A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress

Authors: DHARMAWARDHANA, PALITHA - Oregon State University; REN, LIYA - Cold Spring Harbor Laboratory; AMARASINGHE, VINDHYA - Oregon State University; MONACO, MARCELA - Cold Spring Harbor Laboratory; THOMASON, JAMES - Cold Spring Harbor Laboratory; RAVENSCROFT, DEAN - Cornell University; MCCOUCH, SUSAN - Cornell University; Ware, Doreen; JAISWAL, PANKAJ - Oregon State University

Submitted to: Rice
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/14/2013
Publication Date: 5/29/2013
Citation: Dharmawardhana, P., Ren, L., Amarasinghe, V., Monaco, M.K., Thomason, J., Ravenscroft, D., Mccouch, S., Ware, D., Jaiswal, P. 2013. A genome scale metabolic network for rice and accompanying analysis of tryptophan, auxin and serotonin biosynthesis regulation under biotic stress. Rice. 6(1):1-15.

Interpretive Summary: According to the Food and Agriculture Organization of the United Nations, about 15 crop plants provide 90 percent of the world's food energy intake (excluding meat), with rice, maize and wheat comprising two-thirds of human food consumption. Rice, maize and wheat are the staples of over 4 billion people, and rice alone feeds almost half of humanity. To increase production and alleviate the effects of climate change on rice production, it is imperative that rice breeding incorporates modern techniques that take advantage of the vast amounts of genomic information available for this crop and that underlie the chemical processes by which a plant uses sunlight, water, etc. to make energy to grow and maintain life. Crop improvement requires understanding how a genome sequence (genotype) is translated to physical characteristics of a living organism (phenotype). This article describes the computational reconstruction of a genome-scale network – RiceCyc – connecting rice genes in biological pathways (i.e., how the products of genes interact with one another and with the environment to control the appearance and function of a plant). In its version 3.3, RiceCyc featured 316 pathways and 6,643 genes mapped to 2,103 enzymatic and 87 transport reactions. The RiceCyc pathways database is currently available in Gramene’s BioCyc pathways module at http://pathway.gramene.org/, along with nine plant-specific pathway databases, thus enabling across-species comparative analyses. To further illustrate the significance of this resource as a platform for discovering new knowledge, we analyzed publicly available rice transcriptome datasets (i.e., relating to the expression of genes). Our analyses led us to the creation of novel hypothesis on the regulation of three important plant pathways in the context of the plant’s circadian cycle (i.e., roughly lasting 24 hours) in response to daylight and pathogens. Such hypotheses are suitable for subsequent experimental validation. These exercises demonstrated that the RiceCyc collection of primarily computationally inferred biological pathways is a valuable resource for beginning to understand specific chemical processes by which a plant uses up sunlight and responds to disease-carrying agents to sustain life. The knowledge generated through analyses that combine genomic information and gene interactions in biological pathways, strengthened with the power of comparative analyses across species, is paving the way to the faster generation of testable hypothesis to understand how a plant grows and adapts, ultimately aiming to improve crop yield and address important issues like energy production and climate change.

Technical Abstract: Functional annotations of large plant genome projects mostly provide information on gene function and gene families based on the presence of protein domains and gene homology, but not necessarily in association with gene expression or metabolic and regulatory networks. These additional annotations are necessary to understand the physiology, development and adaptation of a plant and its interaction with the environment. RESULTS: RiceCyc is a metabolic pathway networks database for rice. It is a snapshot of the substrates, metabolites, enzymes, reactions and pathways of primary and intermediary metabolism in rice. RiceCyc version 3.3 features 316 pathways and 6,643 peptide-coding genes mapped to 2,103 enzyme-catalyzed and 87 protein-mediated transport reactions. The initial functional annotations of rice genes with InterPro, Gene Ontology, MetaCyc, and Enzyme Commission (EC) numbers were enriched with annotations provided by KEGG and Gramene databases. The pathway inferences and the network diagrams were first predicted based on MetaCyc reference networks and plant pathways from the Plant Metabolic Network, using the Pathologic module of Pathway Tools. This was enriched by manually adding metabolic pathways and gene functions specifically reported for rice. The RiceCyc database is hierarchically browsable from pathway diagrams to the associated genes, metabolites and chemical structures. Through the integrated tool OMICs Viewer, users can upload transcriptomic, proteomic and metabolomic data to visualize expression patterns in a virtual cell. RiceCyc, along with additional species-specific pathway databases hosted in the Gramene project, facilitates comparative pathway analysis. CONCLUSIONS: Here we describe the RiceCyc network development and discuss its contribution to rice genome annotations. As a case study to demonstrate the use of RiceCyc network as a discovery environment we carried out an integrated bioinformatic analysis of rice metabolic genes that are differentially regulated under diurnal photoperiod and biotic stress treatments. The analysis of publicly available rice transcriptome datasets led to the hypothesis that the complete tryptophan biosynthesis and its dependent metabolic pathways including serotonin biosynthesis are induced by taxonomically diverse pathogens while also being under diurnal regulation. The RiceCyc database is available online for free access at http://pathway.gramene.org/.