| Literature DB >> 31203559 |
Jingru Sun1, Guanghong Cui1, Xiaohui Ma2, Zhilai Zhan1, Ying Ma1, Zhongqiu Teng3, Wei Gao4,5, Yanan Wang1, Tong Chen1, Changjiangsheng Lai1, Yujun Zhao1, Jinfu Tang1, Huixin Lin1, Ye Shen1, Wen Zeng1, Juan Guo6, Luqi Huang7.
Abstract
KEY MESSAGE: Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants.Entities:
Keywords: Functional prediction; Gene expression pattern; Metabolite module; PLS; Terpene synthase
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Year: 2019 PMID: 31203559 DOI: 10.1007/s11103-019-00892-0
Source DB: PubMed Journal: Plant Mol Biol ISSN: 0167-4412 Impact factor: 4.076