ArticleAnalytical and bioanalytical chemistry2022
HPLC-MS/MS method applied to an untargeted metabolomics approach for the diagnosis of "olive quick decline syndrome".
Article in Analytical and bioanalytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 14 citations in OpenAlex.
- Omics approaches to unveil biotic stress responses in olive: current knowledge and the future.Journal of experimental botany · 2026Review
- Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.Nature protocols · 2025Article
- Chemical Profiling of Polar Lipids and the Polyphenolic Fraction of Commercial ItalianBiomolecules · 2024Article
- A Decade after the Outbreak ofPlants (Basel, Switzerland) · 2024Review
- Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review.Sensors (Basel, Switzerland) · 2023Review
- Exploring the xylem-sap to unravel biological features ofFrontiers in plant science · 2023Article
- Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops.International journal of molecular sciences · 2022Review
- Thidiazuron: New Trends and Future Perspectives to FightAntibiotics (Basel, Switzerland) · 2022Review
- A Three-Reagent "Green" Paper-Based Analytical Device for Solid-Phase Spectrometric and Colorimetric Determination of Dihydroquercetin.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Olive quick decline syndrome (OQDS) is a disorder associated with bacterial infections caused by Xylella fastidiosa subsp. pauca ST53 in olive trees. Metabolic profile changes occurring in infected olive trees are still poorly investigated, but have the potential to unravel reliable biomarkers to be exploited for early diagnosis of infections. In this study, an untargeted metabolomic method using high-performance liquid chromatography coupled to quadrupole-time-of-flight high-resolution mass spectrometry (HPLC-ESI-Q-TOF-MS) was used to detect differences in samples (leaves) from healthy (Ctrl) and infected (Xf) olive trees. Both unsupervised and supervised data analysis clearly differentiated the groups. Different metabolites have been identified as potential specific biomarkers, and their characterization strongly suggests that metabolism of flavonoids and long-chain fatty acids is perturbed in Xf samples. In particular, a decrease in the defence capabilities of the host after Xf infection is proposed because of a significant dysregulation of some metabolites belonging to flavonoid family. Moreover, oleic acid is confirmed as a putative diffusible signal factor (DSF). This study provides new insights into the host-pathogen interactions and confirms LC-HRMS-based metabolomics as a powerful approach for disease-associated biomarkers discovery in plants.
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