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Gigascience. 2015 Sep 14;4:42. doi: 10.1186/s13742-015-0084-3. eCollection 2015.

Metabolome of human gut microbiome is predictive of host dysbiosis.

Author information

1
Bioengineering Department, University of Illinois at Chicago, 851 South Morgan, SEO218, Chicago, IL 60607 USA ; Argonne National Laboratory, Biosciences Division, 9700 South Cass Ave, Argonne, IL 60439 USA.
2
Bioengineering Department, University of Illinois at Chicago, 851 South Morgan, SEO218, Chicago, IL 60607 USA.

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. However, 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.

KEYWORDS:

Dysbiosis; Gut microbiome; Human microbiome; Machine learning; Metabolome modeling; Metagenomics; Microbial communities

PMID:
26380076
PMCID:
PMC4570295
DOI:
10.1186/s13742-015-0084-3
[Indexed for MEDLINE]
Free PMC Article
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