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The metabolic diversity of plants, comprising over a million different metabolites across the plant kingdom, harbors enormous potential for pharmaceutical and biotechnological applications. Resin glycoside (RG) acylsugars from the Convolvulaceae are of interest due to their medicinal and agricultural potential. However, understanding the biological relevance of RGs is challenging as they exhibit a high lineage-specific structural diversity. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) coupled with computational peak annotation can provide insights into this diversity. Here, we present a comprehensive protocol for the characterization of RG diversity using a sensitive LC-MS/MS instrument, a knowledge-based computational pipeline, and a web tool for peak annotation. The described experimental approach provides a step-by-step guide for RG sampling, extraction, purification for downstream analyses such as bioassays, and structural annotation using LC-MS/MS and computational metabolomics. The protocol focuses on qualitative analysis for putative annotation (Annotation Level 2 as defined by the Metabolomics Standards Initiative) of RGs and can serve as a valuable template for researchers exploring plant metabolic diversity beyond RGs and acylsugars.

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Extraction, Annotation, and Purification of Resin Glycosides from the Morning Glory Family (Convolvulaceae)

  • 实验方案

  • 发表日期：

    2025年

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  • Lars H. Kruse,
  • Alexandra A. Bennett,
  • Vishwa J. Baruah,
  • Mohammad Irfan,
  • Gaurav D. Moghe

摘要

The metabolic diversity of plants, comprising over a million different metabolites across the plant kingdom, harbors enormous potential for pharmaceutical and biotechnological applications. Resin glycoside (RG) acylsugars from the Convolvulaceae are of interest due to their medicinal and agricultural potential. However, understanding the biological relevance of RGs is challenging as they exhibit a high lineage-specific structural diversity. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) coupled with computational peak annotation can provide insights into this diversity. Here, we present a comprehensive protocol for the characterization of RG diversity using a sensitive LC-MS/MS instrument, a knowledge-based computational pipeline, and a web tool for peak annotation. The described experimental approach provides a step-by-step guide for RG sampling, extraction, purification for downstream analyses such as bioassays, and structural annotation using LC-MS/MS and computational metabolomics. The protocol focuses on qualitative analysis for putative annotation (Annotation Level 2 as defined by the Metabolomics Standards Initiative) of RGs and can serve as a valuable template for researchers exploring plant metabolic diversity beyond RGs and acylsugars.