LAPSE:2019.1525
Published Article
LAPSE:2019.1525
Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling
Siu H. J. Chan, Elliot S. Friedman, Gary D. Wu, Costas D. Maranas
December 10, 2019
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.
Keywords
genome-scale metabolic model, gut microbiome, multi-scale modeling, spatial heterogeneity
Subject
Suggested Citation
Chan SHJ, Friedman ES, Wu GD, Maranas CD. Predicting the Longitudinally and Radially Varying Gut Microbiota Composition using Multi-Scale Microbial Metabolic Modeling. (2019). LAPSE:2019.1525
Author Affiliations
Chan SHJ: Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA [ORCID]
Friedman ES: Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA [ORCID]
Wu GD: Division of Gastroenterology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
Maranas CD: Department of Chemical Engineering, the Pennsylvania State University, State College, PA 16801, USA
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Journal Name
Processes
Volume
7
Issue
7
Article Number
E394
Year
2019
Publication Date
2019-06-26
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr7070394, Publication Type: Journal Article
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LAPSE:2019.1525
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doi:10.3390/pr7070394
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Dec 10, 2019
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CC BY 4.0
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Dec 10, 2019
 
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Dec 10, 2019
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Original Submitter
Calvin Tsay
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