Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

Himel Mallick, Eric A. Franzosa, Lauren J. Mclver, Soumya Banerjee, Alexandra Sirota-Madi, Aleksandar D. Kostic, Clary B. Clish, Hera Vlamakis, Ramnik J. Xavier () and Curtis Huttenhower ()
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Himel Mallick: Broad Institute of MIT and Harvard
Eric A. Franzosa: Broad Institute of MIT and Harvard
Lauren J. Mclver: Broad Institute of MIT and Harvard
Soumya Banerjee: Broad Institute of MIT and Harvard
Alexandra Sirota-Madi: Broad Institute of MIT and Harvard
Aleksandar D. Kostic: Broad Institute of MIT and Harvard
Clary B. Clish: Broad Institute of MIT and Harvard
Hera Vlamakis: Broad Institute of MIT and Harvard
Ramnik J. Xavier: Broad Institute of MIT and Harvard
Curtis Huttenhower: Broad Institute of MIT and Harvard

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this ‘predictive metabolomic’ approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

Date: 2019
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DOI: 10.1038/s41467-019-10927-1

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