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 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.

Rapid classification and identification of chemical components of Astragali radix by UPLC-Q-TOF-MS.

INTRODUCTION: Pharmacological studies indicate that Astragalus (AR) has various bioactivities, including anticancer, antiaging, anti-inflammatory, antiviral, and antioxidant activities. Flavonoids, saponins, amino acids, and polysaccharides are the main active components in AR. However, its complex chemical compositions bring certain difficulties to the analysis of this traditional Chinese medicine (TCM). Therefore, there is an urgent need to establish a method for rapid classification and identification of the chemical constituents in AR. OBJECTIVE: To establish a method for rapid classification and identification of the main components of flavonoids, saponins, and amino acids in AR. METHODS: The samples were analysed with ultra-high-performance liquid chromatography time-of-flight quadrupole mass spectrometry (UPLC-Q-TOF-MS) and data post-processing techniques. Firstly, fragmentation information was obtained in the positive and negative ion modes. Then, to realize the rapid classification and identification of AR components, the characteristic fragmentations (CFs) and neutral losses (NLs) were compared with information described in the literature. RESULTS: A total of 45 chemical constituents were successfully screened out, including 22 flavonoids, 13 saponins, and 10 amino acids. CONCLUSION: The established method realised the efficient classification and identification of flavonoids, saponins, and amino acid compounds in AR, which provided a basis for further study on AR.

A new strategy for the rapid identification and validation of direct toxicity targets of psoralen-induced hepatotoxicity.

The interaction between small-molecule compounds of traditional Chinese medicine and their direct targets is the molecular initiation event, which is the key factor for toxicity efficacy. Psoralen, an active component of Fructus Psoraleae, is toxic to the liver and has various pharmacological properties. Although the mechanism of psoralen-induced hepatotoxicity has been studied, the direct target of psoralen remains unclear. Thus, the aim of this study was to discover direct targets of psoralen. To this end, we initially used proteomics based on drug affinity responsive target stability (DARTS) technology to identify the direct targets of psoralen. Next, we used surface plasmon resonance (SPR) analysis and verified the affinity effect of the 'component-target protein'. This method combines molecular docking technology to explore binding sites between small molecules and proteins. SPR and molecular docking confirmed that psoralen and tyrosine-protein kinase ABL1 could be stably combined. Based on the above experimental results, ABL1 is a potential direct target of psoralen-induced hepatotoxicity. Finally, the targets Nrf2 and mTOR, which are closely related to the hepatotoxicity caused by psoralen, were predicted by integrating proteomics and network pharmacology. The direct target ABL1 is located upstream of Nrf2 and mTOR, Nrf2 can influence the expression of mTOR by affecting the level of reactive oxygen species. Immunofluorescence experiments and western blot results showed that psoralen could affect ROS levels and downstream Nrf2 and mTOR protein changes, whereas the ABL1 inhibitor imatinib and ABL1 agonist DPH could enhance or inhibit this effect. In summary, we speculated that when psoralen causes hepatotoxicity, it acts on the direct target ABL1, resulting in a decrease in Nrf2 expression, an increase in ROS levels and a reduction in mTOR expression, which may cause cell death. We developed a new strategy for predicting and validating the direct targets of psoralen. This strategy identified the toxic target, ABL1, and the potential toxic mechanism of psoralen.

Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources.

In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.