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An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile
Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioact...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627196/ https://www.ncbi.nlm.nih.gov/pubmed/36339047 http://dx.doi.org/10.3389/fchem.2022.1005843 |
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author | Li, Xinyue Liang, ChenRui Su, Rui Wang, Xiang Yao, Yaqi Ding, Haoran Zhou, Guanru Luo, Zhanglong Zhang, Han Li, Yubo |
author_facet | Li, Xinyue Liang, ChenRui Su, Rui Wang, Xiang Yao, Yaqi Ding, Haoran Zhou, Guanru Luo, Zhanglong Zhang, Han Li, Yubo |
author_sort | Li, Xinyue |
collection | PubMed |
description | Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application. |
format | Online Article Text |
id | pubmed-9627196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96271962022-11-03 An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile Li, Xinyue Liang, ChenRui Su, Rui Wang, Xiang Yao, Yaqi Ding, Haoran Zhou, Guanru Luo, Zhanglong Zhang, Han Li, Yubo Front Chem Chemistry Animal bile is an important component of natural medicine and is widely used in clinical treatment. However, it is easy to cause mixed applications during processing, resulting in uneven quality, which seriously affects and harms the interests and health of consumers. Bile acids are the major bioactive constituents of bile and contain a variety of isomeric constituents. Although the components are structurally similar, they exhibit different pharmacological activities. Identifying the characteristics of each animal bile is particularly important for processing and reuse. It is necessary to establish an accurate analysis method to distinguish different types of animal bile. We evaluated the biological activity of key feature markers from various animal bile samples. In this study, a strategy combining metabolomics and machine learning was used to compare the bile of three different animals, and four key markers were screened. Quantitative analysis of the key markers showed that the levels of Glycochenodeoxycholic acid (GCDCA) and Taurodeoxycholic acid (TDCA) were highest in pig bile; Glycocholic acid (GCA) and Cholic acid (CA) were the most abundant in bovine and sheep bile, respectively. In addition, four key feature markers significantly inhibited the production of NO in LPS-stimulated RAW264.7 macrophage cells. These findings will contribute to the targeted development of bile in various animals and provide a basis for its rational application. Frontiers Media S.A. 2022-10-19 /pmc/articles/PMC9627196/ /pubmed/36339047 http://dx.doi.org/10.3389/fchem.2022.1005843 Text en Copyright © 2022 Li, Liang, Su, Wang, Yao, Ding, Zhou, Luo, Zhang and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Chemistry Li, Xinyue Liang, ChenRui Su, Rui Wang, Xiang Yao, Yaqi Ding, Haoran Zhou, Guanru Luo, Zhanglong Zhang, Han Li, Yubo An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title | An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title_full | An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title_fullStr | An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title_full_unstemmed | An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title_short | An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
title_sort | integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9627196/ https://www.ncbi.nlm.nih.gov/pubmed/36339047 http://dx.doi.org/10.3389/fchem.2022.1005843 |
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