Design of synthetic human gut microbiome assembly and butyrate production

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.

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.

HiBC: a publicly available collection of bacterial strains isolated from the human gut

Numerous bacteria in the human gut microbiome remain unknown and/or have yet to be cultured. While collections of human gut bacteria have been published, few strains are accessible to the scientific community. We have therefore created a publicly available collection of bacterial strains isolated from the human gut. The Human intestinal Bacteria Collection (HiBC) ( https://www.hibc.rwth-aachen.de ) contains 340 strains representing 198 species within 29 families and 7 phyla, of which 29 previously unknown species are taxonomically described and named. These included two butyrate-producing species of Faecalibacterium and new dominant species associated with health and inflammatory bowel disease, Ruminococcoides intestinale and Blautia intestinihominis, respectively. Plasmids were prolific within the HiBC isolates, with almost half (46%) of strains containing plasmids, with a maximum of six within a strain. This included a broadly occurring plasmid (pBAC) that exists in three diverse forms across Bacteroidales species. Megaplasmids were identified within two strains, the pMMCAT megaplasmid is globally present within multiple Bacteroidales species. This collection of easily searchable and publicly available gut bacterial isolates will facilitate functional studies of the gut microbiome.

Integrating microbial communities into algal biotechnology: a pathway to enhanced commercialization

Microalgae are increasingly recognized for their potential in wastewater treatment and the sustainable production of feedstock for fuel, feed, food, and other bioproducts. Like conventional agricultural systems, algal cultivation involves complex microbial communities. However, despite their pivotal role in cultivation outcomes, especially at the commodity-scale, the critical interactions between microalgae and their microbiomes are often overlooked. Here we synthesize current knowledge on the taxonomic diversity, ecological roles, and biotechnological potential of algal microbiomes, with a focus on their interactions with algal hosts through nutrient exchange, growth modulation, pathogen defense, and environmental conditioning. We also examine how environmental factors such as nutrient availability, salinity, and temperature influence these interactions. Advances in microbiome engineering, including synthetic biology and ecological approaches, offer opportunities to enhance beneficial algal-microbiome interactions, thereby improving growth, resilience, and yield. These advancements could lead to more sustainable and economically viable microalgae cultivation, with far-reaching implications for environmental management and biotechnological innovation. By addressing key economic and environmental barriers, microbiome engineering holds transformative potential to revolutionize large-scale algae cultivation and provide sustainable solutions to global challenges.

Collective Microbial Effects Drive Toxin Bioremediation and Enable Rational Design

The metabolic activity of microbial communities is essential for host and environmental health, influencing processes from immune regulation to bioremediation. Given this importance, the rational design of microbiomes with targeted functional properties is an important objective. Designing microbial consortia with targeted functions is challenging due to complex community interactions and environmental heterogeneity. Community-function landscapes address this challenge by statistically inferring impacts of species presence or absence on function. Similar to fitness landscapes, community-function landscapes are shaped by both additive effects and interactions (epistasis) among species that influence function. Here, we apply the community-function landscape approach to design synthetic microbial consortia to degrade the toxic environmental contaminant bisphenol-A (BPA). Using synthetic communities of BPA-degrading isolates, we map community-function landscapes across increasing BPA concentrations, where higher BPA means greater toxicity. As toxicity increases, so does epistasis, indicating that collective effects become more important in degradation. Further, we leverage landscapes to rationally design communities with predictable BPA degradation dynamics in vitro. Remarkably, designed synthetic communities are able to remediate BPA in contaminated soils. Our results demonstrate that toxicity can drive epistatic interactions in community-function landscapes and that these landscapes can guide microbial consortia design for bioremediation.

Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

Characterising functional redundancy in microbiome communities via relative entropy

Functional redundancy has been hypothesised to be at the core of the well-evidenced relation between high ecological microbiome diversity and human health. Here, we conceptualise and operationalise functional redundancy on a single-trait level for functionally annotated microbial communities, utilising an information-theoretic approach based on relative entropy that also allows for the quantification of functional interdependency across species. Via constraint-based microbiome community modelling of a public faecal metagenomic dataset, we demonstrate that the strength of the relation between species diversity and functional redundancy is dependent on specific attributes of the function under consideration such as the rarity and the occurring functional interdependencies. Moreover, by integrating faecal metabolome data, we highlight that measures of functional redundancy have correlates in the host's metabolome. We further demonstrate that microbiomes sampled from colorectal cancer patients display higher levels of species-species functional interdependencies than those of healthy controls. By analysing microbiome community models from an inflammatory bowel disease (IBD) study, we show that although species diversity decreased in IBD subjects, functional redundancy increased for certain metabolites, notably hydrogen sulphide. This finding highlights their potential to provide valuable insights beyond species diversity. Here, we formalise the concept of functional redundancy in microbial communities and demonstrate its usefulness in real microbiome data, providing a foundation for a deeper understanding of how microbiome diversity shapes the functional capacities of a microbiome.

A decade of advances in human gut microbiome-derived biotherapeutics

Microbiome science has evolved rapidly in the past decade, with high-profile publications suggesting that the gut microbiome is a causal determinant of human health. This has led to the emergence of microbiome-focused biotechnology companies and pharmaceutical company investment in the research and development of gut-derived therapeutics. Despite the early promise of this field, the first generation of microbiome-derived therapeutics (faecal microbiota products) have only recently been approved for clinical use. Next-generation therapies based on readily culturable and as-yet-unculturable colonic bacterial species (with the latter estimated to comprise 63% of all detected species) have not yet progressed to pivotal phase 3 trials. This reflects the many challenges involved in developing a new class of drugs in an evolving field. Here we discuss the evolution of the live biotherapeutics field over the past decade, from the development of first-generation products to the emergence of rationally designed second- and third-generation live biotherapeutics. Finally, we present our outlook for the future of this field.

Peeling off the layers from microbial dark matter (MDM): recent advances, future challenges, and opportunities

Microbes represent the most common organisms on Earth; however, less than 2% of microbial species in the environment can undergo cultivation for study under laboratory conditions, and the rest of the enigmatic, microbial world remains mysterious, constituting a kind of "microbial dark matter" (MDM). In the last two decades, remarkable progress has been made in culture-dependent and culture-independent techniques. More recently, studies of MDM have relied on culture-independent techniques to recover genetic material through either unicellular genomics or shotgun metagenomics to construct single-amplified genomes (SAGs) and metagenome-assembled genomes (MAGs), respectively, which provide information about evolution and metabolism. Despite the remarkable progress made in the past decades, the functional diversity of MDM still remains uncharacterized. This review comprehensively summarizes the recently developed culture-dependent and culture-independent techniques for characterizing MDM, discussing major challenges, opportunities, and potential applications. These activities contribute to expanding our knowledge of the microbial world and have implications for various fields including Biotechnology, Bioprospecting, Functional genomics, Medicine, Evolutionary and Planetary biology. Overall, this review aims to peel off the layers from MDM, shed light on recent advancements, identify future challenges, and illuminate the exciting opportunities that lie ahead in unraveling the secrets of this intriguing microbial realm.

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.

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.

Dual role for microbial short-chain fatty acids in modifying SIV disease trajectory following anti-α4β7 antibody administration

BACKGROUND

Human Immunodeficiency Virus (HIV)/Simian Immunodeficiency Virus (SIV) infection is associated with significant gut damage, similar to that observed in patients with inflammatory bowel disease (IBD). This pathology includes loss of epithelial integrity, microbial translocation, dysbiosis, and resultant chronic immune activation. Additionally, the levels of all-trans-retinoic acid (atRA) are dramatically attenuated. Data on the therapeutic use of anti-α4β7 antibodies has shown promise in patients with ulcerative colitis and Crohn's disease. Recent evidence has suggested that the microbiome and short-chain fatty acid (SCFA) metabolites it generates may be critical for anti-α4β7 efficacy and maintaining intestinal homeostasis.

MATERIALS AND METHODS

To determine whether the microbiome contributes to gut homeostasis after anti-α4β7 antibody administered to SIV-infected rhesus macaques, faecal SCFA concentrations were determined, 16S rRNA sequencing was performed, plasma viral loads were determined, plasma retinoids were measured longitudinally, and gut retinoid synthesis/response gene expression was quantified.

RESULTS

Our results suggest that anti-α4β7 antibody facilitates the return of retinoid metabolism to baseline levels after SIV infection. Furthermore, faecal SCFAs were shown to be associated with retinoid synthesis gene expression and rebound viral loads after therapy interruption.

CONCLUSIONS

Taken together, these data demonstrate the therapeutic advantages of anti-α4β7 antibody administration during HIV/SIV infection and that the efficacy of anti-α4β7 antibody may depend on microbiome composition and SCFA generation.

Diet, Microbiome, and Inflammation Predictors of Fecal and Plasma Short-Chain Fatty Acids in Humans

BACKGROUND

Gut microbes produce short-chain fatty acids (SCFAs), which are associated with broad health benefits. However, it is not fully known how diet and/or the gut microbiome could be modulated to improve SCFA production.

OBJECTIVES

The objective of this study was to identify dietary, inflammatory, and/or microbiome predictors of SCFAs in a cohort of healthy adults.

METHODS

SCFAs were measured in fecal and plasma samples from 359 healthy adults in the United States Department of Agriculture Nutritional Phenotyping Study. Habitual and recent diet was assessed using a Food Frequency Questionnaire and Automated Self-Administered 24-h Dietary Assesment Tool dietary recalls. Markers of systemic and gut inflammation were measured in fecal and plasma samples. The gut microbiome was assessed using shotgun metagenomics. Using statistics and machine learning, we determined how the abundance and composition of SCFAs varied with measures of diet, inflammation, and the gut microbiome.

RESULTS

We show that fecal pH may be a good proxy for fecal SCFA abundance. A higher Healthy Eating Index for a habitual diet was associated with a compositional increase in fecal butyrate relative to acetate and propionate. SCFAs were associated with markers of subclinical gastrointestinal (GI) inflammation. Fecal SCFA abundance was inversely related to plasma lipopolysaccharide-binding protein. When we analyzed hierarchically organized diet and microbiome data with taxonomy-aware algorithms, we observed that diet and microbiome features were far more predictive of fecal SCFA abundances compared to plasma SCFA abundances. The top diet and microbiome predictors of fecal butyrate included potatoes and the thiamine biosynthesis pathway, respectively.

CONCLUSIONS

These results suggest that resistant starch in the form of potatoes and microbially produced thiamine provide a substrate and essential cofactor, respectively, for butyrate synthesis. Thiamine may be a rate-limiting nutrient for butyrate production in adults. Overall, these findings illustrate the complex biology underpinning SCFA production in the gut. This trial was registered at clinicaltrials.gov as NCT02367287.

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.

A systematic framework for understanding the microbiome in human health and disease: from basic principles to clinical translation

The human microbiome is a complex and dynamic system that plays important roles in human health and disease. However, there remain limitations and theoretical gaps in our current understanding of the intricate relationship between microbes and humans. In this narrative review, we integrate the knowledge and insights from various fields, including anatomy, physiology, immunology, histology, genetics, and evolution, to propose a systematic framework. It introduces key concepts such as the 'innate and adaptive genomes', which enhance genetic and evolutionary comprehension of the human genome. The 'germ-free syndrome' challenges the traditional 'microbes as pathogens' view, advocating for the necessity of microbes for health. The 'slave tissue' concept underscores the symbiotic intricacies between human tissues and their microbial counterparts, highlighting the dynamic health implications of microbial interactions. 'Acquired microbial immunity' positions the microbiome as an adjunct to human immune systems, providing a rationale for probiotic therapies and prudent antibiotic use. The 'homeostatic reprogramming hypothesis' integrates the microbiome into the internal environment theory, potentially explaining the change in homeostatic indicators post-industrialization. The 'cell-microbe co-ecology model' elucidates the symbiotic regulation affecting cellular balance, while the 'meta-host model' broadens the host definition to include symbiotic microbes. The 'health-illness conversion model' encapsulates the innate and adaptive genomes' interplay and dysbiosis patterns. The aim here is to provide a more focused and coherent understanding of microbiome and highlight future research avenues that could lead to a more effective and efficient healthcare system.

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.

DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.

The Relationship Between Maturation Size and Maximum Tree Size From Tropical to Boreal Climates

The fundamental trade-off between current and future reproduction has long been considered to result in a tendency for species that can grow large to begin reproduction at a larger size. Due to the prolonged time required to reach maturity, estimates of tree maturation size remain very rare and we lack a global view on the generality and the shape of this trade-off. Using seed production from five continents, we estimate tree maturation sizes for 486 tree species spanning tropical to boreal climates. Results show that a species' maturation size increases with maximum size, but in a non-proportional way: the largest species begin reproduction at smaller sizes than would be expected if maturation were simply proportional to maximum size. Furthermore, the decrease in relative maturation size is steepest in cold climates. These findings on maturation size drivers are key to accurately represent forests' responses to disturbance and climate change.

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.

Short-chain fatty acids: linking diet, the microbiome and immunity

The short-chain fatty acids (SCFAs) butyrate, propionate and acetate are microbial metabolites and their availability in the gut and other organs is determined by environmental factors, such as diet and use of antibiotics, that shape the diversity and metabolism of the microbiota. SCFAs regulate epithelial barrier function as well as mucosal and systemic immunity via evolutionary conserved processes that involve G protein-coupled receptor signalling or histone deacetylase activity. Indicatively, the anti-inflammatory role of butyrate is mediated through direct effects on the differentiation of intestinal epithelial cells, phagocytes, B cells and plasma cells, and regulatory and effector T cells. Intestinally derived SCFAs also directly and indirectly affect immunity at extra-intestinal sites, such as the liver, the lungs, the reproductive tract and the brain, and have been implicated in a range of disorders, including infections, intestinal inflammation, autoimmunity, food allergies, asthma and responses to cancer therapies. An ecological understanding of microbial communities and their interrelated metabolic states, as well as the engineering of butyrogenic bacteria may support SCFA-focused interventions for the prevention and treatment of immune-mediated diseases.

Higher-order interactions and emergent properties of microbial communities: The power of synthetic ecology

Humans have long relied on microbial communities to create products, produce energy, and treat waste. The microbiota residing within our bodies directly impacts our health, while the soil and rhizosphere microbiomes influence the productivity of our crops. However, the complexity and diversity of microbial communities make them challenging to study and difficult to develop into applications, as they often exhibit the emergence of unpredictable higher-order phenomena. Synthetic ecology aims at simplifying complexity by constituting synthetic or semi-natural microbial communities with reduced diversity that become easier to study and analyze. This strategy combines methodologies that simplify existing complex systems (top-down approach) or build the system from its constituent components (bottom-up approach). Simplified communities are studied to understand how interactions among populations shape the behavior of the community and to model and predict their response to external stimuli. By harnessing the potential of synthetic microbial communities through a multidisciplinary approach, we can advance knowledge of ecological concepts and address critical public health, agricultural, and environmental issues more effectively.

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.

A synbiotic of Anaerostipes caccae and lactulose prevents and treats food allergy in mice

Depletion of beneficial microbes by modern lifestyle factors correlates with the rising prevalence of food allergies. Re-introduction of allergy-protective bacteria may be an effective treatment strategy. We characterized the fecal microbiota of healthy and food-allergic infants and found that the anaerobe Anaerostipes caccae (A. caccae) was representative of the protective capacity of the healthy microbiota. We isolated a strain of A. caccae from the feces of a healthy infant and identified lactulose as a prebiotic to optimize butyrate production by A. caccae in vitro. Administration of a synbiotic composed of our isolated A. caccae strain and lactulose increased luminal butyrate in gnotobiotic mice colonized with feces from an allergic infant and in antibiotic-treated specific pathogen-free (SPF) mice, and prevented or treated an anaphylactic response to allergen challenge. The synbiotic's efficacy in two models and microbial contexts suggests that it may be a promising approach for the treatment of food allergy.

Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

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.

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Global epistasis and the emergence of function in microbial consortia

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

Functional regimes define the response of the soil microbiome to environmental change

The metabolic activity of soil microbiomes plays a central role in carbon and nitrogen cycling. Given the changing climate, it is important to understand how the metabolism of natural communities responds to environmental change. However, the ecological, spatial, and chemical complexity of soils makes understanding the mechanisms governing the response of these communities to perturbations challenging. Here, we overcome this complexity by using dynamic measurements of metabolism in microcosms and modeling to reveal regimes where a few key mechanisms govern the response of soils to environmental change. We sample soils along a natural pH gradient, construct >1500 microcosms to perturb the pH, and quantify the dynamics of respiratory nitrate utilization, a key process in the nitrogen cycle. Despite the complexity of the soil microbiome, a minimal mathematical model with two variables, the quantity of active biomass in the community and the availability of a growth-limiting nutrient, quantifies observed nitrate utilization dynamics across soils and pH perturbations. Across environmental perturbations, changes in these two variables give rise to three functional regimes each with qualitatively distinct dynamics of nitrate utilization over time: a regime where acidic perturbations induce cell death that limits metabolic activity, a nutrient-limiting regime where nitrate uptake is performed by dominant taxa that utilize nutrients released from the soil matrix, and a resurgent growth regime in basic conditions, where excess nutrients enable growth of initially rare taxa. The underlying mechanism of each regime is predicted by our interpretable model and tested via amendment experiments, nutrient measurements, and sequencing. Further, our data suggest that the long-term history of environmental variation in the wild influences the transitions between functional regimes. Therefore, quantitative measurements and a mathematical model reveal the existence of qualitative regimes that capture the mechanisms and dynamics of a community responding to environmental change.

Natural products from the human microbiome: an emergent frontier in organic synthesis and drug discovery

Often referred to as the "second genome", the human microbiome is at the epicenter of complex inter-habitat biochemical networks like the "gut-brain axis", which has emerged as a significant determinant of cognition, overall health and well-being, as well as resistance to antibiotics and susceptibility to diseases. As part of a broader understanding of the nexus between the human microbiome, diseases and microbial interactions, whether encoded secondary metabolites (natural products) play crucial signalling roles has been the subject of intense scrutiny in the recent past. A major focus of these activities involves harvesting the genomic potential of the human microbiome via bioinformatics guided genome mining and culturomics. Through these efforts, an impressive number of structurally intriguing antibiotics, with enhanced chemical diversity vis-à-vis conventional antibiotics have been isolated from human commensal bacteria, thereby generating considerable interest in their total synthesis and expanding their therapeutic space for drug discovery. These developments augur well for the discovery of new drugs and antibiotics, particularly in the context of challenges posed by mycobacterial resistance and emerging new diseases. The current landscape of various synthetic campaigns and drug discovery initiatives on antibacterial natural products from the human microbiome is captured in this review with an intent to stimulate further activities in this interdisciplinary arena among the new generation.

Engineering Gut Symbionts: A Way to Promote Bee Growth?

Bees play a crucial role as pollinators, contributing significantly to ecosystems. However, the honeybee population faces challenges such as global warming, pesticide use, and pathogenic microorganisms. Promoting bee growth using several approaches is therefore crucial for maintaining their roles. To this end, the bacterial microbiota is well-known for its native role in supporting bee growth in several respects. Maximizing the capabilities of these microorganisms holds the theoretical potential to promote the growth of bees. Recent advancements have made it feasible to achieve this enhancement through the application of genetic engineering. In this review, we present the roles of gut symbionts in promoting bee growth and collectively summarize the engineering approaches that would be needed for future applications. Particularly, as the engineering of bee gut symbionts has not been advanced, the dominant gut symbiotic bacteria Snodgrassella alvi and Gilliamella apicola are the main focus of the paper, along with other dominant species. Moreover, we propose engineering strategies that will allow for the improvement in bee growth with listed gene targets for modification to further encourage the use of engineered gut symbionts to promote bee growth.

Designing function-specific minimal microbiomes from large microbial communities

Microorganisms exist in large communities of diverse species, exhibiting various functionalities. The mammalian gut microbiome, for instance, has the functionality of digesting dietary fibre and producing different short-chain fatty acids. Not all microbes present in a community contribute to a given functionality; it is possible to find a minimal microbiome, which is a subset of the large microbiome, that is capable of performing the functionality while maintaining other community properties such as growth rate and metabolite production. Such a minimal microbiome will also contain keystone species for SCFA production in that community. In this work, we present a systematic constraint-based approach to identify a minimal microbiome from a large community for a user-proposed function. We employ a top-down approach with sequential deletion followed by solving a mixed-integer linear programming problem with the objective of minimising the L1-norm of the membership vector. Notably, we consider quantitative measures of community growth rate and metabolite production rates. We demonstrate the utility of our algorithm by identifying the minimal microbiomes corresponding to three model communities of the gut, and discuss their validity based on the presence of the keystone species in the community. Our approach is generic, flexible and finds application in studying a variety of microbial communities. The algorithm is available from https://github.com/RamanLab/minMicrobiome .

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

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

Dissecting Gut-Microbial Community Interactions using a Gut Microbiome-on-a-Chip

While the human gut microbiota has a significant impact on gut health and disease, understanding of the roles of gut microbes, interactions, and collective impact of gut microbes on various aspects of human gut health is limited by the lack of suitable in vitro model system that can accurately replicate gut-like environment and enable the close visualization on causal and mechanistic relationships between microbial constitutents and the gut. , In this study, we present a scalable Gut Microbiome-on-a-Chip (GMoC) with great imaging capability and scalability, providing a physiologically relevant dynamic gut-microbes interfaces. This chip features a reproducible 3D stratified gut epithelium derived from Caco-2 cells (µGut), mimicking key intestinal architecture, functions, and cellular complexity, providing a physiolocially relevant gut environment for microbes residing in the gut. Incorporating tumorigenic bacteria, enterotoxigenic Bacteroides fragilis (ETBF), into the GMoC enable the observation of pathogenic behaviors of ETBF, leading to µGut disruption and pro-tumorigenic signaling activations. Pre-treating the µGut with a beneficial gut microbe Lactobacillus spp., effectively prevent ETBF-mediated gut pathogenesis, preserving the healthy state of the µGut through competition-mediated colonization resistance. The GMoC holds potential as a valuable tool for exploring unknown roles of gut microbes in microbe-induced pathogenesis and microbe-based therapeutic development.

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.

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

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

In Vitro Human Gastrointestinal Tract Simulation Systems: A Panoramic Review

Simulated human gastrointestinal (GI) tract systems are important for their applications in the fields of probiotics, nutrition and health. To date, various in vitro gut systems have been available to study GI tract dynamics and its association with health. In contrast to in vivo investigations, which are constrained by ethical considerations, in vitro models have several benefits despite the challenges involved in mimicking the GI environment. These in vitro models can be used for a range of research, from simple to dynamic, with one compartment to several compartments. In this review, we present a panoramic development of in vitro GI models for the first time through an evolutionary timeline. We tried to provide insight on designing an in vitro gut model, especially for novices. Latest developments and scope for improvement based on the limitations of the existing models were highlighted. In conclusion, designing an in vitro GI model suitable for a particular application is a multifaceted task. The bio-mimicking of the GI tract specific to geometrical, anatomical and mechanical features remains a challenge for the development of effective in vitro GI models. Advances in computer technology, artificial intelligence and nanotechnology are going to be revolutionary for further development. Besides this, in silico high-throughput technologies and miniaturisation are key players in the success of making in vitro modelling cost-effective and reducing the burden of in vivo studies.

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.

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.

The gut microbiome in systemic lupus erythematosus: lessons from rheumatic fever

For more than a century, certain bacterial infections that can breach the skin and mucosal barriers have been implicated as common triggers of autoimmune syndromes, especially post-infection autoimmune diseases that include rheumatic fever and post-streptococcal glomerulonephritis. However, only in the past few years has the importance of imbalances within our own commensal microbiota communities, and within the gut, in the absence of infection, in promoting autoimmune pathogenesis become fully appreciated. A diversity of species and mechanisms have been implicated, including disruption of the gut barrier. Emerging data suggest that expansions (or blooms) of pathobiont species are involved in autoimmune pathogenesis and stimulate clonal expansion of T cells and B cells that recognize microbial antigens. This Review discusses the relationship between the gut microbiome and the immune system, and the potential consequence of disrupting the community balance in terms of autoimmune development, focusing on systemic lupus erythematosus. Notably, inter-relationships between expansions of certain members within gut microbiota communities and concurrent autoimmune responses bear features reminiscent of classical post-infection autoimmune disease. From such insights, new therapeutic opportunities are being considered to restore the balance within microbiota communities or re-establishing the gut-barrier integrity to reinforce immune homeostasis in the host.

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

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

Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

Massively parallel single-cell sequencing of diverse microbial populations

Single-cell genetic heterogeneity is ubiquitous in microbial populations and an important aspect of microbial biology; however, we lack a broadly applicable and accessible method to study this heterogeneity in microbial populations. Here, we show a simple, robust and generalizable method for high-throughput single-cell sequencing of target genetic loci in diverse microbes using simple droplet microfluidics devices (droplet targeted amplicon sequencing; DoTA-seq). DoTA-seq serves as a platform to perform diverse assays for single-cell genetic analysis of microbial populations. Using DoTA-seq, we demonstrate the ability to simultaneously track the prevalence and taxonomic associations of >10 antibiotic-resistance genes and plasmids within human and mouse gut microbial communities. This workflow is a powerful and accessible platform for high-throughput single-cell sequencing of diverse microbial populations.

On the limits to invasion prediction using coexistence outcomes

The dynamics of ecological communities in nature are typically characterized by probabilistic processes involving invasion dynamics. Because of technical challenges, however, the majority of theoretical and experimental studies have focused on coexistence dynamics. Therefore, it has become central to understand the extent to which coexistence outcomes can be used to predict analogous invasion outcomes relevant to systems in nature. Here, we study the limits to this predictability under a geometric and probabilistic Lotka-Volterra framework. We show that while individual survival probability in coexistence dynamics can be fairly closely translated into invader colonization probability in invasion dynamics, the translation is less precise between community persistence and community augmentation, and worse between exclusion probability and replacement probability. These results provide a guiding and testable theoretical framework regarding the translatability of outcomes between coexistence and invasion outcomes when communities are represented by Lotka-Volterra dynamics under environmental uncertainty.

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.

Unraveling the gut-Lung axis: Exploring complex mechanisms in disease interplay

The link between gut and lung starts as early as during organogenesis. Even though they are anatomically distinct, essential bidirectional crosstalk via complex mechanisms supports GLA. Emerging studies have demonstrated the association of gut and lung diseases via multifaceted mechanisms. Advancements in omics and metagenomics technologies revealed a potential link between gut and lung microbiota, adding further complexity to GLA. Despite substantial studies on GLA in various disease models, mechanisms beyond microbial dysbiosis regulating the interplay between gut and lung tissues during disease conditions are not thoroughly reviewed. This review outlines disease specific GLA mechanisms, emphasizing research gaps with a focus on gut-to-lung direction based on current GLA literature. Moreover, the review discusses potential gut microbiota and their products like metabolites, immune modulators, and non-bacterial contributions as a basis for developing treatment strategies for lung diseases. Advanced experimental methods, modern diagnostic tools, and technological advancements are also highlighted as crucial areas for improvement in developing novel therapeutic approaches for GLA-related diseases. In conclusion, this review underscores the importance of exploring additional mechanisms within the GLA to gain a deeper understanding that could aid in preventing and treating a wide spectrum of lung diseases.

Linking active rectal mucosa-attached microbiota to host immunity reveals its role in host-pathogenic STEC O157 interactions

The rectal-anal junction (RAJ) is the major colonization site of Shiga toxin-producing Escherichia coli (STEC) O157 in beef cattle, leading to transmission of this foodborne pathogen from farms to food chains. To date, there is limited understanding regarding whether the mucosa-attached microbiome has a profound impact on host-STEC interactions. In this study, the active RAJ mucosa-attached microbiota and its potential role in host immunity-STEC commensal interactions were investigated using RAJ mucosal biopsies collected from calves orally challenged with two STEC O157 strains with or without functional stx2a (stx2a+ or stx2a-). The results revealed that shifts of microbial diversity, topology, and assembly patterns were subjected to stx2a production post-challenge and Paeniclostridium and Gallibacterium were the keystone taxa for both microbial interactions and assembly. Additional mucosal transcriptome profiling showed stx2a-dependent host immune responses (i.e. B- and T-cell signaling and antigen processing and presentation) post-challenge. Further integrated analysis revealed that mucosa-attached beneficial microbes (i.e. Provotella, Faecalibacterium, and Dorea) interacted with host immune genes pre-challenge to maintain host homeostasis; however, opportunistic pathogenic microbes (i.e. Paeniclostridium) could interact with host immune genes after the STEC O157 colonization and interactions were stx2a-dependent. Furthermore, predicted bacterial functions involved in pathogen (O157 and Paeniclostridium) colonization and metabolism were related to host immunity. These findings suggest that during pathogen colonization, host-microbe interactions could shift from beneficial to opportunistic pathogenic bacteria driven and be dependent on the production of particular virulence factors, highlighting the potential regulatory role of mucosa-attached microbiota in affecting pathogen-commensal host interactions in calves with STEC O157 infection.

Impact of timing on the invasion of synthetic bacterial communities

Microbial communities regularly experience ecological invasions that can lead to changes in composition and function. Factors thought to impact susceptibility to invasions, such as diversity and resource use, vary over the course of community assembly. We used synthetic bacterial communities to evaluate the success and impact of invasions occurring at different times during the community assembly process. Fifteen distinct communities were subjected to each of three bacterial invaders at the initial assembly of the community ("initial invasion"), 24 h into community assembly ("early invasion"), when the community was still undergoing transient dynamics, and 7 days into community assembly ("late invasion"), once the community had settled into its final composition. Communities were passaged daily and characterized through sequencing after reaching a stable composition. Invasions often failed to persist over time, particularly in higher richness communities. However, invasions had their largest effect on composition when they occurred before a community had settled into a stable composition. We found instances where an invader was ultimately excluded yet had profound and long-lasting effects on invaded communities. Invasion outcome was positively associated with lower community richness and resource use efficiency by the community, which varied throughout assembly. Our results demonstrate that microbial communities experiencing transient community dynamics are more affected by, and in some instances prone to, invasion, a finding relevant to efforts to modify the composition of microbial communities.

AnimalMetaOmics: a multi-omics data resources for exploring animal microbial genomes and microbiomes

The Animal Meta-omics landscape database (AnimalMetaOmics, https://yanglab.hzau.edu.cn/animalmetaomics#/) is a comprehensive and freely available resource that includes metagenomic, metatranscriptomic, and metaproteomic data from various non-human animal species and provides abundant information on animal microbiomes, including cluster analysis of microbial cognate genes, functional gene annotations, active microbiota composition, gene expression abundance, and microbial protein identification. In this work, 55 898 microbial genomes were annotated from 581 animal species, including 42 924 bacterial genomes, 12 336 virus genomes, 496 archaea genomes and 142 fungi genomes. Moreover, 321 metatranscriptomic datasets were analyzed from 31 animal species and 326 metaproteomic datasets from four animal species, as well as the pan-genomic dynamics and compositional characteristics of 679 bacterial species and 13 archaea species from animal hosts. Researchers can efficiently access and acquire the information of cross-host microbiota through a user-friendly interface, such as species, genomes, activity levels, expressed protein sequences and functions, and pan-genome composition. These valuable resources provide an important reference for better exploring the classification, functional diversity, biological process diversity and functional genes of animal microbiota.

Reverse engineering the Gut-Brain Axis and microbiome-metabolomics for symbiotic/pathogenic balance in neurodegenerative diseases

Deciphering the molecular communications along the gut-brain axis can help in understanding the pathophysiology of neurodegenerative diseases and exploiting the gut microbiome for therapeutics. However, gut microbes and their metabolites have a multifaceted role in mediating both brain physiology and neurodegenerative pathology. There is a lack of understanding of how and when this role is tipped in neurodegenerative diseases and what are those contributing factors, both at local (gut) and distal (neuronal) levels, that drive this imbalance. Here we have reviewed the gut microbiome and its metabolites in the context of the gut-brain axis and summarized how different factors such as gut-microbial diversity, their metabolites, the role of the native immune system and the integrity of gut epithelial and blood-brain barriers are interconnected and collectively define the involvement of gut-microbiome in neurodegenerative pathologies. It also underlines the need for multidisciplinary tools and animal models to simultaneously reflect on many of these factors and to better correlate with clinical observations and data obtained from human biopsies and fecal samples. Harnessing the gut-brain axis will herald a paradigm shift in medicine for neurodegenerative diseases and aging, emphasizing the significance of the microbiome in the broader spectrum of health and disease.