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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2022.1005843/full |
| _version_ | 1797990606330396672 |
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| author | Xinyue Li Xinyue Li ChenRui Liang Rui Su Rui Su Xiang Wang Xiang Wang Yaqi Yao Haoran Ding Guanru Zhou Zhanglong Luo Han Zhang Han Zhang Yubo Li |
| author_facet | Xinyue Li Xinyue Li ChenRui Liang Rui Su Rui Su Xiang Wang Xiang Wang Yaqi Yao Haoran Ding Guanru Zhou Zhanglong Luo Han Zhang Han Zhang Yubo Li |
| author_sort | Xinyue Li |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T08:39:15Z |
| format | Article |
| id | doaj.art-da046c499dcf46d58466645f558559a0 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-04-11T08:39:15Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-da046c499dcf46d58466645f558559a02022-12-22T04:34:15ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462022-10-011010.3389/fchem.2022.10058431005843An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bileXinyue Li0Xinyue Li1ChenRui Liang2Rui Su3Rui Su4Xiang Wang5Xiang Wang6Yaqi Yao7Haoran Ding8Guanru Zhou9Zhanglong Luo10Han Zhang11Han Zhang12Yubo Li13State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaPharmacy Faculty, Hubei University of Chinese Medicine, Wuhan, Hubei, ChinaCollege of Chemistry and Molecular Sciences, Wuhan University, Wuhan, Hubei, ChinaState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaKey Laboratory of Pharmacology of Traditional Chinese Medicine Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaSchool of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, ChinaAnimal 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.https://www.frontiersin.org/articles/10.3389/fchem.2022.1005843/fullbile acidskey markersmachine learninganti-inflammatory activitymetabolomics |
| spellingShingle | Xinyue Li Xinyue Li ChenRui Liang Rui Su Rui Su Xiang Wang Xiang Wang Yaqi Yao Haoran Ding Guanru Zhou Zhanglong Luo Han Zhang Han Zhang Yubo Li An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile Frontiers in Chemistry bile acids key markers machine learning anti-inflammatory activity metabolomics |
| 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 | bile acids key markers machine learning anti-inflammatory activity metabolomics |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2022.1005843/full |
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