Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

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.

Microbial Community Metabolic Modeling: A Community Data‐Driven Network Reconstruction

“…We also called each gene “active” or “inactive” based on its expression level (see the next section). Finally, we ran transcriptomic FBA (Seaver et al, ) to fit the flux predictions made by our models to gene activity computed from our RNA‐seq data. This approach attempts to force reactions associated with inactive genes off, while forcing reactions associated with active genes on, without preventing the model from producing biomass.…”

Inference and prediction of metabolic network fluxes

“…three isoprenoid compounds) in different tissues was validated by metabolomics technologies, but no assessment of the correspondence between flux distributions and protein abundance was carried out. Finally, Seaver et al (2015) extracted an organ-specific model for the maize leaf and tissue-specific models for embryo and endosperm cells by including (similarly to iMAT) a minimal set of reactions required to fill gaps in the network while maximizing the number of flux-carrying reactions associated with highly expressed genes. The models are accompanied by biomass reactions based on measurements under the same conditions, and their validity was tested by comparing the predicted fluxes with those coming from MFA.…”

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

“…Previously, we used limited data selected primarily from AraCyc to determine the subcellular localization of proteins in our primary reference genome, Arabidopsis. In this release, we switch to using an array of evidence sources, building upon our recently published work (Seaver et al ., ). Our evidence sources for subcellular localization include PPDB (Sun et al ., ), SUBA3 (Tanz et al ., ) and AraCyc (Zhang et al ., ; Schlapfer et al ., ).…”

“…Plants in this nitrogen treatment study were watered with nutrient solution containing either 10 m m KNO 3 (nitrate treatment), 10 m m (NH 4 ) 3 PO 4 (ammonia treatment) or 10 m m urea and root tissue was harvested after 30 days. We used these datasets in combination with the metabolic reconstructions generated by PlantSEED to compute an expression percentile for each reaction under each condition (Seaver et al ., ).…”