The PlantSEED Resource for Functional Annotation and Metabolic Modeling of Plant Genomes and the Generation of Tissue-Specific Metabolic Models

Over the past two decades, there has been progress towards the development of in silico metabolic reconstructions of organisms that enable researchers to explore the complex relationship between genotype and phenotype. The progress has advanced along three paths: the accelerated annotation of enzymes and reactions, the refinement of algorithms and techniques for both propagation of annotation and metabolic analysis, and the growth in the number of sequenced genomes. These paths have come together through the use of subsystems technology in the form of ModelSEED, a framework for the automated reconstruction of metabolic models that has been applied to the construction of genome-scale microbial metabolic models. In conjunction to these microbial developments, several databases specifically targeting different plant species have been developed. As a result, PlantSEED was developed as a unique collaboration between teams at Argonne and the University of Florida to create a specialized niche within the ModelSEED ecosystem geared towards utilizing the plant-specific databases to create an intensively curated set of primary metabolic pathways that can be used in constructing metabolic models for various plant species.

  There is also a growing trend in the generation of a number of –omics datasets, allowing us to systemically apply a variety of experimental evidence to the refinement of in silico metabolic models, in order to computationally replicate metabolic phenotypes and generate more reliable hypotheses.  Here we show highlight the importance of the PlantSEED approach  to limit the gene-reaction associations to those with experimental evidence, namely from AraCyc and PlantSEED itself in order to create a reliable baseline genome-scale metabolic model for Maize. We utilize various sources of protein localization data to assign reactions to various compartments in a metabolic model, and transcriptomics data to filter the reactions that would be active in several different tissue-specific models.  We highlight the differences in pathways and biomass between leaf, endosperm, and embryo-specific metabolic models.

  Finally, the genome-scale metabolic models and the data used in this work, whether through the PlantSEED resource or not, are readily available in the DOE Systems Biology Knowledgebase software and data environment (http://kbase.us).  This emerging framework is designed to enable researchers to collaboratively generate, test and share new hypotheses about gene and protein functions, perform large-scale analyses on a scalable computing infrastructure, and model interactions in microbes, plants, and communities.  As such, the data and results displayed on this poster is publicly accessible. Furthermore, the same approaches can be applied to other plant species.