Data released on September 14, 2015
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 the human microbiomes also vary dramatically from individual to individual and over time, making it difficult to uncover the underlying mechanisms linking 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 the community metabolome of microbiome, an emergent property of microbiome.
Using data from a previously published, longitudinal study of human gut microbiome populations, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the microbiome’s aggregate predicted community enzyme function profiles and modeled metabolomes are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles.