(711a) Predicting the Spatially Differential Gut Microbiota Composition Using Genome-Scale Metabolic Modeling | AIChE

(711a) Predicting the Spatially Differential Gut Microbiota Composition Using Genome-Scale Metabolic Modeling


Chan, S. H. J. - Presenter, Colorado State University
Senftle, M., The Pennsylvania State University
Maranas, C. D., The Pennsylvania State University
The human gut microbiome has tremendous impact on human health, from obesity, immune response to brain function and nerve system. While the majority of the studies on the gut microbiome sampled only the microbiome from feces or from a particular anatomical site of the intestines, the gut microbiome is indeed heterogeneous groups of microbes spatially distributed along various sections of the intestines and across the mucosa and lumen in each section. Understanding the formation of the spatial organization of the microbiome will provide invaluable insights into the theraputic strategies for inflammatory bowel disease and other gastrointestinal diseases. Differential oxygen availability has been proposed as a key factor shaping the spatial organization. To test this hypothesis, we construct 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 connect the mucosally adherent and luminal microbiome between consecutive sections along the intestines, we are able to capture the essential features of the spatially differential distribution of obligate anaerobes vs. facultative anaerobes and aerobes determined experimentally as well as the accumulation of microbial biomass in the lumen. Sensitivity analysis reveals that instead of the oxygen availability, oxygen diffusibility and maximum oxygen uptake rate by the microbes, the vastly different biomass density of the mucosally adherent microbiome (>100-fold changes between different sections of the intestines) is the single most important factor shaping the spatial distribution of the gut microbiome. This study exemplifies the predictive capability of incorporating genome-scale metabolic models in spatiotemporal frameworks for modeling microbial communities.