced changes in Cmic and stress indicator ratios in the litter layer between pine-dominated (800-1100 m) and spruce-dominated (1250-1700 m) forests indicated a shift in the structure and functioning of microbial communities between forest types along the elevational gradient. The study highlights strong changes in microbial community structure and functioning along elevational gradients, but also shows that these changes and their driving factors vary between soil layers. Besides annual variations in temperature and precipitation, carbon accumulation and nitrogen acquisition shape changes in microbial communities with elevation at Changbai Mountain.

Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations

Even in homogeneous conditions, plants facing a soilborne pathogen tend to show a binary outcome with individuals either remaining fully healthy or developing severe to lethal disease symptoms. As the rhizosphere microbiome is a major determinant of plant health, we postulated that such a binary outcome may result from an early divergence in the rhizosphere microbiome assembly that may further cascade into varying disease suppression abilities. We tested this hypothesis by setting up a longitudinal study of tomato plants growing in a natural but homogenized soil infested with the soilborne bacterial pathogen Ralstonia solanacearum. Starting from an originally identical species pool, individual rhizosphere microbiome compositions rapidly diverged into multiple configurations during the plant vegetative growth. This variation in community composition was strongly associated with later disease development during the later fruiting state. Most interestingly, these patterns also significantly predicted disease outcomes 2 weeks before any difference in pathogen density became apparent between the healthy and diseased groups. In this system, a total of 135 bacterial OTUs were associated with persistent healthy plants. Five of these enriched OTUs (Lysinibacillus, Pseudarthrobacter, Bordetella, Bacillus, and Chryseobacterium) were isolated and shown to reduce disease severity by 30.4–100% when co-introduced with the pathogen. Overall, our results demonstrated that an initially homogenized soil can rapidly diverge into rhizosphere microbiomes varying in their ability to promote plant protection. This suggests that early life interventions may have significant effects on later microbiome states, and highlights an exciting opportunity for microbiome diagnostics and plant disease prevention.

pH mediated assemblage of carbon, nitrogen, and sulfur related microbial communities in petroleum reservoirs

Microorganisms are the core drivers of biogeochemistry processes in petroleum reservoirs and have been widely used to enhance petroleum recovery. However, systematic information about the microbial communities related to the C-N-S cycle in petroleum reservoirs under different pH conditions remains poorly understood. In this study, 16S rRNA gene data from 133 petroleum samples were collected, and 756 C-N-S related genera were detected. The Chao1 richness and Shannon diversity indices for the C-N-S-related microbial communities showed significant differences among different pH conditions and at the lowest levels in acidic conditions with pH values of 4.5–6.5. In addition, pH was the most important factor influencing the C-N-S related microbial communities and contributed to 17.95% of the variation in the methanogenesis community. A total of 55 functional genera were influenced by pH, which accounted for 42.08% of the C-N-S related genera. Among them, the genera Pseudomonas and Arcobacter were the highest and were concentrated in acidic conditions with pH values of 4.5–6.5. In parallel, 56 predicted C-N-S related genes were examined, and pH affected 16 of these genes, including putative chitinase, mcrA, mtrB, cysH, narGHIVYZ, nirK, nirB, nifA, sat, aprAB, and dsrAB. Furthermore, the co-occurrence networks of the C-N-S related microbial communities distinctly varied among the different pH conditions. The acidic environment exhibited the lowest complex network with the lowest keystone taxa number, and Escherichia-Shigella was the only keystone group that existed in all three networks. In summary, this study strengthened our knowledge regarding the C-N-S related microbial communities in petroleum reservoirs under different pH conditions, which is of great significance for understanding the microbial ecology and geochemical cycle of petroleum reservoirs.

Carbon-Nitrogen-Sulfur-Related Microbial Taxa and Genes Maintained the Stability of Microbial Communities in Coals

Coal microbes are the predominant form of life in the subsurface ecosystem, which play a vital role in biogeochemical cycles. However, the systematic information about carbon-nitrogen-sulfur (C-N-S)-related microbial communities in coal seams is limited. In this study, 16S rRNA gene data from a total of 93 microbial communities in coals were collected for meta-analysis. The results showed that 718 functional genera were related to the C-N-S cycle, wherein N₂ fixation, denitrification, and C degradation groups dominated in relative abundance, Chao1 richness, Shannon diversity, and niche width. Genus Pseudomonas having the most C-N-S-related functions showed the highest relative abundance, and genus Herbaspirillum with a higher abundance participated in C degradation, CH₄ oxidation, N₂ fixation, ammoxidation, and denitrification. Such Herbaspirillum was a core genus in the co-occurrence network of microbial prokaryotes and showed higher levels in weight degree, betweenness centrality, and eigenvector centrality. In addition, most of the methanogens could fix N₂ and dominated in the N₂ fixation groups. Among them, genera Methanoculleus and Methanosaeta showed higher levels in the betweenness centrality index. In addition, the genus Clostridium was linked to the methanogenesis co-occurrence network module. In parallel, the S reduction gene was present in the highest total relative abundance of genes, followed by the C degradation and the denitrification genes, and S genes (especially cys genes) were the main genes linked to the co-occurrence network of the C-N-S-related genes. In summary, this study strengthened our knowledge regarding the C-N-S-related coal microbial communities, which is of great significance in understanding the microbial ecology and geochemical cycle of coals.

Ecological modelling approaches for predicting emergent properties in microbial communities

Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.

Fast growth can counteract antibiotic susceptibility in shaping microbial community resilience to antibiotics

SignificanceAntibiotic exposure stands among the most used interventions to drive microbial communities away from undesired states. How the ecology of microbial communities shapes their recovery-e.g., posttreatment shifts toward Clostridioides difficile infections in the gut-after antibiotic exposure is poorly understood. We study community response to antibiotics using a model community that can reach two alternative states. Guided by theory, our experiments show that microbial growth following antibiotic exposure can counteract antibiotic susceptibility in driving transitions between alternative community states. This makes it possible to reverse the outcome of antibiotic exposure through modifying growth dynamics, including cooperative growth, of community members. Our research highlights the relevance of simple ecological models to better understand the long-term effects of antibiotic treatment.

Well-mixed Lotka-Volterra model with random strongly competitive interactions

The random Lotka-Volterra model is widely used to describe the dynamical and thermodynamic features of ecological communities. In this work, we consider random symmetric interactions between species and analyze the strongly competitive interaction case. We investigate different scalings for the distribution of the interactions with the number of species and try to bridge the gap with previous works. Our results show two different behaviors for the mean abundance at zero and finite temperature, respectively, with a continuous crossover between the two. We confirm and extend previous results obtained for weak interactions: at zero temperature, even in the strong competitive interaction limit, the system is in a multiple-equilibria phase, whereas at finite temperature only a unique stable equilibrium can exist. Finally, we establish the qualitative phase diagrams and compare the species abundance distributions in the two cases.

A macroecological description of alternative stable states reproduces intra- and inter-host variability of gut microbiome

The most fundamental questions in microbial ecology concern the diversity and variability of communities. Their composition varies widely across space and time, as a result of a nontrivial combination of stochastic and deterministic processes. The interplay between nonlinear community dynamics and environmental fluctuations determines the rich statistical structure of community variability. We analyze long time series of individual human gut microbiomes and compare intra- and intercommunity dissimilarity under a macroecological framework. We show that most taxa have large but stationary fluctuations over time, while a minority of taxa display rapid changes in average abundance that cluster in time, suggesting the presence of alternative stable states. We disentangle interindividual variability in a stochastic component and a deterministic one, the latter recapitulated by differences in carrying capacities. Last, by combining environmental fluctuations and alternative stable states, we introduce a model that quantitatively predicts the statistical properties of both intra- and interindividual community variability, therefore summarizing variation in a unique macroecological framework.

Path-integral solution of MacArthur's resource-competition model for large ecosystems with random species-resources couplings

We solve MacArthur's resource-competition model with random species-resource couplings in the "thermodynamic" limit of infinitely many species and resources using dynamical path integrals à la De Domincis. We analyze how the steady state picture changes upon modifying several parameters, including the degree of heterogeneity of metabolic strategies (encoding the preferences of species) and of maximal resource levels (carrying capacities), and discuss its stability. Ultimately, the scenario obtained by other approaches is recovered by analyzing an effective one-species-one-resource ecosystem that is fully equivalent to the original multi-species one. The technique used here can be applied for the analysis of other model ecosystems related to the version of MacArthur's model considered here.

Estrogen as an Essential Resource and the Coexistence of ER+ and ER– Cancer Cells

Diagnosis of estrogen sensitivity in breast cancer is largely predicated on the ratio of ER+ and ER– cancer cells obtained from biopsies. Estrogen is a growth factor necessary for cell survival and division. It can also be thought of as an essential resource that can act in association with other nutrients, glucose, glutamine, fatty acids, amino acids, etc. All of these nutrients, collectively or individually, may limit the growth of the cancer cells (Liebig’s Law of the Minimum). Here we model estrogen susceptibility in breast cancer as a consumer-resource interaction: ER+ cells require both estrogen and glucose as essential resources, whereas ER– only require the general resource. The model predicts that when estrogen is the limiting factor, other nutrients may go unconsumed and available at higher levels, thus permitting the invasion of ER– cells. Conversely, when ER– cells are less efficient on glucose than ER+ cells, then ER– cells limited by glucose may be susceptible to invasion by ER+ cells, provided that sufficient levels of estrogen are available. ER+ cells will outcompete ER– cells when estrogen is abundant, resulting in low concentrations of interstitial glucose within the tumor. In the absence of estrogen, ER– cells will outcompete ER+ cells, leaving a higher concentration of interstitial glucose. At intermediate delivery rates of estrogen and glucose, ER+ and ER– cells are predicted to coexist. In modeling the dynamics of cells in the same tumor with different resource requirements, we can apply concepts and terms familiar to many ecologists. These include: resource supply points, R∗, ZNGI (zero net growth isoclines), resource depletion, and resource uptake rates. Based on the circumstances favoring ER+ vs. ER– breast cancer, we use the model to explore the consequences of therapeutic regimens that may include hormonal therapies, possible roles of diet in changing cancer cell composition, and potential for evolutionarily informed therapies. More generally, the model invites the viewpoint that cancer’s eco-evolutionary dynamics are a consumer-resource interaction, and that other growth factors such as EGFR or androgens may be best viewed as essential resources within these dynamics.

Trophic Interactions and the Drivers of Microbial Community Assembly

Despite numerous surveys of gene and species content in heterotrophic microbial communities, such as those found in animal guts, oceans, or soils, it is still unclear whether there are generalizable biological or ecological processes that control their dynamics and function. Here, we review experimental and theoretical advances to argue that networks of trophic interactions, in which the metabolic excretions of one species are the primary resource for another, constitute the central drivers of microbial community assembly. Trophic interactions emerge from the deconstruction of complex forms of organic matter into a wealth of smaller metabolic intermediates, some of which are released to the environment and serve as a nutritional buffet for the community. The structure of the emergent trophic network and the rate at which primary resources are supplied control many features of microbial community assembly, including the relative contributions of competition and cooperation and the emergence of alternative community states. Viewing microbial community assembly through the lens of trophic interactions also has important implications for the spatial dynamics of communities as well as the functional redundancy of taxonomic groups. Given the ubiquity of trophic interactions across environments, they impart a common logic that can enable the development of a more quantitative and predictive microbial community ecology.

Properties of Equilibria and Glassy Phases of the Random Lotka-Volterra Model with Demographic Noise

We study a reference model in theoretical ecology, the disordered Lotka-Volterra model for ecological communities, in the presence of finite demographic noise. Our theoretical analysis, valid for symmetric interactions, shows that for sufficiently heterogeneous interactions and low demographic noise the system displays a multiple equilibria phase, which we fully characterize. In particular, we show that in this phase the number of locally stable equilibria is exponential in the number of species. Upon further decreasing the demographic noise, we unveil the presence of a second transition like the so-called "Gardner" transition to a marginally stable phase similar to that observed in the jamming of amorphous materials. We confirm and complement our analytical results by numerical simulations. Furthermore, we extend their relevance by showing that they hold for other interacting random dynamical systems such as the random replicant model. Finally, we discuss their extension to the case of asymmetric couplings.

Multi-Replicated Enrichment Communities as a Model System in Microbial Ecology

Recent advances in robotics and affordable genomic sequencing technologies have made it possible to establish and quantitatively track the assembly of enrichment communities in high-throughput. By conducting community assembly experiments in up to thousands of synthetic habitats, where the extrinsic sources of variation among replicates can be controlled, we can now study the reproducibility and predictability of microbial community assembly at different levels of organization, and its relationship with nutrient composition and other ecological drivers. Through a dialog with mathematical models, high-throughput enrichment communities are bringing us closer to the goal of developing a quantitative predictive theory of microbial community assembly. In this short review, we present an overview of recent research on this growing field, highlighting the connection between theory and experiments and suggesting directions for future work.

Microfluidic and mathematical modeling of aquatic microbial communities

Aquatic microbial communities contribute fundamentally to biogeochemical transformations in natural ecosystems, and disruption of these communities can lead to ecological disasters such as harmful algal blooms. Microbial communities are highly dynamic, and their composition and function are tightly controlled by the biophysical (e.g., light, fluid flow, and temperature) and biochemical (e.g., chemical gradients and cell concentration) parameters of the surrounding environment. Due to the large number of environmental factors involved, a systematic understanding of the microbial community-environment interactions is lacking. In this article, we show that microfluidic platforms present a unique opportunity to recreate well-defined environmental factors in a laboratory setting in a high throughput way, enabling quantitative studies of microbial communities that are amenable to theoretical modeling. The focus of this article is on aquatic microbial communities, but the microfluidic and mathematical models discussed here can be readily applied to investigate other microbiomes.

Modeling microbial cross-feeding at intermediate scale portrays community dynamics and species coexistence

Social interaction between microbes can be described at many levels of details: from the biochemistry of cell-cell interactions to the ecological dynamics of populations. Choosing an appropriate level to model microbial communities without losing generality remains a challenge. Here we show that modeling cross-feeding interactions at an intermediate level between genome-scale metabolic models of individual species and consumer-resource models of ecosystems is suitable to experimental data. We applied our modeling framework to three published examples of multi-strain Escherichia coli communities with increasing complexity: uni-, bi-, and multi-directional cross-feeding of either substitutable metabolic byproducts or essential nutrients. The intermediate-scale model accurately fit empirical data and quantified metabolic exchange rates that are hard to measure experimentally, even for a complex community of 14 amino acid auxotrophies. By studying the conditions of species coexistence, the ecological outcomes of cross-feeding interactions, and each community’s robustness to perturbations, we extracted new quantitative insights from these three published experimental datasets. Our analysis provides a foundation to quantify cross-feeding interactions from experimental data, and highlights the importance of metabolic exchanges in the dynamics and stability of microbial communities.

The behavior of microbial communities such as the human microbiome is hard to predict by its species composition alone. Our efforts to engineer microbiomes—for example to improve human health—would benefit from mathematical models that accurately describe how microbes exchange metabolites with each other and how their environment shapes these exchanges. But what is an appropriate level of details for those models? We propose an intermediate level to model metabolic exchanges between microbes. We show that these models can accurately describe population dynamics in three laboratory communities and predicts their stability in response to perturbations such as changes in the nutrients available in the medium that they grow on. Our work suggests that a highly detailed metabolic network model is unnecessary for extracting ecological insights from experimental data and improves mathematical models so that one day we may be able to predict the behavior of real-world communities such as the human microbiome.

Modeling microbial metabolic trade-offs in a chemostat

Microbes face intense competition in the natural world, and so need to wisely allocate their resources to multiple functions, in particular to metabolism. Understanding competition among metabolic strategies that are subject to trade-offs is therefore crucial for deeper insight into the competition, cooperation, and community assembly of microorganisms. In this work, we evaluate competing metabolic strategies within an ecological context by considering not only how the environment influences cell growth, but also how microbes shape their chemical environment. Utilizing chemostat-based resource-competition models, we exhibit a set of intuitive and general procedures for assessing metabolic strategies. Using this framework, we are able to relate and unify multiple metabolic models, and to demonstrate how the fitness landscape of strategies becomes intrinsically dynamic due to species-environment feedback. Such dynamic fitness landscapes produce rich behaviors, and prove to be crucial for ecological and evolutionarily stable coexistence in all the models we examined.

Metabolic multistability and hysteresis in a model aerobe-anaerobe microbiome community

Major changes in the microbiome are associated with health and disease. Some microbiome states persist despite seemingly unfavorable conditions, such as the proliferation of aerobe-anaerobe communities in oxygen-exposed environments in wound infections or small intestinal bacterial overgrowth. Mechanisms underlying transitions into and persistence of these states remain unclear. Using two microbial taxa relevant to the human microbiome, we combine genome-scale mathematical modeling, bioreactor experiments, transcriptomics, and dynamical systems theory to show that multistability and hysteresis (MSH) is a mechanism describing the shift from an aerobe-dominated state to a resilient, paradoxically persistent aerobe-anaerobe state. We examine the impact of changing oxygen and nutrient regimes and identify changes in metabolism and gene expression that lead to MSH and associated multi-stable states. In such systems, conceptual causation-correlation connections break and MSH must be used for analysis. Using MSH to analyze microbiome dynamics will improve our conceptual understanding of stability of microbiome states and transitions between states.