Metabolome of human gut microbiome is predictive of host dysbiosis
Abstract
Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis frommore »
- Authors:
-
- Univ. of Illinois at Chicago, Chicago, IL (United States). Bioengineering Dept.; Argonne National Lab. (ANL), Argonne, IL (United States)
- Univ. of Illinois at Chicago, Chicago, IL (United States). Bioengineering Dept.
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1224066
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- GigaScience
- Additional Journal Information:
- Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2047-217X
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; dysbiosis; gut microbiome; human microbiome; machine learning; metabolome modeling; metagenomics; microbial communities
Citation Formats
Larsen, Peter E., and Dai, Yang. Metabolome of human gut microbiome is predictive of host dysbiosis. United States: N. p., 2015.
Web. doi:10.1186/s13742-015-0084-3.
Larsen, Peter E., & Dai, Yang. Metabolome of human gut microbiome is predictive of host dysbiosis. United States. https://doi.org/10.1186/s13742-015-0084-3
Larsen, Peter E., and Dai, Yang. Mon .
"Metabolome of human gut microbiome is predictive of host dysbiosis". United States. https://doi.org/10.1186/s13742-015-0084-3. https://www.osti.gov/servlets/purl/1224066.
@article{osti_1224066,
title = {Metabolome of human gut microbiome is predictive of host dysbiosis},
author = {Larsen, Peter E. and Dai, Yang},
abstractNote = {Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.},
doi = {10.1186/s13742-015-0084-3},
journal = {GigaScience},
number = 1,
volume = 4,
place = {United States},
year = {Mon Sep 14 00:00:00 EDT 2015},
month = {Mon Sep 14 00:00:00 EDT 2015}
}
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