Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences

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.

The Integrative Human Microbiome Project

“…Integrated longitudinal molecular profiles of microbial and host activity were generated by analysing 1,785 stool samples (self-collected and sent by mail every two weeks), 651 intestinal biopsies (collected colonoscopically at baseline), and 529 quarterly blood samples. To the extent possible, multiple molecular profiles were generated from the same sets of samples, including stool metagenomes, metatranscriptomes 73 , metaproteomes, viromes, metabolomes 74,75 , host exomes, epigenomes, transcriptomes, and serological profiles, among others, allowing concurrent changes to be observed in multiple types of host and microbial molecular and clinical activity over time. Protocols and results from the study, further information about its infrastructure, and both raw and processed 76,77 data products are available through the IBDMDB data portal (http://ibdmdb.org), from the HMP2 Data Coordination Center (DCC; http://ihmpdcc.org), and in the accompanying manuscript 49 .…”

Computational genomic discovery of diverse gene clusters harboring Fe-S flavoenzymes in anaerobic gut microbiota

“…We present a rich dataset of gene clusters that are candidates for detailed biochemical studies. Moreover, the presence or expression of these GCFs could be used as features alongside standard metabolic pathway annotations to assess whether their presence could explain variation in health/disease phenotypes, or whether they can explain variation observed in gut metabolomes 23,24 . All in all, given the importance of profiling the gut microbiome from a functional point of view, this study provides new ways of exploiting the genomic information present in public repositories, and provides a template for genomic exploration studies centred on key enzyme families to further understand the metabolic potential of the gut microbiome.…”

Multi-omic analysis reveals the anti-aging impact of sulforaphane on the microbiome and metabolome

“…Computation frameworks to integrate microbiome and metabolome for the identification of potential mechanistic links. We explored two different approaches to integrate microbiome and metabolome for the identification of potential mechanistic links: MIMOSA [19] using the updated software (https://borenstein-lab.github.io/MIMOSA2shiny/) and MelonnPan [20]. MIMOSA integrates metabolic potential from bacterial genomes and metabolome into a unified analysis.…”

“…Microbiome and metabolome data integration analysis reveals microbiome dependent metabolic changes. We explored two orthogonal approaches, MIMOSA [19] and MelonnPan [20] which are fundamentally different. MIMOSA uses a metabolic model framework that integrates metabolic potential from bacterial genomes and metabolome composition into a unified analysis.…”