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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
Published on: June 17, 2012
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Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics
Xiuqiong Zhang1,2,3, Fujian Zheng1,2,3, Chunxia Zhao1,2,3
1CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian116023, P. R. China.
Analytical Chemistry
|December 6, 2022
Summary
A new deep annotation method for plant glycosides was developed using mass spectrometry. This approach enhances the identification of diverse glycosides in maize, improving accuracy and reliability in plant metabolomics.
Area of Science:
- Plant metabolomics
- Analytical chemistry
- Biochemistry
Background:
- Glycosides are crucial plant metabolites, but their structural complexity and lack of standards hinder comprehensive annotation.
- Accurate identification of glycosides is essential for understanding plant growth, development, and biochemical pathways.
Purpose of the Study:
- To develop a deep annotation method for plant glycosides using untargeted liquid chromatography-high-resolution tandem mass spectrometry (LC-MS/MS).
- To improve the accuracy and reliability of glycoside identification in complex plant metabolomes, using maize as a model system.
Main Methods:
- Construction of knowledge-based in silico libraries for aglycones and glycosyl/acyl-glycosyl moieties.
- Optimization of MS/MS parameters for rich aglycone ion generation and development of screening rules.
- Filtering glycoside candidates using MS/MS-based chemical classification, aglycon-glycoside pair similarity, and fragmentation patterns for glycosylation site determination.
Main Results:
- A total of 1240 known and potential aglycones were compiled.
- The developed method achieved high annotation accuracy (up to 98.0%) and specificity (up to 99.6%) across different ionization modes.
- 274 glycosides, including 34 acyl-glycosides, were tentatively annotated in maize.
Conclusions:
- The proposed deep annotation method enables effective and reliable identification of plant glycosides.
- This approach significantly advances the comprehensive analysis of glycosides in plant metabolomics studies.
