In vitro interaction network of a synthetic gut bacterial community

Synthetic microbial communities: Novel strategies to enhance the quality of traditional fermented foods

Consumers are attracted to traditional fermented foods due to their unique flavor and nutritional value. However, the traditional fermentation technique can no longer accommodate the requirements of the food industry. Traditional fermented foods produce hazardous compounds, off-odor, and anti-nutritional factors, reducing product stability. The microbial system complexity of traditional fermented foods resulting from the open fermentation process has made it challenging to regulate these problems by modifying microbial behaviors. Synthetic microbial communities (SynComs) have been shown to simplify complex microbial communities and allow for the targeted design of microbial communities, which has been applied in processing traditional fermented foods. Herein, we describe the theoretical information of SynComs, particularly microbial physiological processes and their interactions. This paper discusses current approaches to creating SynComs, including designing, building, testing, and learning, with typical applications and fundamental techniques. Based on various traditional fermented food innovation demands, the potential and application of SynComs in enhancing the quality of traditional fermented foods are highlighted. SynComs showed superior performance in regulating the quality of traditional fermented foods using the interaction of core microorganisms to reduce the hazardous compounds of traditional fermented foods and improve flavor. Additionally, we presented the current status and future perspectives of SynComs for improving the quality of traditional fermented foods.

Unraveling the Metabolic Behavior and Interspecific Interaction Pattern of Lactic Acid Bacteria within Chinese Rice Wine

The synthetic community of lactic acid bacteria (LAB) is commonly utilized in the food industry for manipulating product properties. However, the intermediate interactions and ecological stability resulting from metabolic differences among various LAB types remain poorly understood. We aimed to analyze the metabolic behavior of single and combined lactic acid bacteria in China rice wine based on microbial succession. Three-stage succession patterns with obligate heterofermentative LAB dominating prefermentation and homofermentative LAB prevailing in main fermentation were observed. Facultative heterofermentative LAB exhibited significant growth. Pairwise coculture interactions revealed 63.5% positive, 34.4% negative, and 2.1% neutral interactions, forming nontransitive and transitive competition modes. Nontransitive competitive combinations demonstrated stability over ∼200 generations through amino acid (mainly aspartic acid, glutamine, and serine) cross-feeding and lactic acid detoxification, which also showed potential for controlling biogenic amines and developing LAB starter cultures. Our findings offer insights into the mechanistic underpinnings of LAB interaction networks.

Community interactions among microbes give rise to host-microbiome mutualisms in an aquatic plant

Microbiomes often benefit plants, conferring resistance to pathogens, improving stress tolerance, or promoting plant growth. As potential plant mutualists, however, microbiomes are not a single organism but a community of species with complex interactions among microbial taxa and between microbes and their shared host. The nature of ecological interactions among microbes in the microbiome can have important consequences for the net effects of microbiomes on hosts. Here, we compared the effects of individual microbial strains and 10-strain synthetic communities on microbial productivity and host growth using the common duckweed Lemna minor and a synthetic, simplified version of its native microbiome. Except for Pseudomonas protegens, which was a mutualist when tested alone, all of the single strains we tested were commensals on hosts, benefiting from plant presence but not increasing host growth relative to uninoculated controls. However, 10-strain synthetic microbial communities increased both microbial productivity and duckweed growth more than the average single-strain inoculation and uninoculated controls, meaning that host-microbiome mutualisms can emerge from community interactions among microbes on hosts. The effects of community inoculation were sub-additive, suggesting at least some competition among microbes in the duckweed microbiome. We also investigated the relationship between L. minor fitness and that of its microbes, providing some of the first empirical estimates of broad fitness alignment between plants and members of their microbiomes; hosts grew faster with more productive microbes or microbiomes.

IMPORTANCE

There is currently substantial interest in engineering synthetic microbiomes for health or agricultural applications. One key question is how multi-strain microbial communities differ from single microbial strains in their productivity and effects on hosts. We tested 20 single bacterial strains and 2 distinct 10-strain synthetic communities on plant hosts and found that 10-strain communities led to faster host growth and greater microbial productivity than the average, but not the best, single strain. Furthermore, the microbial strains or communities that achieved the greatest cell densities were also the most beneficial to their hosts, showing that both specific single strains and multi-strain synthetic communities can engage in high-quality mutualisms with their hosts. Our results suggest that ~5% of single strains, as well as multi-strain synthetic communities comprised largely of commensal microbes, can benefit hosts and result in effective host-microbe mutualisms.

Degradation and fermentation of hyaluronic acid by Bacteroides spp. from the human gut microbiota

Bacteroides spp. are prominent members of the human gut microbiota that play critical roles in the metabolism of complex carbohydrates from the daily diet. Hyaluronic acid (HA) is a multifunctional polysaccharide which has been extensively used in the food and biomedical industry. However, how HA is degraded and fermented by Bacteroides spp. has not been fully characterized. Here, we comprehensively investigated the detailed degradation profiles and fermentation characteristics of four different HAs with discrete molecular weight (Mw) by fourteen distinctive Bacteroides spp. from the human gut microbiota. Our results indicated that high-Mw HAs were more degradable and fermentable than low-Mw HAs. Interestingly, B. salyersiae showed the best degrading capability for both high-Mw and low-Mw HAs, making it a keystone species for HA degradation among Bacteroides spp.. Specifically, HA degradation by B. salyersiae produced significant amounts of unsaturated tetrasaccharide (udp4). Co-culture experiments indicated that the produced udp4 could be further fermented and utilized by non-proficient HA-degraders, suggesting a possible cross-feeding interaction in the utilization of HA within the Bacteroides spp.. Altogether, our study provides novel insights into the metabolism of HA by the human gut microbiota, which has considerable implications for the development of new HA-based nutraceuticals and medicines.

Synthetic community derived from grafted watermelon rhizosphere provides protection for ungrafted watermelon against Fusarium oxysporum via microbial synergistic effects

BACKGROUND

Plant microbiota contributes to plant growth and health, including enhancing plant resistance to various diseases. Despite remarkable progress in understanding diseases resistance in plants, the precise role of rhizosphere microbiota in enhancing watermelon resistance against soil-borne diseases remains unclear. Here, we constructed a synthetic community (SynCom) of 16 core bacterial strains obtained from the rhizosphere of grafted watermelon plants. We further simplified SynCom and investigated the role of bacteria with synergistic interactions in promoting plant growth through a simple synthetic community.

RESULTS

Our results demonstrated that the SynCom significantly enhanced the growth and disease resistance of ungrafted watermelon grown in non-sterile soil. Furthermore, analysis of the amplicon and metagenome data revealed the pivotal role of Pseudomonas in enhancing plant health, as evidenced by a significant increase in the relative abundance and biofilm-forming pathways of Pseudomonas post-SynCom inoculation. Based on in vitro co-culture experiments and bacterial metabolomic analysis, we selected Pseudomonas along with seven other members of the SynCom that exhibited synergistic effects with Pseudomonas. It enabled us to further refine the initially constructed SynCom into a simplified SynCom comprising the eight selected bacterial species. Notably, the plant-promoting effects of simplified SynCom were similar to those of the initial SynCom. Furthermore, the simplified SynCom protected plants through synergistic effects of bacteria.

CONCLUSIONS

Our findings suggest that the SynCom proliferate in the rhizosphere and mitigate soil-borne diseases through microbial synergistic interactions, highlighting the potential of synergistic effects between microorganisms in enhancing plant health. This study provides a novel insight into using the functional SynCom as a promising solution for sustainable agriculture. Video Abstract.

Enterococcus faecalis: implications for host health

The microbiota represents a crucial area of research in maintaining human health due to its potential for uncovering novel biomarkers, therapies, and molecular mechanisms relevant to population identification and experimental model characterization. Among these microorganisms, Enterococcus faecalis, a Gram-positive bacterium found in the gastrointestinal tract of humans and animals, holds particular significance. Strains of this bacterial species have sparked considerable debate in the literature due to their dual nature; they can either be utilized as probiotics in the food industry or demonstrate resistance to antibiotics, potentially leading to severe illness, disability, and death. Given the diverse characteristics of Enterococcus faecalis strains, this review aims to provide a comprehensive understanding of their impact on various systems within the host, including the immunological, cardiovascular, metabolic, and nervous systems. Furthermore, we summarize the bacterium-host interaction characteristics and molecular effects to highlight their targets, features, and overall impact on microbial communities and host health.

Gut microbiota and metabolites as predictors of biologics response in inflammatory bowel disease: A comprehensive systematic review

Nonresponse to biologic agents in patients with inflammatory bowel disease (IBD) poses a significant public health burden, and the prediction of response to biologics offers valuable insights for IBD management. Given the pivotal role of gut microbiota and their endogenous metabolites in IBD, we conducted a systematic review to investigate the potential of fecal microbiota and mucosal microbiota and endogenous metabolomic markers as predictors for biotherapy response in IBD patients. A total of 38 studies were included in the review. Following anti-TNF-α treatment, the bacterial community characteristics of IBD patients exhibited a tendency to resemble those observed in healthy controls, indicating an improved clinical response. The levels of endogenous metabolites butyrate and deoxycholic acid were significantly associated with clinical remission following anti-TNF-α therapy. IBD patients who responded well to vedolizumab treatment had higher levels of specific bacteria that produce butyrate, along with increased levels of metabolites such as butyrate, branched-chain amino acids and acetamide following vedolizumab treatment. Crohn's disease patients who responded positively to ustekinumab treatment showed higher levels of Faecalibacterium and lower levels of Escherichia/Shigella. In conclusion, fecal microbiota and mucosal microbiota as well as their endogenous metabolites could provide a predictive tool for assessing the response of IBD patients to various biological agents and serve as a valuable reference for precise drug selection in clinical IBD patients.

Gut microbial interactions based on network construction and bacterial pairwise cultivation

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.

High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants

Our understanding of how microbes respond to micropollutants, such as pesticides, is almost wholly based on single-species responses to individual chemicals. However, in natural environments, microbes experience multiple pollutants simultaneously. Here we perform a matrix of multi-stressor experiments by assaying the growth of model and non-model strains of bacteria in all 255 combinations of 8 chemical stressors (antibiotics, herbicides, fungicides and pesticides). We found that bacterial strains responded in different ways to stressor mixtures, which could not be predicted simply from their phylogenetic relatedness. Increasingly complex chemical mixtures were both more likely to negatively impact bacterial growth in monoculture and more likely to reveal net interactive effects. A mixed co-culture of strains proved more resilient to increasingly complex mixtures and revealed fewer interactions in the growth response. These results show predictability in microbial population responses to chemical stressors and could increase the utility of next-generation eco-toxicological assays.

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.

Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags

Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that require intrastrain resolution. Here we developed a genomic tagging system, termed wild-type isogenic standardized hybrid (WISH)-tags, that can be combined with quantitative polymerase chain reaction and next-generation sequencing for microbial strain enumeration. We experimentally validated the performance of 62 tags and showed that they can be differentiated with high precision. WISH-tags were introduced into model and non-model bacterial members of the mouse and plant microbiota. Intrastrain priority effects were tested using one species of isogenic barcoded bacteria in the murine gut and the Arabidopsis phyllosphere, both with and without microbiota context. We observed colonization resistance against late-arriving strains of Salmonella Typhimurium in the mouse gut, whereas the phyllosphere accommodated Sphingomonas latecomers in a manner proportional to their presence at the late inoculation timepoint. This demonstrates that WISH-tags are a resource for deciphering population dynamics underlying microbiome assembly across biological systems.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Synthetic Communities of Gut Microbes for Basic Research and Translational Approaches in Animal Health and Nutrition

Microbes and animals have a symbiotic relationship that greatly influences nutrient uptake and animal health. This relationship can be studied using selections of microbes termed synthetic communities, or SynComs. SynComs are used in many different animal hosts, including agricultural animals, to investigate microbial interactions with nutrients and how these affect animal health. The most common host focuses for SynComs are currently mouse and human, from basic mechanistic research through to translational disease models and live biotherapeutic products (LBPs) as treatments. We discuss SynComs used in basic research models and findings that relate to human and animal health and nutrition. Translational use cases of SynComs are discussed, followed by LBPs, especially within the context of agriculture. SynComs still face challenges, such as standardization for reproducibility and contamination risks. However, the future of SynComs is hopeful, especially in the areas of genome-guided SynCom design and custom SynCom-based treatments.

Contribution of bacterial and host factors to pathogen "blooming" in a gnotobiotic mouse model for Salmonella enterica serovar Typhimurium-induced enterocolitis

Inflammation has a pronounced impact on the intestinal ecosystem by driving an expansion of facultative anaerobic bacteria at the cost of obligate anaerobic microbiota. This pathogen "blooming" is also a hallmark of enteric Salmonella enterica serovar Typhimurium (S. Tm) infection. Here, we analyzed the contribution of bacterial and host factors to S. Tm "blooming" in a gnotobiotic mouse model for S. Tm-induced enterocolitis. Mice colonized with the Oligo-Mouse-Microbiota (OMM12), a minimal bacterial community, develop fulminant colitis by day 4 after oral infection with wild-type S. Tm but not with an avirulent mutant. Inflammation leads to a pronounced reduction in overall intestinal bacterial loads, distinct microbial community shifts, and pathogen blooming (relative abundance >50%). S. Tm mutants attenuated in inducing gut inflammation generally elicit less pronounced microbiota shifts and reduction in total bacterial loads. In contrast, S. Tm mutants in nitrate respiration, salmochelin production, and ethanolamine utilization induced strong inflammation and S. Tm "blooming." Therefore, individual Salmonella-specific inflammation-fitness factors seem to be of minor importance for competition against this minimal microbiota in the inflamed gut. Finally, we show that antibody-mediated neutrophil depletion normalized gut microbiota loads but not intestinal inflammation or microbiota shifts. This suggests that neutrophils equally reduce pathogen and commensal bacterial loads in the inflamed gut.

Sparse species interactions reproduce abundance correlation patterns in microbial communities

During the last decades, macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the dynamical processes behind them. In particular, abundance fluctuations of different species are found to be correlated, both across time and across communities in metagenomic samples. Reproducing such correlations through appropriate population models remains an open challenge. The present paper tackles this problem and points to sparse species interactions as a necessary mechanism to account for them. Specifically, we discuss several possibilities to include interactions in population models and recognize Lotka-Volterra constants as a successful ansatz. For this, we design a Bayesian inference algorithm to extract sets of interaction constants able to reproduce empirical probability distributions of pairwise correlations for diverse biomes. Importantly, the inferred models still reproduce well-known single-species macroecological patterns concerning abundance fluctuations across both species and communities. Endorsed by the agreement with the empirically observed phenomenology, our analyses provide insights into the properties of the networks of microbial interactions, revealing that sparsity is a crucial feature.

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.

Richness and density jointly determine context dependence in bacterial interactions

Pairwise interactions are often used to predict features of complex microbial communities due to the challenge of measuring multi-species interactions in high dimensional contexts. This assumes that interactions are unaffected by community context. Here, we used synthetic bacterial communities to investigate that assumption by observing how interactions varied across contexts. Interactions were most often weakly negative and showed a phylogenetic signal among genera. Community richness and total density emerged as strong predictors of interaction strength and contributed to an attenuation of interactions as richness increased. Population level and per-capita measures of interactions both displayed such attenuation, suggesting factors beyond systematic changes in population size were involved; namely, changes to the interactions themselves. Nevertheless, pairwise interactions retained some explanatory power across contexts, provided those contexts were not substantially divergent in richness. These results suggest that understanding the emergent properties of microbial interactions can improve our ability to predict the features of microbial communities.

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.

Gut microbiota depletion delays somatic peripheral nerve development and impairs neuromuscular junction maturation

Gut microbiota is responsible for essential functions in human health. Several communication axes between gut microbiota and other organs via neural, endocrine, and immune pathways have been described, and perturbation of gut microbiota composition has been implicated in the onset and progression of an emerging number of diseases. Here, we analyzed peripheral nerves, dorsal root ganglia (DRG), and skeletal muscles of neonatal and young adult mice with the following gut microbiota status: a) germ-free (GF), b) gnotobiotic, selectively colonized with 12 specific gut bacterial strains (Oligo-Mouse-Microbiota, OMM12), or c) natural complex gut microbiota (CGM). Stereological and morphometric analyses revealed that the absence of gut microbiota impairs the development of somatic median nerves, resulting in smaller diameter and hypermyelinated axons, as well as in smaller unmyelinated fibers. Accordingly, DRG and sciatic nerve transcriptomic analyses highlighted a panel of differentially expressed developmental and myelination genes. Interestingly, the type III isoform of Neuregulin1 (NRG1), known to be a neuronal signal essential for Schwann cell myelination, was overexpressed in young adult GF mice, with consequent overexpression of the transcription factor Early Growth Response 2 (Egr2), a fundamental gene expressed by Schwann cells at the onset of myelination. Finally, GF status resulted in histologically atrophic skeletal muscles, impaired formation of neuromuscular junctions, and deregulated expression of related genes. In conclusion, we demonstrate for the first time a gut microbiota regulatory impact on proper development of the somatic peripheral nervous system and its functional connection to skeletal muscles, thus suggesting the existence of a novel 'Gut Microbiota-Peripheral Nervous System-axis.'

Bacteria and bacteriophage consortia are associated with protective intestinal metabolites in patients receiving stem cell transplantation

The microbiome is a predictor of clinical outcome in patients receiving allogeneic hematopoietic stem cell transplantation (allo-SCT). Microbiota-derived metabolites can modulate these outcomes. How bacteria, fungi and viruses contribute to the production of intestinal metabolites is still unclear. We combined amplicon sequencing, viral metagenomics and targeted metabolomics from stool samples of patients receiving allo-SCT (n = 78) and uncovered a microbiome signature of Lachnospiraceae and Oscillospiraceae and their associated bacteriophages, correlating with the production of immunomodulatory metabolites (IMMs). Moreover, we established the IMM risk index (IMM-RI), which was associated with improved survival and reduced relapse. A high abundance of short-chain fatty acid-biosynthesis pathways, specifically butyric acid via butyryl-coenzyme A (CoA):acetate CoA-transferase (BCoAT, which catalyzes EC 2.8.3.8) was detected in IMM-RI low-risk patients, and virome genome assembly identified two bacteriophages encoding BCoAT as an auxiliary metabolic gene. In conclusion, our study identifies a microbiome signature associated with protective IMMs and provides a rationale for considering metabolite-producing consortia and metabolite formulations as microbiome-based therapies.

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.

Integrated multi-omics reveals the roles of cecal microbiota and its derived bacterial consortium in promoting chicken growth

IMPORTANCE

The improvement of chicken growth performance is one of the major concerns for the poultry industry. Gut microbes are increasingly evidenced to be associated with chicken physiology and metabolism, thereby influencing chicken growth and development. Here, through integrated multi-omics analyses, we showed that chickens from the same line differing in their body weight were very different in their gut microbiota structure and host-microbiota crosstalk; microbes in high body weight (HBW) chickens contributed to chicken growth by regulating the gut function and homeostasis. We also verified that a specific bacterial consortium consisting of isolates from the HBW chickens has the potential to be used as chicken growth promoters. These findings provide new insights into the potential links between gut microbiota and chicken phenotypes, shedding light on future manipulation of chicken gut microbiota to improve chicken growth performance.

Metabolic interactions affect the biomass of synthetic bacterial biofilm communities

IMPORTANCE

Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.

Amino acid auxotrophies in human gut bacteria are linked to higher microbiome diversity and long-term stability

Amino acid auxotrophies are prevalent among bacteria. They can govern ecological dynamics in microbial communities and indicate metabolic cross-feeding interactions among coexisting genotypes. Despite the ecological importance of auxotrophies, their distribution and impact on the diversity and function of the human gut microbiome remain poorly understood. This study performed the first systematic analysis of the distribution of amino acid auxotrophies in the human gut microbiome using a combined metabolomic, metagenomic, and metabolic modeling approach. Results showed that amino acid auxotrophies are ubiquitous in the colon microbiome, with tryptophan auxotrophy being the most common. Auxotrophy frequencies were higher for those amino acids that are also essential to the human host. Moreover, a higher overall abundance of auxotrophies was associated with greater microbiome diversity and stability, and the distribution of auxotrophs was found to be related to the human host's metabolome, including trimethylamine oxide, small aromatic acids, and secondary bile acids. Thus, our results suggest that amino acid auxotrophies are important factors contributing to microbiome ecology and host-microbiome metabolic interactions.

Metabolic exchanges are ubiquitous in natural microbial communities

Microbial communities drive global biogeochemical cycles and shape the health of plants and animals-including humans. Their structure and function are determined by ecological and environmental interactions that govern the assembly, stability and evolution of microbial communities. A widely held view is that antagonistic interactions such as competition predominate in microbial communities and are ecologically more important than synergistic interactions-for example, mutualism or commensalism. Over the past decade, however, a more nuanced picture has emerged, wherein bacteria, archaea and fungi exist within interactive networks in which they exchange essential and non-essential metabolites. These metabolic interactions profoundly impact not only the physiology, ecology and evolution of the strains involved, but are also central to the functioning of many, if not all, microbiomes. Therefore, we advocate for a balanced view of microbiome ecology that encompasses both synergistic and antagonistic interactions as key forces driving the structure and dynamics within microbial communities.

Dynamic social interactions and keystone species shape the diversity and stability of mixed-species biofilms - an example from dairy isolates

Identifying interspecies interactions in mixed-species biofilms is a key challenge in microbial ecology and is of paramount importance given that interactions govern community functionality and stability. We previously reported a bacterial four-species biofilm model comprising Stenotrophomonas rhizophila, Bacillus licheniformis, Microbacterium lacticum, and Calidifontibacter indicus that were isolated from the surface of a dairy pasteuriser after cleaning and disinfection. These bacteria produced 3.13-fold more biofilm mass compared to the sum of biofilm masses in monoculture. The present study confirms that the observed community synergy results from dynamic social interactions, encompassing commensalism, exploitation, and amensalism. M. lacticum appears to be the keystone species as it increased the growth of all other species that led to the synergy in biofilm mass. Interactions among the other three species (in the absence of M. lacticum) also contributed towards the synergy in biofilm mass. Biofilm inducing effects of bacterial cell-free-supernatants were observed for some combinations, revealing the nature of the observed synergy, and addition of additional species to dual-species combinations confirmed the presence of higher-order interactions within the biofilm community. Our findings provide understanding of bacterial interactions in biofilms which can be used as an interaction-mediated approach for cultivating, engineering, and designing synthetic bacterial communities.

Inulin-enriched Megamonas funiformis ameliorates metabolic dysfunction-associated fatty liver disease by producing propionic acid

Accumulated evidence supports the beneficial role of inulin in alleviating metabolic dysfunction-associated fatty liver disease (MAFLD) by modulating gut microbiota. However, the underlying mechanisms are not fully understood. Here we used high-fat diet (HFD)-induced laying hen model of MAFLD to investigate the effect of inulin on ameliorating MAFLD and found that the inulin-enriched Megamonas genus was inversely correlated with hepatic steatosis-related parameters. Oral administration of a newly isolated commensal bacterium by culturomics, M. funiformis CML154, to HFD-fed hens and mice ameliorated MAFLD, changed liver gene expression profiles, and increased intestinal propionate concentration. Further evidence demonstrated that the anti-MAFLD effect of M. funiformis CML154 is attributed to propionate-mediated activation of the APN-AMPK-PPARα signaling pathway, thereby inhibiting fatty acid de novo synthesis and promoting β-oxidation. These findings establish the causal relationships among inulin, M. funiformis, and MAFLD, and suggest that M. funiformis CML154 is a probiotic candidate for preventative or therapeutic intervention of MAFLD.

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

Metabolic network construction reveals probiotic-specific alterations in the metabolic activity of a synthetic small intestinal community

IMPORTANCE

The development of probiotic therapies targeted at the small intestinal microbiota represents a significant advancement in the field of probiotic interventions. This region poses unique opportunities due to its low number of gut microbiota, along with the presence of heightened immune and metabolic host responses. However, progress in this area has been hindered by a lack of detailed understanding regarding the molecular mechanisms through which probiotics exert their effects in the small intestine. Our study, utilizing a synthetic community of three small intestinal bacterial strains and the addition of two different probiotic species, and kynurenine as a representative dietary or endogenously produced compound, highlights the importance of selecting probiotic species with diverse genetic capabilities that complement the functional capacity of the resident microbiota, or alternatively, constructing a multispecies formula. This approach holds great promise for the development of effective probiotic therapies and underscores the need to consider the functional capacity of probiotic species when designing interventions.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Inter-bacterial mutualism promoted by public goods in a system characterized by deterministic temperature variation

Mutualism is commonly observed in nature but not often reported for bacterial communities. Although abiotic stress is thought to promote microbial mutualism, there is a paucity of research in this area. Here, we monitor microbial communities in a quasi-natural composting system, where temperature variation (20 °C-70 °C) is the main abiotic stress. Genomic analyses and culturing experiments provide evidence that temperature selects for slow-growing and stress-tolerant strains (i.e., Thermobifida fusca and Saccharomonospora viridis), and mutualistic interactions emerge between them and the remaining strains through the sharing of cobalamin. Comparison of 3000 bacterial pairings reveals that mutualism is common (~39.1%) and competition is rare (~13.9%) in pairs involving T. fusca and S. viridis. Overall, our work provides insights into how high temperature can favour mutualism and reduce competition at both the community and species levels.

Microbial Biofilms for Environmental Bioremediation of Heavy Metals: a Review

Heavy metal pollution caused due to various industrial and mining activities poses a serious threat to all forms of life in the environment because of the persistence and toxicity of metal ions. Microbial-mediated bioremediation including microbial biofilms has received significant attention as a sustainable tool for heavy metal removal as it is considered safe, effective, and feasible. The biofilm matrix is dynamic, having microbial cells as major components with constantly changing and evolving microenvironments. This review summarizes the bioremediation potential of bacterial biofilms for different metal ions. The composition and mechanism of biofilm formation along with interspecies communication among biofilm-forming bacteria have been discussed. The interaction of biofilm-associated microbes with heavy metals takes place through a variety of mechanisms. These include biosorption and bioaccumulation in which the microbes interact with the metal ions leading to their conversion from a highly toxic form to a less toxic form. Such interactions are facilitated via the negative charge of the extracellular polymeric substances on the surface of the biofilm with the positive charge of the metal ions and the high cell densities and high concentrations of cell-cell signaling molecules within the biofilm matrix. Furthermore, the impact of the anodic and cathodic redox potentials in a bioelectrochemical system (BES) for the reduction, removal, and recovery of numerous heavy metal species provides an interesting insight into the bacterial biofilm-mediated bioelectroremediation process. The review concludes that biofilm-linked bioremediation is a viable option for the mitigation of heavy metal pollution in water and ecosystem recovery.

Quantitative differences in synthetic gut microbial inoculums do not affect the final stabilized in vitro community compositions

In vitro studies of synthetic gut microbial communities (SGMCs) can provide valuable insights into the ecological structure and function of gut microbiota. However, the importance of the quantitative composition of an SGMC inoculum and its effect on the eventual stable in vitro microbial community has not been studied. To address this, we constructed two 114-member SGMCs differing only in their quantitative composition-one reflecting the average human fecal microbiome and another mixed in equal proportions based on cell counts. We inoculated each in an automated anaerobic multi-stage in vitro gut fermentor simulating two different colonic conditions, mimicking proximal and distal colons. We replicated this setup with two different nutrient media, periodically sampled the cultures for 27 days, and profiled their microbiome compositions using 16S rRNA gene amplicon sequencing. While nutrient medium explained 36% of the variance in microbiome composition, initial inoculum composition failed to show a statistically significant effect. Under all four conditions, paired fecal and equal SGMC inoculums converged to reach stable community compositions resembling each other. Our results have broad implications for simplifying in vitro SGMC investigations. IMPORTANCE In vitro cultivation of synthetic gut microbial communities (SGMCs) can provide valuable insights into the ecological structure and function of gut microbiota. However, it is currently not known whether the quantitative composition of the initial inoculum can influence the eventual stable in vitro community structure. Hence, using two SGMC inoculums consisting of 114 unique species mixed in either equal proportions (Eq inoculum) or resembling proportions in an average human fecal microbiome (Fec inoculum), we show that initial inoculum compositions did not influence the final stable community structure in a multi-stage in vitro gut fermentor. Under two different nutrient media and two different colon conditions (proximal and distal), both Fec and Eq communities converged to resemble each other's community structure. Our results suggest that the time-consuming preparation of SGMC inoculums may not be needed and has broad implications for in vitro SGMC studies.

Nutritional and host environments determine community ecology and keystone species in a synthetic gut bacterial community

A challenging task to understand health and disease-related microbiome signatures is to move beyond descriptive community-level profiling towards disentangling microbial interaction networks. Using a synthetic gut bacterial community, we aimed to study the role of individual members in community assembly, identify putative keystone species and test their influence across different environments. Single-species dropout experiments reveal that bacterial strain relationships strongly vary not only in different regions of the murine gut, but also across several standard culture media. Mechanisms involved in environment-dependent keystone functions in vitro include exclusive access to polysaccharides as well as bacteriocin production. Further, Bacteroides caecimuris and Blautia coccoides are found to play keystone roles in gnotobiotic mice by impacting community composition, the metabolic landscape and inflammatory responses. In summary, the presented study highlights the strong interdependency between bacterial community ecology and the biotic and abiotic environment. These results question the concept of universally valid keystone species in the gastrointestinal ecosystem and underline the context-dependency of both, keystone functions and bacterial interaction networks.

Antibiotic resistance in bacterial communities

Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem.

Bacteriophages targeting protective commensals impair resistance against Salmonella Typhimurium infection in gnotobiotic mice

Gut microbial communities protect the host against a variety of major human gastrointestinal pathogens. Bacteriophages (phages) are ubiquitous in nature and frequently ingested via food and drinking water. Moreover, they are an attractive tool for microbiome engineering due to the lack of known serious adverse effects on the host. However, the functional role of phages within the gastrointestinal microbiome remain poorly understood. Here, we investigated the effects of microbiota-directed phages on infection with the human enteric pathogen Salmonella enterica serovar Typhimurium (S. Tm), using a gnotobiotic mouse model (OMM14) for colonization resistance (CR). We show, that phage cocktails targeting Escherichia coli and Enterococcus faecalis acted in a strain-specific manner. They transiently reduced the population density of their respective target before establishing coexistence for up to 9 days. Infection susceptibility to S. Tm was markedly increased at an early time point after challenge with both phage cocktails. Surprisingly, OMM14 mice were also susceptible 7 days after a single phage inoculation, when the targeted bacterial populations were back to pre-phage administration density. Concluding, our work shows that phages that dynamically modulate the density of protective members of the gut microbiota can provide opportunities for invasion of bacterial pathogens, in particular at early time points after phage application. This suggests, that phages targeting protective members of the microbiota may increase the risk for Salmonella infection.

Metabolic interaction models recapitulate leaf microbiota ecology

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

Universal gut microbial relationships in the gut microbiome of wild baboons

Ecological relationships between bacteria mediate the services that gut microbiomes provide to their hosts. Knowing the overall direction and strength of these relationships is essential to learn how ecology scales up to affect microbiome assembly, dynamics, and host health. However, whether bacterial relationships are generalizable across hosts or personalized to individual hosts is debated. Here, we apply a robust, multinomial logistic-normal modeling framework to extensive time series data (5534 samples from 56 baboon hosts over 13 years) to infer thousands of correlations in bacterial abundance in individual baboons and test the degree to which bacterial abundance correlations are 'universal'. We also compare these patterns to two human data sets. We find that, most bacterial correlations are weak, negative, and universal across hosts, such that shared correlation patterns dominate over host-specific correlations by almost twofold. Further, taxon pairs that had inconsistent correlation signs (either positive or negative) in different hosts always had weak correlations within hosts. From the host perspective, host pairs with the most similar bacterial correlation patterns also had similar microbiome taxonomic compositions and tended to be genetic relatives. Compared to humans, universality in baboons was similar to that in human infants, and stronger than one data set from human adults. Bacterial families that showed universal correlations in human infants were often universal in baboons. Together, our work contributes new tools for analyzing the universality of bacterial associations across hosts, with implications for microbiome personalization, community assembly, and stability, and for designing microbiome interventions to improve host health.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Gut-on-a-Chip for the Analysis of Bacteria-Bacteria Interactions in Gut Microbial Community: What Would Be Needed for Bacterial Co-Culture Study to Explore the Diet-Microbiota Relationship?

Bacterial co-culture studies using synthetic gut microbiomes have reported novel research designs to understand the underlying role of bacterial interaction in the metabolism of dietary resources and community assembly of complex microflora. Since lab-on-a-chip mimicking the gut (hereafter "gut-on-a-chip") is one of the most advanced platforms for the simulative research regarding the correlation between host health and microbiota, the co-culture of the synthetic bacterial community in gut-on-a-chip is expected to reveal the diet-microbiota relationship. This critical review analyzed recent research on bacterial co-culture with perspectives on the ecological niche of commensals, probiotics, and pathogens to categorize the experimental approaches for diet-mediated management of gut health as the compositional and/or metabolic modulation of the microbiota and the control of pathogens. Meanwhile, the aim of previous research on bacterial culture in gut-on-a-chip has been mainly limited to the maintenance of the viability of host cells. Thus, the integration of study designs established for the co-culture of synthetic gut consortia with various nutritional resources into gut-on-a-chip is expected to reveal bacterial interspecies interactions related to specific dietary patterns. This critical review suggests novel research topics for co-culturing bacterial communities in gut-on-a-chip to realize an ideal experimental platform mimicking a complex intestinal environment.

Nodule-associated diazotrophic community succession is driven by developmental phases combined with microhabitat of Sophora davidii

Nodule-associated nitrogen-fixing microorganisms (diazotrophs) residing in legume root nodules, and they have the potential to enhance legume survival. However, the succession characteristics and mechanisms of leguminous diazotrophic communities remain largely unexplored. We performed a high-throughput nifH amplicon sequencing with samples of root nodules and soil in the three developmental phases (young nodules, active nodules and senescent nodules) of the Sophora davidii (Franch.) Skeels root nodules, aiming to investigate the dynamics of nodule-endophytic diazotrophs during three developmental phases of root nodules. The results demonstrated the presence of diverse diazotrophic bacteria and successional community shifting dominated by Mesorhizobium and Bradyrhizobium inside the nodule according to the nodule development. The relative abundance decreased for Mesorhizobium, while decreased first and then increased for Bradyrhizobium in nodule development from young to active to senescent. Additionally, strains M. amorphae BT-30 and B. diazoefficiens B-26 were isolated and selected to test the interaction between them in co-cultured conditions. Under co-culture conditions: B. diazoefficiens B-26 significantly inhibited the growth of M. amorphae BT-30. Intriguingly, growth of B. diazoefficiens B-26 was significantly promoted by co'culture with M. amorphae BT-30 and could utilize some carbon and nitrogen sources that M. amorphae BT-30 could not. Additionally, the composition of microbial community varied in root nodules, in rhizosphere and in bulk soil. Collectively, our study highlights that developmental phases of nodules and the host microhabitat were the key driving factors for the succession of nodule-associated diazotrophic community.

The Biological Significance of Pyruvate Sensing and Uptake in Salmonella enterica Serovar Typhimurium

Pyruvate (CH₃COCOOH) is the simplest of the alpha-keto acids and is at the interface of several metabolic pathways both in prokaryotes and eukaryotes. In an amino acid-rich environment, fast-growing bacteria excrete pyruvate instead of completely metabolizing it. The role of pyruvate uptake in pathological conditions is still unclear. In this study, we identified two pyruvate-specific transporters, BtsT and CstA, in Salmonella enterica serovar Typhimurium (S. Typhimurium). Expression of btsT is induced by the histidine kinase/response regulator system BtsS/BtsR upon sensing extracellular pyruvate, whereas expression of cstA is maximal in the stationary phase. Both pyruvate transporters were found to be important for the uptake of this compound, but also for chemotaxis to pyruvate, survival under oxidative and nitrosative stress, and persistence of S. Typhimurium in response to gentamicin. Compared with the wild-type cells, the ΔbtsTΔcstA mutant has disadvantages in antibiotic persistence in macrophages, as well as in colonization and systemic infection in gnotobiotic mice. These data demonstrate the surprising complexity of the two pyruvate uptake systems in S. Typhimurium.

Probiotics, Prebiotics, and Phytogenic Substances for Optimizing Gut Health in Poultry

The gut microbiota has been designated as a hidden metabolic ‘organ’ because of its enormous impact on host metabolism, physiology, nutrition, and immune function. The connection between the intestinal microbiota and their respective host animals is dynamic and, in general, mutually beneficial. This complicated interaction is seen as a determinant of health and disease; thus, intestinal dysbiosis is linked with several metabolic diseases. Therefore, tractable strategies targeting the regulation of intestinal microbiota can control several diseases that are closely related to inflammatory and metabolic disorders. As a result, animal health and performance are improved. One of these strategies is related to dietary supplementation with prebiotics, probiotics, and phytogenic substances. These supplements exert their effects indirectly through manipulation of gut microbiota quality and improvement in intestinal epithelial barrier. Several phytogenic substances, such as berberine, resveratrol, curcumin, carvacrol, thymol, isoflavones and hydrolyzed fibers, have been identified as potential supplements that may also act as welcome means to reduce the usage of antibiotics in feedstock, including poultry farming, through manipulation of the gut microbiome. In addition, these compounds may improve the integrity of tight junctions by controlling tight junction-related proteins and inflammatory signaling pathways in the host animals. In this review, we discuss the role of probiotics, prebiotics, and phytogenic substances in optimizing gut function in poultry.

Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities

With a growing world population and increasing frequency of climate disturbance events, we are in dire need of methods to improve plant productivity, resilience, and resistance to both abiotic and biotic stressors, both for agriculture and conservation efforts. Microorganisms play an essential role in supporting plant growth, environmental response, and susceptibility to disease. However, understanding the specific mechanisms by which microbes interact with each other and with plants to influence plant phenotypes is a major challenge due to the complexity of natural communities, simultaneous competition and cooperation effects, signalling interactions, and environmental impacts. Synthetic communities are a major asset in reducing the complexity of these systems by simplifying to dominant components and isolating specific variables for controlled experiments, yet there still remains a large gap in our understanding of plant microbiome interactions. This perspectives article presents a brief review discussing ways in which metabolic modelling can be used in combination with synthetic communities to continue progress toward understanding the complexity of plant-microbe-environment interactions. We highlight the utility of metabolic models as applied to a community setting, identify different applications for both flux balance and elementary flux mode simulation approaches, emphasize the importance of ecological theory in guiding data interpretation, and provide ideas for how the integration of metabolic modelling techniques with big data may bridge the gap between simplified synthetic communities and the complexity of natural plant-microbe systems.

Resistance is futile? Mucosal immune mechanisms in the context of microbial ecology and evolution

In the beginning it was simple: we injected a protein antigen and studied the immune responses against the purified protein. This elegant toolbox uncovered thousands of mechanisms via which immune cells are activated. However, when we consider immune responses against real infectious threats, this elegant simplification misses half of the story: the infectious agents are typically evolving orders-of-magnitude faster than we are. Nowhere is this more pronounced than in the mammalian large intestine. A bacterium representing only 0.1% of the human gut microbiota will have a population size of 10⁹ clones, each actively replicating. Moreover, the evolutionary pressure from other microbes is at least as profound as direct effects of the immune system. Therefore, to really understand intestinal immune mechanisms, we need to understand both the host response and how rapid microbial evolution alters the apparent outcome of the response. In this review we use the examples of intestinal inflammation and secretory immunoglobulin A (SIgA) to highlight what is already known (Fig. 1). Further, we will explore how these interactions can inform immunotherapy and prophylaxis. This has major implications for how we design effective mucosal vaccines against increasingly drug-resistant bacterial pathogens Fig. 1 THE IMMUNE RESPONSE SHAPES THE FITNESS LANDSCAPE IN THE GASTRO-INTESTINAL TRACT.: The red arrows depict possible evolutionary paths of a novel colonizer along adaptive peaks in the intestinal fitness landscapes that change with the status of the host immune system. The flat surfaces represent the non-null fitness baselines (values x or y) at which a bacterium can establish at minimum carrying capacity. a In the healthy gut, metabolic competence, resistance to aggressions by competitors and predators, swift adaptation to rapid fluctuations as well as surviving acidic pH and the flow of the intestinal content, represent potent selective pressures and as many opportunities for bacteria to increase fitness by phenotypic or genetic variations. b When pathogens trigger acute inflammation, bacteria must adapt to iron starvation, killing by immune cells and antimicrobial peptides, and oxidative stress, while new metabolic opportunities emerge. c When high-affinity SIgA are produced against a bacterium, e.g., after oral vaccination, escape of SIgA by altering or losing surface epitopes becomes crucial for maximum fitness. However, escaping polyvalent SIgA responses after vaccination with "evolutionary trap" vaccines leads to evolutionary trade-offs: A fitness maximum is reached in the vaccinated host gut that represents a major disadvantage for transmission into naïve hosts (fitness diminished below x) (d).