Suboptimal community growth mediated through metabolite crossfeeding promotes species diversity in the gut microbiota

Effect of microbial interactions on performance of community metabolic modeling algorithms: flux balance analysis (FBA), community FBA (cFBA) and SteadyCom

To explore the impact of microbial interactions on outcomes from three prevalent algorithms (Flux Balance Analysis (FBA), community FBA (cFBA), and SteadyCom) analyzing microbial community metabolic networks, five toy community models representing common microbial interactions were designed. These include commensalism, mutualism, competition, mutualism-competition, and commensalism-competition. Various scenarios, considering different biomass yields and substrate constraints, were examined for each type. In commensal communities, all algorithms consistently produced similar results. However, changes in biomass yields and substrate constraints led to variable abundances (0.33-0.8) and community growth rates (2-5 1/h) within a broad range. For competitive communities, all algorithms predicted growth of fastest-growing member. To comply with the natural coexistence of members, suboptimal solutions over optimal point are recommended. FBA faced challenges in modeling mutualism, consistently predicting growth of only one member. Although cFBA and SteadyCom resulted in a lower community growth rate, coexistence of both members were satisfied. In toy models with dual interactions, more realistic outcomes were achieved contrary to purely competitive model as the dependency fosters the coexistence which was missing in the competitive only scenarios. These findings emphasize the importance of algorithm choice based on specific microbial interaction types for reliable community behavior predictions.​.

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Interplay Between Drug-Induced Liver Injury and Gut Microbiota: A Comprehensive Overview

Drug-induced liver injury is a prevalent severe adverse event in clinical settings, leading to increased medical burdens for patients and presenting challenges for the development and commercialization of novel pharmaceuticals. Research has revealed a close association between gut microbiota and drug-induced liver injury in recent years. However, there has yet to be a consensus on the specific mechanism by which gut microbiota is involved in drug-induced liver injury. Gut microbiota may contribute to drug-induced liver injury by increasing intestinal permeability, disrupting intestinal metabolite homeostasis, and promoting inflammation and oxidative stress. Alterations in gut microbiota were found in drug-induced liver injury caused by antibiotics, psychotropic drugs, acetaminophen, antituberculosis drugs, and antithyroid drugs. Specific gut microbiota and their abundance are associated closely with the severity of drug-induced liver injury. Therefore, gut microbiota is expected to be a new target for the treatment of drug-induced liver injury. This review focuses on the association of gut microbiota with common hepatotoxic drugs and the potential mechanisms by which gut microbiota may contribute to the pathogenesis of drug-induced liver injury, providing a more comprehensive reference for the interaction between drug-induced liver injury and gut microbiota.

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 .

Unveiling abundance-dependent metabolic phenotypes of microbial communities

IMPORTANCE

In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.

Human Milk Microbiome and Microbiome-Related Products: Potential Modulators of Infant Growth

Infant growth trajectory may influence later-life obesity. Human milk provides a wide range of nutritional and bioactive components that are vital for infant growth. Compared to formula-fed infants, breastfed infants are less likely to develop later-onset obesity, highlighting the potential role of bioactive components present in human milk. Components of particular interest are the human milk microbiota, human milk oligosaccharides (HMOs), short-chain fatty acids (SCFAs), and antimicrobial proteins, each of which influence the infant gut microbiome, which in turn has been associated with infant body composition. SCFAs and antimicrobial proteins from human milk may also systemically influence infant metabolism. Although inconsistent, multiple studies have reported associations between HMOs and infant growth, while studies on other bioactive components in relation to infant growth are sparse. Moreover, these microbiome-related components may interact with each other within the mammary gland. Here, we review the evidence around the impact of human milk microbes, HMOs, SCFAs, and antimicrobial proteins on infant growth. Breastfeeding is a unique window of opportunity to promote optimal infant growth, with aberrant growth trajectories potentially creating short- and long-term public health burdens. Therefore, it is important to understand how bioactive components of human milk influence infant growth.

Optimal control and Bayes inference applied to complex microbial communities

Interactions between species are essential in ecosystems, but sometimes competition dominates over mutualism. The transition between mutualism-competition can have several implications and consequences, and it has hardly been studied in experimental settings. This work studies the mutualism between cross-feeding bacteria in strains that supply an essential amino acid for their mutualistic partner when both strains are exposed to antimicrobials. When the strains are free of antimicrobials, we found that, depending on the amount of amino acids freely available in the environment, the strains can exhibit extinction, mutualism, or competition. The availability of resources modulates the behavior of both species. When the strains are exposed to antimicrobials, the population dynamics depend on the proportion of bacteria resistant to the antimicrobial, finding that the extinction of both strains is eminent for low levels of the resource. In contrast, competition between both strains continues for high levels of the resource. An optimal control problem was then formulated to reduce the proportion of resistant bacteria, which showed that under cooperation, both strains (sensitive and resistant) are immediately controlled, while under competition, only the density of one of the strains is decreased. In contrast, its mutualist partner with control is increased. Finally, using our experimental data, we did parameters estimation in order to fit our mathematical model to the experimental data.

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.

Metabolome-Microbiome Responses of Growing Pigs Induced by Time-Restricted Feeding

Time-restricted feeding (TRF) mode is a potential strategy in improving the health and production of farm animals. However, the effect of TRF on microbiota and their metabolism in the large intestine of the host remains unclear. Therefore, the present study aimed to investigate the responses of microbiome and metabolome induced by TRF based on a growing-pig model. Twelve crossbred growing barrows were randomly allotted into two groups with six replicates (1 pig/pen), namely, the free-access feeding group (FA) and TRF group. Pigs in the FA group were fed free access while the TRF group were fed free access within a regular time three times per day at 07:00–08:00, 12:00–13:00, and 18:00–19:00, respectively. Results showed that the concentrations of NH4-N, putrescine, cadaverine, spermidine, spermine, total biogenic amines, isobutyrate, butyrate, isovalerate, total SCFA, and lactate were increased while the pH value in the colonic digesta and the concentration of acetate was decreased in the TRF group. The Shannon index was significantly increased in the TRF group; however, no significant effects were found in the Fisher index, Simpson index, ACE index, Chao1 index, and observed species between the two groups. In the TRF group, the relative abundances of Prevotella 1 and Eubacterium ruminantium group were significantly increased while the relative abundances of Clostridium sensu sticto 1, Lactobacillus, and Eubacterium coprostanoligenes group were decreased compared with the FA group. PLS-DA analysis revealed an obvious and regular variation between the FA and TRF groups, further pathway enrichment analysis showed that these differential features were mainly enriched in pyrimidine metabolism, nicotinate and nicotinamide metabolism, glycerolipid metabolism, and fructose and mannose metabolism. In addition, Pearson's correlation analysis indicated that the changes in the microbial genera were correlated with the colonic metabolites. In conclusion, these results together indicated that although the overall microbial composition in the colon was not changed, TRF induced the gradient changes of the nutrients and metabolites which were correlated with certain microbial genera including Lactobacillus, Eubacterium_ruminantium group, Eubacterium coprostanoligenes group, Prevotella 1, and Clostridium sensu sticto 1. However, more studies are needed to understand the impacts of TRF on the health and metabolism of growing pigs.

Dynamic metabolic modelling predicts efficient acetogen-gut bacterium cocultures for CO-to-butyrate conversion

AIMS

While gas-fermenting acetogens have been engineered to secrete non-native metabolites such as butyrate, acetate remains the most thermodynamically favourable product. An alternative to metabolic engineering is to exploit native capabilities for CO-to-acetate conversion by coculturing an acetogen with a second bacterium that provides efficient acetate-butyrate conversion.

METHODS AND RESULTS

We used dynamic metabolic modelling to computationally evaluate the CO-to-butyrate conversion capabilities of candidate coculture systems by exploiting the diversity of human gut bacteria for anaerobic synthesis of butyrate from acetate and ethanol. A preliminary screening procedure based on flux balance analysis was developed to identify 48 gut bacteria which satisfied minimal growth rate and acetate-to-butyrate conversion requirements when cultured on minimal medium containing acetate and a simple sugar not consumed by the paired acetogen. A total of 170 acetogen/gut bacterium/sugar combinations were dynamically simulated for continuous growth using a 70/30 CO/CO₂ feed gas mixture and minimal medium computationally determined for each combination.

CONCLUSIONS

While coculture systems involving the acetogens Eubacterium limosum or Blautia producta yielded low butyrate productivities and CO-to-ethanol conversion had minimal impact on system performance, dynamic simulations predicted a large number of promising coculture designs with Clostridium ljungdahlii or C. autoethanogenum as the CO-to-acetate converter. Pairings with the gut bacterium Clostridium hylemonae or Roseburia hominis were particularly promising due to their ability to generate high butyrate productivities over a range of dilution rates with a variety of sugars. The higher specific acetate secretion rate of C. ljungdahlii proved more beneficial than the elevated growth rate of C. autoethanogenum for coculture butyrate productivity.

SIGNIFICANCE AND IMPACT OF THE STUDY

Our study demonstrated that metabolic modelling could provide useful insights into coculture design that can guide future experimental studies. More specifically, our predictions generated several favourable designs, which could serve as the first coculture systems realized experimentally.

Metabolic Modeling to Interrogate Microbial Disease: A Tale for Experimentalists

The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

Predicting Nash equilibria for microbial metabolic interactions

MOTIVATION

Microbial metabolic interactions impact ecosystems, human health and biotechnology profoundly. However, their determination remains elusive, invoking an urgent need for predictive models seamlessly integrating metabolism with evolutionary principles that shape community interactions.

RESULTS

Inspired by the evolutionary game theory, we formulated a bi-level optimization framework termed NECom for which any feasible solutions are Nash equilibria of microbial community metabolic models with/without an outer-level (community) objective function. Distinct from discrete matrix games, NECom models the continuous interdependent strategy space of metabolic fluxes. We showed that NECom successfully predicted several classical games in the context of metabolic interactions that were falsely or incompletely predicted by existing methods, including prisoner's dilemma, snowdrift and cooperation. The improved capability originates from the novel formulation to prevent 'forced altruism' hidden in previous static algorithms while allowing for sensing all potential metabolite exchanges to determine evolutionarily favorable interactions between members, a feature missing in dynamic methods. The results provided insights into why mutualism is favorable despite seemingly costly cross-feeding metabolites and demonstrated similarities and differences between games in the continuous metabolic flux space and matrix games. NECom was then applied to a reported algae-yeast co-culture system that shares typical cross-feeding features of lichen, a model system of mutualism. 488 growth conditions corresponding to 3,221 experimental data points were simulated. Without training any parameters using the data, NECom is more predictive of species' growth rates given uptake rates compared with flux balance analysis with an overall 63.5% and 81.7% reduction in root-mean-square error for the two species.

AVAILABILITY

Simulation code and data are available at https://github.com/Jingyi-Cai/NECom.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The gut microbiome: an unexpected player in cancer immunity

Numerous independent studies link gut microbiota composition and disease and imply a causal role of select commensal microbes in disease etiology. In the gut, commensal microbiota or pathobionts secrete metabolites that underlie pathological conditions, often impacting proximal tissues and gaining access to the bloodstream. Here we focus on extrinsic and intrinsic factors affecting composition of gut microbiota and their impact on the immune system, as key drivers of anti-tumor immunity. In discussing exciting advances relevant to microbiome-tumor interaction, we note existing knowledge gaps that need to be filled to advance basic and clinical research initiatives.

Metabolic modelling of chronic wound microbiota predicts mutualistic interactions that drive community composition

AIMS

To identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling.

METHODS AND RESULTS

We developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data.

CONCLUSIONS

Mutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression.

SIGNIFICANCE AND IMPACT OF THE STUDY

Approximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.

Predicting the Longitudinally and Radially Varying Gut Microbiota Composition Using Multi-Scale Microbial Metabolic Modeling

Background: The gut microbiota is a heterogeneous group of microbes that is spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the dynamics between the spatially differential microbial populations and the driving forces for the observed spatial organization will provide valuable insights into important questions such as the nature of colonization of the infant gut and different types of inflammatory bowel disease localized in different regions of the intestines. However, in most studies, the microbiota is sampled only at a single site (often feces) or from a particular anatomical site of the intestines. Differential oxygen availability is putatively a key factor shaping the spatial organization. Results: To test this hypothesis, we constructed a community genome-scale metabolic model consisting of representative organisms for the major phyla present in the human gut microbiome. By solving step-wise optimization problems embedded in a dynamic framework to predict community metabolism and integrate the mucosally-adherent with the luminal microbiome between consecutive sections along the intestines, we were able to capture (i) the essential features of the spatially differential composition of obligate anaerobes vs. facultative anaerobes and aerobes determined experimentally, and (ii) the accumulation of microbial biomass in the lumen. Sensitivity analysis suggests that the spatial organization depends primarily on the oxygen-per-microbe availability in each region. Oxygen availability is reduced relative to the ~100-fold increase in mucosal microbial density along the intestines, causing the switch between aerobes and anaerobes. Conclusion: The proposed integrated dynamic framework is able to predict spatially differential gut microbiota composition using microbial genome-scale metabolic models and test hypotheses regarding the dynamics of the gut microbiota. It can potentially become a valuable tool for exploring therapeutic strategies for site-specific perturbation of the gut microbiota and the associated metabolic activities.

Meta-Omics- and Metabolic Modeling-Assisted Deciphering of Human Microbiota Metabolism

The human microbiota is a complex community of commensal, symbiotic, and pathogenic microbes that play a crucial role in maintaining the homeostasis of human health. Such a homeostasis is maintained through the collective functioning of enzymatic genes responsible for the production of metabolites, enabling the interaction and signaling within microbiota as well as between microbes and the human host. Understanding microbial genes, their associated chemistries and functions would be valuable for engineering systemic metabolic pathways within the microbiota to manage human health and diseases. Given that there are many unknown gene metabolic functions and interactions, increasing efforts have been made to gain insights into the underlying functions of microbiota metabolism. This can be achieved through culture-independent metagenomic approaches and metabolic modeling to simulate the microenvironment of human microbiota. In this article, the recent advances in metagenome mining and functional profiling for the discovery of the genetic and biochemical links in human microbiota metabolism as well as metabolic modeling for simulation and prediction of metabolic fluxes in the human microbiota are reviewed. This review provides useful insights into the understanding, reconstruction, and modulation of the human microbiota guided by the knowledge acquired from the basic understanding of the human microbiota metabolism.

Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance

Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the “rare” pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy.

IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Metabolic Modeling of Clostridium difficile Associated Dysbiosis of the Gut Microbiota

Recent in vitro experiments have demonstrated the ability of the pathogen Clostridium difficile and commensal gut bacteria to form biofilms on surfaces, and biofilm development in vivo is likely. Various studies have reported that 3%–15% of healthy adults are asymptomatically colonized with C. difficile, with commensal species providing resistance against C. difficile pathogenic colonization. C. difficile infection (CDI) is observed at a higher rate in immunocompromised patients previously treated with broad spectrum antibiotics that disrupt the commensal microbiota and reduce competition for available nutrients, resulting in imbalance among commensal species and dysbiosis conducive to C. difficile propagation. To investigate the metabolic interactions of C. difficile with commensal species from the three dominant phyla in the human gut, we developed a multispecies biofilm model by combining genome-scale metabolic reconstructions of C. difficile, Bacteroides thetaiotaomicron from the phylum Bacteroidetes, Faecalibacterium prausnitzii from the phylum Firmicutes, and Escherichia coli from the phylum Proteobacteria. The biofilm model was used to identify gut nutrient conditions that resulted in C. difficile-associated dysbiosis characterized by large increases in C. difficile and E. coli abundances and large decreases in F. prausnitzii abundance. We tuned the model to produce species abundances and short-chain fatty acid levels consistent with available data for healthy individuals. The model predicted that experimentally-observed host-microbiota perturbations resulting in decreased carbohydrate/increased amino acid levels and/or increased primary bile acid levels would induce large increases in C. difficile abundance and decreases in F. prausnitzii abundance. By adding the experimentally-observed perturbation of increased host nitrate secretion, the model also was able to predict increased E. coli abundance associated with C. difficile dysbiosis. In addition to rationalizing known connections between nutrient levels and disease progression, the model generated hypotheses for future testing and has the capability to support the development of new treatment strategies for C. difficile gut infections.