Deciphering microbial interactions in synthetic human gut microbiome communities

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

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.

Unveiling the fungal diversity and associated secondary metabolism on black apples

Black apples are the result of late-stage microbial decomposition after falling to the ground. This phenomenon is highly comparable from year to year, with the filamentous fungus Monilinia fructigena most commonly being the first invader, followed by Penicillium expansum. Motivated by the fact that only little chemistry has been reported from apple microbiomes, we set out to investigate the chemical diversity and potential ecological roles of secondary metabolites (SMs) in a total of 38 black apples. Metabolomics analyses were conducted on either whole apples or small excisions of fungal biomass derived from black apples. Annotation of fungal SMs in black apple extracts was aided by the cultivation of 15 recently isolated fungal strains on 9 different substrates in a One Strain Many Compounds (OSMAC) approach, leading to the identification of 3,319 unique chemical features. Only 6.4% were attributable to known compounds based on analysis of high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS/MS) data using spectral library matching tools. Of the 1,606 features detected in the black apple extracts, 32% could be assigned as fungal-derived, due to their presence in the OSMAC-based training data set. Notably, the detection of several antifungal compounds indicates the importance of such compounds for the invasion of and control of other microbial competitors on apples. In conclusion, the diversity and abundance of microbial SMs on black apples were found to be much higher than that typically observed for other environmental microbiomes. Detection of SMs known to be produced by the six fungal species tested also highlights a succession of fungal growth following the initial invader M. fructigena.IMPORTANCEMicrobial secondary metabolites constitute a significant reservoir of biologically potent and clinically valuable chemical scaffolds. However, their usefulness is hampered by rapidly developing resistance, resulting in reduced profitability of such research endeavors. Hence, the ecological role of such microbial secondary metabolites must be considered to understand how best to utilize such compounds as chemotherapeutics. Here, we explore an under-investigated environmental microbiome in the case of black apples; a veritable "low-hanging fruit," with relatively high abundances and diversity of microbially produced secondary metabolites. Using both a targeted and untargeted metabolomics approach, the interplay between metabolites, other microbes, and the apple host itself was investigated. This study highlights the surprisingly low incidence of known secondary metabolites in such a system, highlighting the need to study the functionality of secondary metabolites in microbial interactions and complex microbiomes.

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.

Framework for exploring the sensory repertoire of the human gut microbiota

Bacteria sense changes in their environment and transduce signals to adjust their cellular functions accordingly. For this purpose, bacteria employ various sensors feeding into multiple signal transduction pathways. Signal recognition by bacterial sensors is studied mainly in a few model organisms, but advances in genome sequencing and analysis offer new ways of exploring the sensory repertoire of many understudied organisms. The human gut is a natural target of this line of study: it is a nutrient-rich and dynamic environment and is home to thousands of bacterial species whose activities impact human health. Many gut commensals are also poorly studied compared to model organisms and are mainly known through their genome sequences. To begin exploring the signals human gut commensals sense and respond to, we have designed a framework that enables the identification of sensory domains, prediction of signals that they recognize, and experimental verification of these predictions. We validate this framework's functionality by systematically identifying amino acid sensors in selected bacterial genomes and metagenomes, characterizing their amino acid binding properties, and demonstrating their signal transduction potential.IMPORTANCESignal transduction is a central process governing how bacteria sense and respond to their environment. The human gut is a complex environment with many living organisms and fluctuating streams of nutrients. One gut inhabitant, Escherichia coli, is a model organism for studying signal transduction. However, E. coli is not representative of most gut microbes, and signaling pathways in the thousands of other organisms comprising the human gut microbiota remain poorly understood. This work provides a foundation for how to explore signals recognized by these organisms.

Cooperative growth in microbial communities is a driver of multistability

Microbial communities often exhibit more than one possible stable composition for the same set of external conditions. In the human microbiome, these persistent changes in species composition and abundance are associated with health and disease states, but the drivers of these alternative stable states remain unclear. Here we experimentally demonstrate that a cross-kingdom community, composed of six species relevant to the respiratory tract, displays four alternative stable states each dominated by a different species. In pairwise coculture, we observe widespread bistability among species pairs, providing a natural origin for the multistability of the full community. In contrast with the common association between bistability and antagonism, experiments reveal many positive interactions within and between community members. We find that multiple species display cooperative growth, and modeling predicts that this could drive the observed multistability within the community as well as non-canonical pairwise outcomes. A biochemical screening reveals that glutamate either reduces or eliminates cooperativity in the growth of several species, and we confirm that such supplementation reduces the extent of bistability across pairs and reduces multistability in the full community. Our findings provide a mechanistic explanation of how cooperative growth rather than competitive interactions can underlie multistability in microbial communities.

Responses of growth performance, immunity, disease resistance of shrimp and microbiota in Penaeus vannamei culture system to Bacillus subtilis BSXE-1601 administration: Dietary supplementation versus water addition

This study evaluated the effects of Bacillus subtilis BSXE-1601, applied either as dietary supplementation or water addition, on growth performance, immune responses, disease resistance of Penaeus vannamei, and microbiota in shrimp gut and rearing water. During the 42-day feeding experiment, shrimp were fed with basal diet (CO and BW group), basal diet supplemented with live strain BSXE-1601 at the dose of 1 × 109 CFU kg-1 feed (BD group) and 15 mg kg-1 florfenicol (FL group), and basal diet with strain BSXE-1601 added to water at the concentration of 1 × 107 CFU L-1 every five days (BW group). Results showed that dietary supplementation of strain BSXE-1601 significantly promoted growth performance of shrimp, both in the diet and water, enhanced disease resistance against Vibrio parahaemolyticus (P < 0.05). The BD and BW groups exhibited significant increases in acid phosphatase, alkaline phosphatase, lysozyme, peroxidase, superoxide dismutase activities, phenonoloxidase content in the serum of shrimp compared to the control (P < 0.05). Meanwhile, the expression of immune-related genes proPO, LZM, SOD, LGBP, HSP70, Imd, Toll, Relish, TOR, 4E-BP, eIF4E1α, eIF4E2 were significantly up-regulated compared to the control (P < 0.05). When added in rearing water, strain BSXE-1601 induced greater immune responses in shrimp than the dietary supplement (P < 0.05). Chao1 and Shannon indices of microbiota in rearing water were significantly lower in BD group than in the control. The microbiota in rearing water were significantly altered in BD, BW and FL groups compared to the control, while no significant impacts were observed on the microbiota of shrimp gut. When supplemented into the feed, strain BSXE-1601 obviously reduced the number of nodes, edges, modules in the ecological network of rearing water. The results suggested that dietary supplementation of BSXE-1601 could be more suitable than water addition in the practice of shrimp rearing when growth performance, non-specific immunity, disease resistance against V. parahaemolyticus in shrimp were collectively considered.

Relative dispersion ratios following fecal microbiota transplant elucidate principles governing microbial migration dynamics

Microorganisms frequently migrate from one ecosystem to another. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial dispersion is still lacking. Moreover, while theoretical models and in-vitro experiments have highlighted the contribution of species interactions to community assembly, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To address this gap, here we introduce a robust and rigorous computational framework, termed Relative Dispersion Ratio (RDR) analysis, and leverage data from well-characterized fecal microbiota transplant trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal tract. Our analysis identifies numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrate that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we explore metabolic dependencies between these taxa and characterize the functional properties that facilitate effective dispersion. Collectively, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

Critical role of the gut microbiota in immune responses and cancer immunotherapy

The gut microbiota plays a critical role in the progression of human diseases, especially cancer. In recent decades, there has been accumulating evidence of the connections between the gut microbiota and cancer immunotherapy. Therefore, understanding the functional role of the gut microbiota in regulating immune responses to cancer immunotherapy is crucial for developing precision medicine. In this review, we extract insights from state-of-the-art research to decipher the complicated crosstalk among the gut microbiota, the systemic immune system, and immunotherapy in the context of cancer. Additionally, as the gut microbiota can account for immune-related adverse events, we discuss potential interventions to minimize these adverse effects and discuss the clinical application of five microbiota-targeted strategies that precisely increase the efficacy of cancer immunotherapy. Finally, as the gut microbiota holds promising potential as a target for precision cancer immunotherapeutics, we summarize current challenges and provide a general outlook on future directions in this field.

Anti-inflammatory Effects of Bacteroidota Strains Derived From Outstanding Donors of Fecal Microbiota Transplantation for the Treatment of Ulcerative Colitis

BACKGROUND

The proportion of certain Bacteroidota species decreased in patients with ulcerative colitis, and the recovery of Bacteroidota is associated with the efficacy of fecal microbiota transplantation therapy. We hypothesized that certain Bacteroidota may advance ulcerative colitis treatment. Accordingly, we aimed to evaluate the anti-inflammatory effects of Bacteroidota strains isolated from donors.

METHODS

Donors with proven efficacy of fecal microbiota transplantation for ulcerative colitis were selected, and Bacteroidota strains were isolated from their stools. The immune function of Bacteroidota isolates was evaluated through in vitro and in vivo studies.

RESULTS

Twenty-four Bacteroidota strains were isolated and identified. Using an in vitro interleukin (IL)-10 induction assay, we identified 4 Bacteroidota strains with remarkable IL-10-induction activity. Of these, an Alistipes putredinis strain exhibited anti-inflammatory effects in a mouse model of colitis induced by sodium dextran sulfate and oxazolone. However, 16S rRNA gene-based sequencing analysis of A. putredinis cultures in the in vivo study revealed unexpected Veillonella strain contamination. A second in vitro study confirmed that the coculture exhibited an even more potent IL-10-inducing activity. Furthermore, the production of A. putredinis-induced IL-10 was likely mediated via toll-like receptor 2 signaling.

CONCLUSIONS

This study demonstrated that A. putredinis, a representative Bacteroidota species, exhibits anti-inflammatory effects in vivo and in vitro; however, the effects of other Bacteroidota species remain unexplored. Our fecal microbiota transplantation-based reverse translation approach using promising bacterial species may represent a breakthrough in microbiome drug development for controlling dysbiosis during ulcerative colitis.

Cooperative interactions between invader and resident microbial community members weaken the negative diversity-invasion relationship

The negative diversity-invasion relationship observed in microbial invasion studies is commonly explained by competition between the invader and resident populations. However, whether this relationship is affected by invader-resident cooperative interactions is unknown. Using ecological and mathematical approaches, we examined the survival and functionality of Aminobacter niigataensis MSH1 to mineralize 2,6-dichlorobenzamide (BAM), a groundwater micropollutant affecting drinking water production, in sand microcosms when inoculated together with synthetic assemblies of resident bacteria. The assemblies varied in richness and in strains that interacted pairwise with MSH1, including cooperative and competitive interactions. While overall, the negative diversity-invasion relationship was retained, residents engaging in cooperative interactions with the invader had a positive impact on MSH1 survival and functionality, highlighting the dependency of invasion success on community composition. No correlation existed between community richness and the delay in BAM mineralization by MSH1. The findings suggest that the presence of cooperative residents can alleviate the negative diversity-invasion relationship.

Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes

The human gut pathogen Clostridioides difficile displays extreme genetic variability and confronts a changeable nutrient landscape in the gut. We mapped gut microbiota inter-species interactions impacting the growth and toxin production of diverse C. difficile strains in different nutrient environments. Although negative interactions impacting C. difficile are prevalent in environments promoting resource competition, they are sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile strains display differences in interactions with Clostridium scindens and the ability to compete for proline. C. difficile toxin production displays substantial community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile shows substantial differences in transcriptional profiles in the presence of the closely related species C. hiranonis or C. scindens. In co-culture with C. hiranonis, C. difficile exhibits massive alterations in metabolism and other cellular processes, consistent with their high metabolic overlap. Further, Clostridium hiranonis inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and ameliorates the disease severity of a C. difficile challenge in a murine model. In sum, strain-level variability and nutrient environments are major variables shaping gut microbiota interactions with C. difficile.

Formation of a constructed microbial community in a nutrient-rich environment indicates bacterial interspecific competition

Understanding the organizational principles of microbial communities is essential for interpreting ecosystem stability. Previous studies have investigated the formation of bacterial communities under nutrient-poor conditions or obligate relationships to observe cooperative interactions among different species. How microorganisms form stabilized communities in nutrient-rich environments, without obligate metabolic interdependency for growth, is still not fully disclosed. In this study, three bacterial strains isolated from the Populus deltoides rhizosphere were co-cultured in complex medium, and their growth behavior was tracked. These strains co-exist in mixed culture over serial transfer for multiple growth-dilution cycles. Competition is proposed as an emergent interaction relationship among the three bacteria based on their significantly decreased growth levels. The effects of different initial inoculum ratios, up to three orders of magnitude, on community structure were investigated, and the final compositions of the mixed communities with various starting composition indicate that community structure is not dependent on the initial inoculum ratio. Furthermore, the competitive relationships within the community were not altered by different initial inoculum ratios. The community structure was simulated by generalized Lotka-Volterra and dynamic flux balance analysis to provide mechanistic predictions into emergence of community structure under a nutrient-rich environment. Metaproteomic analyses provide support for the metabolite exchanges predicted by computational modeling and for highly altered physiologies when microbes are grown in co-culture. These findings broaden our understanding of bacterial community dynamics and metabolic diversity in higher-order interactions and could be significant in the management of rhizospheric bacterial communities.

IMPORTANCE

Bacteria naturally co-exist in multispecies consortia, and the ability to engineer such systems can be useful in biotechnology. Despite this, few studies have been performed to understand how bacteria form a stable community and interact with each other under nutrient-rich conditions. In this study, we investigated the effects of initial inoculum ratios on bacterial community structure using a complex medium and found that the initial inoculum ratio has no significant impact on resultant community structure or on interaction patterns between community members. The microbial population profiles were simulated using computational tools in order to understand intermicrobial relationships and to identify potential metabolic exchanges that occur during stabilization of the bacterial community. Studying microbial community assembly processes is essential for understanding fundamental ecological principles in microbial ecosystems and can be critical in predicting microbial community structure and function.

Shaping human gut community assembly and butyrate production by controlling the arginine dihydrolase pathway

The arginine dihydrolase pathway (arc operon) present in a subset of diverse human gut species enables arginine catabolism. This specialized metabolic pathway can alter environmental pH and nitrogen availability, which in turn could shape gut microbiota inter-species interactions. By exploiting synthetic control of gene expression, we investigated the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles in vitro and in the murine gut. By stabilizing environmental pH, the arc operon reduced variability in community composition across different initial pH perturbations. The abundance of butyrate producing bacteria were altered in response to arc operon activity and butyrate production was enhanced in a physiologically relevant pH range. While the presence of the arc operon altered community dynamics, it did not impact production of short chain fatty acids. Dynamic computational modeling of pH-mediated interactions reveals the quantitative contribution of this mechanism to community assembly. In sum, our framework to quantify the contribution of molecular pathways and mechanism modalities on microbial community dynamics and functions could be applied more broadly.

Positive modulation of a new reconstructed human gut microbiota by Maitake extract helpfully boosts the intestinal environment in vitro

The human gut is a complex environment where the microbiota and its metabolites play a crucial role in the maintenance of a healthy state. The aim of the present work is the reconstruction of a new in vitro minimal human gut microbiota resembling the microbe-microbe networking comprising the principal phyla (Bacillota, Bacteroidota, Pseudomonadota, and Actinomycetota), to comprehend the intestinal ecosystem complexity. In the reductionist model, we mimicked the administration of Maitake extract as prebiotic and a probiotic formulation (three strains belonging to Lactobacillus and Bifidobacterium genera), evaluating the modulation of strain levels, the release of beneficial metabolites, and their health-promoting effects on human cell lines of the intestinal environment. The administration of Maitake and the selected probiotic strains generated a positive modulation of the in vitro bacterial community by qPCR analyses, evidencing the prominence of beneficial strains (Lactiplantibacillus plantarum and Bifidobacterium animalis subsp. lactis) after 48 hours. The bacterial community growths were associated with the production of metabolites over time through GC-MSD analyses such as lactate, butyrate, and propionate. Their effects on the host were evaluated on cell lines of the intestinal epithelium and the immune system, evidencing positive antioxidant (upregulation of SOD1 and NQO1 genes in HT-29 cell line) and anti-inflammatory effects (production of IL-10 from all the PBMCs). Therefore, the results highlighted a positive modulation induced by the synergic activities of probiotics and Maitake, inducing a tolerogenic microenvironment.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

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.

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.

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.

The impact of phage and phage resistance on microbial community dynamics

Where there are bacteria, there will be bacteriophages. These viruses are known to be important players in shaping the wider microbial community in which they are embedded, with potential implications for human health. On the other hand, bacteria possess a range of distinct immune mechanisms that provide protection against bacteriophages, including the mutation or complete loss of the phage receptor, and CRISPR-Cas adaptive immunity. While our previous work showed how a microbial community may impact phage resistance evolution, little is known about the inverse, namely how interactions between phages and these different phage resistance mechanisms affect the wider microbial community in which they are embedded. Here, we conducted a 10-day, fully factorial evolution experiment to examine how phage impact the structure and dynamics of an artificial four-species bacterial community that includes either Pseudomonas aeruginosa wild-type or an isogenic mutant unable to evolve phage resistance through CRISPR-Cas. Additionally, we used mathematical modelling to explore the ecological interactions underlying full community behaviour, as well as to identify general principles governing the impacts of phage on community dynamics. Our results show that the microbial community structure is drastically altered by the addition of phage, with Acinetobacter baumannii becoming the dominant species and P. aeruginosa being driven nearly extinct, whereas P. aeruginosa outcompetes the other species in the absence of phage. Moreover, we find that a P. aeruginosa strain with the ability to evolve CRISPR-based resistance generally does better when in the presence of A. baumannii, but that this benefit is largely lost over time as phage is driven extinct. Finally, we show that pairwise data alone is insufficient when modelling our microbial community, both with and without phage, highlighting the importance of higher order interactions in governing multispecies dynamics in complex communities. Combined, our data clearly illustrate how phage targeting a dominant species allows for the competitive release of the strongest competitor while also contributing to community diversity maintenance and potentially preventing the reinvasion of the target species, and underline the importance of mapping community composition before therapeutically applying phage.

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.

Chaotic turnover of rare and abundant species in a strongly interacting model community

The composition of ecological communities varies not only between different locations but also in time. Understanding the fundamental processes that drive species toward rarity or abundance is crucial to assessing ecosystem resilience and adaptation to changing environmental conditions. In plankton communities in particular, large temporal fluctuations in species abundances have been associated with chaotic dynamics. On the other hand, microbial diversity is overwhelmingly sustained by a "rare biosphere" of species with very low abundances. We consider here the possibility that interactions within a species-rich community can relate both phenomena. We use a Lotka-Volterra model with weak immigration and strong, disordered, and mostly competitive interactions between hundreds of species to bridge single-species temporal fluctuations and abundance distribution patterns. We highlight a generic chaotic regime where a few species at a time achieve dominance but are continuously overturned by the invasion of formerly rare species. We derive a focal-species model that captures the intermittent boom-and-bust dynamics that every species undergoes. Although species cannot be treated as effectively uncorrelated in their abundances, the community's effect on a focal species can nonetheless be described by a time-correlated noise characterized by a few effective parameters that can be estimated from time series. The model predicts a nonunitary exponent of the power-law abundance decay, which varies weakly with ecological parameters, consistent with observation in marine protist communities. The chaotic turnover regime is thus poised to capture relevant ecological features of species-rich microbial communities.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

Tipping points emerge from weak mutualism in metacommunities

The coexistence of obligate mutualists is often precariously close to tipping points where small environmental changes can drive catastrophic shifts in species composition. For example, microbial ecosystems can collapse by the decline of a strain that provides an essential resource on which other strains cross-feed. Here, we show that tipping points, ecosystem collapse, bistability and hysteresis arise even with very weak (non-obligate) mutualism provided the population is spatially structured. Based on numeric solutions of a metacommunity model and mean-field analyses, we demonstrate that weak mutualism lowers the minimal dispersal rate necessary to avoid stochastic extinction, while species need to overcome a mean threshold density to survive in this low dispersal rate regime. Our results allow us to make numerous predictions for mutualistic metacommunities regarding tipping points, hysteresis effects, and recovery from external perturbations, and let us draw general conclusions for ecosystems even with random, not necessarily mutualistic, interactions and systems with density-dependent dispersal rather than direct mutualistic interactions.

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.

Randomly barcoded transposon mutant libraries for gut commensals I: Strategies for efficient library construction

Randomly barcoded transposon mutant libraries are powerful tools for studying gene function and organization, assessing gene essentiality and pathways, discovering potential therapeutic targets, and understanding the physiology of gut bacteria and their interactions with the host. However, construction of high-quality libraries with uniform representation can be challenging. In this review, we survey various strategies for barcoded library construction, including transposition systems, methods of transposon delivery, optimal library size, and transconjugant selection schemes. We discuss the advantages and limitations of each approach, as well as factors to consider when selecting a strategy. In addition, we highlight experimental and computational advances in arraying condensed libraries from mutant pools. We focus on examples of successful library construction in gut bacteria and their application to gene function studies and drug discovery. Given the need for understanding gene function and organization in gut bacteria, we provide a comprehensive guide for researchers to construct randomly barcoded transposon mutant libraries.

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.

Gut toxicity of polystyrene microplastics and polychlorinated biphenyls to Eisenia fetida: Single and co-exposure effects with a focus on links between gut bacteria and bacterial translocation stemming from gut barrier damage

Microplastics' (MPs) ability to sorb and transport polychlorinated biphenyls (PCBs) in soil ecosystems warrants significant attention. Although organisms mainly encounter pollutants through the gut, the combined pollution impact of MPs and PCBs on soil fauna gut toxicity remains incompletely understood. Consequently, this study examined the gut toxicity of polystyrene MPs (PS-MPs) and PCB126 on Eisenia fetida, emphasizing the links between gut bacteria and bacterial translocation instigated by gut barrier impairment. Our findings underscored that E. fetida could ingest PS-MPs, which mitigated the PCB126 accumulation in E. fetida by 9.43 %. Exposure to PCB126 inhibited the expression of gut tight junction (TJ) protein genes. Although the presence of PS-MPs attenuated this suppression, it didn't alleviate gut barrier damage and bacterial translocation in the co-exposure group. This group demonstrated a significantly increased level of gut bacterial load (BLT, ANOVA, p = 0.005 vs control group) and lipopolysaccharide-binding protein (LBP, ANOVA, all p < 0.001 vs control, PCB, and PS groups), both of which displayed significant positive correlations with antibacterial defense. Furthermore, exposure to PS-MPs and PCB126, particularly within the co-exposure group, results in a marked decline in the dispersal ability of gut bacteria. This leads to dysbiosis (Adonis, R2 = 0.294, p = 0.001), with remarkable signature taxa such as Janthinobacterium, Microbacterium and Pseudomonas, being implicated in gut barrier dysfunction. This research illuminates the mechanism of gut toxicity induced by PS-MPs and PCB126 combined pollution in earthworms, providing novel insights for the ecological risk assessment of soil.

Evaluation of ready-to-use freezer stocks of a synthetic microbial community for maize root colonization

IMPORTANCE

Synthetic communities (SynComs) are an invaluable tool to characterize and model plant-microbe interactions. Multimember SynComs approximate intricate real-world interactions between plants and their microbiome, but the complexity and time required for their construction increase enormously for each additional member added to the SynCom. Therefore, researchers who study a diversity of microbiomes using SynComs are looking for ways to simplify the use of SynComs. In this manuscript, we evaluate the feasibility of creating ready-to-use freezer stocks of a well-studied seven-member SynCom for maize roots. The frozen ready-to-use SynCom stocks work according to the principle of "just add buffer and apply to sterilized seeds or seedlings" and thus can save time applied in multiple days of laborious growing and combining of multiple microorganisms. We show that ready-to-use SynCom stocks provide comparable results to those of freshly constructed SynComs and thus allow for significant time savings when working with SynComs.

Subtractive modification of bacterial consortium using antisense peptide nucleic acids

Microbiome engineering is an emerging research field that aims to design an artificial microbiome and modulate its function. In particular, subtractive modification of the microbiome allows us to create an artificial microbiome without the microorganism of interest and to evaluate its functions and interactions with other constituent bacteria. However, few techniques that can specifically remove only a single species from a large number of microorganisms and can be applied universally to a variety of microorganisms have been developed. Antisense peptide nucleic acid (PNA) is a potent designable antimicrobial agent that can be delivered into microbial cells by conjugating with a cell-penetrating peptide (CPP). Here, we tested the efficacy of the conjugate of CPP and PNA (CPP-PNA) as microbiome modifiers. The addition of CPP-PNA specifically inhibited the growth of Escherichia coli and Pseudomonas putida in an artificial bacterial consortium comprising E. coli, P. putida, Pseudomonas fluorescens, and Lactiplantibacillus plantarum. Moreover, the growth inhibition of P. putida promoted the growth of P. fluorescens and inhibited the growth of L. plantarum. These results indicate that CPP-PNA can be used not only for precise microbiome engineering but also for analyzing the growth relationships among constituent microorganisms in the microbiome.

A single intranasal dose of essential oil spray confers modulation of the nasopharyngeal microbiota and short-term inhibition of Mannheimia in feedlot cattle: a pilot study

Five essential oils (EOs) were previously characterized in vitro and identified as candidate EOs for the development of an intranasal EO spray to mitigate bovine respiratory disease (BRD) pathogens. In the present study, these EOs were evaluated for their potential to (i) reduce BRD pathogens, (ii) modulate nasopharyngeal microbiota, and (iii) influence animal performance, feeding behavior and immune response when a single dose administered intranasally to feedlot cattle. Forty beef steer calves (7-8 months old, Initial body weight = 284 ± 5 kg [SE]) received either an intranasal EO spray (ajowan, thyme, fennel, cinnamon leaf, and citronella) or PBS (Control; n = 20/group) on day 0. Deep nasopharyngeal swabs were collected on days (d) -1, 1, 2, 7, 14, 28, and 42 and processed for 16S rRNA gene sequencing, qPCR, and culturing. Significant effects of EO on community structure (d1), microbial richness and diversity, relative abundance of some dominant phyla (d1, d2, and d14), and the overall interaction network structure of the nasopharyngeal microbiota were detected. The relative abundance of Mannheimia was lower in the EO calves (4.34%) than in Control calves (10.4%) on d2, and M. haemolytica prevalence on d7 as compared to control calves. Feed intake, average daily gain, feeding behavior, and blood cell counts were not affected by EO treatment. Overall, a single intranasal dose of EO spray resulted in moderate modulation of nasopharyngeal microbiota and short-term inhibition of Mannheimia while not influencing animal performance, feeding behavior or immune response. Our study, for the first time, shows the potential use of intranasal EO to mitigate BRD in feedlot cattle.

Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution

MOTIVATION

Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.

RESULTS

Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.

AVAILABILITY AND IMPLEMENTATION

The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.

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.

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.

Targeted rRNA depletion enables efficient mRNA sequencing in diverse bacterial species and complex co-cultures

IMPORTANCE

Microbes present one of the most diverse sources of biochemistry in nature, and mRNA sequencing provides a comprehensive view of this biological activity by quantitatively measuring microbial transcriptomes. However, efficient mRNA capture for sequencing presents significant challenges in prokaryotes as mRNAs are not poly-adenylated and typically make up less than 5% of total RNA compared with rRNAs that exceed 80%. Recently developed methods for sequencing bacterial mRNA typically rely on depleting rRNA by tiling large probe sets against rRNAs; however, such approaches are expensive, time-consuming, and challenging to scale to varied bacterial species and complex microbial communities. Therefore, we developed EMBR-seq+, a method that requires fewer than 10 short oligonucleotides per rRNA to achieve up to 99% rRNA depletion in diverse bacterial species. Finally, EMBR-seq+ resulted in a deeper view of the transcriptome, enabling systematic quantification of how microbial interactions result in altering the transcriptional state of bacteria within co-cultures.

Control points for design of taxonomic composition in synthetic human gut communities

Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states. Furthermore, systematically varied initial abundances drive variation in community assembly and enable inference of pairwise inter-species interactions via a dynamic ecological model. These interactions are overall consistent with conditioned media experiments, demonstrating that specific perturbations to a high-richness community can provide rich information for building dynamic ecological models. This model is subsequently used to design low-richness communities that display low or high temporal taxonomic variability over an extended period. A record of this paper's transparent peer review process is included in the supplemental information.

Sequencing and culture-based characterization of the vaginal and uterine microbiota in beef cattle that became pregnant or remained open following artificial insemination

IMPORTANCE

Emerging evidence suggests that microbiome-targeted approaches may provide a novel opportunity to reduce the incidence of reproductive failures in cattle. To develop such microbiome-based strategies, one of the first logical steps is to identify reproductive microbiome features related to fertility and to isolate the fertility-associated microbial species for developing a future bacterial consortium that could be administered before breeding to enhance pregnancy outcomes. Here, we characterized the vaginal and uterine microbiota in beef cattle that became pregnant or remained open via artificial insemination and identified microbiota features associated with fertility. We compared similarities between vaginal and uterine microbiota and between heifers and cows. Using culturing, we provided new insights into the culturable fraction of the vaginal and uterine microbiota and their antimicrobial resistance. Overall, our findings will serve as an important basis for future research aimed at harnessing the vaginal and uterine microbiome for improved cattle fertility.

Impact of bacteriocin-producing strains on bacterial community composition in a simplified human intestinal microbiota

Bacteriocins are antimicrobial peptides that have been studied for decades as food bio-preservatives or as alternatives to antibiotics. They also have potential as modulators of the gut microbiome, which has been linked to human health. However, it is difficult to predict a priori how bacteriocins will impact complex microbial communities through direct and indirect effects. Here we assess the effect of different bacteriocin-producing strains on a Simplified Human Intestinal Microbiota (SIHUMI) model, using a set of bacteriocin-producing strains (Bac+) and otherwise isogenic non-producers (Bac-). Bacteriocins from different classes and with different activity spectra were selected, including lantibiotics such as lacticin 3147 and nisin A, and pediocin-like bacteriocins such as pediocin PA-1 among other peptides. SIHUMI is a bacterial consortium of seven diverse human gut species that assembles to a predictable final composition in a particular growth medium. Each member can be individually tracked by qPCR. Bac+ and Bac- strains were superimposed on the SIHUMI system, and samples were taken at intervals up to 48 h. The genome copy number of each SIHUMI member was evaluated using specific primers. We establish that the composition of the community changes in response to the presence of either broad- or narrow-spectrum bacteriocin producers and confirm that there are significant off-target effects. These effects were analyzed considering antagonistic inter-species interactions within the SIHUMI community, providing a comprehensive insight into the possible mechanisms by which complex communities can be shaped by bacteriocins.

Ecological network structure in response to community assembly processes over evolutionary time

The dynamic structure of ecological communities results from interactions among taxa that change with shifts in species composition in space and time. However, our ability to study the interplay of ecological and evolutionary processes on community assembly remains relatively unexplored due to the difficulty of measuring community structure over long temporal scales. Here, we made use of a geological chronosequence across the Hawaiian Islands, representing 50 years to 4.15 million years of ecosystem development, to sample 11 communities of arthropods and their associated plant taxa using semiquantitative DNA metabarcoding. We then examined how ecological communities changed with community age by calculating quantitative network statistics for bipartite networks of arthropod-plant associations. The average number of interactions per species (linkage density), ratio of plant to arthropod species (vulnerability) and uniformity of energy flow (interaction evenness) increased significantly in concert with community age. The index of specializationH 2 ' has a curvilinear relationship with community age. Our analyses suggest that younger communities are characterized by fewer but stronger interactions, while biotic associations become more even and diverse as communities mature. These shifts in structure became especially prominent on East Maui (~0.5 million years old) and older volcanos, after enough time had elapsed for adaptation and specialization to act on populations in situ. Such natural progression of specialization during community assembly is probably impeded by the rapid infiltration of non-native species, with special risk to younger or more recently disturbed communities that are composed of fewer specialized relationships.

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

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

Mathematical models of cystic fibrosis as a systemic disease

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

High-throughput microbial culturomics using automation and machine learning

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

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.

A spatially structured mathematical model of the gut microbiome reveals factors that increase community stability

Given the importance of gut microbial communities for human health, we may want to ensure their stability in terms of species composition and function. Here, we built a mathematical model of a simplified gut composed of two connected patches where species and metabolites can flow from an upstream patch, allowing upstream species to affect downstream species' growth. First, we found that communities in our model are more stable if they assemble through species invasion over time compared to combining a set of species from the start. Second, downstream communities are more stable when species invade the downstream patch less frequently than the upstream patch. Finally, upstream species that have positive effects on downstream species can further increase downstream community stability. Despite it being quite abstract, our model may inform future research on designing more stable microbial communities or increasing the stability of existing ones.

More Than the Sum of Its Parts: Unlocking the Power of Network Structure for Understanding Organization and Function in Microbiomes

Plant and soil microbiomes are integral to the health and productivity of plants and ecosystems, yet researchers struggle to identify microbiome characteristics important for providing beneficial outcomes. Network analysis offers a shift in analytical framework beyond "who is present" to the organization or patterns of coexistence between microbes within the microbiome. Because microbial phenotypes are often significantly impacted by coexisting populations, patterns of coexistence within microbiomes are likely to be especially important in predicting functional outcomes. Here, we provide an overview of the how and why of network analysis in microbiome research, highlighting the ways in which network analyses have provided novel insights into microbiome organization and functional capacities, the diverse network roles of different microbial populations, and the eco-evolutionary dynamics of plant and soil microbiomes.

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.

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.

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.