Abstract
In 2013, Xylella fastidiosa (Xf) was detected for the first time in Apulia and, subsequently, recognized as the causal agent of the olive quick decline syndrome (OQDS). To contain the disease, the olive germplasm was evaluated for resistance to Xf, identifying cultivars with different susceptibility to the pathogen. Regarding this, the resistant cultivar Leccino has generally a lower bacterial titer compared with the susceptible cultivar Ogliarola salentina. Among biomolecules, lipids could have a pivotal role in the interaction of Xf with its host. In the grapevine Pierce's disease, fatty acid molecules, the diffusible signaling factors (DSFs), act as regulators of Xf lifestyle and are crucial for its virulence. Other lipid compounds derived from fatty acid oxidation, namely, oxylipins, can affect, in vitro, biofilm formation in Xf subsp. pauca (Xfp) strain De Donno, that is, the strain causing OQDS. In this study, we combined high-performance liquid chromatography-mass spectrometry-MS-based targeted lipidomics with supervised learning algorithms (random forest, support vector machine, and neural networks) to classify olive tree samples from Salento. The dataset included samples from either OQDS-positive or OQDS-negative olive trees belonging either to cultivar Ogliarola salentina or Leccino treated or not with the zinc-copper-citric acid biocomplex Dentamet®. We built classifiers using the relative differences in lipid species able to discriminate olive tree samples, namely, (1) infected and non-infected, (2) belonging to different cultivars, and (3) treated or untreated with Dentamet®. Lipid entities emerging as predictors of the thesis are free fatty acids (C16:1, C18:1, C18:2, C18:3); the LOX-derived oxylipins 9- and 13-HPOD/TrE; the DOX-derived oxylipin 10-HPOME; and diacylglyceride DAG36:4(18:1/18:3).
Keywords:
Xylella fastidiosa; detection; lipids; machine learning algorithms; olive trees; oxylipins.