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

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 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.