Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Samuel M D Seaver,Eytan Ruppin,Raphy Zarecki,Andrew D Hanson,Christopher S Henry,Océane Frelin,Louis M T Bradbury

doi:10.3389/fpls.2015.00142

Abstract

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

Highlights

The ability of a plant to grow and survive is linked to its metabolic network (Stitt et al, 2010), which indicates that a capacity to predict and understand plant metabolism will improve our understanding of plant response to changing environments and genetic perturbations
Poolman et al (2009) built the first genome-scale plant metabolic reconstruction, which could respire on heterotrophic media in silico and produce biomass components in proportions that matched in vivo observations. de Oliveira Dal’molin et al (2010a) investigated autotrophic biosynthesis of plant biomass, showing that the model correctly predicted the reactions used for both photosynthesis and photorespiration. de Oliveira Dal’Molin et al developed a metabolic reconstruction of a C4 plant containing plastidial reactions for photosynthesis
A High-Quality Evidence-Based Genome-Scale Metabolic Reconstruction of Maize In order to generate a metabolic reconstruction based on available evidence, as described in the Materials and Methods Section, we started by building a full genome-scale metabolic reconstruction that integrated every reaction and gene-reaction association from all available resources

Summary

Introduction

The ability of a plant to grow and survive is linked to its metabolic network (Stitt et al, 2010), which indicates that a capacity to predict and understand plant metabolism will improve our understanding of plant response to changing environments and genetic perturbations (Mo et al, 2009; Reliable maize tissue-specific metabolic modelsChang et al, 2011; Saha et al, 2011). Many external and internal perturbations lead to systemic responses, and a systems-level understanding of plant metabolism is required to fully explain these system responses To build this systems-level understanding, several genomescale metabolic reconstructions have recently been published for plant species (Poolman et al, 2009; de Oliveira Dal’molin et al, 2010a,b; Saha et al, 2011; Poolman et al, 2013). De Oliveira Dal’Molin et al developed a metabolic reconstruction of a C4 plant (de Oliveira Dal’molin et al, 2010b) containing plastidial reactions for photosynthesis. In other work, Saha et al (2011) show that genetic perturbations in the phenylpropanoid biosynthesis pathway could be simulated in silico, producing an impact on cell wall composition that compared favorably with experimental data from known maize mutants

Methods

Results

Conclusion