BackgroundInflammatory bowel diseases (IBD), which include ulcerative colitis and Crohn’s disease, cause chronic inflammation of the digestive tract in approximately 1.6 million Americans. A signature of IBD is dysbiosis of the gut microbiota marked by a significant reduction of obligate anaerobes and a sharp increase in facultative anaerobes. Numerous experimental studies have shown that IBD is strongly correlated with a decrease of Faecalibacterium prausnitzii and an increase of Escherichia coli. One hypothesis is that chronic inflammation induces increased oxygen levels in the gut, which in turn causes an imbalance between obligate and facultative anaerobes.ResultsTo computationally investigate the oxygen hypothesis, we developed a multispecies biofilm model based on genome-scale metabolic reconstructions of F. prausnitzii, E. coli and the common gut anaerobe Bacteroides thetaiotaomicron. Application of low bulk oxygen concentrations at the biofilm boundary reproduced experimentally observed behavior characterized by a sharp decrease of F. prausnitzii and a large increase of E. coli, demonstrating that dysbiosis consistent with IBD disease progression could be qualitatively predicted solely based on metabolic differences between the species. A diet with balanced carbohydrate and protein content was predicted to represent a metabolic “sweet spot” that increased the oxygen range over which F. prausnitzii could remain competitive and IBD could be sublimated. Host-microbiota feedback incorporated via a simple linear feedback between the average F. prausnitzii concentration and the bulk oxygen concentration did not substantially change the range of oxygen concentrations where dysbiosis was predicted, but the transition from normal species abundances to severe dysbiosis was much more dramatic and occurred over a much longer timescale. Similar predictions were obtained with sustained antibiotic treatment replacing a sustained oxygen perturbation, demonstrating how IBD might progress over several years with few noticeable effects and then suddenly produce severe disease symptoms.ConclusionsThe multispecies biofilm metabolic model predicted that oxygen concentrations of ∼1 micromolar within the gut could cause microbiota dysbiosis consistent with those observed experimentally for inflammatory bowel diseases. Our model predictions could be tested directly through the development of an appropriate in vitro system of the three species community and testing of microbiota-host interactions in gnotobiotic mice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0522-1) contains supplementary material, which is available to authorized users.

BackgroundMicrobial systems in which the extracellular environment varies both spatially and temporally are very common in nature and in engineering applications. While the use of genome-scale metabolic reconstructions for steady-state flux balance analysis (FBA) and extensions for dynamic FBA are common, the development of spatiotemporal metabolic models has received little attention.ResultsWe present a general methodology for spatiotemporal metabolic modeling based on combining genome-scale reconstructions with fundamental transport equations that govern the relevant convective and/or diffusional processes in time and spatially varying environments. Our solution procedure involves spatial discretization of the partial differential equation model followed by numerical integration of the resulting system of ordinary differential equations with embedded linear programs using DFBAlab, a MATLAB code that performs reliable and efficient dynamic FBA simulations. We demonstrate our methodology by solving spatiotemporal metabolic models for two systems of considerable practical interest: (1) a bubble column reactor with the syngas fermenting bacterium Clostridium ljungdahlii; and (2) a chronic wound biofilm with the human pathogen Pseudomonas aeruginosa. Despite the complexity of the discretized models which consist of 900 ODEs/600 LPs and 250 ODEs/250 LPs, respectively, we show that the proposed computational framework allows efficient and robust model solution.ConclusionsOur study establishes a new paradigm for formulating and solving genome-scale metabolic models with both time and spatial variations and has wide applicability to natural and engineered microbial systems.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0259-2) contains supplementary material, which is available to authorized users.

BackgroundChronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia.ResultsWe developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies.ConclusionsOur biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0334-8) contains supplementary material, which is available to authorized users.

Abstract:The gut microbiome is a highly complex microbial community that strongly impacts human health and disease. The two dominant phyla in healthy humans are Bacteroidetes and Firmicutes, with minor phyla such as Proteobacteria having elevated abundances in various disease states. While the gut microbiome has been widely studied, relatively little is known about the role of interspecies interactions in promoting microbiome stability and function. We developed a biofilm metabolic model of a very simple gut microbiome community consisting of a representative bacteroidete (Bacteroides thetaiotaomicron), firmicute (Faecalibacterium prausnitzii) and proteobacterium (Escherichia coli) to investigate the putative role of metabolic byproduct cross feeding between species on community stability, robustness and flexibility. The model predicted coexistence of the three species only if four essential cross-feeding relationships were present. We found that cross feeding allowed coexistence to be robustly maintained for large variations in biofilm thickness and nutrient levels. However, the model predicted that community composition and short chain fatty acid levels could be strongly affected only over small ranges of byproduct uptake rates, indicating a possible lack of flexibility in our cross-feeding mechanism. Our model predictions provide new insights into the impact of byproduct cross feeding and yield experimentally testable hypotheses about gut microbiome community stability.

The gut microbiota represent a highly complex ecosystem comprised of approximately 1000 species that forms a mutualistic relationship with the human host. A critical attribute of the microbiota is high species diversity, which provides system robustness through overlapping and redundant metabolic capabilities. The gradual loss of bacterial diversity has been associated with a broad array of gut pathologies and diseases including malnutrition, obesity, diabetes and inflammatory bowel disease. We formulated an in silico community model of the gut microbiota by combining genome-scale metabolic reconstructions of 28 representative species to explore the relationship between species diversity and community growth. While the individual species offered a broad range of metabolic capabilities, communities optimized for maximal growth on simulated Western and high-fiber diets had low diversities and imbalances in short-chain fatty acid (SCFA) synthesis characterized by acetate overproduction. Community flux variability analysis performed with the 28-species model and a reduced 20-species model suggested that enhanced species diversity and more balanced SCFA production were achievable at suboptimal growth rates. We developed a simple method for constraining species abundances to sample the growth-diversity tradeoff and used the 20-species model to show that tradeoff curves for Western and high-fiber diets resembled Pareto-optimal surfaces. Compared to maximal growth solutions, suboptimal growth solutions were characterized by higher species diversity, more balanced SCFA synthesis and lower exchange rates of crossfed metabolites between more species. We hypothesized that modulation of crossfeeding relationships through host-microbiota interactions could be an important means for maintaining species diversity and suggest that community metabolic modeling approaches that allow multiobjective optimization of growth and diversity are needed for more realistic simulation of complex communities.