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Use of the Metabolomics Approach to Characterize Chinese Medicinal Material Huangqi

Molecular Plant 2012年第2期5卷 376-386页

作者： Li-Xin Duan Tian-Lu Chen Ming Li Min Chen Ye-Qing Zhou Guang-Hong Cui Ai-Hua Zhao Wei Jia Lu-Qi Huang Xiaoquan Qi The Key Laboratory of Plant Molecular Physiology Institute of BotanyChinese Academy of Sciences20 Nanxincun Xiangshan RoadBeijing 100093China Shanghai Center for Systems Biomedicine(Ministry of Education Key Laboratory of Systems Biomedicine) Shanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China Institute of Chinese Materia Medica Chinese Academy of Chinese Medical Sciences16 Nanxiao RoadBeijing 100700China School of Pharmacy Shanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China

Integration of the genetic and metabolic fingerprinting can provide a new approach to differentiate similar Traditional Chinese Medical （TCM） materials. Two leguminous plants, Mojia Huangqi and Menggu Huangqi, are important medical herbs and share great similarities in morphology, chemical constituent, and genomic DNA sequence. The taxonomy of Mojia Huangqi and Menggu Huangqi has been debated for more than 50 years and discrimination of TCM materials directly affects the pharmacological and clinical effects. AFLP based genetic fingerprinting and GC-TOF/MS-based meta- bolic fingerprinting were used to successfully discriminate the two species. The results of AFLP supported the opinion that Menggu Huangqi was a variant of Mojia Huangqi. The metabolic fingerprinting showed growth locations have greater impacts on the metabolite composition and quantity than the genotypes （cultivated versus wild） in Menggu Huangqi. The difference of some soluble sugars, fatty acids, proline, and polyamine reflected plant adaptation to different growth environments. Using multivariate and univariate statistical analysis, three AFLP markers and eight metabolites were identified as candidate DNA and metabolic markers to distinguish the two herb materials. The correlation network between AFLP markers and metabolites revealed a complex correlation network, which indicated the special metabolic pathways and the regulation networks of Huangqi.

关键词： Astragalus membranaceus metabolic fingerprinting amplified fragment length polymorphisms gas chromatography time-of-flight mass spectrometry.

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Utilizing metabolomic approach to study the mode of action of fungicides and corresponding resistance in plant pathogens

Advanced Agrochem

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Advanced Agrochem 2024年第3期3卷 197-205页

作者： Zhaochen Wu Ziqi Liu Zhihong Hu Tingting Wang Lijie Teng Tan Dai Pengfei Liu Jianjun Hao Xili Liu Department of Plant Pathology and MOA Key Laboratory of Pest Monitoring and Green Management College of Plant ProtectionChina Agricultural UniversityBeijing100093China Institute of Genetics and Developmental Biology Chinese Academy of SciencesBeijing100101China School of Food and Agriculture University of MaineOrono ME04469USA

Fungicides are an indispensable tool in plant disease *** modes of action(MOAs)have been identified in different fungicides to suppress plant *** combined use of fungicides with distinct MOAs has been recommended to prevent the development of pathogen *** studying MOAs,metabolomics has been proven to be a robust and high-throughput *** metabolites are unique and distinct depending on the biological activities of an organism,MOAs can be identified and classified by establishing metabolic fingerprinting and metabolic ***,if fungicide resistance is developed in a pathogen,the metabolome will change,which can be *** this review,we have discussed the principles and advanced applications of metabolomics in the study of MOAs and resistance mechanisms of fungicides,and the potential of metabolic data in understanding the interaction between fungicides and *** are also discussed in the application of metabolomics,improvement of the study on the mechanism of fungicides in their functions against pathogens and advancing the development of novel fungicides.

关键词： Fungicide resistance Plant pathogens Gas chromatography-mass spectrometry metabolic fingerprinting

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