Relative metabolomic flux: Predicting changes in the environmental metabolome from the metagenome

Peter E Larsen, Frank R Collart, Dawn Field, Folker Meyer, Kevin P Keegan, Christopher S Henry, John McGrath, John Quinn, Jack A Gilbert

Research output: Contribution to journalArticlepeer-review


Background: The world's oceans are home to a diverse array of microbial life whose metabolic activity helps to drive the earth's biogeochemical cycles. Metagenomic analysis has revolutionized our access to these communities, providing a system-scale perspective of microbial community interactions. However, while metagenome sequencing can provide useful estimates of the relative change in abundance of specific genes and taxa between environments or over time, this does not investigate the relative changes in the production or consumption of different metabolites.
Results: We propose a methodology, Predicted Relative Metabolic Turnover (PRMT) that defines and enables exploration of metabolite-space inferred from the metagenome. Our analysis of metagenomic data from a time-series study in the Western English Channel demonstrated considerable correlations between predicted relative metabolic turnover and seasonal changes in abundance of measured environmental parameters as well as with observed seasonal changes in bacterial population structure.
Conclusions: The PRMT method was successfully applied to metagenomic data to explore the Western English Channel microbial metabalome to generate specific, biologically testable hypotheses. Generated hypotheses linked organic phosphate utilization to Gammaproteobactaria, Plantcomycetes, and Betaproteobacteria, chitin degradation to Actinomycetes, and potential small molecule biosynthesis pathways for Lentisphaerae, Chlamydiae, and Crenarchaeota. The PRMT method can be applied as a general tool for the analysis of additional metagenomic or transcriptomic datasets.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalMicrobial Informatics and Experimentation
Issue number4
Publication statusPublished - 14 Jun 2011

Bibliographical note

The work was funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Biological Systems Science Division under contract DE-AC02-06CH11357 at Argonne, IL. Sequence data was produced with a grant from the Natural Environmental Research Council (NERC - NE/F00138X/1).


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