Evidence for a multi-level trophic organization of the human gut microbiome

Evolution and stability of complex microbial communities driven by trade-offs

Microbial communities are ubiquitous in nature and play an important role in ecology and human health. Cross-feeding is thought to be core to microbial communities, though it remains unclear precisely why it emerges. Why have multi-species microbial communities evolved in many contexts and what protects microbial consortia from invasion? Here, we review recent insights into the emergence and stability of coexistence in microbial communities. A particular focus is the long-term evolutionary stability of coexistence, as observed for microbial communities that spontaneously evolved in the E. coli long-term evolution experiment (LTEE). We analyze these findings in the context of recent work on trade-offs between competing microbial objectives, which can constitute a mechanistic basis for the emergence of coexistence. Coexisting communities, rather than monocultures of the 'fittest' single strain, can form stable endpoints of evolutionary trajectories. Hence, the emergence of coexistence might be an obligatory outcome in the evolution of microbial communities. This implies that rather than embodying fragile metastable configurations, some microbial communities can constitute formidable ecosystems that are difficult to disrupt.

Food webs in food webs: the micro-macro interplay of multilayered networks

Food webs are typically defined as being macro-organism-based (e.g., plants, mammals, birds) or microbial (e.g., bacteria, fungi, viruses). However, these characterizations have limits. We propose a multilayered food web conceptual model where microbial food webs are nested within food webs composed of macro-organisms. Nesting occurs through host-microbe interactions, which influence the health and behavior of host macro-organisms, such that host microbiomes likely alter population dynamics of interacting macro-organisms and vice versa. Here, we explore the theoretical underpinnings of multilayered food webs and the implications of this new conceptual model on food web ecology. Our framework opens avenues for new empirical investigations into complex ecological networks and provides a new lens through which to view a network's response to ecosystem changes.

Eco-evolutionary feedbacks in the human gut microbiome

Gut microbiota can evolve within their hosts on human-relevant timescales, but little is known about how these changes influence (or are influenced by) the composition of their local community. Here, by combining ecological and evolutionary analyses of a large cohort of human gut metagenomes, we show that the short-term evolution of the microbiota is linked with shifts in its ecological structure. These correlations are not simply explained by expansions of the evolving species, and often involve additional fluctuations in distantly related taxa. We show that similar feedbacks naturally emerge in simple resource competition models, even in the absence of cross-feeding or predation. These results suggest that the structure and function of host microbiota may be shaped by their local evolutionary history, which could have important implications for personalized medicine and microbiome engineering.

Disease-specific loss of microbial cross-feeding interactions in the human gut

Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.

The value of connections

High proportions of gut bacteria that produce their own food can be an indicator for poor gut health.

Alterations of gut microbiome following gastrointestinal surgical procedures and their potential complications

Intestinal microorganisms play a crucial role in shaping the host immunity and maintaining homeostasis. Nevertheless, alterations in gut bacterial composition may occur and these alterations have been linked with the pathogenesis of several diseases. In surgical practice, studies revealed that the microbiome of patients undergoing surgery changes and several post-operative complications seem to be associated with the gut microbiota composition. In this review, we aim to provide an overview of gut microbiota (GM) in surgical disease. We refer to several studies which describe alterations of GM in patients undergoing different types of surgery, we focus on the impacts of peri-operative interventions on GM and the role of GM in development of post-operative complications, such as anastomotic leak. The review aims to enhance comprehension regarding the correlation between GM and surgical procedures based in the current knowledge. However, preoperative and postoperative synthesis of GM needs to be further examined in future studies, so that GM-targeted measures could be assessed and the different surgery complications could be reduced.

Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium

The human gut is home to trillions of microorganisms that influence several aspects of our health. This dense microbial community targets almost all dietary polysaccharides and releases multiple metabolites, some of which have physiological effects on the host. A healthy equilibrium between members of the gut microbiota, its microbial diversity, and their metabolites is required for intestinal health, promoting regulatory or anti-inflammatory immune responses. In contrast, the loss of this equilibrium due to antibiotics, low fiber intake, or other conditions results in alterations in gut microbiota composition, a term known as gut dysbiosis. This dysbiosis can be characterized by a reduction in health-associated microorganisms, such as butyrate-producing bacteria, enrichment of a small number of opportunistic pathogens, or a reduction in microbial diversity. Bifidobacterium species are key species in the gut microbiome, serving as primary degraders and contributing to a balanced gut environment in various ways. Colonization resistance is a fundamental property of gut microbiota for the prevention and control of infections. This community competes strongly with foreign microorganisms, such as gastrointestinal pathogens, antibiotic-resistant bacteria, or even probiotics. Resistance to colonization is based on microbial interactions such as metabolic cross-feeding, competition for nutrients, or antimicrobial-based inhibition. These interactions are mediated by metabolites and metabolic pathways, representing the inner workings of the gut microbiota, and play a protective role through colonization resistance. This review presents a rationale for how microbial interactions provide resistance to colonization and gut dysbiosis, highlighting the protective role of Bifidobacterium species.

Cross-feeding in the gut microbiome: Ecology and mechanisms

Microbial communities are shaped by positive and negative interactions ranging from competition to mutualism. In the context of the mammalian gut and its microbial inhabitants, the integrated output of the community has important impacts on host health. Cross-feeding, the sharing of metabolites between different microbes, has emergent roles in establishing communities of gut commensals that are stable, resistant to invasion, and resilient to external perturbation. In this review, we first explore the ecological and evolutionary implications of cross-feeding as a cooperative interaction. We then survey mechanisms of cross-feeding across trophic levels, from primary fermenters to H2 consumers that scavenge the final metabolic outputs of the trophic network. We extend this analysis to also include amino acid, vitamin, and cofactor cross-feeding. Throughout, we highlight evidence for the impact of these interactions on each species' fitness as well as host health. Understanding cross-feeding illuminates an important aspect of microbe-microbe and host-microbe interactions that establishes and shapes our gut communities.

"What I cannot create, I do not understand": elucidating microbe-microbe interactions to facilitate plant microbiome engineering

Plant-microbe interactions are important for both physiological and pathological processes. Despite the significance of plant-microbe interactions, microbe-microbe interactions themselves represent an important, complex, dynamic network that warrants deeper investigation. To understand how microbe-microbe interactions affect plant microbiomes, one approach is to systematically understand all the factors involved in successful engineering of a microbial community. This follows the physicist Richard Feynman's declaration: "what I cannot create, I do not understand". This review highlights recent studies that focus on aspects that we believe are important for building (ergo understanding) microbe-microbe interactions in the plant environment, including pairwise screening, intelligent application of cross-feeding models, spatial distributions of microbes, and understudied interactions between bacteria and fungi, phages, and protists. We offer a framework for systematic collection and centralized integration of data of plant microbiomes that could organize all the factors that can help ecologists understand microbiomes and help synthetic ecologists engineer beneficial microbiomes.

Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

Characterizing the metabolic profile of a microbial community is crucial for understanding its biological function and its impact on the host or environment. Metabolomics experiments directly measuring these profiles are difficult and expensive, while sequencing methods quantifying the species composition of microbial communities are well-developed and relatively cost-effective. Computational methods that are capable of predicting metabolomic profiles from microbial compositions can save considerable efforts needed for metabolomic profiling experimentally. Yet, despite existing efforts, we still lack a computational method with high prediction power, general applicability, and great interpretability. Here we develop a method - mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), based on a state-of-the-art family of deep neural network models. We show compelling evidence that mNODE outperforms existing methods in predicting the metabolomic profiles of human microbiomes and several environmental microbiomes. Moreover, in the case of human gut microbiomes, mNODE can naturally incorporate dietary information to further enhance the prediction of metabolomic profiles. Besides, susceptibility analysis of mNODE enables us to reveal microbe-metabolite interactions, which can be validated using both synthetic and real data. The presented results demonstrate that mNODE is a powerful tool to investigate the microbiome-diet-metabolome relationship, facilitating future research on precision nutrition.

Metabolic models of human gut microbiota: Advances and challenges

The human gut is a complex ecosystem consisting of hundreds of microbial species interacting with each other and with the human host. Mathematical models of the gut microbiome integrate our knowledge of this system and help to formulate hypotheses to explain observations. The generalized Lotka-Volterra model has been widely used for this purpose, but it does not describe interaction mechanisms and thus does not account for metabolic flexibility. Recently, models that explicitly describe gut microbial metabolite production and consumption have become popular. These models have been used to investigate the factors that shape gut microbial composition and to link specific gut microorganisms to changes in metabolite concentrations found in diseases. Here, we review how such models are built and what we have learned so far from their application to human gut microbiome data. In addition, we discuss current challenges of these models and how these can be addressed in the future.

Cystic Fibrosis-Related Gut Dysbiosis: A Systematic Review

BACKGROUND AND AIMS

Cystic Fibrosis (CF) is associated with gut dysbiosis, local and systemic inflammation, and impaired immune function. Gut microbiota dysbiosis results from changes in the complex gut milieu in response to CF transmembrane conductance regulator (CFTR) dysfunction, pancreatic malabsorption, diet, medications, and environmental influences. In several diseases, alteration of the gut microbiota influences local and systemic inflammation and disease outcomes. We conducted a systematic review of the gut microbiota in CF and explored factors influencing dysbiosis.

METHODS

An electronic search of three databases was conducted in January 2019, and re-run in June 2021. Human, animal, and in vitro studies were included. The primary outcome was differences in the gut microbiota between people with CF (pwCF) and healthy controls. Secondary outcomes included the relationship between the gut microbiota and other factors, including diet, medication, inflammation, and pulmonary function in pwCF.

RESULTS

Thirty-eight studies were identified. The literature confirmed the presence of CF-related gut dysbiosis, characterized by reduced diversity and several taxonomic changes. There was a relative increase of bacteria associated with a pro-inflammatory response coupled with a reduction of those considered anti-inflammatory. However, studies linking gut dysbiosis to systemic and lung inflammation were limited. Causes of gut dysbiosis were multifactorial, and findings were variable. Data on the impact of CFTR modulators on the gut microbiota were limited.

CONCLUSIONS

CF-related gut dysbiosis is evident in pwCF. Whether this influences local and systemic disease and is amenable to interventions with diet and drugs, such as CFTR modulators, requires further investigation.

Metabolic Modeling and Bidirectional Culturing of Two Gut Microbes Reveal Cross-Feeding Interactions and Protective Effects on Intestinal Cells

The gut microbiota is constituted by thousands of microbial interactions, some of which correspond to the exchange of metabolic by-products or cross-feeding. Inulin and xylan are two major dietary polysaccharides that are fermented by members of the human gut microbiota, resulting in different metabolic profiles. Here, we integrated community modeling and bidirectional culturing assays to study the metabolic interactions between two gut microbes, Phocaeicola dorei and Lachnoclostridium symbiosum, growing in inulin or xylan, and how they provide a protective effect in cultured cells. P. dorei (previously belonging to the Bacteroides genus) was able to consume inulin and xylan, while L. symposium only used certain inulin fractions to produce butyrate as a major end product. Constrained-based flux simulations of refined genome-scale metabolic models of both microbes predicted high lactate and succinate cross-feeding fluxes between P. dorei and L. symbiosum when growing in each fiber. Bidirectional culture assays in both substrates revealed that L. symbiosum growth increased in the presence of P. dorei. Carbohydrate consumption analyses showed a faster carbohydrate consumption in cocultures compared to monocultures. Lactate and succinate concentrations in bidirectional cocultures were lower than in monocultures, pointing to cross-feeding as initially suggested by the model. Butyrate concentrations were similar across all conditions. Finally, supernatants from both bacteria cultured in xylan in bioreactors significantly reduced tumor necrosis factor-α-induced inflammation in HT-29 cells and exerted a protective effect against the TcdB toxin in Caco-2 epithelial cells. Surprisingly, this effect was not observed in inulin cocultures. Overall, these results highlight the predictive value of metabolic models integrated with microbial culture assays for probing microbial interactions in the gut microbiota. They also provide an example of how metabolic exchange could lead to potential beneficial effects in the host. IMPORTANCE Microbial interactions represent the inner connections in the gut microbiome. By integrating mathematical modeling tools and microbial bidirectional culturing, we determined how two gut commensals engage in the exchange of cross-feeding metabolites, lactate and succinate, for increased growth in two fibers. These interactions underpinned butyrate production in cocultures, resulting in a significant reduction in cellular inflammation and protection against microbial toxins when applied to cellular models.

Small molecule modulation of microbiota: a systems pharmacology perspective

BACKGROUND

Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution.

RESULTS

We construct a disease-centric signed microbe-microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins.

CONCLUSION

The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.

Stability criteria for the consumption and exchange of essential resources

Models of consumer effects on a shared resource environment have helped clarify how the interplay of consumer traits and resource supply impact stable coexistence. Recent models generalize this picture to include the exchange of resources alongside resource competition. These models exemplify the fact that although consumers shape the resource environment, the outcome of consumer interactions is context-dependent: such models can have either stable or unstable equilibria, depending on the resource supply. However, these recent models focus on a simplified version of microbial metabolism where the depletion of resources always leads to consumer growth. Here, we model an arbitrarily large system of consumers governed by Liebig’s law, where species require and deplete multiple resources, but each consumer’s growth rate is only limited by a single one of these resources. Resources that are taken up but not incorporated into new biomass are leaked back into the environment, possibly transformed by intracellular reactions, thereby tying the mismatch between depletion and growth to cross-feeding. For this set of dynamics, we show that feasible equilibria can be either stable or unstable, again depending on the resource environment. We identify special consumption and production networks which protect the community from instability when resources are scarce. Using simulations, we demonstrate that the qualitative stability patterns derived analytically apply to a broader class of network structures and resource inflow profiles, including cases where multiple species coexist on only one externally supplied resource. Our stability criteria bear some resemblance to classic stability results for pairwise interactions, but also demonstrate how environmental context can shape coexistence patterns when resource limitation and exchange are modeled directly.

Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties

Microbe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.

Mutualistic interplay between bacteriophages and bacteria in the human gut

Bacteriophages (phages) are often described as obligate predators of their bacterial hosts, and phage predation is one of the leading forces controlling the density and distribution of bacterial populations. Every 48 h half of all bacteria on Earth are killed by phages. Efficient killing also forms the basis of phage therapy in humans and animals and the use of phages as food preservatives. In turn, bacteria have a plethora of resistance systems against phage attack, but very few bacterial species, if any, have entirely escaped phage predation. However, in complex communities and environments such as the human gut, this antagonistic model of attack and counter-defence does not fully describe the scope of phage-bacterium interactions. In this Review, we explore some of the more mutualistic aspects of phage-bacterium interactions in the human gut, and we suggest that the relationship between phages and their bacterial hosts in the gut is best characterized not as a fight to the death between enemies but rather as a mutualistic relationship between partners.

Environment Constrains Fitness Advantages of Division of Labor in Microbial Consortia Engineered for Metabolite Push or Pull Interactions

Fitness benefits from division of labor are well documented in microbial consortia, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a “push” interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a “pull” interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild-type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of by-product accumulation all relative to the WT. However, the LAE consortium had the trade-off of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered performance metric. Performance advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the performance advantages compared to WT.

IMPORTANCE Most naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence performance properties of consortia. Environmental context ultimately controlled the division of labor performance as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning, which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.

Diauxic lags explain unexpected coexistence in multi-resource environments

How the coexistence of species is affected by the presence of multiple resources is a major question in microbial ecology. We experimentally demonstrate that differences in diauxic lags, which occur as species deplete their own environments and adapt their metabolisms, allow slow-growing microbes to stably coexist with faster-growing species in multi-resource environments despite being excluded in single-resource environments. In our focal example, an Acinetobacter species (Aci2) competitively excludes Pseudomonas aurantiaca (Pa) on alanine and on glutamate. However, they coexist on the combination of both resources. Experiments reveal that Aci2 grows faster but Pa has shorter diauxic lags. We establish a tradeoff between Aci2's fast growth and Pa's short lags as their mechanism for coexistence. We model this tradeoff to accurately predict how environmental changes affect community composition. We extend our work by surveying a large set of competitions and observe coexistence nearly four times as frequently when the slow-grower is the fast-switcher. Our work illustrates a simple mechanism, based entirely on supplied-resource growth dynamics, for the emergence of multi-resource coexistence.

Functional Microbes and Their Incorporation into Foods and Food Supplements: Probiotics and Postbiotics

Life expectancy has dramatically increased over the past 200 years, but modern life factors such as environmental exposure, antibiotic overuse, C-section deliveries, limited breast-feeding, and diets poor in fibers and microbes could be associated with the rise of noncommunicable diseases such as overweight, obesity, diabetes, food allergies, and colorectal cancer as well as other conditions such as mental disorders. Microbial interventions that range from transplanting a whole undefined microbial community from a healthy gut to an ill one, e.g., so-called fecal microbiota transplantation or vaginal seeding, to the administration of selected well-characterized microbes, either live (probiotics) or not (postbiotics), with efficacy demonstrated in clinical trials, may be effective tools to treat or prevent acute and chronic diseases that humans still face, enhancing the quality of life.

Engineering temporal dynamics in microbial communities

Microbial communities are a key part to tackling global challenges in human health, environmental conservation, and sustainable agriculture in the coming decade. Recent advances in synthetic biology to study and modify microbial communities have led to important insights into their physiology and ecology. Understanding how targeted changes to microbial communities result in reproducible alterations of the community's intrinsic fluctuations and function is important for mechanistic reconstruction of microbiomes. Studies of synthetic microbial consortia and comparative analysis of communities in normal and disrupted states have revealed ecological principles that can be leveraged to engineer communities towards desired functions. Tools enabling temporal modulation and sensing of the community dynamics offer precise spatiotemporal control of functions, help to dissect microbial interaction networks, and improve predictions of population temporal dynamics. Here we discuss recent advances to manipulate microbiome dynamics through control of specific strain engraftment and abundance, modulation of cell-cell signaling for tuning population dynamics, infiltration of new functions in the existing community with in situ engineering, and in silico modeling of microbial consortia to predict community function and ecology.

The Clash of Microbiomes: From the Food Matrix to the Host Gut

Food fermentation has led to the improvement of the safety characteristics of raw materials and the production of new foodstuffs with elevated organoleptic characteristics. The empirical observation that these products could have a potential health benefit has garnered the attention of the scientific community. Therefore, several studies have been conducted in animal and human hosts to decipher which of these products may have a beneficial outcome against specific ailments. However, despite the accumulating literature, a relatively small number of products have been authorized as ‘functional foods’ by regulatory bodies. Data inconsistency and lack of in-depth preclinical characterization of functional products could heavily contribute to this issue. Today, the increased availability of omics platforms and bioinformatic algorithms for comprehensive data analysis can aid in the systematic characterization of microbe–microbe, microbe–matrix, and microbe–host interactions, providing useful insights about the maximization of their beneficial effects. The incorporation of these platforms in food science remains a challenge; however, coordinated efforts and interdisciplinary collaboration could push the field toward the dawn of a new era.

Resource-allocation constraint governs structure and function of microbial communities in metabolic modeling

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.

Modeling approaches for probing cross-feeding interactions in the human gut microbiome

Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.

Complementary resource preferences spontaneously emerge in diauxic microbial communities

Many microbes grow diauxically, utilizing the available resources one at a time rather than simultaneously. The properties of communities of microbes growing diauxically remain poorly understood, largely due to a lack of theory and models of such communities. Here, we develop and study a minimal model of diauxic microbial communities assembling in a serially diluted culture. We find that unlike co-utilizing communities, diauxic community assembly repeatably and spontaneously leads to communities with complementary resource preferences, namely communities where species prefer different resources as their top choice. Simulations and theory explain that the emergence of complementarity is driven by the disproportionate contribution of the top choice resource to the growth of a diauxic species. Additionally, we develop a geometric approach for analyzing serially diluted communities, with or without diauxie, which intuitively explains several additional emergent community properties, such as the apparent lack of species which grow fastest on a resource other than their most preferred resource. Overall, our work provides testable predictions for the assembly of natural as well as synthetic communities of diauxically shifting microbes.

Resource-diversity relationships in bacterial communities reflect the network structure of microbial metabolism

The relationship between the number of available nutrients and community diversity is a central question in ecological research that remains unanswered. Here we studied the assembly of hundreds of soil-derived microbial communities on a wide range of well-defined resource environments, from single carbon sources to combinations of up to 16. We found that, while single resources supported multispecies communities varying from 8 to 40 taxa, mean community richness increased only one-by-one with additional resources. Cross-feeding could reconcile these seemingly contrasting observations, with the metabolic network seeded by the supplied resources explaining the changes in richness due to both the identity and the number of resources, as well as the distribution of taxa across different communities. By using a consumer-resource model incorporating the inferred cross-feeding network, we provide further theoretical support to our observations and a framework to link the type and number of environmental resources to microbial community diversity.

Resource Competition and Host Feedbacks Underlie Regime Shifts in Gut Microbiota

AbstractThe spread of an enteric pathogen in the human gut depends on many interacting factors, including pathogen exposure, diet, host gut environment, and host microbiota, but how these factors jointly influence infection outcomes remains poorly characterized. Here we develop a model of host-mediated resource competition between mutualistic and pathogenic taxa in the gut that aims to explain why similar hosts, exposed to the same pathogen, can have such different infection outcomes. Our model successfully reproduces several empirically observed phenomena related to transitions between healthy and infected states, including (1) the nonlinear relationship between pathogen inoculum size and infection persistence, (2) the elevated risk of chronic infection during or after treatment with broad-spectrum antibiotics, (3) the resolution of gut dysbiosis with fecal microbiota transplants, and (4) the potential protection from infection conferred by probiotics. We then use the model to explore how host-mediated interventions-namely, shifts in the supply rates of electron donors (e.g., dietary fiber) and respiratory electron acceptors (e.g., oxygen)-can potentially be used to direct gut community assembly. Our study demonstrates how resource competition and ecological feedbacks between the host and the gut microbiota can be critical determinants of human health outcomes. We identify several testable model predictions ready for experimental validation.

Spatial distribution of gut microbes along the intestinal duct

We studied the spatial pattern of two microbial strains along the intestinal duct. Probiotic bacteria acidify the environment and suppress their competitors, non-probiotic bacteria. Food resources are supplied from the proximal end, and there exists a flow from the proximal end to the distal end. In the steady state, we observed three major patterns. In the "standard" pattern (ST), the abundance of probiotic bacteria was high in the proximal end, and it decreased toward the distal end; in contrast, the abundance of non-probiotic bacteria was low in the proximal end, and it increased toward the distal end. In the "proximal reversion" pattern (PR), non-probiotic bacteria were dominant and probiotic bacteria were suppressed in the proximal portion of the duct. Subsequently, the abundance values of the two competitors switched, followed by a spatial pattern similar to ST. In the "distal suppression" pattern (DS), the pattern was similar to ST in the proximal portion; however, toward the distal end, the abundance of probiotic bacteria remained at an intermediate level and suppressed the abundance of non-probiotic bacteria, resulting in a peak abundance of non-probiotic bacteria in the middle portion of the duct. We additionally discuss the nonmonotonic increase in the abundance of non-probiotic bacteria in ST and the transition of the spatial pattern from one type to another due to changes in the resource abundance in the influx.

Ecology-guided prediction of cross-feeding interactions in the human gut microbiome

Understanding a complex microbial ecosystem such as the human gut microbiome requires information about both microbial species and the metabolites they produce and secrete. These metabolites are exchanged via a large network of cross-feeding interactions, and are crucial for predicting the functional state of the microbiome. However, till date, we only have information for a part of this network, limited by experimental throughput. Here, we propose an ecology-based computational method, GutCP, using which we predict hundreds of new experimentally untested cross-feeding interactions in the human gut microbiome. GutCP utilizes a mechanistic model of the gut microbiome with the explicit exchange of metabolites and their effects on the growth of microbial species. To build GutCP, we combine metagenomic and metabolomic measurements from the gut microbiome with optimization techniques from machine learning. Close to 65% of the cross-feeding interactions predicted by GutCP are supported by evidence from genome annotations, which we provide for experimental testing. Our method has the potential to greatly improve existing models of the human gut microbiome, as well as our ability to predict the metabolic profile of the gut.

Applying the core microbiome to understand host-microbe systems

The host-associated core microbiome was originally coined to refer to common groups of microbes or genes that were likely to be particularly important for host biological function. However, the term has evolved to encompass variable definitions across studies, often identifying key microbes with respect to their spatial distribution, temporal stability or ecological influence, as well as their contribution to host function and fitness. A major barrier to reaching a consensus over how to define the core microbiome and its relevance to biological, ecological and evolutionary theory is a lack of precise terminology and associated definitions, as well the persistent association of the core microbiome with host function. Common, temporal and ecological core microbiomes can together generate insights into ecological processes that act independently of host function, while functional and host-adapted cores distinguish between facultative and near-obligate symbionts that differ in their effects on host fitness. This commentary summarizes five broad definitions of the core microbiome that have been applied across the literature, highlighting their strengths and limitations for advancing our understanding of host-microbe systems, noting where they are likely to overlap, and discussing their potential relevance to host function and fitness. No one definition of the core microbiome is likely to capture the range of key microbes across a host population. Applied together, they have the potential to reveal different layers of microbial organization from which we can begin to understand the ecological and evolutionary processes that govern host-microbe interactions.