Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Academic Article uri icon

Overview

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.

authors

  • Mallick, Himel
  • Franzosa, Eric A
  • Mclver, Lauren J
  • Banerjee, Soumya
  • Sirota-Madi, Alexandra
  • Kostic, Aleksandar D
  • Clish, Clary B
  • Vlamakis, Hera
  • Xavier, Ramnik J
  • Huttenhower, Curtis

publication date

  • July 17, 2019

Research

keywords

  • Gastrointestinal Microbiome
  • Metabolomics
  • Microbiota
  • Models, Genetic

Identity

PubMed Central ID

  • PMC6637180

Scopus Document Identifier

  • 85069442556

Digital Object Identifier (DOI)

  • 10.1371/journal.pgen.0020190

PubMed ID

  • 31316056

Additional Document Info

volume

  • 10

issue

  • 1