Tong K, Li ZL, Sun X, Yan S, Jiang MJ, Deng MS, Chen J, Li JW, Tian ML. Metabolomics approach reveals annual metabolic variation in roots of
Cyathula officinalis Kuan based on gas chromatography-mass spectrum.
Chin Med 2017;
12:12. [PMID:
28469699 PMCID:
PMC5414129 DOI:
10.1186/s13020-017-0133-1]
[Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Accepted: 04/18/2017] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND
Herbal quality is strongly influenced by harvest time. It is therefore one of crucial factors that should be well respected by herbal producers when optimizing cultivation techniques, so that to obtain herbal products of high quality. In this work, we paid attention on one of common used Chinese herbals, Cyathula officinalis Kuan. According to previous studies, its quality may be related with growth years because of the variation of several main bioactive components in different growth years. However, information about the whole chemical composition is still scarce, which may jointly determine the herbal quality.
METHODS
Cyathula officinalis samples were collected in 1-4 growth years after sowing. To obtain a global insight on chemical profile of herbs, we applied a metabolomics approach based on gas chromatography-mass spectrum. Analysis of variance, principal component analysis, partial least squares discriminant analysis and hierarchical cluster analysis were combined to explore the significant difference in different growth years.
RESULTS
166 metabolites were identified by using gas chromatography-mass spectrum method. 63 metabolites showed significant change in different growth years in terms of analysis of variance. Those metabolites then were grouped into 4 classes by hierarchical cluster analysis, characterizing the samples of different growth ages. Samples harvested in the earliest years (1-2) were obviously differ with the latest years (3-4) as reported by principal component analysis. Further, partial least squares discriminant analysis revealed the detail difference in each growth year. Gluconic acid, xylitol, glutaric acid, pipecolinic acid, ribonic acid, mannose, oxalic acid, digalacturonic acid, lactic acid, 2-deoxyerythritol, acetol, 3-hydroxybutyric acid, citramalic acid, N-carbamylglutamate, and cellobiose are the main 15 discrimination metabolites between different growth years.
CONCLUSION
Harvest time should be well considered when producing C. officinalis. In order to boost the consistency of herbal quality, C. officinalis is recommended to harvest in 4th growth year. The method of GC-MS combined with multivariate analysis was a powerful tool to evaluate the herbal quality.
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