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Gene Coexpression Network Reveals Insights into the Origin and Evolution of a Theanine-Associated Regulatory Module in NonCamellia and Camellia Species

更新日期:2021-05-17

Title

Gene Coexpression Network Reveals Insights into the Origin and Evolution of a Theanine-Associated Regulatory Module in NonCamellia and Camellia Species

Authors

Jinke Du, Xiaolong He, Yeman Zhou, Chenchen Zhai, Deen Yu, Shihua Zhang, Qi Chen,*and Xiaochun Wan*

Journal

Journal of Agricultural and Food Chemistry

DOI

https://dx.doi.org/10.1021/acs.jafc.0c06490

ABSTRACT:

Theanine (thea) is one of the most important plant-derived characteristic secondary metabolites and a major

healthcare product because of its benefificial biological activities, such as being an antianxiety agent, promoting memory, and lowering

blood pressure. Thea mostly accumulates in Camellia plants and is especially rich in Camellia sinensis (tea plant). Although some

functional genes (e.g., TS, GOGAT, and GS) attributed to thea accumulation have been separately well explored in tea plants, the

evolution of a regulatory module (highly interacting gene group) related to thea metabolism remains to be elaborated. Herein, a

thea-associated regulatory module (TARM) was mined by using a comprehensive analysis of a weighted gene coexpression network

in Camellia and non-Camellia species. Comparative genomic analysis of 84 green plant species revealed that TARM originated from

the ancestor of green plants (algae) and that TARM genes were recruited from difffferent evolutionary nodes with the most gene

duplication events at the early stage. Among the TARM genes, two core transcription factors of NAC080 and LBD38 were deduced,

which may play a crucial role in regulating the biosynthesis of thea. Our fifindings provide the fifirst insights into the origin and

evolution of TARM and indicate a promising paradigm for identifying vital regulatory genes involved in thea metabolism.

KEYWORDS: theanine, regulatory module, origin and evolution, gene coexpression network, comparative genomic analysis