QT–GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits

全基因组关联研究 生物 计算生物学 遗传学 数量性状位点 进化生物学 基因 单核苷酸多态性 基因型
作者
Marlies Brouckaert,Peng Meng,René Höfer,Ilias El Houari,Chiarina Darrah,Véronique Storme,Yvan Saeys,Ruben Vanholme,Geert Goeminne,Vitaliy I. Timokhin,John Ralph,Kris Morreel,Wout Boerjan
出处
期刊:Molecular Plant [Elsevier]
卷期号:16 (7): 1212-1227 被引量:2
标识
DOI:10.1016/j.molp.2023.06.004
摘要

Although the plant kingdom provides an enormous diversity of metabolites with potentially beneficial applications for humankind, a large fraction of these metabolites and their biosynthetic pathways remain unknown. Resolving metabolite structures and their biosynthetic pathways is key to gaining biological understanding and to allow metabolic engineering. In order to retrieve novel biosynthetic genes involved in specialized metabolism, we developed a novel untargeted method designated as qualitative trait GWAS (QT-GWAS) that subjects qualitative metabolic traits to a genome-wide association study, while the conventional metabolite GWAS (mGWAS) mainly considers the quantitative variation of metabolites. As a proof of the validity of QT-GWAS, 23 and 15 of the retrieved associations identified in Arabidopsis thaliana by QT-GWAS and mGWAS, respectively, were supported by previous research. Furthermore, seven gene-metabolite associations retrieved by QT-GWAS were confirmed in this study through reverse genetics combined with metabolomics and/or in vitro enzyme assays. As such, we established that CYTOCHROME P450 706A5 (CYP706A5) is involved in the biosynthesis of chroman derivatives, UDP-GLYCOSYLTRANSFERASE 76C3 (UGT76C3) is able to hexosylate guanine in vitro and in planta, and SULFOTRANSFERASE 202B1 (SULT202B1) catalyzes the sulfation of neolignans in vitro. Collectively, our study demonstrates that the untargeted QT-GWAS method can retrieve valid gene-metabolite associations at the level of enzyme-encoding genes, even new associations that cannot be found by the conventional mGWAS, providing a new approach for dissecting qualitative metabolic traits.
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