Using metacommunity ecology to understand environmental metabolomes

Robert E. Danczak, Rosalie K. Chu, Sarah J. Fansler, Amy E. Goldman, Emily B. Graham, Malak M. Tfaily, Jason Toyoda and James C. Stegen ()
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Robert E. Danczak: Pacific Northwest National Laboratory
Rosalie K. Chu: Environmental Molecular Sciences Laboratory
Sarah J. Fansler: Pacific Northwest National Laboratory
Amy E. Goldman: Pacific Northwest National Laboratory
Emily B. Graham: Pacific Northwest National Laboratory
Malak M. Tfaily: Environmental Molecular Sciences Laboratory
Jason Toyoda: Environmental Molecular Sciences Laboratory
James C. Stegen: Pacific Northwest National Laboratory

Nature Communications, 2020, vol. 11, issue 1, 1-16

Abstract: Abstract Environmental metabolomes are fundamentally coupled to microbially-linked biogeochemical processes within ecosystems. However, significant gaps exist in our understanding of their spatiotemporal organization, limiting our ability to uncover transferrable principles and predict ecosystem function. We propose that a theoretical paradigm, which integrates concepts from metacommunity ecology, is necessary to reveal underlying mechanisms governing metabolomes. We call this synthesis between ecology and metabolomics ‘meta-metabolome ecology’ and demonstrate its utility using a mass spectrometry dataset. We developed three relational metabolite dendrograms using molecular properties and putative biochemical transformations and performed ecological null modeling. Based upon null modeling results, we show that stochastic processes drove molecular properties while biochemical transformations were structured deterministically. We further suggest that potentially biochemically active metabolites were more deterministically assembled than less active metabolites. Understanding variation in the influences of stochasticity and determinism provides a way to focus attention on which meta-metabolomes and which parts of meta-metabolomes are most likely to be important to consider in mechanistic models. We propose that this paradigm will allow researchers to study the connections between ecological systems and their molecular processes in previously inaccessible detail.

Date: 2020
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DOI: 10.1038/s41467-020-19989-y

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