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Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum

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...

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Autores principales: Tong, Kai, Li, Zhao-ling, Sun, Xu, Yan, Shen, Jiang, Mei-jie, Deng, Meng-sheng, Chen, Ji, Li, Jing-wei, Tian, Meng-liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414129/
https://www.ncbi.nlm.nih.gov/pubmed/28469699
http://dx.doi.org/10.1186/s13020-017-0133-1
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author Tong, Kai
Li, Zhao-ling
Sun, Xu
Yan, Shen
Jiang, Mei-jie
Deng, Meng-sheng
Chen, Ji
Li, Jing-wei
Tian, Meng-liang
author_facet Tong, Kai
Li, Zhao-ling
Sun, Xu
Yan, Shen
Jiang, Mei-jie
Deng, Meng-sheng
Chen, Ji
Li, Jing-wei
Tian, Meng-liang
author_sort Tong, Kai
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13020-017-0133-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-54141292017-05-03 Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum Tong, Kai Li, Zhao-ling Sun, Xu Yan, Shen Jiang, Mei-jie Deng, Meng-sheng Chen, Ji Li, Jing-wei Tian, Meng-liang Chin Med Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13020-017-0133-1) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-03 /pmc/articles/PMC5414129/ /pubmed/28469699 http://dx.doi.org/10.1186/s13020-017-0133-1 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Tong, Kai
Li, Zhao-ling
Sun, Xu
Yan, Shen
Jiang, Mei-jie
Deng, Meng-sheng
Chen, Ji
Li, Jing-wei
Tian, Meng-liang
Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title_full Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title_fullStr Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title_full_unstemmed Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title_short Metabolomics approach reveals annual metabolic variation in roots of Cyathula officinalis Kuan based on gas chromatography–mass spectrum
title_sort metabolomics approach reveals annual metabolic variation in roots of cyathula officinalis kuan based on gas chromatography–mass spectrum
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5414129/
https://www.ncbi.nlm.nih.gov/pubmed/28469699
http://dx.doi.org/10.1186/s13020-017-0133-1
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