AUTHOR=Li Xinyue , Liang ChenRui , Su Rui , Wang Xiang , Yao Yaqi , Ding Haoran , Zhou Guanru , Luo Zhanglong , Zhang Han , Li Yubo 

TITLE=An integrated strategy combining metabolomics and machine learning for the evaluation of bioactive markers that differentiate various bile

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2022.1005843

DOI=10.3389/fchem.2022.1005843

ISSN=2296-2646

ABSTRACT=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, which 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. And evaluate the biological activity of key feature markers from various animal bile. In this study, a strategy combined with 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 level of GCDCA and TDCA were highest in pig bile; GCA and CA are the most abundant in bovine and sheep bile respectively. In addition, four key feature markers significantly inhibited 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.