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Unification of Environmental Metabolomics with Metacommunity Ecology

View ORCID ProfileRobert E Danczak, View ORCID ProfileRosalie K Chu, Sarah J Fansler, Amy E Goldman, Emily B. Graham, View ORCID ProfileMalak M Tfaily, View ORCID ProfileJason G Toyoda, James C Stegen
doi: https://doi.org/10.1101/2020.01.31.929364
Robert E Danczak
Pacific Northwest National Laboratory;
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  • For correspondence: robert.danczak@pnnl.gov
Rosalie K Chu
Environmental Molecular Sciences Laboratory;
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  • For correspondence: rosalie.chu@pnnl.gov
Sarah J Fansler
Pacific Northwest National Laboratory;
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  • For correspondence: sarah.fansler@pnnl.gov
Amy E Goldman
Pacific Northwest National Laboratory;
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  • For correspondence: amy.goldman@pnnl.gov
Emily B. Graham
Pacific Northwest National Laboratory;
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  • For correspondence: emily.graham@pnnl.gov
Malak M Tfaily
University of Arizona;
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  • For correspondence: tfaily@email.arizona.edu
Jason G Toyoda
Environmental Molecular Science Laboratory
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James C Stegen
Pacific Northwest National Laboratory;
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  • For correspondence: james.stegen@pnnl.gov
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Abstract

Environmental metabolomics, enabled by high-resolution mass spectrometric techniques, have demonstrated the biogeochemical importance of the metabolites which comprise natural organic matter (NOM). However, significant gaps exist in our understanding of the spatiotemporal organization of NOM composition. We suggest that the underlying mechanisms governing NOM can be revealed by applying tools and concepts from metacommunity ecology to environmental metabolomics. After illustrating the similarities between metabolomes and ecological communities, we call this conceptual synthesis "meta-metabolome ecology" and demonstrate its potential utility using a freshwater mass spectrometry dataset. Specifically, we developed three relational metabolite dendrograms using combinations of molecular properties (i.e., aromaticity index, double-bond equivalents, etc.) and putative biochemical transformations. Using these dendrograms, which are similar to phylogenetic or functional trait trees in ecological communities, we illustrate potential analytical techniques by investigating relationally-informed α-diversity and β-diversity metrics (e.g., MPD, MNTD, UniFrac), and null model analyses (e.g., NRI, NTI, and βNTI). Furthermore, we demonstrate that this synthesis allows ecological communities (e.g., microbes) and the metabolites they produce and consume using the same framework. We propose that applying this framework to a broad range of ecosystems will reveal generalizable principles that can advance our predictive capabilities regarding NOM dynamics.

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Posted February 02, 2020.
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Unification of Environmental Metabolomics with Metacommunity Ecology
Robert E Danczak, Rosalie K Chu, Sarah J Fansler, Amy E Goldman, Emily B. Graham, Malak M Tfaily, Jason G Toyoda, James C Stegen
bioRxiv 2020.01.31.929364; doi: https://doi.org/10.1101/2020.01.31.929364
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Unification of Environmental Metabolomics with Metacommunity Ecology
Robert E Danczak, Rosalie K Chu, Sarah J Fansler, Amy E Goldman, Emily B. Graham, Malak M Tfaily, Jason G Toyoda, James C Stegen
bioRxiv 2020.01.31.929364; doi: https://doi.org/10.1101/2020.01.31.929364

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