Untargeted Metabolomics Based on GC-MS and Chemometrics: A New Tool for the Early Diagnosis of Strawberry Anthracnose Caused by Colletotrichum theobromicola.

To prevent the spread of anthracnose in strawberry plants and characterize the metabolic changes occurring during plant-pathogen interactions, we developed a method for the early diagnosis of disease based on an analysis of the metabolome by gas chromatography-mass spectrometry. An examination of the metabolic profile revealed 189 and 202 total ion chromatogram peaks for the control and inoculated plants, respectively. A partial least squares discriminant analysis (PLS-DA) model was conducted for the reliable and accurate discrimination between healthy and diseased strawberry plants, even in the absence of disease symptoms (e.g., early stages of infection). ANOVA (analysis of variance) and orthogonal partial least squares analysis (OPLS) identified 20 metabolites as tentative biomarkers of Colletotrichum theobromicola infection (e.g., citric acid, d-xylose, erythrose, galactose, gallic acid, malic acid, methyl α-galactopyranoside, phosphate, and shikimic acid). At least some of these potential biomarkers may be applicable for the early diagnosis of anthracnose in strawberry plants. Moreover, these metabolites may be useful for characterizing pathogen infections and plant defense responses. This study confirms the utility of metabolomics research for developing diagnostic tools and clarifying the mechanism underlying plant-pathogen interactions. Furthermore, the data presented herein may be relevant for developing new methods for preventing anthracnose in strawberry seedlings cultivated under field conditions.

Metabolic Profiling to Identify the Latent Infection of Strawberry by Botrytis cinerea.

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, ß-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.