Genomic structure predicts metabolite dynamics in microbial communities

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.

Candidate Phyla Radiation (CPR) bacteria from hyperalkaline ecosystems provide novel insight into their symbiotic lifestyle and ecological implications

BACKGROUND

Candidate Phyla Radiation (CPR) represents a unique superphylum characterized by ultra-small cell size and symbiotic lifestyle. Although CPR bacteria have been identified in varied environments, their broader distribution, associations with hosts, and ecological roles remain largely unexplored. To address these knowledge gaps, a serpentinite-like environment was selected as a simplified model system to investigate the CPR communities in hyperalkaline environments and their association with hosts in extreme conditions. Additionally, the enzymatic activity, global distribution, and evolution of the CPR-derived genes encoding essential metabolites (e.g., folate or vitamin B9) were analyzed and assessed.

RESULTS

In the highly alkaline serpentinite-like ecosystem (pH = 10.9-12.4), metagenomic analyses of the water and sediment samples revealed that CPR bacteria constituted 1.93-34.8% of the microbial communities. Metabolic reconstruction of 12 high-quality CPR metagenome-assembled genomes (MAGs) affiliated to the novel taxa from orders UBA6257, UBA9973, and Paceibacterales suggests that these bacteria lack the complete biosynthetic pathways for amino acids, lipids, and nucleotides. Notably, the CPR bacteria commonly harbored the genes associated with essential folate cofactor biosynthesis and metabolism, including dihydrofolate reductase (folA), serine hydroxymethyltransferase (glyA), and methylenetetrahydrofolate reductase (folD). Additionally, two presumed auxotrophic hosts, incapable of forming tetrahydrofolate (THF) due to the absence of folA, were identified as potential hosts for some CPR bacteria harboring folA genes. The functionality of these CPR-derived folA genes was experimentally verified by heterologous expression in the folA-deletion mutant Escherichia coli MG1655 ΔfolA. Further assessment of the available CPR genomes (n = 4,581) revealed that the genes encoding the proteins for the synthesis of bioactive folate derivatives (e.g., folA, glyA, and/or folD genes) were present in 90.8% of the genomes examined. It suggests potential widespread metabolic complementarity in folate biosynthesis between CPR and their hosts.

CONCLUSIONS

This finding deepens our understanding of the mechanisms of CPR-host symbiosis, providing novel insight into essential cofactor-dependent mutualistic CPR-host interactions. Our observations suggest that CPR bacteria may contribute to auxotrophic organisms and indirectly influence biogeochemical processes. Video Abstract.

Wine Fermentation as a Model System for Microbial Ecology and Evolution

In vitro microbial communities have proven to be invaluable model systems for studying ecological and evolutionary processes experimentally. However, it remains unclear whether quantitative insights obtained from these laboratory systems can be applied to complex communities assembling and evolving in their natural ecological context. To bridge the gap between the lab and the 'real-world', there is a need for laboratory model systems that better approximate natural and semi-natural ecosystems. Wine fermentation presents an ideal system for this purpose, balancing experimental tractability with rich ecological and evolutionary dynamics. In this perspective piece we outline the key features that make wine fermentation a fruitful model system for ecologists and evolutionary biologists. We highlight the diversity of environmentally mediated interactions that shape community dynamics during fermentation, the complex evolutionary history of wine microbial populations, and the opportunity to study the impact of complex ecologies on evolutionary dynamics. By integrating knowledge from both wine research and microbial ecology and evolution we aim to enhance understanding and foster collaboration between these fields.

Unlocking the potential of ecofriendly guardians for biological control of plant diseases, crop protection and production in sustainable agriculture

Several beneficial microbial strains inhibit the growth of different phytopathogens and commercialized worldwide as biocontrol agents (BCAs) for plant disease management. These BCAs employ different strategies for growth inhibition of pathogens, which includes production of antibiotics, siderophores, lytic enzymes, bacteriocins, hydrogen cyanide, volatile organic compounds, biosurfactants and induction of systemic resistance. The efficacy of antagonistic strains could be further improved through genetic engineering for better disease suppression in sustainable farming practices. Some antagonistic microbial strains also possess plant-growth-promoting activities and their inoculation improved plant growth in addition to disease suppression. This review discusses the characterization of antagonistic microbes and their antimicrobial metabolites, and the application of these BCAs for disease control. The present review also provides a comprehensive summary of the genetic organization and regulation of the biosynthesis of different antimicrobial metabolites in antagonistic strains. Use of molecular engineering to improve production of metabolites in BCAs and their efficacy in disease control is also discussed. The application of these biopesticides will reduce use of conventional pesticides in disease control and help in achieving sustainable and eco-friendly agricultural systems.

Advances in the study of the correlation between balanoposthitis and skin microecology

Balanoposthitis is a common heterogeneous skin condition involving the glans penis and prepuce, and is seen from infancy to old age. Its predisposing factors are intertrigo, lengthy foreskin, localized irritation, and mucosal injury. The itching and skin inflammation at the glans penis seriously affects the quality of life of patients. As a result of advances in high-throughput sequencing technology, the prepuce microbial colonization patterns and glans penis are now known better. Previous studies have shown that an imbalance of flora can cause balanoposthitis. This article summarizes the progress of research on balanoposthitis and skin microecology, and provides a direction for the subsequent exploration of immunological mechanisms in balanoposthitis.

Competition for shared resources increases dependence on initial population size during coalescence of gut microbial communities

The long-term success of introduced populations depends on both their initial size and ability to compete against existing residents, but it remains unclear how these factors collectively shape colonization dynamics. Here, we investigate how initial population (propagule) size shapes the outcome of community coalescence by systematically mixing eight pairs of in vitro microbial communities at ratios that vary over six orders of magnitude, and we compare our results to neutral ecological theory. Although the composition of the resulting cocultures deviated substantially from neutral expectations, each coculture contained species whose relative abundance depended on propagule size even after ~40 generations of growth. Using a consumer-resource model, we show that this dose-dependent colonization can arise when resident and introduced species have high niche overlap and consume shared resources at similar rates. Strain isolates displayed longer-lasting dose dependence when introduced into diverse communities than in pairwise cocultures, consistent with our model's prediction that propagule size should have larger, more persistent effects in diverse communities. Our model also successfully predicted that species with similar resource-utilization profiles, as inferred from growth in spent media and untargeted metabolomics, would show stronger dose dependence in pairwise coculture. This work demonstrates that transient, dose-dependent colonization dynamics can emerge from resource competition and exert long-term effects on the outcomes of community coalescence.

Microbial functional guilds respond cohesively to rapidly fluctuating environments

Microbial communities experience environmental fluctuations across timescales from rapid changes in moisture, temperature, or light levels to long-term seasonal or climactic variations. Understanding how microbial populations respond to these changes is critical for predicting the impact of perturbations, interventions, and climate change on communities. Since communities typically harbor tens to hundreds of distinct taxa, the response of microbial abundances to perturbations is potentially complex. However, while taxonomic diversity is high, in many communities taxa can be grouped into functional guilds of strains with similar metabolic traits. These guilds effectively reduce the complexity of the system by providing a physiologically motivated coarse-graining. Here, using a combination of simulations, theory, and experiments, we show that the response of guilds to nutrient fluctuations depends on the timescale of those fluctuations. Rapid changes in nutrient levels drive cohesive, positively correlated abundance dynamics within guilds. For slower timescales of environmental variation, members within a guild begin to compete due to similar resource preferences, driving negative correlations in abundances between members of the same guild. Our results provide a route to understanding the relationship between functional guilds and community response to changing environments, as well as an experimental approach to discovering functional guilds via designed nutrient perturbations to communities.

Unravelling the secrets of soil microbiome and climate change for sustainable agroecosystems

The soil microbiota exhibits an important function in the ecosystem, and its response to climate change is of paramount importance for sustainable agroecosystems. The macronutrients, micronutrients, and additional constituents vital for the growth of plants are cycled biogeochemically under the regulation of the soil microbiome. Identifying and forecasting the effect of climate change on soil microbiomes and ecosystem services is the need of the hour to address one of the biggest global challenges of the present time. The impact of climate change on the structure and function of the soil microbiota is a major concern, explained by one or more sustainability factors around resilience, reluctance, and rework. However, the past research has revealed that microbial interventions have the potential to regenerate soils and improve crop resilience to climate change factors. The methods used therein include using soil microbes' innate capacity for carbon sequestration, rhizomediation, bio-fertilization, enzyme-mediated breakdown, phyto-stimulation, biocontrol of plant pathogens, antibiosis, inducing the antioxidative defense pathways, induced systemic resistance response (ISR), and releasing volatile organic compounds (VOCs) in the host plant. Microbial phytohormones have a major role in altering root shape in response to exposure to drought, salt, severe temperatures, and heavy metal toxicity and also have an impact on the metabolism of endogenous growth regulators in plant tissue. However, shelf life due to the short lifespan and storage time of microbial formulations is still a major challenge, and efforts should be made to evaluate their effectiveness in crop growth based on climate change. This review focuses on the influence of climate change on soil physico-chemical status, climate change adaptation by the soil microbiome, and its future implications.

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.

Inferring resource competition in microbial communities from time series

The competition for resources is a defining feature of microbial communities. In many contexts, from soils to host-associated communities, highly diverse microbes are organized into metabolic groups or guilds with similar resource preferences. The resource preferences of individual taxa that give rise to these guilds are critical for understanding fluxes of resources through the community and the structure of diversity in the system. However, inferring the metabolic capabilities of individual taxa, and their competition with other taxa, within a community is challenging and unresolved. Here we address this gap in knowledge by leveraging dynamic measurements of abundances in communities. We show that simple correlations are often misleading in predicting resource competition. We show that spectral methods such as the cross-power spectral density (CPSD) and coherence that account for time-delayed effects are superior metrics for inferring the structure of resource competition in communities. We first demonstrate this fact on synthetic data generated from consumer-resource models with time-dependent resource availability, where taxa are organized into groups or guilds with similar resource preferences. By applying spectral methods to oceanic plankton time-series data, we demonstrate that these methods detect interaction structures among species with similar genomic sequences. Our results indicate that analyzing temporal data across multiple timescales can reveal the underlying structure of resource competition within communities.

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.

Ecological dynamics explain modular denitrification in the ocean

Microorganisms in marine oxygen minimum zones (OMZs) drive globally impactful biogeochemical processes. One such process is multistep denitrification (NO3-→NO2-→NO→N2O→N2), which dominates OMZ bioavailable nitrogen (N) loss and nitrous oxide (N2O) production. Denitrification-derived N loss is typically measured and modeled as a single step, but observations reveal that most denitrifiers in OMZs contain subsets ("modules") of the complete pathway. Here, we identify the ecological mechanisms sustaining diverse denitrifiers, explain the prevalence of certain modules, and examine the implications for N loss. We describe microbial functional types carrying out diverse denitrification modules by their underlying redox chemistry, constraining their traits with thermodynamics and pathway length penalties, in an idealized OMZ ecosystem model. Biomass yields of single-step modules increase along the denitrification pathway when organic matter (OM) limits growth, which explains the viability of populations respiring NO2- and N2O in a NO3--filled ocean. Results predict denitrifier community succession along environmental gradients: Pathway length increases as the limiting substrate shifts from OM to N, suggesting a niche for the short NO3-→NO2- module in free-living, OM-limited communities, and for the complete pathway in organic particle-associated communities, consistent with observations. The model captures and mechanistically explains the observed dominance and higher oxygen tolerance of the NO3-→NO2- module. Results also capture observations that NO3- is the dominant source of N2O. Our framework advances the mechanistic understanding of the relationship between microbial ecology and N loss in the ocean and can be extended to other processes and environments.

Nitrogen fertilization and soil nitrogen cycling: Unraveling the links among multiple environmental factors, functional genes, and transformation rates

Anthropogenic nitrogen (N) inputs substantially influence the N cycle in agricultural ecosystems. However, the potential links among various environmental factors, nitrogen functional genes, and transformation rates under N fertilization remain poorly understood. Here, we conducted a five-year field experiment and collected 54 soil samples from three 0-4 m boreholes across different treatments: control, N-addition (nitrogen fertilizer) and NPK-addition (combined application of nitrogen, phosphorus and potassium fertilizers) treatments. Our results revealed pronounced variations in soil physiochemical parameters, metal concentrations and antibiotic levels under both N and NPK treatments. These alternations induced significant shifts in bacterial and fungal communities, altered NFG abundance and composition, and greatly enhanced rates of nitrate reduction processes. Notably, nutrients, antibiotics and bacteria exerted a more pronounced influence on NFGs and nitrate reduction under N treatment, whereas nutrients, metals, bacteria and fungi had a significant impact under NPK treatment. Furthermore, we established multidimensional correlations between nitrate reduction gene profiles and the activity rates under N and NPK treatments, contrasting with the absence of significant relationships in the control treatment. These findings shed light on the intricate relationships between microbial genetics and ecosystem functions in agricultural ecosystem, which is of significance for predicting and managing metabolic processes effectively.

Environmentally dependent interactions shape patterns in gene content across natural microbiomes

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

Quantifying microbial interactions: concepts, caveats, and applications

Microbial communities are fundamental to every ecosystem on Earth and hold great potential for biotechnological applications. However, their complex nature hampers our ability to study and understand them. A common strategy to tackle this complexity is to abstract the community into a network of interactions between its members - a phenomenological description that captures the overall effects of various chemical and physical mechanisms that underpin these relationships. This approach has proven useful for numerous applications in microbial ecology, including predicting community dynamics and stability and understanding community assembly and evolution. However, care is required in quantifying and interpreting interactions. Here, we clarify the concept of an interaction and discuss when interaction measurements are useful despite their context-dependent nature. Furthermore, we categorize different approaches for quantifying interactions, highlighting the research objectives each approach is best suited for.

Diel variations of airborne microbes and antibiotic resistance genes in Response to urban PM2.5 chemical properties during the heating season

Among the components of fine particulate matter (PM2.5), the contributions of airborne microorganisms and antibiotic resistance genes (ARGs) to health risks have been overlooked. Airborne microbial dynamics exhibit a unique diurnal cycle due to environmental influences. However, the specific roles of PM2.5 chemical properties resulting from fossil fuel combustion in driving circadian fluctuations in microbial populations and ARGs remain unclear. This study explored the interactions between toxic components and microbial communities during the heating period to understand the variations in ARGs. Bacterial and fungal communities showed a higher susceptibility to diel variations in PM2.5 compared to their chemical properties. Mantel tests revealed that chemical properties and microbial community interactions contribute differently to ARG variations, both directly and indirectly, during circadian fluctuations. Our findings highlight that, during the daytime, the enrichment of pathogenic microorganisms and ARGs increases the risk of PM2.5 toxicity. Conversely, during the nighttime, the utilization of water-soluble ions by the fungal community increased, leading to a significant increase in fungal biomass. Notably, Aspergillus exhibited a significant correlation with mobile genetic elements and ARGs, implying that this genus is a crucial driver of airborne ARGs. This study provides novel insights into the interplay between the chemical composition, microbial communities, and ARGs in PM, underscoring the urgent need for a comprehensive understanding of effective air pollution control strategies.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Global epistasis and the emergence of function in microbial consortia

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Unlocking bacterial potential to reduce farmland N2O emissions

Farmed soils contribute substantially to global warming by emitting N2O (ref. 1), and mitigation has proved difficult2. Several microbial nitrogen transformations produce N2O, but the only biological sink for N2O is the enzyme NosZ, catalysing the reduction of N2O to N2 (ref. 3). Although strengthening the NosZ activity in soils would reduce N2O emissions, such bioengineering of the soil microbiota is considered challenging4,5. However, we have developed a technology to achieve this, using organic waste as a substrate and vector for N2O-respiring bacteria selected for their capacity to thrive in soil6-8. Here we have analysed the biokinetics of N2O reduction by our most promising N2O-respiring bacterium, Cloacibacterium sp. CB-01, its survival in soil and its effect on N2O emissions in field experiments. Fertilization with waste from biogas production, in which CB-01 had grown aerobically to about 6 × 109 cells per millilitre, reduced N2O emissions by 50-95%, depending on soil type. The strong and long-lasting effect of CB-01 is ascribed to its tenacity in soil, rather than its biokinetic parameters, which were inferior to those of other strains of N2O-respiring bacteria. Scaling our data up to the European level, we find that national anthropogenic N2O emissions could be reduced by 5-20%, and more if including other organic wastes. This opens an avenue for cost-effective reduction of N2O emissions for which other mitigation options are lacking at present.

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.

Cooperation among nitrifying microorganisms promotes the irreversible biotransformation of sulfamonomethoxine

Ammonia-oxidizing microorganisms, including AOA (ammonia-oxidizing archaea), AOB (ammonia-oxidizing bacteria), and Comammox (complete ammonia oxidization) Nitrospira, have been reported to possess the capability for the biotransformation of sulfonamide antibiotics. However, given that nitrifying microorganisms coexist and operate as communities in the nitrification process, it is surprising that there is a scarcity of studies investigating how their interactions would affect the biotransformation of sulfonamide antibiotics. This study aims to investigate the sulfamonomethoxine (SMM) removal efficiency and mechanisms among pure cultures of phylogenetically distinct nitrifiers and their combinations. Our findings revealed that AOA demonstrated the highest SMM removal efficiency and rate among the pure cultures, followed by Comammox Nitrospira, NOB, and AOB. However, the biotransformation of SMM by AOA N. gargensis is reversible, and the removal efficiency significantly decreased from 63.84 % at 167 h to 26.41 % at 807 h. On the contrary, the co-culture of AOA and NOB demonstrated enhanced and irreversible SMM removal efficiency compared to AOA alone. Furthermore, the presence of NOB altered the SMM biotransformation of AOA by metabolizing TP202 differently, possibly resulting from reduced nitrite accumulation. This study offers novel insights into the potential application of nitrifying communities for the removal of sulfonamide antibiotics (SAs) in engineered ecosystems.

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.

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.

Predicting the final metabolic profile based on the succession-related microbiota during spontaneous fermentation of the starter for Chinese liquor making

Microbial inoculation is an effective way to improve the quality of fermented foods via affecting the microbiota structure. However, it is unclear how the inoculation regulates the microbiota structure, and it is still difficult to directionally control the microbiota function via the inoculation. In this work, using the spontaneous fermentation of the starter (Daqu) for Chinese liquor fermentation as a case, we inoculated different microbiota groups at different time points in Daqu fermentation, and analyzed the effect of the inoculation on the final metabolic profile of Daqu. The inoculated microbiota and inoculated time points both significantly affected the final metabolites via regulating the microbial succession (P < 0.001), and multiple inoculations can promote deterministic assembly. Twenty-seven genera were identified to be related to microbial succession, and drove the variation of 121 metabolites. We then constructed an elastic network model to predict the profile of these 121 metabolites based on the abundances of 27 succession-related genera in Daqu fermentation. Procrustes analysis showed that the model could accurately predict the metabolic abundances (average Spearman correlation coefficients >0.3). This work revealed the effect of inoculation on the microbiota succession and the metabolic profile. The established predicted model of metabolic profile would be beneficial for directionally improving the food quality.IMPORTANCEThis work revealed the importance of microbial succession to microbiota structure and metabolites. Multi-inoculations would promote deterministic assembly. It would facilitate the regulation of microbiota structure and metabolic profile. In addition, we established a model to predict final metabolites based on microbial genera related to microbial succession. This model was beneficial for optimizing the inoculation of the microbiota. This work would be helpful for controlling the spontaneous food fermentation and directionally improving the food quality.

Nitrous oxide reduction by two partial denitrifying bacteria requires denitrification intermediates that cannot be respired

Denitrification is a form of anaerobic respiration wherein nitrate (NO3-) is sequentially reduced via nitrite (NO2-), nitric oxide, and nitrous oxide (N2O) to dinitrogen gas (N2) by four reductase enzymes. Partial denitrifying bacteria possess only one or some of these four reductases and use them as independent respiratory modules. However, it is unclear if partial denitrifiers sense and respond to denitrification intermediates outside of their reductase repertoire. Here, we tested the denitrifying capabilities of two purple nonsulfur bacteria, Rhodopseudomonas palustris CGA0092 and Rhodobacter capsulatus SB1003. Each had denitrifying capabilities that matched their genome annotation; CGA0092 reduced NO2- to N2, and SB1003 reduced N2O to N2. For each bacterium, N2O reduction could be used both for electron balance during growth on electron-rich organic compounds in light and for energy transformation via respiration in darkness. However, N2O reduction required supplementation with a denitrification intermediate, including those for which there was no associated denitrification enzyme. For CGA0092, NO3- served as a stable, non-catalyzable molecule that was sufficient to activate N2O reduction. Using a β-galactosidase reporter, we found that NO3- acted, at least in part, by stimulating N2O reductase gene expression. In SB1003, NO2- but not NO3- activated N2O reduction, but NO2- was slowly removed, likely by a promiscuous enzyme activity. Our findings reveal that partial denitrifiers can still be subject to regulation by denitrification intermediates that they cannot use.IMPORTANCEDenitrification is a form of microbial respiration wherein nitrate is converted via several nitrogen oxide intermediates into harmless dinitrogen gas. Partial denitrifying bacteria, which individually have some but not all denitrifying enzymes, can achieve complete denitrification as a community by cross-feeding nitrogen oxide intermediates. However, the last intermediate, nitrous oxide (N2O), is a potent greenhouse gas that often escapes, motivating efforts to understand and improve the efficiency of denitrification. Here, we found that at least some partial denitrifying N2O reducers can sense and respond to nitrogen oxide intermediates that they cannot otherwise use. The regulatory effects of nitrogen oxides on partial denitrifiers are thus an important consideration in understanding and applying denitrifying bacterial communities to combat greenhouse gas emissions.

Machine learning interpretable models of cell mechanics from protein images

Cellular form and function emerge from complex mechanochemical systems within the cytoplasm. Currently, no systematic strategy exists to infer large-scale physical properties of a cell from its molecular components. This is an obstacle to understanding processes such as cell adhesion and migration. Here, we develop a data-driven modeling pipeline to learn the mechanical behavior of adherent cells. We first train neural networks to predict cellular forces from images of cytoskeletal proteins. Strikingly, experimental images of a single focal adhesion (FA) protein, such as zyxin, are sufficient to predict forces and can generalize to unseen biological regimes. Using this observation, we develop two approaches-one constrained by physics and the other agnostic-to construct data-driven continuum models of cellular forces. Both reveal how cellular forces are encoded by two distinct length scales. Beyond adherent cell mechanics, our work serves as a case study for integrating neural networks into predictive models for cell biology.

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 often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing 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. Our model bypasses specific interactions (e.g., nutrient starvation, stress, aggregation) that lead to different combinations of physiological states, referred to collectively as "community states", and modeled by specifying the growth preference of each species along a global ecological coordinate, taken here to be the total community biomass density. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: increased tolerance of community diversity to fast growth rates of species dominating different community states, enhanced community stability through staggered dominance of different species in different community states, and increased requirement on growth dominance for the inclusion of late-growing species. These features, derived explicitly for simplified models, are proposed here to be principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on idealized inter-species 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.

Denitrification genotypes of endospore-forming Bacillota

Denitrification is a key metabolic process in the global nitrogen cycle and is performed by taxonomically diverse microorganisms. Despite the widespread importance of this metabolism, challenges remain in identifying denitrifying populations and predicting their metabolic end-products based on their genotype. Here, genome-resolved metagenomics was used to explore the denitrification genotype of Bacillota enriched in nitrate-amended high temperature incubations with confirmed N2O and N2 production. A set of 12 hidden Markov models (HMMs) was created to target the diversity of denitrification genes in members of the phylum Bacillota. Genomic potential for complete denitrification was found in five metagenome-assembled genomes from nitrate-amended enrichments, including two novel members of the Brevibacillaceae family. Genomes of complete denitrifiers encode N2O reductase gene clusters with clade II-type nosZ and often include multiple variants of the nitric oxide reductase gene. The HMM set applied to all genomes of Bacillota from the Genome Taxonomy Database identified 17 genera inferred to contain complete denitrifiers based on their gene content. Among complete denitrifiers it was common for three distinct nitric oxide reductases to be present (qNOR, bNOR, and sNOR) that may reflect the metabolic adaptability of Bacillota in environments with variable redox conditions.

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.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Statistical prediction of microbial metabolic traits from genomes

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

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

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

Statistically learning the functional landscape of microbial communities

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

Interfacial morphodynamics of proliferating microbial communities

In microbial communities, various cell types often coexist by occupying distinct spatial domains. What determines the shape of the interface between such domains-which in turn influences the interactions between cells and overall community function? Here, we address this question by developing a continuum model of a 2D spatially-structured microbial community with two distinct cell types. We find that, depending on the balance of the different cell proliferation rates and substrate friction coefficients, the interface between domains is either stable and smooth, or unstable and develops finger-like protrusions. We establish quantitative principles describing when these different interfacial behaviors arise, and find good agreement both with the results of previous experimental reports as well as new experiments performed here. Our work thus helps to provide a biophysical basis for understanding the interfacial morphodynamics of proliferating microbial communities, as well as a broader range of proliferating active systems.

Genome content predicts the carbon catabolic preferences of heterotrophic bacteria

Heterotrophic bacteria-bacteria that utilize organic carbon sources-are taxonomically and functionally diverse across environments. It is challenging to map metabolic interactions and niches within microbial communities due to the large number of metabolites that could serve as potential carbon and energy sources for heterotrophs. Whether their metabolic niches can be understood using general principles, such as a small number of simplified metabolic categories, is unclear. Here we perform high-throughput metabolic profiling of 186 marine heterotrophic bacterial strains cultured in media containing one of 135 carbon substrates to determine growth rates, lag times and yields. We show that, despite high variability at all levels of taxonomy, the catabolic niches of heterotrophic bacteria can be understood in terms of their preference for either glycolytic (sugars) or gluconeogenic (amino and organic acids) carbon sources. This preference is encoded by the total number of genes found in pathways that feed into the two modes of carbon utilization and can be predicted using a simple linear model based on gene counts. This allows for coarse-grained descriptions of microbial communities in terms of prevalent modes of carbon catabolism. The sugar-acid preference is also associated with genomic GC content and thus with the carbon-nitrogen requirements of their encoded proteome. Our work reveals how the evolution of bacterial genomes is structured by fundamental constraints rooted in metabolism.

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.

Genome-centric investigation of the potential succession pattern in gut microbiota and altered functions under high-protein diet

Excessive intake of protein has been considered as a factor leading to intestinal microecological disorder, but why and how intestinal microbes change under the high-protein diet (HPD) have yet to be fully elucidated. Here, we performed 16S rRNA gene amplicon sequencing and metagenomic sequencing on contents of cecum, colon and feces from two groups of mice with standard diet (SD) and HPD. And then the microbial alteration of composition and function were deeply analyzed by using several statistical models and bioinformatic methods. Among the three niches, the microbes in the colon are observed to show the most significant change with lower alpha-diversity and higher beta-diversity after HPD. In addition, this alteration of microbial structure may be related to the replacement process and co-occurring community. Most species are also enriched or impoverished in the colon during this process. After analyzing the functional genes related to protein and carbohydrate hydrolysis in different niches, we found that the carbon source provided by poor carbohydrates compared with the rich protein may be the potential factor driving the enrichment of mucin degraders and desulphaters in the colon under HPD. Therefore, our study provided a new insight to understand the underlying mechanism of HPD affecting intestinal health from the perspective of microbial functional ecology.

Are Bacteria Leaky? Mechanisms of Metabolite Externalization in Bacterial Cross-Feeding

The metabolism of a bacterial cell stretches beyond its boundaries, often connecting with the metabolism of other cells to form extended metabolic networks that stretch across communities, and even the globe. Among the least intuitive metabolic connections are those involving cross-feeding of canonically intracellular metabolites. How and why are these intracellular metabolites externalized? Are bacteria simply leaky? Here I consider what it means for a bacterium to be leaky, and I review mechanisms of metabolite externalization from the context of cross-feeding. Despite common claims, diffusion of most intracellular metabolites across a membrane is unlikely. Instead, passive and active transporters are likely involved, possibly purging excess metabolites as part of homeostasis. Re-acquisition of metabolites by a producer limits the opportunities for cross-feeding. However, a competitive recipient can stimulate metabolite externalization and initiate a positive-feedback loop of reciprocal cross-feeding.

Predictability of the community-function landscape in wine yeast ecosystems

Predictively linking taxonomic composition and quantitative ecosystem functions is a major aspiration in microbial ecology, which must be resolved if we wish to engineer microbial consortia. Here, we have addressed this open question for an ecological function of major biotechnological relevance: alcoholic fermentation in wine yeast communities. By exhaustively phenotyping an extensive collection of naturally occurring wine yeast strains, we find that most ecologically and industrially relevant traits exhibit phylogenetic signal, allowing functional traits in wine yeast communities to be predicted from taxonomy. Furthermore, we demonstrate that the quantitative contributions of individual wine yeast strains to the function of complex communities followed simple quantitative rules. These regularities can be integrated to quantitatively predict the function of newly assembled consortia. Besides addressing theoretical questions in functional ecology, our results and methodologies can provide a blueprint for rationally managing microbial processes of biotechnological relevance.

Revealing the Host-Dependent Nature of an Engineered Genetic Inverter in Concordance with Physiology

Broad-host-range synthetic biology is an emerging frontier that aims to expand our current engineerable domain of microbial hosts for biodesign applications. As more novel species are brought to "model status," synthetic biologists are discovering that identically engineered genetic circuits can exhibit different performances depending on the organism it operates within, an observation referred to as the "chassis effect." It remains a major challenge to uncover which genome-encoded and biological determinants will underpin chassis effects that govern the performance of engineered genetic devices. In this study, we compared model and novel bacterial hosts to ask whether phylogenomic relatedness or similarity in host physiology is a better predictor of genetic circuit performance. This was accomplished using a comparative framework based on multivariate statistical approaches to systematically demonstrate the chassis effect and characterize the performance dynamics of a genetic inverter circuit operating within 6 Gammaproteobacteria. Our results solidify the notion that genetic devices are strongly impacted by the host context. Furthermore, we formally determined that hosts exhibiting more similar metrics of growth and molecular physiology also exhibit more similar performance of the genetic inverter, indicating that specific bacterial physiology underpins measurable chassis effects. The result of this study contributes to the field of broad-host-range synthetic biology by lending increased predictive power to the implementation of genetic devices in less-established microbial hosts.

Complete Genome Sequencing of Three Clade-1 Xanthomonads Reveals Genetic Determinants for a Lateral Flagellin and the Biosynthesis of Coronatine-Like Molecules in Xanthomonas

Evolutionarily, early-branching xanthomonads, also referred to as clade-1 xanthomonads, include major plant pathogens, most of which colonize monocotyledonous plants. Seven species have been validly described, among them the two sugarcane pathogens Xanthomonas albilineans and Xanthomonas sacchari, as well as Xanthomonas translucens, which infects small-grain cereals and diverse grasses but also asparagus and pistachio trees. Single-gene sequencing and genomic approaches have indicated that this clade likely contains more, yet-undescribed species. In this study, we sequenced representative strains of three novel species using long-read sequencing technology. Xanthomonas campestris pv. phormiicola strain CFBP 8444 causes bacterial streak on New Zealand flax, another monocotyledonous plant. Xanthomonas sp. strain CFBP 8443 has been isolated from common bean, and Xanthomonas sp. strain CFBP 8445 originated from banana. Complete assemblies of the chromosomes confirmed their unique phylogenetic position within clade 1 of Xanthomonas. Genome mining revealed novel genetic features, hitherto undescribed in other members of the Xanthomonas genus. In strain CFBP 8444, we identified genes related to the synthesis of coronatine-like compounds, a phytotoxin produced by several pseudomonads, which raises interesting questions about the evolution and pathogenicity of this pathogen. Furthermore, strain CFBP 8444 was found to contain a second, atypical flagellar gene cluster in addition to the canonical flagellar gene cluster. Overall, this research represents an important step toward better understanding the evolutionary history and biology of early-branching xanthomonads.

Polymeric carbohydrates utilization separates microbiomes into niches: insights into the diversity of microbial carbohydrate-active enzymes in the inner shelf of the Pearl River Estuary, China

Polymeric carbohydrates are abundant and their recycling by microbes is a key process of the ocean carbon cycle. A deeper analysis of carbohydrate-active enzymes (CAZymes) can offer a window into the mechanisms of microbial communities to degrade carbohydrates in the ocean. In this study, metagenomic genes encoding microbial CAZymes and sugar transporter systems were predicted to assess the microbial glycan niches and functional potentials of glycan utilization in the inner shelf of the Pearl River Estuary (PRE). The CAZymes gene compositions were significantly different between in free-living (0.2-3 μm, FL) and particle-associated (>3 μm, PA) bacteria of the water column and between water and surface sediments, reflecting glycan niche separation on size fraction and selective degradation in depth. Proteobacteria and Bacteroidota had the highest abundance and glycan niche width of CAZymes genes, respectively. At the genus level, Alteromonas (Gammaproteobacteria) exhibited the greatest abundance and glycan niche width of CAZymes genes and were marked by a high abundance of periplasmic transporter protein TonB and members of the major facilitator superfamily (MFS). The increasing contribution of genes encoding CAZymes and transporters for Alteromonas in bottom water contrasted to surface water and their metabolism are tightly related with particulate carbohydrates (pectin, alginate, starch, lignin-cellulose, chitin, and peptidoglycan) rather than on the utilization of ambient-water DOC. Candidatus Pelagibacter (Alphaproteobacteria) had a narrow glycan niche and was primarily preferred for nitrogen-containing carbohydrates, while their abundant sugar ABC (ATP binding cassette) transporter supported the scavenging mode for carbohydrate assimilation. Planctomycetota, Verrucomicrobiota, and Bacteroidota had similar potential glycan niches in the consumption of the main component of transparent exopolymer particles (sulfated fucose and rhamnose containing polysaccharide and sulfated-N-glycan), developing considerable niche overlap among these taxa. The most abundant CAZymes and transporter genes as well as the widest glycan niche in the abundant bacterial taxa implied their potential key roles on the organic carbon utilization, and the high degree of glycan niches separation and polysaccharide composition importantly influenced bacterial communities in the coastal waters of PRE. These findings expand the current understanding of the organic carbon biotransformation, underlying the size-fractionated glycan niche separation near the estuarine system.

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.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study

BACKGROUND

Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD.

METHODS

OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS).

RESULTS

A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization.

CONCLUSIONS

This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

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.

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.

Phylogenomics reveals insights into the functional evolution of the genus Agrobacterium and enables the description of Agrobacterium divergens sp. nov

The genus Agrobacterium was initially described as mainly phytopathogenic strains. Nowadays, the genus includes phytopathogenic and non-phytopathogenic bacteria that are distinctive among the Rhizobiaceae family. Recently we have isolated two closely related strains, LMG 31531^T and LMG 31532, from soil and plant roots, respectively. Both strains differ from previously reported species based on the genomic and phenotypic data. A. arsenijevicii KFB 330^T and A. fabacearum LMG 31642^T showed the highest 16S rRNA similarity (98.9 %), followed by A. nepotum LMG 26435^T (98.7 %). A clear genomic feature that distinguishes LMG 31531^T and LMG 31532 from other Agrobacterium species is the absence of a linear chromid. Nevertheless, typical values of the core-proteome Average Amino Acid Identity (cpAAI > 85 %) and 16S rRNA gene sequence similarity (>96 %) when compared to other members of the genus confirm the position of these two strains as part of the Agrobacterium genus. They are therefore described as Agrobacterium divergens sp. nov. Besides, our comparative genomic study and survey for clade-specific markers resulted in the discovery of conserved proteins that provide insights into the functional evolution of this genus.

Environmental microbiome engineering for the mitigation of climate change

Environmental microbiome engineering is emerging as a potential avenue for climate change mitigation. In this process, microbial inocula are introduced to natural microbial communities to tune activities that regulate the long-term stabilization of carbon in ecosystems. In this review, we outline the process of environmental engineering and synthesize key considerations about ecosystem functions to target, means of sourcing microorganisms, strategies for designing microbial inocula, methods to deliver inocula, and the factors that enable inocula to establish within a resident community and modify an ecosystem function target. Recent work, enabled by high-throughput technologies and modeling approaches, indicate that microbial inocula designed from the top-down, particularly through directed evolution, may generally have a higher chance of establishing within existing microbial communities than other historical approaches to microbiome engineering. We address outstanding questions about the determinants of inocula establishment and provide suggestions for further research about the possibilities and challenges of environmental microbiome engineering as a tool to combat climate change.