Competitive and cooperative metabolic interactions in bacterial communities

MetaBiome: a multiscale model integrating agent-based and metabolic networks to reveal spatial regulation in gut mucosal microbial communities

Mucosal microbial communities (MMCs) are complex ecosystems near the mucosal layers of the gut essential for maintaining health and modulating disease states. Despite advances in high-throughput omics technologies, current methodologies struggle to capture the dynamic metabolic interactions and spatiotemporal variations within MMCs. In this work, we present MetaBiome, a multiscale model integrating agent-based modeling (ABM), finite volume methods, and constraint-based models to explore the metabolic interactions within these communities. Integrating ABM allows for the detailed representation of individual microbial agents each governed by rules that dictate cell growth, division, and interactions with their surroundings. Through a layered approach-encompassing microenvironmental conditions, agent information, and metabolic pathways-we simulated different communities to showcase the potential of the model. Using our in-silico platform, we explored the dynamics and spatiotemporal patterns of MMCs in the proximal small intestine and the cecum, simulating the physiological conditions of the two gut regions. Our findings revealed how specific microbes adapt their metabolic processes based on substrate availability and local environmental conditions, shedding light on spatial metabolite regulation and informing targeted therapies for localized gut diseases. MetaBiome provides a detailed representation of microbial agents and their interactions, surpassing the limitations of traditional grid-based systems. This work marks a significant advancement in microbial ecology, as it offers new insights into predicting and analyzing microbial communities.IMPORTANCEOur study presents a novel multiscale model that combines agent-based modeling, finite volume methods, and genome-scale metabolic models to simulate the complex dynamics of mucosal microbial communities in the gut. This integrated approach allows us to capture spatial and temporal variations in microbial interactions and metabolism that are difficult to study experimentally. Key findings from our model include the following: (i) prediction of metabolic cross-feeding and spatial organization in multi-species communities, (ii) insights into how oxygen gradients and nutrient availability shape community composition in different gut regions, and (iii) identification of spatiallyregulated metabolic pathways and enzymes in E. coli. We believe this work represents a significant advance in computational modeling of microbial communities and provides new insights into the spatial regulation of gut microbiome metabolism. The multiscale modeling approach we have developed could be broadly applicable for studying other complex microbial ecosystems.

Assembly of stool-derived bacterial communities follows "early-bird" resource utilization dynamics

Diet can impact host health through changes to the gut microbiota, yet we lack mechanistic understanding linking nutrient availability and microbiota composition. Here, we use thousands of microbial communities cultured in vitro from human stool to develop a predictive model of community composition upon addition of single nutrients from central carbon metabolism to a complex medium. Among these communities, membership was largely determined by the donor stool, whereas relative abundances were determined by the supplemental carbon source. The absolute abundance of most taxa was independent of the supplementing nutrient due to the ability of a few organisms to quickly exhaust their niche in the complex medium and then exploit and monopolize the supplemental carbon source. Relative abundances of dominant taxa could be predicted from the nutritional preferences and growth dynamics of species in isolation, and exceptions were consistent with strain-level variation in growth capabilities. Our study reveals that assembly of this community of gut commensals can be explained by nutrient utilization dynamics that provide a predictive framework for manipulating community composition through nutritional perturbations.

Directing microbial co-culture composition using cybernetic control

We demonstrate a cybernetic approach to control the composition of a P. putida and E. coli co-culture that does not rely on genetic engineering to interface cells with computers. We first show how composition information can be extracted from different bioreactor measurements and then combined with a system model using an extended Kalman filter to generate accurate estimates of a noisy system. We then demonstrate that adjusting the culture temperature can drive the composition due to the species' different optimal temperatures. Using a proportional-integral control algorithm, we are able to track dynamic references with real-time noise rejection and independence from starting conditions such as inoculation ratio. We stabilize the co-culture for 7 days (∼250 generations) with the experiment ending before the cells could adapt out of the control. This cybernetic framework is broadly applicable, with different microbes' unique characteristics enabling robust control over diverse co-cultures.

Cellulase enhancing rumen microbiome of Tan sheep indicates plastic responses to seasonal variations of diet in the typical steppe

BACKGROUND

Climate and geographical changes significantly influence food availability and nutrient composition over time and space, Which in turn affects the selection of microbial communities essential for maintaining gastrointestinal homeostasis and facilitating dietary adaptation. Therefore, it is essential to understand the specific responses of the gut microbiota to dietary and seasonal variations in order to improve animal conservation strategies based on solid scientific knowledge.

RESULTS

In summer, due to the higher nutritional quality of forage, Tan sheep exhibited enhanced forage degradation and fermentation. This was reflected by increased populations of key rumen bacteria, including Bacteroidetes, Prevotella_1, Prevotellaceae_UCG-003, Ruminococcus_1, Saccharofermentans, and Ruminococcaceae_UCG-014. Supplementation with cellulase further facilitated these processes, optimizing the utilization of available nutrients. In contrast, during winter, when the nutritional quality of forage decline, we observed lower indicators of forage degradation and fermentation in Tan sheep. Additionally, there was a significant increase in the Firmicutes/Bacteroidetes ratio, microbial diversity, microbial interactions, and metabolic activity.

CONCLUSIONS

The rumen microbiota adapts to enhance the breakdown of forage biomass and maintain energy balance during periods of inadequate nutritional value. Supplementing the diet with cellulase during these times can help mitigate the reduced digestibility associated with low-quality forage. This study highlights the dynamic adaptation of the rumen microbiota to seasonal variations in forage quality and emphasizes the potential benefits of cellulase supplementation in supporting rumen function and improving animal performance under varying environmental conditions.

The role of the gut microbiota in regulating responses to vaccination: current knowledge and future directions

Antigen-specific B and T cell responses play a critical role in vaccine-mediated protection against infectious diseases, but these responses are highly variable between individuals and vaccine immunogenicity is frequently sub-optimal in infants, the elderly and in people living in low- and middle-income countries. Although many factors such as nutrition, age, sex, genetics, environmental exposures, and infections may all contribute to variable vaccine immunogenicity, mounting evidence indicates that the gut microbiota is an important and targetable factor shaping optimal immune responses to vaccination. In this review, we discuss evidence from human, preclinical and experimental studies supporting a role for a healthy gut microbiota in mediating optimal vaccine immunogenicity, including the immunogenicity of COVID-19 vaccines. Furthermore, we provide an overview of the potential mechanisms through which this could occur and discuss strategies that could be used to target the microbiota to boost vaccine immunogenicity where it is currently sub-optimal.

Adaptive survival strategies of rumen microbiota with solid diet deficiency in early life cause epithelial mitochondrial dysfunction

With extreme nutritional substrate deficiency, the adaptive responses of the gastrointestinal microbiota and host metabolism are largely unknown. Here, we successfully established a microbial substrate deficiency model in the rumen without solid diet introduction in neonatal lambs. In the absence of solid diet, we observed a reduction in the Simpson Index of rumen bacteria, along with a marked decline in the abundance of keystone microorganisms such as Prevotella, Selenomonas, Megasphaera, and Succiniclasticum, indicating a simplified microbial interaction network. Additionally, more urea and NH3-N production facilitated microbial efficient nitrogen utilization to prioritize ammonia as a nitrogen source for survival, reallocating energy to overcome nutritional limitations and sustain their viability. In addition, enriched archaea (Methanosarcina, Methanomicrobium, Methanobrevibacter, and Methanobacterium) promoted hydrogen removal and the growth of nitrogen-producing microorganisms (Pecoramyces, Piromyces, Caecomyces, and Orpinomyces). It also reinforced the glutamate-glutamine pathway, as evidenced by the higher expression of glnA, GLUL, gdhA, and ureAB, suggesting enhanced internal cycling of nitrogen for microbial survival. This selfish microbial survival strategy deprived the host of adequate volatile fatty acids for energy metabolism, resulting in the downregulation of rumen epithelial cell cycle proteins (CCNB1, CCNE), abnormal mitochondrial morphology, and reduced mitochondrial deoxyribonucleic acid copy number and adenosine triphosphate production. Overall, these findings revealed the adaptive survival strategies of rumen microbiota with solid diet deficiency in early life, which caused alterations in epithelial cell mitochondrial function.

Biotic and abiotic factors acting on community assembly in parallel anaerobic digestion systems from a brewery wastewater treatment plant

Anaerobic digestion is a complex microbial process that mediates the transformation of organic waste into biogas. The performance and stability of anaerobic digesters relies on the structure and function of the microbial community. In this study, we asked whether the deterministic effect of wastewater composition outweighs the effect of reactor configuration on the structure and dynamics of anaerobic digester archaeal and bacterial communities. Biotic and abiotic factors acting on microbial community assembly in two parallel anaerobic digestion systems, an upflow anaerobic sludge blanket digestor (UASB) and a closed digester tank with a solid recycling system (CDSR), from a brewery WWTP were analysed utilizing 16S rDNA and mcrA amplicon sequencing and genome-centric metagenomics. This study confirmed the deterministic effect of the wastewater composition on bacterial community structure, while the archaeal community composition resulted better explained by organic loading rate (ORL) and volatile free acids (VFA). According to the functions assigned to the differentially abundant metagenome-assembled genomes (MAGs) between reactors, CDSR was enriched in genes related to methanol and methylamines methanogenesis, protein degradation, and sulphate and alcohol utilization. Conversely, the UASB reactor was enriched in genes associated with carbohydrate and lipid degradation, as well as amino acid, fatty acid, and propionate fermentation. By comparing interactions derived from the co-occurrence network with predicted metabolic interactions of the prokaryotic communities in both anaerobic digesters, we conclude that the overall community structure is mainly determined by habitat filtering.

The dominant lineage of an emerging pathogen harbours contact-dependent inhibition systems

Bacteria from the Stenotrophomonas maltophilia complex (Smc) are important multidrug-resistant pathogens that cause a broad range of infections. Smc is genomically diverse and has been classified into 23 lineages. Lineage Sm6 is the most common among sequenced strains, but it is unclear why this lineage has evolved to be dominant. Antagonistic interactions can significantly affect the evolution of bacterial populations. These interactions may be mediated by secreted contact-dependent proteins, which allow inhibitor cells to intoxicate adjacent target bacteria. Contact-dependent inhibition (CDI) requires three proteins: CdiA, CdiB and CdiI. CdiA is a large, filamentous protein exported to the surface of inhibitor cells through the pore-like CdiB. The CdiA C-terminal domain (CdiA-CT) is toxic when delivered into target cells of the same species or genus. CdiI immunity proteins neutralize the toxicity of cognate CdiA-CT toxins. We found that all complete Smc genomes from the Sm6 lineage harbour at least one CDI locus. By contrast, less than a quarter of strains from other lineages have CDI genes. Smc CdiA-CT domains are diverse and have a broad range of predicted functions. Most Sm6 strains harbour non-cognate cdiI genes predicted to provide protection against foreign toxins from other strains. Finally, we demonstrated that an Smc CdiA-CT toxin has antibacterial properties and is neutralized by its cognate CdiI.

Salinity mediates the damage caused by acute and chronic ammonia stress in largemouth bass (Micropterus salmoides)

Ammonia is a critical pollutant in aquatic environments, posing significant risks to aquaculture by accumulating in culture systems due to fish excretion and organic matter decomposition. This study investigated the effects of ammonia toxicity on juvenile largemouth bass (Micropterus salmoides) under varying salinity conditions (0 and 5 psu), focusing on physiological responses and gut microbiota changes. Results indicated that ammonia exposure led to increased mortality, oxidative stress, liver damage, and significant shifts in gut microbial communities, especially under freshwater conditions. Elevated salinity mitigated these effects by reducing the bioavailability of toxic un-ionized ammonia (UIA) and enhancing the fish's physiological resilience, particularly in the kidney and intestine. Ammonia exposure significantly increased the IBR index values in all three organs, with the gills showing the most pronounced stress response, followed by the kidney and intestine. Salinity had a significant mitigating effect by reducing the oxidative stress response in comparison to freshwater conditions. However, in the gills, the protective effect of salinity was not enough to fully counteract the oxidative stress induced by ammonia. Ammonia exposure in freshwater favored pathogenic gut bacteria genera such as Aeromonas, while higher salinity enriched stress-resistant genera like Ralstonia and Klebsiella. These findings contribute to a better understanding of the interaction between salinity and ammonia toxicity, suggesting that moderate salinity increases within the fish's tolerance range could be an effective strategy in aquaculture to reduce ammonia toxicity and promote fish health.

Deciphering and designing microbial communities by genome-scale metabolic modelling

Microbial communities are shaped by the complex interactions among organisms and the environment. Genome-scale metabolic models (GEMs) can provide deeper insights into the complexity and ecological properties of various microbial communities, revealing their intricate interactions. Many researchers have modified GEMs for the microbial communities based on specific needs. Thus, GEMs need to be comprehensively summarized to better understand the trends in their development. In this review, we summarized the key developments in deciphering and designing microbial communities using different GEMs. A timeline of selected highlights in GEMs indicated that this area is evolving from the single-strain level to the microbial community level. Then, we outlined a framework for constructing GEMs of microbial communities. We also summarized the models and resources of static and dynamic community-level GEMs. We focused on the role of external environmental and intracellular resources in shaping the assembly of microbial communities. Finally, we discussed the key challenges and future directions of GEMs, focusing on the integration of GEMs with quorum sensing mechanisms, microbial ecology interactions, machine learning algorithms, and automatic modeling, all of which contribute to consortia-based applications in different fields.

Exploring interspecific interaction variability in microbiota: A review

Interspecific interactions are an important component and a strong selective force in microbial communities. Over the past few decades, there has been a growing awareness of the variability in microbial interactions, and various studies are already unraveling the inner working dynamics in microbial communities. This has prompted scientists to develop novel techniques for characterizing the varying interspecific interactions among microbes. Here, we review the precise definitions of pairwise and high-order interactions, summarize the key concepts related to interaction variability, and discuss the strengths and weaknesses of emerging characterization techniques. Specifically, we found that most methods can accurately predict or provide direct information about microbial pairwise interactions. However, some of these methods inevitably mask the underlying high-order interactions in the microbial community. Making reasonable assumptions and choosing a characterization method to explore varying microbial interactions should allow us to better understand and engineer dynamic microbial systems.

Algorithm of spatial-temporal simulation for environment-strain interactions in strain-strain consortia based on resource competition mechanism

Interaction simulation for co-culture systems is important for optimizing culture conditions and improving yields. For industrial production, the environment significantly affects the spatial-temporal microbial interactions. However, the current research on polymicrobial interactions mainly focuses on interaction patterns among strains, and neglects the environment influence. Based on the resource competition relationship between two strains, this research set up the modules of cellular physicochemical properties, nutrient uptake and metabolite release, cellular survival, cell swimming and substrate diffusion, and investigated the spatial-temporal strain-environment interactions through module coupling and data mining. Furthermore, in an Escherichia coli-Saccharomyces cerevisiae consortium, the total net reproduction rate decreased as glucose was consumed. E. coli gradually dominated favorable positions due to its higher glucose utilization capacity, reaching 100 % abundance with a competitive strength of 0.86 for glucose. Conversely, S. cerevisiae decreased to 0 % abundance with a competitive strength of 0.14. The simulation results of environment influence on strain competitiveness showed that inoculation ratio and dissolved oxygen strongly influenced strain competitiveness. Specifically, strain competitiveness increased with higher inoculation ratio, whereas E. coli competitiveness increased as dissolved oxygen increased, in contrast to S. cerevisiae. On the other hand, substrate diffusion condition, micronutrients and toxins had minimal influence on strain competitiveness. This method offers a straightforward procedure without featured downscaling and provides novel insights into polymicrobial interaction simulation.

Adjusted bacterial cooperation in anammox community to adapt to high ammonium in wastewater treatment plant

Bacterial cooperation is very important for anammox bacteria which perform low-carbon and energy-efficient nitrogen removal, yet its variation to adapt to high NH4 +-N concentration in actual wastewater treatment plants (WWTPs) remains unclear. Here, we found wide and varied cross-feedings of anammox bacteria and symbiotic bacteria in the two series connected full-scale reactors with different NH4 +-N concentrations (297.95 ± 54.84 and 76.03 ± 34.01 mg/L) treating sludge digester liquor. The uptake of vitamin B6 as highly effective antioxidants secreted by the symbiotic bacteria was beneficial for anammox bacteria to resist the high NH4 +-N concentration and varied dissolved oxygen (DO). When NH4 +-N concentration in influent (1785.46 ± 228.5 mg/L) increased, anammox bacteria tended to reduce the amino acids supply to symbiotic bacteria to save metabolic costs. A total of 26.1% bacterial generalists switched to specialists to increase the stability and functional heterogeneity of the microbial community at high NH4 +-N conditions. V/A-type ATPase for anammox bacteria to adapt to the change of NH4 +-N was highly important to strive against cellular alkalization caused by free ammonia. This study expands the understanding of the adjusted bacterial cooperation within anammox consortia at high NH4 +-N conditions, providing new insights into bacterial adaptation to adverse environments from a sociomicrobiology perspective.

Game-Theoretic Flux Balance Analysis Model for Predicting Stable Community Composition

Models for microbial interactions attempt to understand and predict the steady state network of inter-species relationships in a community, e.g. competition for shared metabolites, and cooperation through cross-feeding. Flux balance analysis (FBA) is an approach that was introduced to model the interaction of a particular microbial species with its environment. This approach has been extended to analyzing interactions in a community of microbes; however, these approaches have two important drawbacks: first, one has to numerically solve a differential equation to identify the steady state, and second, there are no methods available to analyze the stability of the steady state. We propose a game theory based community FBA model wherein species compete to maximize their individual growth rate, and the state of the community is given by the resulting Nash equilibrium. We develop a computationally efficient method for directly computing the steady state biomasses and fluxes without solving a differential equation. We also develop a method to determine the stability of a steady state to perturbations in the biomasses and to invasion by new species. We report the results of applying our proposed framework to a small community of four E. coli mutants that compete for externally supplied glucose, as well as cooperate since the mutants are auxotrophic for metabolites exported by other mutants, and a more realistic model for a gut microbiome consisting of nine species.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

Metabolic interactions shape emergent biofilm structures in a conceptual model of gut mucosal bacterial communities

The gut microbiome plays a major role in human health; however, little is known about the structural arrangement of microbes and factors governing their distribution. In this work, we present an in silico agent-based model (ABM) to conceptually simulate the dynamics of gut mucosal bacterial communities. We explored how various types of metabolic interactions, including competition, neutralism, commensalism, and mutualism, affect community structure, through nutrient consumption and metabolite exchange. Results showed that, across scenarios with different initial species abundances, cross-feeding promotes species coexistence. Morphologically, competition and neutralism resulted in segregation, while mutualism and commensalism fostered high intermixing. In addition, cooperative relations resulted in community properties with little sensitivity to the selective uptake of metabolites produced by the host. Moreover, metabolic interactions strongly influenced colonization success following the invasion of newcomer species. These results provide important insights into the utility of ABM in deciphering complex microbiome patterns.

The Role of Phosphate-Solubilizing Microbial Interactions in Phosphorus Activation and Utilization in Plant-Soil Systems: A Review

To address the issue of phosphorus limitation in agricultural and forestry production and to identify green and economical alternatives to chemical phosphorus fertilizers, this paper reviews the utilization of phosphorus in plant-soil systems and explores the considerable potential for exploiting endogenous phosphorus resources. The application of phosphate-solubilizing microorganisms (PSMs) is emphasized for their role in phosphorus activation and plant growth promotion. A focus is placed on microbial interactions as an entry point to regulate the functional rhizosphere microbiome, introducing the concept of synthetic communities. This approach aims to deepen the understanding of PSM interactions across plant root, soil, and microbial interfaces, providing a theoretical foundation for the development and application of biological regulation technologies to enhance phosphorus utilization efficiency.

Metabolic Footprint of Treponema phagedenis and Treponema pedis Reveals Potential Interaction Towards Community Succession and Pathogenesis in Bovine Digital Dermatitis

Bovine digital dermatitis (BDD) is a cattle infection causing hoof lesions and lameness, with treponemes as key pathogens. We analyzed the metabolic activity of Treponema phagedenis and Treponema pedis using gas chromatography-mass spectrometry for organic acids (OAs), amino acids (AAs), and fatty acids (FAs), and high-performance liquid chromatography for short-chain fatty acids (SCFAs). Key findings include a 61.5% reduction in pyruvic acid in T. pedis and 81.0% in T. phagedenis. 2-hydroxybutyric acid increased by 493.8% in T. pedis, while succinic acid increased by 31.3%, potentially supporting T. phagedenis. Among AAs, glycine was reduced by 97.4% in T. pedis but increased by 64.1% in T. phagedenis. Proline increased by 76.6% in T. pedis but decreased by 13.6% in T. phagedenis. Methionine and glutamic acid were competitively utilized, with methionine reduced by 41.8% in T. pedis and 11.9% in T. phagedenis. Both species showed significant utilization of palmitic acid (reduced by 82.8% in T. pedis and 87.2% in T. phagedenis). Butyric acid production increased by 620.2% in T. phagedenis, and propionic acid increased by 932.8% in T. pedis and 395.6% in T. phagedenis. These reveal metabolic interactions between the pathogens, contributing to disease progression and offering insights to BDD pathogenesis.

Assembly processes and co-occurrence of bacterial communities in tree rhizosphere under Pb-Zn contamination

Rhizosphere bacteria are critical for supporting plant performance in stressful environments. Understanding the assembly and co-occurrence of rhizosphere bacterial communities contributes significantly to both plant growth and heavy metal accumulation. In this study, Ligustrum lucidum and Melia azedarach were planted in soils with simulated varying levels of Pb-Zn contamination. The Rhizosphere bacterial communities were investigated by using 16S rRNA gene sequencing. The impacts of Pb-Zn contamination on the diversity and structure of the rhizosphere bacterial community were found to be greater than those of both tree species. The variation in bacterial community structure in both trees was mainly driven by the combinations of Pb-Zn and soil properties. Deterministic processes (non-planted, 82 %; L. lucidum, 73 %; M. azedarach, 55 %) proved to be the most important assembly processes for soil bacterial communities, but both trees increased the importance of stochastic processes (18 %, 27 %, 45 %). The rhizosphere co-occurrence networks exhibited greater stability compared to the non-planted soil networks. Rare taxa played a dominant role in maintaining the stability of rhizosphere networks, as most of the keystone taxa within rhizosphere networks belonged to rare taxa. Dissimilarities in the structure and network complexity of rhizosphere bacterial communities were significantly associated with differences in tree biomass and metal accumulation. These variations in response varied between both trees, with L. lucidum exhibiting greater potential for phytoremediation in its rhizosphere compared to M. azedarach. Our results offer valuable insights for designing effective microbe-assisted phytoremediation systems.

Disentangling the factors defining Bacillus subtilis group species abundance in natural soils

Bacillus subtilis is ubiquitously and broadly distributed in various environments but is mostly isolated from soil. Given that B. subtilis is known as a plant growth-promoting rhizobacterium in agriculture, we aimed to describe the natural distribution of this species and uncover how biotic and abiotic factors affect its distribution. When comparing different soils, we discovered that B. subtilis group species are most abundant in grasslands but can rarely be isolated from forest soil, even if the soil sample sites are situated in proximity. Differential analysis revealed that spore-forming bacteria exhibited enrichments in the grassland, suggesting niche overlap or synergistic interactions leading to the proliferation of certain Bacillus species in grassland environments. Network analysis further revealed that Bacillus and other Bacillota established a densely interconnected hub module in the grassland, characterised by positive associations indicating co-occurrence, a pattern not observed in the forest soil. Speculating that this difference was driven by abiotic factors, we combined amplicon sequencing with physico-chemical analysis of soil samples and found multiple chemical variables, mainly pH, to affect microbial composition. Our study pinpoints the factors that influence B. subtilis abundance in natural soils and, therefore, offers insights for designing B. subtilis-based biocontrol products in agricultural settings.

Convergent evolution in toxin detection and resistance provides evidence for conserved bacterial-fungal interactions

Microbes rarely exist in isolation and instead form complex polymicrobial communities. As a result, microbes have developed intricate offensive and defensive strategies that enhance their fitness in these complex communities. Thus, identifying and understanding the molecular mechanisms controlling polymicrobial interactions is critical for understanding the function of microbial communities. In this study, we show that the gram-negative opportunistic human pathogen Pseudomonas aeruginosa, which frequently causes infection alongside a plethora of other microbes including fungi, encodes a genetic network which can detect and defend against gliotoxin, a potent, disulfide-containing antimicrobial produced by the ubiquitous filamentous fungus Aspergillus fumigatus. We show that gliotoxin exposure disrupts P. aeruginosa zinc homeostasis, leading to transcriptional activation of a gene encoding a previously uncharacterized dithiol oxidase (herein named as DnoP), which detoxifies gliotoxin and structurally related toxins. Despite sharing little homology to the A. fumigatus gliotoxin resistance protein (GliT), the enzymatic mechanism of DnoP from P. aeruginosa appears to be identical that used by A. fumigatus. Thus, DnoP and its transcriptional induction by low zinc represent a rare example of both convergent evolution of toxin defense and environmental cue sensing across kingdoms. Collectively, these data provide compelling evidence that P. aeruginosa has evolved to survive exposure to an A. fumigatus disulfide-containing toxin in the natural environment.

Superiority of native soil core microbiomes in supporting plant growth

Native core microbiomes represent a unique opportunity to support food provision and plant-based industries. Yet, these microbiomes are often neglected when developing synthetic communities (SynComs) to support plant health and growth. Here, we study the contribution of native core, native non-core and non-native microorganisms to support plant production. We construct four alternative SynComs based on the excellent growth promoting ability of individual stain and paired non-antagonistic action. One of microbiome based SynCom (SC2) shows a high niche breadth and low average variation degree in-vitro interaction. The promoting-growth effect of SC2 can be transferred to non-sterile environment, attributing to the colonization of native core microorganisms and the improvement of rhizosphere promoting-growth function including nitrogen fixation, IAA production, and dissolved phosphorus. Further, microbial fertilizer based on SC2 and composite carrier (rapeseed cake fertilizer + rice husk carbon) increase the net biomass of plant by 129%. Our results highlight the fundamental importance of native core microorganisms to boost plant production.

Systematic Evaluation of Biotic and Abiotic Factors in Antifungal Microorganism Screening

Microorganisms have significant potential to control fungal contamination in various foods. However, the identification of strains that exhibit robust antifungal activity poses challenges due to highly context-dependent responses. Therefore, to fully exploit the potential of isolates as antifungal agents, it is crucial to systematically evaluate them in a variety of biotic and abiotic contexts. Here, we present an adaptable and scalable method using a robotic platform to study the properties of 1022 isolates obtained from maple sap. We tested the antifungal activity of isolates alone or in pairs on M17 + lactose (LM17), plate count agar (PCA), and sucrose-allantoin (SALN) culture media against Kluyveromyces lactis, Candida boidinii, and Saccharomyces cerevisiae. Microorganisms exhibited less often antifungal activity on SALN and PCA than LM17, suggesting that the latter is a better screening medium. We also analyzed the results of ecological interactions between pairs. Isolates that showed consistent competitive behaviors were more likely to show antifungal activity than expected by chance. However, co-culture rarely improved antifungal activity. In fact, an interaction-mediated suppression of activity was more prevalent in our dataset. These findings highlight the importance of incorporating both biotic and abiotic factors into systematic screening designs for the bioprospection of microorganisms with environmentally robust antifungal activity.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

BACKGROUND

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains, and microbiome composition typically remains stable across a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. Therefore, the long-term persistence of microbiome diversity calls for an explanation.

RESULTS

To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis were obtained, namely, pure competition, host-parasite relationship, and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environment.

CONCLUSIONS

The results of this modeling study show that basic phenomena that are universal in microbial communities, namely, environmental variation and HGT, provide for stabilization and persistence of microbial diversity, and emergence of ecological complexity.

Evaluating a polymicrobial biofilm model for structural components by co-culturing Komagataeibacter hansenii produced bacterial cellulose with Pseudomonas aeruginosa PAO1

A polymicrobial biofilm model of Komagataeibacter hansenii and Pseudomonas aeruginosa was developed to understand whether a pre-existing matrix affects the ability of another species to build a biofilm. P. aeruginosa was inoculated onto the preformed K. hansenii biofilm consisting of a cellulose matrix. P. aeruginosa PAO1 colonized and infiltrated the K. hansenii bacterial cellulose biofilm (BC), as indicated by the presence of cells at 19 μm depth in the translucent hydrogel matrix. Bacterial cell density increased along the imaged depth of the biofilm (17-19 μm). On day 5, the average bacterial count across sections was 67 ± 4 % P. aeruginosa PAO1 and 33 ± 6 % K. hansenii. Biophysical characterization of the biofilm indicated that colonization by P. aeruginosa modified the biophysical properties of the BC matrix, which inlcuded increased density, heterogeneity, degradation temperature and thermal stability, and reduced crystallinity, swelling ability and moisture content. This further indicates colonization of the biofilm by P. aeruginosa. While eDNA fibres - a key viscoelastic component of P. aeruginosa biofilm - were present on the surface of the co-cultured biofilm on day 1, their abundance decreased over time, and by day 5, no eDNA was observed, either on the surface or within the matrix. P. aeruginosa-colonized biofilm devoid of eDNA retained its mechanical properties. The observations demonstrate that a pre-existing biofilm scaffold of K. hansenii inhibits P. aeruginosa PAO1 eDNA production and suggest that eDNA production is a response by P. aeruginosa to the viscoelastic properties of its environment.

Screening Microbial Interactions During Inulin Utilization Reveals Strong Competition and Proteomic Changes in Lacticaseibacillus paracasei M38

Competition for resources is a common microbial interaction in the gut microbiome. Inulin is a well-studied prebiotic dietary fiber that profoundly shapes gut microbiome composition. Several community members and some probiotics, such as Lacticaseibacillus paracasei, deploy multiple molecular strategies to access fructans. In this work, we screened bacterial interactions during inulin utilization in representative gut microbes. Unidirectional and bidirectional assays were used to evaluate the effects of microbial interactions and global proteomic changes on inulin utilization. Unidirectional assays showed the total or partial consumption of inulin by many gut microbes. Partial consumption was associated with cross-feeding of fructose or short oligosaccharides. However, bidirectional assays showed strong competition from L. paracasei M38 against other gut microbes, reducing the growth and quantity of proteins found in the latter. L. paracasei dominated and outcompeted other inulin utilizers, such as Ligilactobacillus ruminis PT16, Bifidobacterium longum PT4, and Bacteroides fragilis HM714. The importance of strain-specific characteristics of L. paracasei, such as its high fitness for inulin consumption, allows it to be favored for bacterial competence. Proteomic studies indicated an increase in inulin-degrading enzymes in co-cultures, such as β-fructosidase, 6-phosphofructokinase, the PTS D-fructose system, and ABC transporters. These results reveal that intestinal metabolic interactions are strain-dependent and might result in cross-feeding or competition depending on total or partial consumption of inulin. Partial degradation of inulin by certain bacteria favors coexistence. However, when L. paracasei M38 totally degrades the fiber, this does not happen. The synergy of this prebiotic with L. paracasei M38 could determine the predominance in the host as a potential probiotic.

GEM-based computational modeling for exploring metabolic interactions in a microbial community

Microbial communities play fundamental roles in every complex ecosystem, such as soil, sea and the human body. The stability and diversity of the microbial community depend precisely on the composition of the microbiota. Any change in the composition of these communities affects microbial functions. An important goal of studying the interactions between species is to understand the behavior of microbes and their responses to perturbations. These interactions among species are mediated by the exchange of metabolites within microbial communities. We developed a computational model for the microbial community that has a separate compartment for exchanging metabolites. This model can predict possible metabolites that cause competition, commensalism, and mutual interactions between species within a microbial community. Our constraint-based community metabolic modeling approach provides insights to elucidate the pattern of metabolic interactions for each common metabolite between two microbes. To validate our approach, we used a toy model and a syntrophic co-culture of Desulfovibrio vulgaris and Methanococcus maripaludis, as well as another in co-culture between Geobacter sulfurreducens and Rhodoferax ferrireducens. For a more general evaluation, we applied our algorithm to the honeybee gut microbiome, composed of seven species, and the epiphyte strain Pantoea eucalypti 299R. The epiphyte strain Pe299R has been previously studied and cultured with six different phyllosphere bacteria. Our algorithm successfully predicts metabolites, which imply mutualistic, competitive, or commensal interactions. In contrast to OptCom, MRO, and MICOM algorithms, our COMMA algorithm shows that the potential for competitive interactions between an epiphytic species and Pe299R is not significant. These results are consistent with the experimental measurements of population density and reproductive success of the Pe299R strain.

Dual stressors of infection and warming can destabilize host microbiomes

Climate change is causing extreme heating events and intensifying infectious disease outbreaks. Animals harbour microbial communities, which are vital for their survival and fitness under stressful conditions. Understanding how microbiome structures change in response to infection and warming may be important for forecasting host performance under global change. Here, we evaluated alterations in the microbiomes of several wild Caenorhabditis elegans isolates spanning a range of latitudes, upon warming temperatures and infection by the parasite Leucobacter musarum. Using 16S rRNA sequencing, we found that microbiome diversity decreased, and dispersion increased over time, with the former being more prominent in uninfected adults and the latter aggravated by infection. Infection reduced dominance of specific microbial taxa, and increased microbiome dispersion, indicating destabilizing effects on host microbial communities. Exposing infected hosts to warming did not have an additive destabilizing effect on their microbiomes. Moreover, warming during pre-adult development alleviated the destabilizing effects of infection on host microbiomes. These results revealed an opposing interaction between biotic and abiotic factors on microbiome structure. Lastly, we showed that increased microbiome dispersion might be associated with decreased variability in microbial species interaction strength. Overall, these findings improve our understanding of animal microbiome dynamics amidst concurrent climate change and epidemics. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Using metabolic networks to predict cross-feeding and competition interactions between microorganisms

Understanding the interactions between microorganisms and their impact on bacterial behavior at the community level is a key research topic in microbiology. Different methods, relying on experimental or mathematical approaches based on the diverse properties of bacteria, are currently employed to study these interactions. Recently, the use of metabolic networks to understand the interactions between bacterial pairs has increased, highlighting the relevance of this approach in characterizing bacteria. In this study, we leverage the representation of bacteria through their metabolic networks to build a predictive model aimed at reducing the number of experimental assays required for designing bacterial consortia with specific behaviors. Our novel method for predicting cross-feeding or competition interactions between pairs of microorganisms utilizes metabolic network features. Machine learning classifiers are employed to determine the type of interaction from automatically reconstructed metabolic networks. Several algorithms were assessed and selected based on comprehensive testing and careful separation of manually compiled data sets obtained from literature sources. We used different classification algorithms, including K Nearest Neighbors, XGBoost, Support Vector Machine, and Random Forest, tested different parameter values, and implemented several data curation approaches to reduce the biological bias associated with our data set, ultimately achieving an accuracy of over 0.9. Our method holds substantial potential to advance the understanding of community behavior and contribute to the development of more effective approaches for consortia design.IMPORTANCEUnderstanding bacterial interactions at the community level is critical for microbiology, and leveraging metabolic networks presents an efficient and effective approach. The introduction of this novel method for predicting interactions through machine learning classifiers has the potential to advance the field by reducing the number of experimental assays required and contributing to the development of more effective bacterial consortia.

Biological Control of Escherichia coli O157:H7 in Dairy Manure-Based Compost Using Competitive Exclusion Microorganisms

BACKGROUND

Animal manure-based compost is a valuable organic fertilizer and biological soil amendment. To ensure the microbiological safety of compost products, the effectiveness of competitive exclusion microorganisms (CE) in reducing Escherichia coli O157:H7 in dairy manure-based compost was evaluated.

METHODS

A cocktail of E. coli O157:H7 strains were inoculated into dairy compost along with CE strains isolated from compost, and the reduction in E. coli O157:H7 by CE was determined in compost with 20%, 30%, and 40% moisture levels at 22 °C and 30 °C under laboratory conditions, as well as in fall, winter, and summer seasons under greenhouse settings.

RESULTS

Under lab conditions, CE addition resulted in 1.1-3.36 log reductions in E. coli O157:H7 in compost, with enhanced pathogen reduction by higher moisture and lower temperature. In the greenhouse, >99% of the E. coli O157:H7 population in compost with ≥30% moisture due to cross-contamination can be effectively inactivated by CE within 2 days during colder seasons. However, it took ≥8 days to achieve the same level of reduction for heat-adapted E. coli O157:H7 cells.

CONCLUSIONS

Our results demonstrated that the competitive exclusion of microorganisms can be an effective tool for controlling foodborne pathogens in compost and reducing the potential for soil and crop contamination.

Differences in the intestinal microbiota and association of host metabolism with hair coat status in cattle

Introduction

The hair coat status of cattle serves as an easily observed indicator of economic value in livestock production; however, the underlying mechanism remains largely unknown. Therefore, the objective of the current study was to determine differences in the intestinal microbiota and metabolome of cattle based on a division of with either slick and shining (SHC) or rough and dull (MHC) hair coat in Simmental cows.

Methods

Eight SHC and eight MHC late-pregnancy Simmental cows (with similar parities, body weights, and body conditions) were selected based on their hair coat status, and blood samples (plasma) from coccygeal venipuncture and fecal samples from the rectum were collected. The intestinal microbiota (in the fecal samples) was characterized by employing 16S rRNA gene sequencing targeting the V3-V4 hypervariable region on the Illumina MiSeq PE300 platform, and plasma samples were subjected to LC-MS/MS-based metabolomics with Progenesis QI 2.3. Plasma macromolecular metabolites were examined for differences in the metabolism of lipids, proteins, mineral elements, and hormones.

Results

Notable differences between the SHC and MHC groups related to host hair coat status were observed in the host metabolome and intestinal microbiota (P < 0.05). The host metabolome was enriched in histidine metabolism, cysteine and methionine metabolism, and purine metabolism in the SHC group, and the intestinal microbiota were also enriched in histidine metabolism (P < 0.05). In the MHC group, the symbiotic relationship transitioned from cooperation to competition in the MHC group, and an uncoupling effect was present in the microbe-metabolite association of intestine microbiota-host interactions. The hubs mediating the relationships between intestinal microbiota and plasma metabolites were the intestinal bacterial genus g__norank_f__Eubacterium_coprostanoligenes_group, plasma inosine, triiodothyronine, and phosphorus, which could be used to differentiate cows' hair coat status (P < 0.05).

Conclusion

Overall, the present study identified the relationships between the features of the intestinal microbiota and host hair coat status, thereby providing evidence and a new direction (intestine microbiota-host interplay) for future studies aimed at understanding the hair coat status of cattle.

Rational management of the plant microbiome for the Second Green Revolution

The Green Revolution of the mid-20th century transformed agriculture worldwide and has resulted in environmental challenges. A new approach, the Second Green Revolution, seeks to enhance agricultural productivity while minimizing negative environmental impacts. Plant microbiomes play critical roles in plant growth and stress responses, and understanding plant-microbiome interactions is essential for developing sustainable agricultural practices that meet food security and safety challenges, which are among the United Nations Sustainable Development Goals. This review provides a comprehensive exploration of key deterministic processes crucial for developing microbiome management strategies, including the host effect, the facilitator effect, and microbe-microbe interactions. A hierarchical framework for plant microbiome modulation is proposed to bridge the gap between basic research and agricultural applications. This framework emphasizes three levels of modulation: single-strain, synthetic community, and in situ microbiome modulation. Overall, rational management of plant microbiomes has wide-ranging applications in agriculture and can potentially be a core technology for the Second Green Revolution.

Modeling Dynamics of Human Gut Microbiota Derived from Gluten Metabolism: Obtention, Maintenance and Characterization of Complex Microbial Communities

Western diets are rich in gluten-containing products, which are frequently poorly digested. The human large intestine harbors microorganisms able to metabolize undigested gluten fragments that have escaped digestion by human enzymatic activities. The aim of this work was obtaining and culturing complex human gut microbial communities derived from gluten metabolism to model the dynamics of healthy human large intestine microbiota associated with different gluten forms. For this purpose, stool samples from six healthy volunteers were inoculated in media containing predigested gluten or predigested gluten plus non-digested gluten. Passages were carried out every 24 h for 15 days in the same medium and community composition along time was studied via V3-V4 16S rDNA sequencing. Diverse microbial communities were successfully obtained. Moreover, communities were shown to be maintained in culture with stable composition for 14 days. Under non-digested gluten presence, communities were enriched in members of Bacillota, such as Lachnospiraceae, Clostridiaceae, Streptococcaceae, Peptoniphilaceae, Selenomonadaceae or Erysipelotrichaceae, and members of Actinomycetota, such as Bifidobacteriaceae and Eggerthellaceae. Contrarily, communities exposed to digested gluten were enriched in Pseudomonadota. Hence, this study shows a method for culture and stable maintenance of gut communities derived from gluten metabolism. This method enables the analysis of microbial metabolism of gluten in the gut from a community perspective.

Dynamic nesting of Anaplasma marginale in the microbial communities of Rhipicephalus microplus

Interactions within the tick microbiome involving symbionts, commensals, and tick-borne pathogens (TBPs) play a pivotal role in disease ecology. This study explored temporal changes in the microbiome of Rhipicephalus microplus, an important cattle tick vector, focusing on its interaction with Anaplasma marginale. To overcome limitations inherent in sampling methods relying on questing ticks, which may not consistently reflect pathogen presence due to variations in exposure to infected hosts in nature, our study focused on ticks fed on chronically infected cattle. This approach ensures continuous pathogen exposure, providing a more comprehensive understanding of the nesting patterns of A. marginale in the R. microplus microbiome. Using next-generation sequencing, microbiome dynamics were characterized over 2 years, revealing significant shifts in diversity, composition, and abundance. Anaplasma marginale exhibited varying associations, with its increased abundance correlating with reduced microbial diversity. Co-occurrence networks demonstrated Anaplasma's evolving role, transitioning from diverse connections to keystone taxa status. An integrative approach involving in silico node removal unveils the impact of Anaplasma on network stability, highlighting its role in conferring robustness to the microbial community. This study provides insights into the intricate interplay between the tick microbiome and A. marginale, shedding light on potential avenues for controlling bovine anaplasmosis through microbiome manipulation.

Lose-lose consequences of bacterial community-driven invasions in soil

BACKGROUND

Community-driven invasion, also known as community coalescence, occurs widely in natural ecosystems. Despite that, our knowledge about the process and mechanisms controlling community-driven invasion in soil ecosystems is lacking. Here, we performed a set of coalescence experiments in soil microcosms and assessed impacts up to 60 days after coalescence by quantifying multiple traits (compositional, functional, and metabolic) of the invasive and coalescent communities.

RESULTS

Our results showed that coalescences significantly triggered changes in the resident community's succession trajectory and functionality (carbohydrate metabolism), even when the size of the invasive community is small (~ 5% of the resident density) and 99% of the invaders failed to survive. The invasion impact was mainly due to the high suppression of constant residents (65% on average), leading to a lose-lose situation where both invaders and residents suffered with coalescence. Our results showed that surviving residents could benefit from the coalescence, which supports the theory of "competition-driven niche segregation" at the microbial community level. Furthermore, the result showed that both short- and long-term coalescence effects were predicted by similarity and unevenness indexes of compositional, functional, and metabolic traits of invasive communities. This indicates the power of multi-level traits in monitoring microbial community succession. In contrast, the varied importance of different levels of traits suggests that competitive processes depend on the composition of the invasive community.

CONCLUSIONS

Our results shed light on the process and consequence of community coalescences and highlight that resource competition between invaders and residents plays a critical role in soil microbial community coalescences. These findings provide valuable insights for understanding and predicting soil microbial community succession in frequently disturbed natural and agroecosystems. Video Abstract.

Distinct microbial characteristics of the robust single-stage coupling system during the conversion from anammox-denitritation to anammox-denitratation patterns

Simultaneous anammox-denitrification is effectively operated in two types, i.e., the anammox-denitritation (SAD pattern) and the anammox-denitratation (PDA pattern). The nitrate derived from inevitable nitrite oxidization likely determines the practical operational pattern of the coupling system, while little information is available regarding the microbial characteristics during the pattern conversion. Here, the single-stage bioreactor coupling anammox with denitrification was operated under conditions with a changed ratio of influent nitrite and nitrate. Results showed that the bioreactor exhibited a robust performance during the conversion from SAD to PDA patterns, corresponding with the total nitrogen removal efficiency ranging from 89.5% to 92.4%. Distinct community structures were observed in two patterns, while functional bacteria including the genera Denitratisoma, Thauera, Candidatus Brocadia, and Ca. Jettenia steadily co-existed. Meanwhile, the high transcription of hydrazine synthase genes demonstrated a stable anammox process, while the up-regulated transcription of nitrite and nitrous oxide reductase genes indicated that the complete denitrification process was enhanced for total nitrogen removal during the PDA pattern. Ecologically, stochastic processes dominantly governed the community assembly in two patterns. The PDA pattern improved the interconnectivity of communities, especially for the cooperative behaviors between dominant denitrifying bacteria and low-abundant species. These findings deepen our understanding of the microbial mechanism underlying the different patterns of the coupling system and potentially expand its engineering application.

Metagenomic insights into the diversity of 2,4-dichlorophenol degraders and the cooperation patterns in a bacterial consortium

2,4-Dichlorophenol, which is largely employed in herbicides and industrial production, is frequently detected in ecosystems and poses risks to human health and environmental safety. Microbial communities are thought to perform better than individual strains in the complete degradation of organic contaminants. However, the synergistic degradation mechanisms of the microbial consortia involved in 2,4-dichlorophenol degradation are still not widely understood. In this study, a bacterial consortium named DCP-2 that is capable of degrading 2,4-dichlorophenol was obtained. Metagenomic analysis, cultivation-dependent functional verification, and co-occurrence network analysis were combined to reveal the primary 2,4-dichlorophenol degraders and the cooperation patterns in the consortium DCP-2. Metagenomic analysis showed that Pseudomonas, Achromobacter, and Pigmentiphaga were the primary degraders for the complete degradation of 2,4-dichlorophenol. Thirty-nine phylogenetically diverse bacterial genera, such as Brucella, Acinetobacter, Aeromonas, Allochromatium and Bosea, were identified as keystone taxa for 2,4-dichlorophenol degradation by keystone taxa analysis of the co-occurrence networks. In addition, a stable synthetic consortium of isolates from DCP-2 was constructed, consisting of Pseudomonas sp. DD-13 and Brucella sp. FZ-1; this synthetic consortium showed superior degradation capability for 2,4-dichlorophenol in both mineral salt medium and wastewater compared with monoculture. The findings provide valuable insights into the practical bioremediation of 2,4-dichlorophenol-contaminated sites.

Predicting microbial interactions with approaches based on flux balance analysis: an evaluation

BACKGROUND

Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed.

RESULTS

Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data.

CONCLUSIONS

Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model

Microbiomes are generally characterized by high diversity of coexisting microbial species and strains that remains stable within a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis obtained, namely, pure competition, host-parasite relationship and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environments. These findings show that basic phenomena that are universal in microbial communities, environmental variation and HGT, provide for stabilization of microbial diversity and ecological complexity.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Microbial collaborations and conflicts: unraveling interactions in the gut ecosystem

The human gut microbiota constitutes a vast and complex community of microorganisms. The myriad of microorganisms present in the intestinal tract exhibits highly intricate interactions, which play a crucial role in maintaining the stability and balance of the gut microbial ecosystem. These interactions, in turn, influence the overall health of the host. The mammalian gut microbes have evolved a wide range of mechanisms to suppress or even eliminate their competitors for nutrients and space. Simultaneously, extensive cooperative interactions exist among different microbes to optimize resource utilization and enhance their own fitness. This review will focus on the competitive mechanisms among members of the gut microorganisms and discuss key modes of actions, including bacterial secretion systems, bacteriocins, membrane vesicles (MVs) etc. Additionally, we will summarize the current knowledge of the often-overlooked positive interactions within the gut microbiota, and showcase representative machineries. This information will serve as a reference for better understanding the complex interactions occurring within the mammalian gut environment. Understanding the interaction dynamics of competition and cooperation within the gut microbiota is crucial to unraveling the ecology of the mammalian gut microbial communities. Targeted interventions aimed at modulating these interactions may offer potential therapeutic strategies for disease conditions.

Soil organic carbon loss decreases biodiversity but stimulates multitrophic interactions that promote belowground metabolism

Soil organic carbon (SOC) plays an essential role in mediating community structure and metabolic activities of belowground biota. Unraveling the evolution of belowground communities and their feedback mechanisms on SOC dynamics helps embed the ecology of soil microbiome into carbon cycling, which serves to improve biodiversity conservation and carbon management strategy under global change. Here, croplands with a SOC gradient were used to understand how belowground metabolisms and SOC decomposition were linked to the diversity, composition, and co-occurrence networks of belowground communities encompassing archaea, bacteria, fungi, protists, and invertebrates. As SOC decreased, the diversity of prokaryotes and eukaryotes also decreased, but their network complexity showed contrasting patterns: prokaryotes increased due to intensified niche overlap, while that of eukaryotes decreased possibly because of greater dispersal limitation owing to the breakdown of macroaggregates. Despite the decrease in biodiversity and SOC stocks, the belowground metabolic capacity was enhanced as indicated by increased enzyme activity and decreased enzymatic stoichiometric imbalance. This could, in turn, expedite carbon loss through respiration, particularly in the slow-cycling pool. The enhanced belowground metabolic capacity was dominantly driven by greater multitrophic network complexity and particularly negative (competitive and predator-prey) associations, which fostered the stability of the belowground metacommunity. Interestingly, soil abiotic conditions including pH, aeration, and nutrient stocks, exhibited a less significant role. Overall, this study reveals a greater need for soil C resources across multitrophic levels to maintain metabolic functionality as declining SOC results in biodiversity loss. Our researchers highlight the importance of integrating belowground biological processes into models of SOC turnover, to improve agroecosystem functioning and carbon management in face of intensifying anthropogenic land-use and climate change.

Reciprocal inhibition and competitive hierarchy cause negative biodiversity-ecosystem function relationships

The relationship between biodiversity and ecosystem function (BEF) captivates ecologists, but the factors responsible for the direction of this relationship remain unclear. While higher ecosystem functioning at higher biodiversity levels ('positive BEF') is not universal in nature, negative BEF relationships seem puzzlingly rare. Here, we develop a dynamical consumer-resource model inspired by microbial decomposer communities in pitcher plant leaves to investigate BEF. We manipulate microbial diversity via controlled colonization and measure their function as total ammonia production. We test how niche partitioning among bacteria and other ecological processes influence BEF in the leaves. We find that a negative BEF can emerge from reciprocal interspecific inhibition in ammonia production causing a negative complementarity effect, or from competitive hierarchies causing a negative selection effect. Absent these factors, a positive BEF was the typical outcome. Our findings provide a potential explanation for the rarity of negative BEF in empirical data.

The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. - Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. - Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. - Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Soil bacterium manipulates antifungal weapons by sensing intracellular type IVA secretion system effectors of a competitor

Soil beneficial bacteria can effectively inhibit bacterial pathogens by assembling contact-dependent killing weapons, such as the type IVA secretion system (T4ASS). It's not clear whether these antibacterial weapons are involved in biotrophic microbial interactions in soil. Here we showed that an antifungal antibiotic 2,4-DAPG production of the soil bacterium, Pseudomonas protegens can be triggered by another soil bacterium, Lysobacter enzymogenes, via T4ASS by co-culturing on agar plates to mimic cell-to-cell contact. We demonstrated that the induced 2,4-DAPG production of P. protegens is achieved by intracellular detection of the T4ASS effector protein Le1519 translocated from L. enzymogenes. We defined Le1519 as LtaE (Lysobacter T4E triggering antifungal effects), which specifically stimulates the expression of 2,4-DAPG biosynthesis genes in P. protegens, thereby protecting soybean seedlings from infection by the fungus Rhizoctonia solani. We further found that LtaE directly bound to PhlF, a pathway-specific transcriptional repressor of the 2,4-DAPG biosynthesis, then activated the 2,4-DAPG production. Our results highlight a novel pattern of microbial interspecies and interkingdom interactions, providing a unique case for expanding the diversity of soil microbial interactions.

Xylose and shikimate transporters facilitates microbial consortium as a chassis for benzylisoquinoline alkaloid production

Plant-sourced aromatic amino acid (AAA) derivatives are a vast group of compounds with broad applications. Here, we present the development of a yeast consortium for efficient production of (S)-norcoclaurine, the key precursor for benzylisoquinoline alkaloid biosynthesis. A xylose transporter enables the concurrent mixed-sugar utilization in Scheffersomyces stipitis, which plays a crucial role in enhancing the flux entering the highly regulated shikimate pathway located upstream of AAA biosynthesis. Two quinate permeases isolated from Aspergillus niger facilitates shikimate translocation to the co-cultured Saccharomyces cerevisiae that converts shikimate to (S)-norcoclaurine, resulting in the maximal titer (11.5 mg/L), nearly 110-fold higher than the titer reported for an S. cerevisiae monoculture. Our findings magnify the potential of microbial consortium platforms for the economical de novo synthesis of complex compounds, where pathway modularization and compartmentalization in distinct specialty strains enable effective fine-tuning of long biosynthetic pathways and diminish intermediate buildup, thereby leading to increases in production.

The impact of primary colonizers on the community composition of river biofilm

As a strategy for minimizing microbial infections in fish hatcheries, we have investigated how putatively probiotic bacterial populations influence biofilm formation. All surfaces that are exposed to the aquatic milieu develop a microbial community through the selective assembly of microbial populations into a surface-adhering biofilm. In the investigations reported herein, we describe laboratory experiments designed to determine how initial colonization of a surface by nonpathogenic isolates from sturgeon eggs influence the subsequent assembly of populations from a pelagic river community, into the existing biofilm. All eight of the tested strains altered the assembly of river biofilm in a strain-specific manner. Previously formed isolate biofilm was challenged with natural river populations and after 24 hours, two strains and two-isolate combinations proved highly resistant to invasion, comprising at least 80% of the biofilm community, four isolates were intermediate in resistance, accounting for at least 45% of the biofilm community and two isolates were reduced to 4% of the biofilm community. Founding biofilms of Serratia sp, and combinations of Brevundimonas sp.-Hydrogenophaga sp. and Brevundimonas sp.-Acidovorax sp. specifically blocked populations of Aeromonas and Flavobacterium, potential fish pathogens, from colonizing the biofilm. In addition, all isolate biofilms were effective at blocking invading populations of Arcobacter. Several strains, notably Deinococcus sp., recruited specific low-abundance river populations into the top 25 most abundant populations within biofilm. The experiments suggest that relatively simple measures can be used to control the assembly of biofilm on the eggs surface and perhaps offer protection from pathogens. In addition, the methodology provides a relatively rapid way to detect potentially strong ecological interactions between bacterial populations in the formation of biofilms.

New microbial tools to boost restoration and soil organic matter

Anthropogenic activities are causing unprecedented rates of soil and ecosystem degradation, and the current restoration practices take decades and are prone to high rates of failure. Here we propose, the development and application of emerging microbiome tools that can potentially improve the contents and diversity of soil organic matters, enhancing the efficacy and consistency of restoration outcomes.

Seasonal dynamics of bacterial community and co-occurrence with eukaryotic phytoplankton in the Pearl River Estuary

In this study, we investigated the taxonomic composition of the bacteria and phytoplankton communities in the Pearl River Estuary (PRE) through Illumina sequencing of the V3-V4 region of the 16 S rRNA gene. Furthermore, their relationships as well as recorded environmental variables were explored by co-occurrence networks. Bacterial community composition was different in two size fractions, as well as along the salinity gradient across two seasons. Free-living (FL) communities were dominated by pico-sized Cyanobacteria (Synechococcus CC9902) while Exiguobacterium, Halomonas and Pseudomonas were predominantly associated with particle-associated (PA) lifestyle, and Cyanobium PCC-6307 exhibited seasonal shifts in lifestyles in different seasons. In wet season, bacterial community composition was characterized by abundance of Cyanobacteria, Actinobacteria, and Bacteroidetes, which were tightly linked with high riverine inflow. While in dry season, Proteobacteria increased in prevalence, especially for Psychrobacter, NOR5/OM60 clade and Pseudomonas, which were thrived in lower water temperature and higher salinity. Moreover, we discovered that differences between PA and FL composition were more significant in the wet season than in the dry season, which may be due to better nutritional conditions of particles (indicated by POC%) in the wet season and then attract more diverse PA populations. Based on the analysis of plastidial 16 S rRNA genes, abundant small-sized mixotrophic phytoplankton (Dinophyceae, Euglenida and Haptophyta) were identified in the PRE. The complexity of co-occurrence network increased from FL to PA fractions in both seasons, which suggested that suspended particles can provide ecological niches for particle-associated colonizers contributing to the maintenance of a more stable community structure. In addition, the majority of phytoplankton species exhibited positive co-occurrences with both other phytoplankton species and bacterial counterparts, indicating the mutual cooperation between phytoplankton assemblages and specific bacterial populations e likely benefited from phytoplankton-derived organic compounds. This study enhances our understanding of the seasonal and spatial dynamics of bacterial communities and their potential relationship with phytoplankton assembly in estuarine waters.

Construction of stable microbial consortia for effective biochemical synthesis

Microbial consortia can complete otherwise arduous tasks through the cooperation of multiple microbial species. This concept has been applied to produce commodity chemicals, natural products, and biofuels. However, metabolite incompatibility and growth competition can make the microbial composition unstable, and fluctuating microbial populations reduce the efficiency of chemical production. Thus, controlling the populations and regulating the complex interactions between different strains are challenges in constructing stable microbial consortia. This Review discusses advances in synthetic biology and metabolic engineering to control social interactions within microbial cocultures, including substrate separation, byproduct elimination, crossfeeding, and quorum-sensing circuit design. Additionally, this Review addresses interdisciplinary strategies to improve the stability of microbial consortia and provides design principles for microbial consortia to enhance chemical production.