Identification and Structural Elucidation of Acylsugars in Tomato Leaves Using Liquid Chromatography–Ion Mobility–Tandem Mass Spectrometry (LC-IM-MS/MS)
收藏NIAID Data Ecosystem2026-05-02 收录
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https://figshare.com/articles/dataset/Identification_and_Structural_Elucidation_of_Acylsugars_in_Tomato_Leaves_Using_Liquid_Chromatography_Ion_Mobility_Tandem_Mass_Spectrometry_LC-IM-MS_MS_/28038107
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Leaves of tomato
plants contain various glandular trichomes that
produce a wide range of metabolic products including acylsugars, which
may serve as a defense mechanism against various insect pests. Acylsugars
exhibit significant structural diversity, differing in their sugar
cores, acylated positions, and type of acyl chains. This work demonstrated
a comprehensive approach using multidimensional separation techniques,
specifically liquid chromatography–ion mobility–tandem
mass spectrometry (LC-IM-MS/MS), for structural characterization,
and the discrimination of different tomato plants (one cultivar and
five accessions) was demonstrated using tomato leaf extracts; six
genotypes from five species of Solanum were represented.
As a result, we identified 16 acylsugars through their molecular formulas
and annotations using LC and MS analyses. The incorporation of ion
mobility (IM) analysis revealed an additional 9 isomeric forms, resulting
in a comprehensive total of 25 isomeric acylsugars identified. Furthermore,
the experimental collision cross section (CCSexp) values
agreed reasonably well with the corresponding predicted values (CCSpred), with an overall estimated error of less than 2%. These
findings pave the way for research into how the different structural
isomers of acylsugars might influence the self-defense mechanism in
plants. Moreover, this work demonstrated that the investigated cultivar
and accessions of tomatoes can be distinguished from each other based
on their metabolite profile, e.g., acylsugars, with principal component
analysis (PCA) and linear discriminant analysis (LDA) statistical
models, yielding a prediction rate of 98.3%.
创建时间:
2024-12-16



