Cloutier Mathieu, Xiang Daoquan, Gao Peng, Kochian Leon V, Zou Jitao, Datla Raju, Wang Edwin
Laboratory of Bioinformatics and Systems Biology, National Research Council Canada, Montreal, QC, Canada.
Aquatic and Crop Resource Development, National Research Council Canada, Saskatoon, SK, Canada.
Front Plant Sci. 2021 Apr 6;12:642938. doi: 10.3389/fpls.2021.642938. eCollection 2021.
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 seed development. Through 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.
作物种子中的脂肪酸是植物油和工业应用的主要来源。脂肪酸组成和油含量的遗传改良对于满足当前和未来对植物基可再生种子油的需求至关重要。应对这一挑战可以通过网络建模来实现，以捕捉种子代谢的关键贡献者，并确定与种子油组成和含量相关性状工程的潜在遗传靶点。在此，我们提出了一个动态模型，使用常微分方程模型并整合时间进程基因表达数据，来描述种子发育过程中的代谢网络。通过对基因的扰动，预测了种子油性状的靶点。利用科学文献中的已发表报告，对其中一些预测获得了验证和支持证据。此外，我们使用来自种子胚的基因表达和代谢物组学数据集研究了两个预测靶点，并证明了这种基于网络的模型的适用性。这项工作强调，动态基因表达图谱的整合产生了信息丰富的模型，可用于剖析代谢途径并导致鉴定与种子油性状相关因果基因。
