PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency. (9th August 2018)

• Record Type: Journal Article
• Authors: Seaver, Samuel M. D.; Lerma‐Ortiz, Claudia; Conrad, Neal; Mikaili, Arman; Sreedasyam, Avinash; Hanson, Andrew D.; Henry, Christopher S.
• Is Part Of: Plant journal. Volume 95:Number 6(2018)
• Page Start: 1102
• Page End: 1113
• Publication Date: 2018-08-09
• Subjects: metabolic reconstruction -- metabolic modeling -- plant metabolism -- plant genomes -- flux balance analysis; Plant molecular biology -- Periodicals; Plant cells and tissues -- Periodicals; Botany -- Periodicals; 580
• DOI: 10.1111/tpj.14003
• Languages: English
• ISSNs: 0960-7412

Abstract

Summary: Genome‐scale metabolic reconstructions help us to understand and engineer metabolism. Next‐generation sequencing technologies are delivering genomes and transcriptomes for an ever‐widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k‐mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at http://modelseed.org, which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species. Significance Statement: An improved version of the PlantSEED resource is released with numerous enhancements: improved assignment of metabolic pathways to subcellular compartments; refined annotation of primary metabolism for 39 reference genomes from Phytozome; a pipeline for automated annotation and modeling of user‐uploaded plant sequences; and an upgraded website (http://modelseed.org ). PlantSEED provides highly consistent reconstructions and simulations of primary metabolism for any newly sequenced plant species, simplifying the process for comparison of omics data across different species.