Serrano-García Irene, Martakos Ioannis C, Olmo-García Lucía, León Lorenzo, de la Rosa Raúl, Gómez-Caravaca Ana M, Belaj Angjelina, Serrano Alicia, Dasenaki Marilena E, Thomaidis Nikolaos S, Carrasco-Pancorbo Alegría
Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Ave. Fuentenueva s/n, Granada 18071, Spain.
Analytical Chemistry Laboratory, Chemistry Department, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, Athens 15771, Greece.
J Agric Food Chem. 2024 Dec 11;72(49):27561-27574. doi: 10.1021/acs.jafc.4c07155. Epub 2024 Nov 22.
The limited effectiveness of current strategies to control Verticillium wilt of olive (VWO) prompts the need for innovative approaches. This study explores the basal metabolome of 43 olive cultivars with varying resistance levels to , offering alternative insights for olive crossbreeding programmes. The use of an innovative UHPLC-ESI-TimsTOF MS/MS platform enabled the annotation of more than 70 compounds across different olive organs (root, stem, and leaf) and the creation of a preliminary compilation of CCS experimental data for more reliable metabolite annotation. Moreover, it allowed the documentation of numerous isomeric species in the studied olive organs by resolving hidden compounds. Multivariate statistical analyses revealed significant metabolome variability between highly resistant and susceptible cultivars, which was further investigated through supervised PLS-DA. Key markers indicative of VWO susceptibility were annotated and characteristic compositional patterns were established. Stem tissue exhibited the highest discriminative capability, while root and leaf tissues also showed significant predictive potential.
当前控制油橄榄黄萎病(VWO)策略的效果有限,因此需要创新方法。本研究探索了43个对[病害名称未给出]具有不同抗性水平的油橄榄品种的基础代谢组,为油橄榄杂交育种计划提供了新的见解。使用创新的UHPLC-ESI-TimsTOF MS/MS平台能够注释不同油橄榄器官(根、茎和叶)中的70多种化合物,并创建了CCS实验数据的初步汇编,以实现更可靠的代谢物注释。此外,通过解析隐藏化合物,该平台还记录了所研究油橄榄器官中的众多同分异构体。多变量统计分析揭示了高抗和感病品种之间显著的代谢组差异,并通过监督式PLS-DA进一步研究。注释了指示VWO易感性的关键标志物,并建立了特征性组成模式。茎组织表现出最高的鉴别能力,而根和叶组织也显示出显著的预测潜力。