Metabolic Profiling to Identify the Latent Infection of Strawberry by Botrytis cinerea. (April 2019)

• Record Type: Journal Article
• Authors: Hu, Zhihong; Chang, Xunian; Dai, Tan; Li, Lei; Liu, Panqing; Wang, Guozhen; Liu, Pengfei; Huang, Zhongqiao; Liu, Xili
• Is Part Of: Evolutionary bioinformatics online. Volume 15(2019)
• Publication Date: 2019-04
• Subjects: metabolomics -- strawberry -- interaction -- biomarker -- diagnosis; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934319838518
• Languages: English
• ISSNs: 1176-9343

Abstract

In plant-pathogen interaction systems, plant metabolism is usually agitated in the early stages of infection and much before visible symptoms appear. To identify the latent infection of strawberry by Botrytis cinerea by metabolome profiling, a metabolomics method based on gas chromatography and mass spectrometry was applied to identify the affected metabolites and discriminate diseased plants from healthy ones. An orthogonal partial least squares (OPLS) score plot showed that the metabolic profiling well separated B. cinerea -infected strawberry plants at 2, 5, and 7 days after infection from non-infected healthy plants. Combined analysis of variance (ANOVA) and OPLS analysis revealed candidate biomarkers of plant resistance and of infection and expansion of the pathogen in the plants. Among them, hexadecanoic acid, octadecanoic acid, sucrose, β-lyxopyranose, melibiose, and 1, 1, 4a-Trimethyl-5, 6-dimethylenedecahydronaphthalene were closely related to the early stage of disease development when symptoms were not visible. A discrimination method that could distinguish Botrytis gray mold diseased strawberry plants from healthy ones was established based on the partial least squares discriminant analysis (PLS-DA) model with a correct recognition accuracy of 100%. This research offers a good application of metabolome profiling for early diagnosis of plant disease and interaction mechanism exploration.