Summary
The inter-species exchange of metabolites plays a key role in the spatio-temporal dynamics of microbial communities. This raises the question whether ecosystem-level behavior of structured communities can be predicted using genome-scale models of metabolism for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice, and applied it to engineered consortia. First, we predicted, and experimentally confirmed, the species-ratio to which a 2-species mutualistic consortium converges, and the equilibrium composition of a newly engineered 3-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the “eclipse dilemma”: does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding, that the net outcome is beneficial, highlights the complex nature of metabolic interactions in microbial communities, while at the same time demonstrating their predictability.
Background
Inflammatory bowel diseases (IBD) are characterized by intermittent relapses, and their course is heterogeneous and unpredictable. Our aim was to determine the ability of protein, metabolite, or microbial biomarkers to predict relapse in patients with quiescent disease.
Methods
This prospective study enrolled patients with quiescent Crohn disease and ulcerative colitis, defined as the absence of clinical symptoms (Harvey-Bradshaw Index ≤ 4, Simple Clinical Colitis Activity Index ≤ 2) and endoscopic remission within the prior year. The primary outcome was relapse within 2 years, defined as symptomatic worsening accompanied by elevated inflammatory markers resulting in a change in therapy or IBD-related hospitalization or surgery. Biomarkers were tested in a derivation cohort, and their performance was examined in an independent validation cohort.
Results
Our prospective cohort study included 164 patients with IBD (108 with Crohn disease, 56 with ulcerative colitis). Upon follow-up for a median of 1 year, 22 patients (13.4%) experienced a relapse. Three protein biomarkers (interleukin-10, glial cell line–derived neurotrophic factor, and T-cell surface glycoprotein CD8 alpha chain) and 4 metabolomic markers (propionyl-L-carnitine, carnitine, sarcosine, and sorbitol) were associated with relapse in multivariable models. Proteomic and metabolomic risk scores independently predicted relapse with a combined area under the curve of 0.83. A high proteomic risk score (odds ratio = 9.11; 95% confidence interval, 1.90-43.61) or metabolomic risk score (odds ratio = 5.79; 95% confidence interval, 1.24-27.11) independently predicted a higher risk of relapse over 2 years. Fecal metagenomics showed an increased abundance of Proteobacteria (P = 0.0019, q = 0.019) and Fusobacteria (P = 0.0040, q = 0.020) and at the species level Lachnospiraceae_bacterium_2_1_58FAA (P = 0.000008, q = 0.0009) among the relapses.
Conclusions
Proteomic, metabolomic, and microbial biomarkers identify a proinflammatory state in quiescent IBD that predisposes to clinical relapse.
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