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The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula
BACKGROUND: It is essential to identify the chemical components for the quality control methods establishment of Chinese Classical Formula (CCF). However, CCF are complex mixture of several herbal medicines with huge number of different compounds and they are not equal to the combination of chemical...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254261/ https://www.ncbi.nlm.nih.gov/pubmed/34215302 http://dx.doi.org/10.1186/s13020-021-00459-6 |
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author | Xue, Xiaoxia Jiao, Qishu Jin, Runa Wang, Xueyuan Li, Pengyue Shi, Shougang Huang, Zhengjun Dai, Yuntao Chen, Shilin |
author_facet | Xue, Xiaoxia Jiao, Qishu Jin, Runa Wang, Xueyuan Li, Pengyue Shi, Shougang Huang, Zhengjun Dai, Yuntao Chen, Shilin |
author_sort | Xue, Xiaoxia |
collection | PubMed |
description | BACKGROUND: It is essential to identify the chemical components for the quality control methods establishment of Chinese Classical Formula (CCF). However, CCF are complex mixture of several herbal medicines with huge number of different compounds and they are not equal to the combination of chemical components from each herb due to particular formula ratio and preparation techniques. Therefore, it is time-consuming to identify compounds in a CCF by analyzing the LC–MS/MS data one by one, especially for unknown components. METHODS: An ultra-high pressure liquid chromatography-linear ion trap-orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS) approach was developed to comprehensively profile and characterize multi-components in CCF with Erdong decoction composed of eight herbal medicines as an example. Then the MS data of Erdong decoction was analyzed by MS/MS-based molecular networking and these compounds with similar structures were connected to each other into a cluster in the network map. Then the unknown compounds connected to known compounds in a cluster of the network map were identified due to their similar structures. RESULTS: Based on the clusters of the molecular networking, 113 compounds were rapidly tentative identification from Erdong decoction for the first time in the negative mode, which including steroidal saponins, triterpenoid saponins, flavonoid O-glycosides and flavonoid C-glycosides. In addition, 10 alkaloids were tentatively identified in the positive mode from Nelumbinis folium by comparison with literatures. CONCLUSION: MS/MS-based molecular networking technique is very useful for the rapid identification of components in CCF. In Erdong decoction, this method was very suitable for the identification of major steroidal saponins, triterpenoid saponins, and flavonoid C-glycosides. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-021-00459-6. |
format | Online Article Text |
id | pubmed-8254261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82542612021-07-06 The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula Xue, Xiaoxia Jiao, Qishu Jin, Runa Wang, Xueyuan Li, Pengyue Shi, Shougang Huang, Zhengjun Dai, Yuntao Chen, Shilin Chin Med Research BACKGROUND: It is essential to identify the chemical components for the quality control methods establishment of Chinese Classical Formula (CCF). However, CCF are complex mixture of several herbal medicines with huge number of different compounds and they are not equal to the combination of chemical components from each herb due to particular formula ratio and preparation techniques. Therefore, it is time-consuming to identify compounds in a CCF by analyzing the LC–MS/MS data one by one, especially for unknown components. METHODS: An ultra-high pressure liquid chromatography-linear ion trap-orbitrap high resolution mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS) approach was developed to comprehensively profile and characterize multi-components in CCF with Erdong decoction composed of eight herbal medicines as an example. Then the MS data of Erdong decoction was analyzed by MS/MS-based molecular networking and these compounds with similar structures were connected to each other into a cluster in the network map. Then the unknown compounds connected to known compounds in a cluster of the network map were identified due to their similar structures. RESULTS: Based on the clusters of the molecular networking, 113 compounds were rapidly tentative identification from Erdong decoction for the first time in the negative mode, which including steroidal saponins, triterpenoid saponins, flavonoid O-glycosides and flavonoid C-glycosides. In addition, 10 alkaloids were tentatively identified in the positive mode from Nelumbinis folium by comparison with literatures. CONCLUSION: MS/MS-based molecular networking technique is very useful for the rapid identification of components in CCF. In Erdong decoction, this method was very suitable for the identification of major steroidal saponins, triterpenoid saponins, and flavonoid C-glycosides. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13020-021-00459-6. BioMed Central 2021-07-02 /pmc/articles/PMC8254261/ /pubmed/34215302 http://dx.doi.org/10.1186/s13020-021-00459-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xue, Xiaoxia Jiao, Qishu Jin, Runa Wang, Xueyuan Li, Pengyue Shi, Shougang Huang, Zhengjun Dai, Yuntao Chen, Shilin The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title | The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title_full | The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title_fullStr | The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title_full_unstemmed | The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title_short | The combination of UHPLC-HRMS and molecular networking improving discovery efficiency of chemical components in Chinese Classical Formula |
title_sort | combination of uhplc-hrms and molecular networking improving discovery efficiency of chemical components in chinese classical formula |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254261/ https://www.ncbi.nlm.nih.gov/pubmed/34215302 http://dx.doi.org/10.1186/s13020-021-00459-6 |
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