Deciphering microbial interactions in synthetic human gut microbiome communities

Unravelling microbial interactions in a synthetic broad bean paste microbial community

The biotic factors governing the assembly and functionality of broad bean paste microbiota remain largely unexplored due to its highly complex fermentation ecosystem. This study constructed a synthetic community comprising Zygosaccharomyces rouxii, Staphylococcus carnosus, Bacillus subtilis, Bacillus amyloliquefaciens, Tetragenococcus halophilus and Weissella confusa, representing key microorganisms involved in broad bean paste fermentation. The generalized Lotka-Volterra (gLV) model revealed that the microbial interaction network among the six species was dominated by pairwise interactions. The abundances of most species in the multi-species communities at 2 and 4 days were accurately predicted using the gLV model, based on pairwise species combinations outcomes. Among pairwise interactions, negative interactions (57 %) were significantly more prevalent than positive interactions (37 %), with the former generally being stronger. Subsequent investigations demonstrated that the tested Z. rouxii inhibited acid accumulation by acid-producing bacteria, while the two strains belonging to the genus Bacillus stimulated lactic acid bacteria growth and lactic acid accumulation. The sequential inoculation strategy, informed by the interaction network, enhanced the synthetic community's bioaugmentation in broad bean paste, significantly improving ester and mellow flavors, reducing unpleasant odors, and increasing volatile flavor substances to 9.43 times that of natural fermentation. Overall, this study revealed the interaction network of six key microorganisms in broad bean paste using the gLV model and guided the application of the synthetic community in its fermentation, significantly enhancing flavor quality.

Designing synthetic microbial communities for enhanced anaerobic waste treatment

Synthetic microbial communities (SynComs) are powerful tools for investigating microbial interactions and community assembly by focusing on minimal yet functionally representative members. Here, we will highlight key principles for designing SynComs, specifically emphasizing the anaerobic digestion (AD) microbiome for waste treatment and upcycling. The AD process has traditionally been used to reduce organic waste volume while producing biogas as a renewable energy source. Its microbiome features well-defined trophic layers and metabolic groups. There has been growing interest in repurposing the AD process to produce value-added products and chemical precursors, contributing to sustainable waste management and the goals of a circular economy. Optimizing the AD process requires a better understanding of microbial interactions and the influence of both biotic and abiotic parameters, where SynComs offer great promise. Focusing on AD microbiomes, we review the principles of SynComs' design, including keystone taxa and function, cross-feeding interactions, and metabolic redundancy, as well as how modeling approaches could guide SynComs design. Furthermore, we address practical considerations for working with AD SynComs and examine constructed SynComs designed for anaerobic waste digestion. Finally, we discuss the challenges associated with designing and applying SynComs to enhance our understanding of the AD process. This review aims to explore the use of synthetic communities in studying anaerobic digestion and highlights their potential for developing innovative biotechnological processes.

Decoding the role of the arginine dihydrolase pathway in shaping human gut community assembly and health-relevant metabolites

The arginine dihydrolase pathway (arc operon) provides a metabolic niche by transforming arginine into metabolic byproducts. We investigate the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles. By stabilizing environmental pH, the arc operon reduces variability in community composition in response to pH perturbations and frequently enhances butyrate production in synthetic communities. We use a tailored machine learning model for microbiomes to predict community assembly in response to variation in initial media pH and arc operon activity. This model uncovers the pH- and arc operon-dependent interactions shaping community assembly. Human gut species display altered colonization dynamics in response to the arc operon in the murine gut. In sum, our framework to quantify the contribution of a specific pathway to microbial community assembly and metabolite production can reveal new engineering strategies. A record of this paper's transparent peer review process is included in the supplemental information.

Multi-Layer Autocatalytic Feedback Enables Integral Control Amidst Resource Competition and Across Scales

Integral feedback control strategies have proven effective in regulating protein expression in unpredictable cellular environments. These strategies, grounded in model-based designs and control theory, have advanced synthetic biology applications. Autocatalytic integral feedback controllers, utilizing positive autoregulation for integral action, are one class of simplest architectures to design integrators. This class of controllers offers unique features, such as robustness against dilution effects and cellular growth, as well as the potential for synthetic realizations across different biological scales, owing to their similarity to self-regenerative behaviors widely observed in nature. Despite this, their potential has not yet been fully exploited. One key reason, we discuss, is that their effectiveness is often hindered by resource competition and context-dependent couplings. This study addresses these challenges using a multilayer feedback strategy. Our designs enabled population-level integral feedback and multicellular integrators, where the control function emerges as a property of coordinated interactions distributed across different cell populations coexisting in a multicellular consortium. We provide a generalized mathematical framework for modeling resource competition in complex genetic networks, supporting the design of intracellular control circuits. The use of our proposed multilayer autocatalytic controllers is examined in two typical control tasks that pose significant relevance to synthetic biology applications: concentration regulation and ratiometric control. We define a ratiometric control task and solve it using a variant of our controller. The effectiveness of our controller motifs is demonstrated through a range of application examples, from precise regulation of gene expression and gene ratios in embedded designs to population growth and coculture composition control in multicellular designs within engineered microbial ecosystems. These findings offer a versatile approach to achieving robust adaptation and homeostasis from subcellular to multicellular scales.

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

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

Genome-scale metabolic modeling reveals specific vaginal Lactobacillus strains and their metabolites as key inhibitors of Candida albicans

As the predominant constituents of the vaginal microbiome in healthy women, Lactobacillus species are considered essential in maintaining a homeostatic vaginal microbiome. Specific Lactobacillus species can produce beneficial metabolites to support their persistence within the host environment and inhibit Candida albicans colonization. Due to the extensive diversity of Lactobacillus species and their metabolites, comprehensively investigating all possible interactions remains challenging. This study employed an integrative approach combining genome-scale metabolic modeling, metagenomic sequencing, and in vitro validation to explore Lactobacillus and C. albicans interactions. Pairwise simulations of 159 Lactobacillus strains with C. albicans revealed that most strains exhibit inhibitory effects, altering fungal amino acid and carbohydrate metabolism. Key inhibitory metabolites identified included formate, L-lactate, and L-malate. Metagenomic analysis of vaginal swabs from 20 vulvovaginal candidiasis (VVC) patients and 20 healthy women showed a correlation between Lactobacillus species abundance and reduced C. albicans colonization. In vitro experiments confirmed the inhibitory effects of these metabolites and the selected Lactobacillus strains on C. albicans growth, thereby validating our computational predictions. These findings provide insights into the metabolic interactions within the vaginal microbiome and pave the way for targeted microbial or metabolite-based therapeutic strategies to manage VVC.IMPORTANCEVulvovaginal candidiasis is a prevalent fungal infection with significant implications for women's health, caused primarily by Candida albicans. Although the protective role of a Lactobacillus-dominated vaginal microbiome is well established, the metabolic mechanisms underlying the interactions between Lactobacillus species and C. albicans remain inadequately understood. Specifically, the Lactobacillus species that effectively inhibit C. albicans and the metabolic pathways involved warrant further investigation. This study offers novel insights into the metabolic mechanisms underlying Lactobacillus antagonism against C. albicans. By identifying critical metabolic pathways and inhibitory metabolites, this study enhances our understanding of vaginal microbiome dynamics and host-microbe interactions. The findings suggest that key Lactobacillus strains and their metabolites could significantly reduce harmful levels of C. albicans, paving the way for future therapeutic strategies that leverage these microbial characteristics to promote vaginal health.

MetaFlowTrain: a highly parallelized and modular fluidic system for studying exometabolite-mediated inter-organismal interactions

Metabolic fluxes between cells, organisms, or communities drive ecosystem assembly and functioning and explain higher-level biological organization. Exometabolite-mediated inter-organismal interactions, however, remain poorly described due to technical challenges in measuring these interactions. Here, we present MetaFlowTrain, an easy-to-assemble, cheap, semi-high-throughput, and modular fluidic system in which multiple media can be flushed at adjustable flow rates into gnotobiotic microchambers accommodating diverse micro-organisms, ranging from bacteria to small eukaryotes. These microchambers can be used alone or connected in series to create microchamber trains within which metabolites, but not organisms, directionally travel between microchambers to modulate organismal growth. Using MetaFlowTrain, we uncover soil conditioning effects on synthetic community structure and plant growth, and reveal microbial antagonism mediated by exometabolite production. Our study highlights MetaFlowTrain as a versatile system for investigating plant-microbe-microbe metabolic interactions. We also discuss the system´s potential to discover metabolites that function as signaling molecules, drugs, or antimicrobials across various systems.

A Novel Frontier in Gut-Brain Axis Research: The Transplantation of Fecal Microbiota in Neurodegenerative Disorders

The gut-brain axis (GBA) represents a sophisticated bidirectional communication system connecting the central nervous system (CNS) and the gastrointestinal (GI) tract. This interplay occurs primarily through neuronal, immune, and metabolic pathways. Dysbiosis in gut microbiota has been associated with multiple neurodegenerative diseases, such as Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS). In recent years, fecal microbiota transplantation (FMT) has gained attention as an innovative therapeutic approach, aiming to restore microbial balance in the gut while influencing neuroinflammatory and neurodegenerative pathways. This review explores the mechanisms by which FMT impacts the gut-brain axis. Key areas of focus include its ability to reduce neuroinflammation, strengthen gut barrier integrity, regulate neurotransmitter production, and reinstate microbial diversity. Both preclinical and clinical studies indicate that FMT can alleviate motor and cognitive deficits in PD and AD, lower neuroinflammatory markers in MS, and enhance respiratory and neuromuscular functions in ALS. Despite these findings, several challenges remain, including donor selection complexities, uncertainties about long-term safety, and inconsistencies in clinical outcomes. Innovations such as synthetic microbial communities, engineered probiotics, and AI-driven analysis of the microbiome hold the potential to improve the precision and effectiveness of FMT in managing neurodegenerative conditions. Although FMT presents considerable promise as a therapeutic development, its widespread application for neurodegenerative diseases requires thorough validation through well-designed, large-scale clinical trials. It is essential to establish standardized protocols, refine donor selection processes, and deepen our understanding of the molecular mechanisms behind its efficacy.

Antibiotic-mediated microbial community restructuring is dictated by variability in antibiotic-induced lysis rates and population interactions

It is widely known that faster-growing bacterial cells are more susceptible to many antibiotics. Given this notion, it appears intuitive that antibiotic treatment would enrich slower-growing cells in a clonal population or slower-growing populations in a microbial community, which has been commonly observed. However, experimental observations also show the enrichment of faster-growing subpopulations under certain conditions. Does this apparent discrepancy suggest the uniqueness about different growth environments or the role of additional confounding factors? If so, what could be the major determinant in antibiotic-mediated community restructuring? Combining modeling and quantitative measurements using a barcoded heterogeneous E. coli library, we show that the outcome of antibiotic-mediated community restructuring can be driven by two major factors. One is the variability among the clonal responses of different subpopulations to the antibiotic; the other is their interactions. Our results suggest the importance of quantitative measurements of antibiotic responses in individual clones in predicting community responses to antibiotics and addressing subpopulation interactions.

Applying 3D cultures and high-throughput technologies to study host-pathogen interactions

Recent advances in cell culturing and DNA sequencing have dramatically altered the field of human microbiome research. Three-dimensional (3D) cell culture is an important tool in cell biology, in cancer research, and for studying host-microbe interactions, as it mimics the in vivo characteristics of the host environment in an in vitro system, providing reliable and reproducible models. This work provides an overview of the main 3D culture techniques applied to study interactions between host cells and pathogenic microorganisms, how these systems can be integrated with high-throughput molecular methods, and how multi-species model systems may pave the way forward to pinpoint interactions among host, beneficial microbes and pathogens.

Harnessing gut microbial communities to unravel microbiome functions

The gut microbiome impacts human health in direct and indirect ways. While many associations have been discovered between specific microbiome compositions and diseases, establishing causality, understanding the underlying mechanisms, and developing successful microbiome-based therapies require novel experimental approaches. In this opinion, we discuss how in vitro cultivation of diverse communities enables systematic investigation of the individual and collective functions of gut microbes. Up to now, the field has relied mostly on simple, bottom-up assembled synthetic communities or more complex, undefined stool-derived communities. Although powerful for dissecting interactions and mapping causal effects, these communities suffer either from ignoring the complexity, diversity, coevolution, and dynamics of natural communities or from lack of control of community composition. These limitations can be overcome in the future by establishing personalized culture collections from stool samples of different donors and assembling personalized communities to investigate native interactions and ecological relationships in a controlled manner.

The architecture of theory and data in microbiome design: towards an S-matrix for microbiomes

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models - a middle, third way for using theory to inform design.

Phocaeicola vulgatus shapes the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile

Clostridioides difficile can transiently or persistently colonize the human gut, posing a risk for infections. This colonization is influenced by complex molecular and ecological interactions with the human gut microbiota. By investigating C. difficile dynamics in human gut communities over hundreds of generations, we show patterns of stable coexistence, instability, or competitive exclusion. Lowering carbohydrate concentrations shifted a community containing C. difficile and the prevalent human gut symbiont Phocaeicola vulgatus from competitive exclusion to coexistence, facilitated by increased cross-feeding. In this environment, two key mutations in C. difficile altered its metabolic niche from proline to glucose utilization. These metabolic changes in C. difficile substantially impacted gut microbiota inter-species interactions and reduced disease severity in mice. In sum, interactions with P. vulgatus are crucial in shaping the long-term growth dynamics and evolutionary adaptations of C. difficile, offering key insights for developing anti-C. difficile strategies.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21 567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles and FR profiles, and identified 14 636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Effects of data transformation and model selection on feature importance in microbiome classification data

BACKGROUND

Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

RESULTS

Our analysis of over 8500 samples from 24 shotgun metagenomic datasets showed that it is possible to classify healthy and diseased individuals using microbiome data with minimal dependence on the choice of algorithm or transformation. Presence-absence transformations performed comparably to abundance-based transformations, and only a small subset of predictors is necessary for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlights the need to reevaluate machine learning-based biomarker detection.

CONCLUSIONS

Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.

Metagenomic symphony of the intestinal ecosystem: How the composition affects the mind

Mental health disorders and neurodegenerative diseases place a heavy burden on patients and societies, and, although great strides have been made to understand the pathophysiology of these conditions, advancement in drug development is lagging. The importance of gastrointestinal health in maintaining overall health and preventing disease is not a new concept. Hundreds of years ago, healers from various cultures and civilizations recognized the crucial role of the gut in sustaining health. More than a century ago, scientists began exploring the restorative effects of probiotics, marking the early recognition of the importance of gut microbes. The omics era brought more enlightenment and enabled researchers to identify the complexity of the microbial ecosystems we harbour, encompassing bacteria, eukaryotes (including fungi), archaea, viruses, and other microorganisms. The extensive genetic capacity of the microbiota is dynamic and influenced by the environment. The microbiota therefore serves as a significant entity within us, with evolutionarily preserved functions in host metabolism, immunity, development, and behavior. The significant role of the bacterial gut microbiome in mental health and neurodegenerative disorders has been realized and described within the framework of the microbiota-gut-brain axis. However, the bacterial members do not function unaccompanied, but rather in concert, and there is a substantial knowledge gap regarding the involvement of non-bacterial microbiome members in these disorders. In this review, we will explore the current literature that implicates a role for the entire metagenomic ensemble, and how their complex interkingdom relationships could influence CNS functioning in mental health disorders and neurodegenerative diseases.

Central Taxa Are Keystone Microbes During Early Succession

Microorganisms underpin numerous ecosystem processes and support biodiversity globally. Yet, we understand surprisingly little about what structures environmental microbiomes, including how to efficiently identify key players. Microbiome network theory predicts that highly connected hubs act as keystones, but this has never been empirically tested in nature. Combining culturing, sequencing, networks and field experiments, we isolated 'central' (highly connected, hub taxa), 'intermediate' (moderately connected), and 'peripheral' (weakly/unconnected) microbes and experimentally evaluated their effects on soil microbiome assembly during early succession in nature. Central early colonisers significantly (1) enhanced biodiversity (35%-40% richer communities), (2) reshaped trajectories of microbiome assembly and (3) increased recruitment of additional influential microbes by > 60%. In contrast, peripheral microbes did not increase diversity and were transient taxa, minimally affected by the presence of other microbes. This work elucidates fundamental principles of network theory in microbial ecology and demonstrates for the first time in nature that central microbes act as keystone taxa.

Synthetic Ecosystems: From the Test Tube to the Biosphere

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move toward advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modeling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative roadmap for open problems and further exploration.

Exploring interspecific interaction variability in microbiota: A review

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

Spatiotemporal dynamics during niche remodeling by super-colonizing microbiota in the mammalian gut

While fecal microbiota transplantation (FMT) has been shown to be effective in reversing gut dysbiosis, we lack an understanding of the fundamental processes underlying microbial engraftment in the mammalian gut. Here, we explored a murine gut colonization model leveraging natural inter-individual variations in gut microbiomes to elucidate the spatiotemporal dynamics of FMT. We identified a natural "super-donor" consortium that robustly engrafts into diverse recipients and resists reciprocal colonization. Temporal profiling of the gut microbiome showed an ordered succession of rapid engraftment by early colonizers within 72 h, followed by a slower emergence of late colonizers over 15-30 days. Moreover, engraftment was localized to distinct compartments of the gastrointestinal tract in a species-specific manner. Spatial metagenomic characterization suggested engraftment was mediated by simultaneous transfer of spatially co-localizing species from the super-donor consortia. These results offer a mechanism of super-donor colonization by which nutritional niches are expanded in a spatiotemporally dependent manner. A record of this paper's transparent peer review process is included in the supplemental information.

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.

Predicting and Prioritising Community Assembly: Learning Outcomes via Experiments

Community assembly provides the foundation for applications in biodiversity conservation, climate change, invasion, restoration and synthetic ecology. However, predicting and prioritising assembly outcomes remains difficult. We address this challenge via a mechanism-free approach useful when little data or knowledge exist (LOVE; Learning Outcomes Via Experiments). We carry out assembly experiments ('actions', here, random combinations of species additions) potentially in multiple environments, wait, and measure abundance outcomes. We then train a model to predict outcomes of novel actions or prioritise actions that would yield the most desirable outcomes. Across 10 single- and multi-environment datasets, when trained on 89 randomly selected actions, LOVE predicts outcomes with 0.5%-3.4% mean error, and prioritises actions for maximising richness, maximising abundance, or removing unwanted species, with 94%-99% mean true positive rate and 10%-84% mean true negative rate across tasks. LOVE complements existing mechanism-first approaches for community ecology and may help address numerous applied challenges.

mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data

Alterations in human microbial communities are intricately linked to the onset and progression of diseases. Identifying the key microbes driving these community changes is crucial, as they may serve as valuable biomarkers for disease prevention, diagnosis, and treatment. However, there remains a need for further research to develop effective methods for addressing this critical task. This is primarily because defining the driver microbe requires consideration not only of each microbe's individual contributions but also their interactions. This paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (i) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (ii) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (iii) quantification of each microbe's contribution to the community's steady state by manipulating the causal graph implied by gLV equations. The performance of nonparametric spline-based denoising and regularized least squares estimation is comprehensively evaluated on simulated datasets, demonstrating superiority over existing methods. Furthermore, the practical applicability and effectiveness of mbDriver are showcased using a dietary fiber intervention dataset and an ulcerative colitis dataset. Notably, driver microbes identified in the dietary fiber intervention dataset exhibit significant effects on the abundances of short-chain fatty acids, while those identified in the ulcerative colitis dataset show a significant correlation with metabolism-related pathways.

Engineering Microbial Consortia as Living Materials: Advances and Prospectives

The field of Engineered Living Materials (ELMs) integrates engineered living organisms into natural biomaterials to achieve diverse objectives. Multiorganism consortia, prevalent in both naturally occurring and synthetic microbial cultures, exhibit complex functionalities and interrelationships, extending the scope of what can be achieved with individual engineered bacterial strains. However, the ELMs comprising microbial consortia are still in the developmental stage. In this Review, we introduce two strategies for designing ELMs constituted of microbial consortia: a top-down strategy, which involves characterizing microbial interactions and mimicking and reconstructing natural ecosystems, and a bottom-up strategy, which entails the rational design of synthetic consortia and their assembly with material substrates to achieve user-defined functions. Next, we summarize technologies from synthetic biology that facilitate the efficient engineering of microbial consortia for performing tasks more complex than those that can be done with single bacterial strains. Finally, we discuss essential challenges and future perspectives for microbial consortia-based ELMs.

Fragmented micro-growth habitats present opportunities for alternative competitive outcomes

Bacteria in nature often thrive in fragmented environments, like soil pores, plant roots or plant leaves, leading to smaller isolated habitats, shared with fewer species. This spatial fragmentation can significantly influence bacterial interactions, affecting overall community diversity. To investigate this, we contrast paired bacterial growth in tiny picoliter droplets (1-3 cells per 35 pL up to 3-8 cells per species in 268 pL) with larger, uniform liquid cultures (about 2 million cells per 140 µl). We test four interaction scenarios using different bacterial strains: substrate competition, substrate independence, growth inhibition, and cell killing. In fragmented environments, interaction outcomes are more variable and sometimes even reverse compared to larger uniform cultures. Both experiments and simulations show that these differences stem mostly from variation in initial cell population growth phenotypes and their sizes. These effects are most significant with the smallest starting cell populations and lessen as population size increases. Simulations suggest that slower-growing species might survive competition by increasing growth variability. Our findings reveal how microhabitat fragmentation promotes diverse bacterial interaction outcomes, contributing to greater species diversity under competitive conditions.

Effects of dietary restriction and one-carbon metabolite supplementation during the first 63 days of gestation on the maternal gut, vaginal, and blood microbiota in cattle

BACKGROUND

Maternal diet quality and quantity have significant impacts on both maternal and fetal health and development. The composition and function of the maternal gut microbiome is also significantly influenced by diet; however, little is known about the impact of gestational nutrient restriction on the bovine maternal microbiome during early gestation, which is a critical stage for maternal microbiome-mediated fetal programming to take place. The objective of the present study was to evaluate the impacts of diet restriction and one-carbon metabolite (OCM) supplementation during early gestation on maternal ruminal, vaginal, and blood microbiota in cattle. Thirty-three beef heifers (approx. 14 months old) were used in a 2 × 2 factorial experiment with main factors of target gain (control [CON]; targeted 0.45 kg/d gain vs restricted [RES]; targeted - 0.23 kg/d gain), and OCM supplementation (+ OCM vs - OCM; n = 8/treatment; except n = 9 for RES-OCM). Heifers were individually fed, starting treatment at breeding (d 0) and concluding at d 63 of gestation. Ruminal fluid and vaginal swabs were collected on d - 2, d 35, and d 63 (at necropsy) and whole blood was collected on d 63 (necropsy). Bacterial microbiota was assessed using 16S rRNA gene (V3-V4) sequencing.

RESULTS

Overall ruminal microbiota structure was affected by gain, OCM, time, and their interactions. The RES heifers had greater microbial richness (observed ASVs) but neither Shannon nor Inverse Simpson diversity was significantly influenced by gain or OCM supplementation; however, on d 63, 34 bacterial genera showed differential abundance in the ruminal fluid, with 25 genera enriched in RES heifers as compared to CON heifers. In addition, the overall interaction network structure of the ruminal microbiota changed due to diet restriction. The vaginal microbiota community structure was influenced by gain and time. Overall microbial richness and diversity of the vaginal microbiota steadily increased as pregnancy progressed. The vaginal ecological network structure was distinctive between RES and CON heifers with genera-genera interactions being intensified in RES heifers. A relatively diverse bacterial community was detected in blood samples, and the composition of the blood microbiota differed from that of ruminal and vaginal microbiota.

CONCLUSION

Restricted dietary intake during early gestation induced significant alterations in the ruminal microbiota which also extended to the vaginal microbiota. The composition of these two microbial communities was largely unaffected by OCM supplementation. Blood associated microbiota was largely distinctive from the ruminal and vaginal microbiota.

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

The human gut pathogen Clostridioides difficile displays substantial inter-strain genetic variability and confronts a changeable nutrient landscape in the gut. We examined how human gut microbiota inter-species interactions influence the growth and toxin production of various C. difficile strains across different nutrient environments. Negative interactions influencing C. difficile growth are prevalent in an environment containing a single highly accessible resource and sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile toxin production displays significant community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile strains exhibit differences in interactions with Clostridium scindens and the ability to compete for proline. Further, C. difficile shows substantial differences in transcriptional profiles in co-culture with C. scindens or Clostridium hiranonis. C. difficile exhibits massive alterations in metabolism and other cellular processes in co-culture with C. hiranonis, reflecting their similar metabolic niches. C. hiranonis uniquely inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and robustly ameliorates disease severity in mice. In sum, understanding the impact of C. difficile strain variability and nutrient environments on inter-species interactions could help improve the effectiveness of anti-C. difficile strategies.

Environmentally dependent interactions shape patterns in gene content across natural microbiomes

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

Roseburia intestinalis-derived extracellular vesicles ameliorate colitis by modulating intestinal barrier, microbiome, and inflammatory responses

Inflammatory bowel disease (IBD) is a chronic disorder characterized by recurrent gastrointestinal inflammation, lacking a precise aetiology and definitive cure. The gut microbiome is vital in preventing and treating IBD due to its various physiological functions. In the interplay between the gut microbiome and human health, extracellular vesicles secreted by gut bacteria (BEVs) are key mediators. Herein, we explore the role of Roseburia intestinalis (R)-derived EVs (R-EVs) as potent anti-inflammatory mediators in treating dextran sulfate sodium-induced colitis. R was selected as an optimal BEV producer for IBD treatment through ANCOM analysis. R-EVs with a 76 nm diameter were isolated from R using a tangential flow filtration system. Orally administered R-EVs effectively accumulated in inflamed colonic tissues and increased the abundance of Bifidobacterium on microbial changes, inhibiting colonic inflammation and prompting intestinal recovery. Due to the presence of Ile-Pro-Ile in the vesicular structure, R-EVs reduced the DPP4 activity in inflamed colonic tissue and increased the active GLP-1, thereby downregulating the NFκB and STAT3 via the PI3K pathway. Our results shed light on the impact of BEVs on intestinal recovery and gut microbiome alteration in treating IBD.

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.

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.

In vitro analysis of postbiotic antimicrobial activity against Candida Species in a minimal synthetic model simulating the gut mycobiota in obesity

Gut fungal imbalances, particularly increased Candida spp., are linked to obesity. This study explored the potential of Lactiplantibacillus plantarum cell-free extracts (postbiotics) to modulate the growth of Candida albicans and Candida kefyr, key members of the gut mycobiota. A minimal synthetic gut model was employed to evaluate the effects of Lactiplantibacillus plantarum postbiotics on fungal growth in mono- and mixed cultures. Microreactors were employed for culturing, fungal growth was quantified using CFU counting, and regression analysis was used to evaluate the effects of postbiotics on fungal growth. Postbiotics at a concentration of 12.5% significantly reduced the growth of both Candida species. At 24 h, both C. albicans and C. kefyr in monocultures exhibited a decrease in growth of 0.11 log CFU/mL. In contrast, mixed cultures showed a more pronounced antifungal effect, with C. albicans and C. kefyr reductions of 0.62 log CFU/mL and 0.64 log CFU/mL, respectively. Regression analysis using the Gompertz model supported the antifungal activity of postbiotics and revealed species-specific differences in growth parameters. These findings suggest that L. plantarum postbiotics have the potential to modulate the gut mycobiota by reducing Candida growth, potentially offering a therapeutic approach for combating fungal overgrowth associated with obesity.

Human gut microbiota interactions shape the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile

Clostridioides difficile can transiently or persistently colonize the human gut, posing a risk factor for infections. This colonization is influenced by complex molecular and ecological interactions with human gut microbiota. By investigating C. difficile dynamics in human gut communities over hundreds of generations, we show patterns of stable coexistence, instability, or competitive exclusion. Lowering carbohydrate concentration shifted a community containing C. difficile and the prevalent human gut symbiont Phocaeicola vulgatus from competitive exclusion to coexistence, facilitated by increased cross-feeding. In this environment, C. difficile adapted via single-point mutations in key metabolic genes, altering its metabolic niche from proline to glucose utilization. These metabolic changes substantially impacted inter-species interactions and reduced disease severity in the mammalian gut. In sum, human gut microbiota interactions are crucial in shaping the long-term growth dynamics and evolutionary adaptations of C. difficile, offering key insights for developing anti-C. difficile strategies.

Quantifying microbial interactions: concepts, caveats, and applications

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

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

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.

Software sensor to monitor a synthetic microbial community emulating the dietary fiber impact on the gut microbiota

Human health is related to the gut microbiota, a complex ecosystem integrated by microorganisms and host cells. In vivo study of microbiota implies sophisticated analytical techniques, making it hard to study its dynamic changes. In vitro studies have proved helpful in deciphering interactions using representative synthetic microbial communities. Its characterization still depends on multiple sensors and analytical and molecular techniques. This work presents a SynCom including three bacteria with inulin as a fiber source to synthesize acetic, propionic, and butyric acids. A software sensor based on the Differential Neural Networks was implemented to estimate the SynCom dynamic. Species growth and byproduct concentration were estimated from available information (pH and optical density). We assessed the approach based on reported datasets; less than 5% of the error was achieved in less than 0.5 h, and we proved that our algorithm is suitable in noisy environments. This approach could accelerate the dynamic description of gut microbiota and reduce cost and time investment.

Emerging tools and best practices for studying gut microbial community metabolism

The human gut microbiome vastly extends the set of metabolic reactions catalysed by our own cells, with far-reaching consequences for host health and disease. However, our knowledge of gut microbial metabolism relies on a handful of model organisms, limiting our ability to interpret and predict the metabolism of complex microbial communities. In this Perspective, we discuss emerging tools for analysing and modelling the metabolism of gut microorganisms and for linking microorganisms, pathways and metabolites at the ecosystem level, highlighting promising best practices for researchers. Continued progress in this area will also require infrastructure development to facilitate cross-disciplinary synthesis of scientific findings. Collectively, these efforts can enable a broader and deeper understanding of the workings of the gut ecosystem and open new possibilities for microbiome manipulation and therapy.

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.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

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.

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.

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.

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.