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Keywords

LC-MS/MS; Medicinal property; Metabolome mining; Molecular networking; Traditional Chinese medicine

Abstract

Traditional Chinese medicine (TCM) has been applied to improve human health for millennia. In the TCM system, “medicinal property” (yao xing; hot and cold properties) is a core concept used to describe the influences of medicinal materials on human physiological conditions, and metabolites are believed to be one of the major ingredients of TCMs that affect their medicinal property. However, due to a lack of comprehensive analyses of TCM metabolomes, information about the relationships between TCM metabolite composition and medicinal property remains limited. In this pilot study, a mass spectral molecular networking-based platform was established and applied to systematically profile the metabolome of 24 TCMs with various medicinal properties. The molecular networks were built based on the liquid chromatography-tandem mass spectrometry (LC-MS/MS) data from 50% EtOH extracts of 24 TCMs. The results showed that various classes of metabolites were clustered in the molecular networks, and the potential medicinal property-associated molecular families were filtered by screening the medicinal property and the diversity of TCM sources. For example, some specific types of flavonoids were identified in the representative cold-property (han xing) molecular families. In contrast, due to the limited sample size, the representative and universal hot-property (re xing) molecular family has not been well revealed. In summary, this study provides methodology and information on the potential relationships between the metabolite composition and the concept of medicinal property in TCM. Furthermore, the results can serve as a foundation for mass spectral molecular networking-based analysis of TCM metabolomes, facilitating TCM research and development.

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Research highlights_6.pdf (67 kB)
Research highlights_6

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Graphical Abstract_6

Supporting information.pdf (920 kB)
Supporting information

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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