Abstract
Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We developed a general approach for reconstructing metabolic pathway complements of plant genomes. Two new reference databases were created and added to the core of the infrastructure: a comprehensive, all-plant reference pathway database, PlantCyc, and a reference enzyme sequence database, RESD, for annotating metabolic functions of protein sequences. PlantCyc (version 3.0) includes 714 metabolic pathways and 2,619 reactions from over 300 species. RESD (version 1.0) contains 14,187 literature-supported enzyme sequences from across all kingdoms. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. PoplarCyc (version 1.0) contains 321 pathways with 1,807 assigned enzymes. Comparing PoplarCyc (version 1.0) with AraCyc (version 6.0, Arabidopsis [Arabidopsis thaliana]) showed comparable numbers of pathways distributed across all domains of metabolism in both databases, except for a higher number of AraCyc pathways in secondary metabolism and a 1.5-fold increase in carbohydrate metabolic enzymes in PoplarCyc. Here, we introduce these new resources and demonstrate the feasibility of using them to identify candidate enzymes for specific pathways and to analyze metabolite profiling data through concrete examples. These resources can be searched by text or BLAST, browsed, and downloaded from our project Web site (http://plantcyc.org).
Highlights
Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution
We developed a general approach for facilitating the reconstruction of plant metabolic networks from sequenced genomes or transcriptomes
Four components were created for the system: (1) PlantCyc, a pan-plant reference database of metabolic pathways and enzymes; (2) RESD, a reference enzyme sequence database containing protein sequences with literaturesupported enzyme activities; (3) an enzyme sequence annotation pipeline that predicts enzyme functions from predicted protein sequences based on sequence similarity to RESD sequences; and (4) a modified pathway prediction procedure that uses both PlantCyc and MetaCyc as the reference for reconstructing single-species metabolic networks from the predicted enzymes
Summary
Metabolic networks reconstructed from sequenced genomes or transcriptomes can help visualize and analyze large-scale experimental data, predict metabolic phenotypes, discover enzymes, engineer metabolic pathways, and study metabolic pathway evolution. We used RESD, PlantCyc, and MetaCyc (an all-species reference metabolic pathway database), in conjunction with the pathway prediction software Pathway Tools, to reconstruct a metabolic pathway database, PoplarCyc, from the recently sequenced genome of Populus trichocarpa. The general approach developed at PMN includes four components: (1) PlantCyc, a reference database composed solely of plant metabolic pathways and enzymes; (2) RESD, a reference enzyme sequence database; (3) an enzyme sequence annotation pipeline using RESD; and (4) a modified pathway prediction procedure that uses both PlantCyc and MetaCyc as references in reconstructing single-species metabolic networks from the predicted enzymes. We provide some specific examples of how the PMN resources can be used to guide experimentation and discovery in studying plant metabolism
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