Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes.

Peng Gao,Raju Datla,Leon V Kochian,Daoquan Xiang,Edwin Wang,Jitao Zou,Mathieu Cloutier

doi:10.3389/fpls.2021.642938

Abstract

Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

Highlights

Fatty acids (FAs) in crop seeds are a major source for human nutrition and potential biodiesel fuels (Tang et al, 2015; Kumar et al, 2016)
We present a mathematical modeling approach that integrates the dynamics of gene expression into an Ordinary Differential Equations (ODE) model of the metabolic networks representing the major seed development biochemical pathways in the embryo
It should be noted that even with this limitation regarding model assumptions, as seen in Figure 1 we were able to obtain good agreements between model simulation results and experimental data. This does not mean that the model will not be improved if we provide actual column ratios for the 3 subcellular compartments of cells of the plant embryo, as we expect that will improve the accuracy of the model

Summary

Introduction

Fatty acids (FAs) in crop seeds are a major source for human nutrition and potential biodiesel fuels (Tang et al, 2015; Kumar et al, 2016). Combined network analysis for prediction of metabolic pathways based on metabolomics data, in silico analysis and machine learning was recently conducted in tomato, displaying the potential of artificial intelligence in model simulation of metabolic networks (Beckers et al, 2016; de Luis Balaguer and Sozzani, 2017; Toubiana et al, 2019). These modeling efforts are either limited to steady-state conditions or conducted in the context of a single pathway. Dynamic modeling (i.e., by integrating gene expression data into the modeling) of seed metabolic networks has not been explored in-depth or in great detail

Methods

Results

Conclusion