文献类型：期刊文献

英文题名：Integrated transcriptomics and metabolites at different growth stages reveals the regulation mechanism of bolting and flowering of Angelica sinensis

作者：Li, J.[1];Li, M. L.[1];Zhu, T. T.[2];Zhang, X. N.[3];Li, M. F.[1,4];Wei, J. H.[4]

第一作者：Li, J.

通信作者：Li, MF[1];Wei, JH[2]

机构：[1]Gansu Agr Univ, Key Lab Arid Land Crop Sci, Lanzhou 730070, Peoples R China;[2]Gansu Univ Chinese Med, Coll Pharm, Lanzhou 730000, Peoples R China;[3]Gansu Herbal Med Planting Co Ltd, Lanzhou 730000, Peoples R China;[4]Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Plant Dev, Beijing 100193, Peoples R China

第一机构：Gansu Agr Univ, Key Lab Arid Land Crop Sci, Lanzhou 730070, Peoples R China

通信机构：[1]corresponding author), Gansu Agr Univ, Key Lab Arid Land Crop Sci, Lanzhou 730070, Peoples R China;[2]corresponding author), Chinese Acad Med Sci & Peking Union Med Coll, Inst Med Plant Dev, Beijing 100193, Peoples R China.

年份：2021

卷号：23

期号：4

起止页码：574

外文期刊名：PLANT BIOLOGY

收录：;Scopus(收录号：2-s2.0-85107409280);WOS:【SCI-EXPANDED(收录号:WOS:000645122800001)】；

基金：This work was financially supported by funds from the National Industrial System of Traditional Chinese Medicine of China (CARS-21), Gansu Agricultural University of China (GSCS-2018-1 and Gaufx-02J04), and Key talent projects of Gansu Province of China (2020RCXM103).

语种：英文

外文关键词：Angelica sinensis; bolting and flowering; transcriptomic analysis; gene expression; gibberellin metabolism; soluble sugar metabolism

摘要：The root of Angelica sinensis is one of the most widely used traditional Chinese medicines. In commercial planting, early bolting and flowering (EBF) of ca. 40% of 2-year-old plants reduces root yield and quality. Although changes in physiology in bolted plants have been investigated, the mechanism activating EBF has not been identified. Here, transcriptomics profiles at four different growth stages (S1 to S4) were performed, gene expression was validated by qRT-PCR and the accumulation of endogenous hormones quantified by HPLC. A total of 60,282 unigenes were generated, with 2,282, 1,359 and 2,246 differentially expressed genes (DEGs) observed at S2 versus S1, S3 versus S2 and S4 versus S3, respectively; 558 genes that co-exist in at least three stages from S1 to S4 were obtained. Functional annotation classified 38 DEGs linked to flowering pathways: photoperiodism, hormone signalling, carbohydrate metabolism and floral development. The levels of gene expression, hormones (GA(1), GA(4) and IAA) and soluble sugars were consistent with the EBF. It can be concluded that the EBF of A. sinensis is controlled by multiple genes. This integrated analysis of transcriptomics, together with targeted hormones and soluble sugars, provides new insights into the regulation of EBF of A. sinensis.