Plant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology. One-sentence Summary The Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.
Plant Metabolic Network: A multi-species resource of plant metabolic information
Charles L. Hawkins,Daniel N. Ginzburg,Kangmei Zhao,William Dwyer,Bo Xue,Angela Xu,Selena L Rice,Benjamin J. Cole,Suzanne M. Paley,P. Karp,S. Rhee
Published 2021 in bioRxiv
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- Publication year
2021
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bioRxiv
- Publication date
2021-03-31
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Biology, Computer Science, Environmental Science
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