4.6 Article

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

期刊

PROCESSES
卷 7, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/pr7070394

关键词

gut microbiome; genome-scale metabolic model; spatial heterogeneity; multi-scale modeling

资金

  1. Bioenergy Research Center-Center for Bioenergy Innovation (CBI)
  2. US. Department of Energy (DOE), Office of Biological and Environmental Research (OBER) [DE-AC05-000R22725, DE-SC0012377]
  3. U.S. Department of Energy (DOE) [DE-SC0012377] Funding Source: U.S. Department of Energy (DOE)

向作者/读者索取更多资源

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 similar to 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.

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