Rice metabolic regulatory network spanning its entire life cycle

Yang, Chenkun; Shen, Shuangqian; Zhou, Shen; Li, Yufei; Mao, Yuyuan; Zhou, Junjie; Shi, Yuheng; An, Longxu; Zhou, Qianqian; Peng, Wenju; Lyu, Yuanyuan; Liu, Xuemei; Chen, Wei; Wang, Shouchuang; Qu, Lianghuan; Liu, Xianqing; Fernie, A. R.; Luo, Jie

doi:10.1016/j.molp.2021.10.005

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Rice metabolic regulatory network spanning its entire life cycle

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Fernie, A. R.
Central Metabolism, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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引用

Yang, C., Shen, S., Zhou, S., Li, Y., Mao, Y., Zhou, J., Shi, Y., An, L., Zhou, Q., Peng, W., Lyu, Y., Liu, X., Chen, W., Wang, S., Qu, L., Liu, X., Fernie, A. R., & Luo, J. (2021). Rice metabolic regulatory network spanning its entire life cycle. Molecular Plant. doi:10.1016/j.molp.2021.10.005.

引用: https://hdl.handle.net/21.11116/0000-0009-6934-7

要旨

ABSTRACT
As one of the most important crops in the world, rice (Oryza sativa L.) is a model plant for metabolome research. Although many studies have focused on the analysis of specific tissues, the dynamics of metabolite abundances across the entire life cycle has not yet been realized. Combining both targeted and nontargeted metabolite profiling methods, a total of 825 annotated metabolites were quantified in rice samples from different tissues covering the entire life cycle. The contents of metabolites in different tissues of rice were significantly different, with various metabolites were accumulating in the plumule and radicle during seed germination. Combining these data with transcriptome data obtained from the same time period, we constructed the Rice Metabolic Regulation Network (RMRN). These metabolites and the genes co-expressed with them were further divided into 12 clusters according to their accumulation patterns, within which they displayed a uniform and clear pattern of abundance across development. Using this dataset, we established a comprehensive metabolic profile of the rice life cycle and used two independent strategies to identify novel transcription factors