Metabolomics by NMR Spectroscopy in Plant Disease diagnostic: Huanglongbing as a Case Study

Candidatus Liberibacter spp. is the pathogen associated with Huanglongbing (HLB), a disease with an economic impact in the order of billions of dollars to the worldwide citrus industry. A key point to reduce HLB economic losses lies on early detection on asymptomatic stages of the infection by new detection methods as it is still not possible to cultivate Candidatus Liberibacter spp. in vitro, and the polymerase chain reaction (PCR) method used nowadays is not manageable in large scale. In this study, we search for metabolic biomarkers from Citrus sinensis leaves in different disease stages using a combined approach of NMR spectroscopy and chemometrics. Chemometric clustering was observed, providing excellent tools for class discrimination, with high accuracy, therefore enabling metabolic profile differentiation on disease early stages. Around 20 different key biomarkers, metabolites responsible for the clustering of each group, were identified using 2D NMR experimental data. Currently 1D and 2D NMR Spectroscopy tools together with Chemometric Analysis, such as Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS‐DA) have been an excellent and alternative approach employed for biomarkers identification in plant diseases research and for plant‐pathogen interactions studies. In this work, using spectroscopic assignments, multivariate statistical analysis and databases comparison, the twenty key metabolites for the Huanglongbing (HLB) disease were identified.