In this research, metabolic profiling/pathways of Thymus vulgaris (thyme) plant were assessed during a water deficit stress using an FTICR mass spectrometry-based metabolomics strategy incorporating multivariate data analysis and bioinformatics techniques. Herein, differences of MS signals in specific time courses after water deficit stress and control cases without any timing period were distinguished significantly by common pattern recognition techniques, i.e., PCA, HCA-Heatmap, and PLS-DA. Subsequently, the results were compared with supervised Kohonen neural network (SKN) ones as a non-linear data visualization and capable mapping tool. The classification models showed excellent performance to predict the level of drought stress. By assessing variances contribution on the PCA-loadings of the MS data, the discriminant variables related to the most critical metabolites were identified and then confirmed by ANOVA. Indeed, FTICR MS-based multivariate analysis strategy could explore distinctive metabolites and metabolic pathways/profiles, grouped into three metabolism categories including amino acids, carbohydrates (i.e., galactose, glucose, fructose, sucrose, and mannose), and other metabolites (rosmarinic acid and citrate), to indicate biological mechanisms in response to drought stress for thyme. It was achieved and approved through the MS signals, genomics databases, and transcriptomics factors to interpret and predict the plant metabolic behavior. Eventually, a comprehensive pathway analysis was used to provide a pathway enrichment analysis and explore topological pathway characteristics dealing with the remarkable metabolites to demonstrate that galactose metabolism is the most significant pathway in the biological system of thyme.
Keywords: FTICR mass spectrometry; Metabolic pathways; Metabolomics; Multivariate data analysis; Thymus vulgaris plant; Water deficit stress.
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