Untargeted metabolomics based on GC-MS and chemometrics: A new tool for the early diagnosis of strawberry anthracnose caused by colletotrichum theobromicola

Abstract

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 a-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.