Multi-omics and chemical profiling approaches to understand the material foundation and pharmacological mechanism of sophorae tonkinensis radix et rhizome-induced liver injury in mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Sophorae tonkinensis Radix et Rhizoma (STR) is an extensively applied traditional Chinese medicine (TCM) in southwest China. However, its clinical application is relatively limited due to its hepatotoxicity effects. AIM OF THE STUDY: To understand the material foundation and liver injury mechanism of STR. MATERIALS AND METHODS: Chemical compositions in STR and its prototypes in mice were profiled by ultra-performance liquid chromatography coupled quadrupole-time of flight mass spectrometry (UPLC-Q/TOF MS). STR-induced liver injury (SILI) was comprehensively evaluated by STR-treated mice mode. The histopathologic and biochemical analyses were performed to evaluate liver injury levels. Subsequently, network pharmacology and multi-omics were used to analyze the potential mechanism of SILI in vivo. And the target genes were further verified by Western blot. RESULTS: A total of 152 compounds were identified or tentatively characterized in STR, including 29 alkaloids, 21 organic acids, 75 flavonoids, 1 quinone, and 26 other types. Among them, 19 components were presented in STR-medicated serum. The histopathologic and biochemical analysis revealed that hepatic injury occurred after 4 weeks of intragastric administration of STR. Network pharmacology analysis revealed that IL6, TNF, STAT3, etc. were the main core targets, and the bile secretion might play a key role in SILI. The metabolic pathways such as taurine and hypotaurine metabolism, purine metabolism, and vitamin B6 metabolism were identified in the STR exposed groups. Among them, taurine, hypotaurine, hypoxanthine, pyridoxal, and 4-pyridoxate were selected based on their high impact value and potential biological function in the process of liver injury post STR treatment. CONCLUSIONS: The mechanism and material foundation of SILI were revealed and profiled by a multi-omics strategy combined with network pharmacology and chemical profiling. Meanwhile, new insights were taken into understand the pathological mechanism of SILI.

Integrative Analysis of Transcriptomic and Metabolomic Data for Identification of Pathways Related to Matrine-Induced Hepatotoxicity.

Matrine (MT) is a major bioactive compound extracted from Sophorae tonkinensis. However, the clinical application of MT is relatively restricted due to its potentially toxic effects, especially hepatotoxicity. Although MT-induced liver injury has been reported, little is known about the underlying molecular mechanisms. In this study, transcriptomics and metabolomics were applied to investigate the hepatotoxicity of MT in mice. The results indicated that liver injury occurred when the administration of MT (30 or 60 mg/kg, i.g) lasted for 2 weeks, including dramatically increased alanine aminotransferase (ALT), aspartate aminotransferase (AST), etc. The metabolomic results revealed that steroid biosynthesis, purine metabolism, glutathione metabolism, and pyruvate metabolism were involved in the occurrence and development of MT-induced hepatotoxicity. Further, the transcriptomic data indicated that the downregulation of NSDHL with CYP51, FDFT1, and DHCR7, involved in steroid biosynthesis, resulted in a lower level of cholic acid. Besides, Gstps and Nat8f1 were related to the disorder of glutathione metabolism, and HMGCS1 could be treated as the marker gene of the development of MT-induced hepatotoxicity. In addition, other metabolites, such as taurine, flavin mononucleotide (FMN), and inosine monophosphate (IMP), also made a contribution to the boosting of MT-induced hepatotoxicity. In this work, our results provide clues for the mechanism investigation of MT-induced hepatotoxicity, and several biomarkers (metabolites and genes) closely related to the liver injury caused by MT are also provided. Meanwhile, new insights into the understanding of the development of MT-induced hepatotoxicity or other monomer-induced hepatotoxicity were also provided.

Spectrum-Effect Relationship Analysis of Bioactive Compounds in Zanthoxylum nitidum (Roxb.) DC. by Ultra-High Performance Liquid Chromatography Mass Spectrometry Coupled With Comprehensive Filtering Approaches.

Zanthoxylum nitidum (Roxb.) DC. (ZN), with strong effects of anti-inflammation and antioxidant activities is treated as a core herb in traditional Chinese medicine (TCM) preparation for treating stomachache, toothache, and rheumatoid arthritis. However, the active ingredients of ZN are not fully clarified due to its chemical complexity. In the present study, a double spectrum-effect analysis strategy was developed and applied to explore the bioactive components in herbs, and ZN was used as an example. Here, the chemical components in ZN were rapidly and comprehensively profiled based on the mass defect filtering-based structure classification (MDFSC) and diagnostic fragment-ion-based extension approaches. Furthermore, the fingerprints of 20 batches of ZN samples were analyzed by high-performance liquid chromatography, and the anti-inflammatory and antioxidant activities of the 20 batches of ZN samples were studied. Finally, the partial least squares regression (PLSR), gray relational analysis models, and Spearman's rank correlation coefficient (SRCC) were applied to discover the bioactive compounds in ZN. As a result, a total of 48 compounds were identified or tentatively characterized in ZN, including 35 alkaloids, seven coumarins, three phenolic acids, two flavonoids, and one lignan. The results achieved by three prediction models indicated that peaks 4, 12, and 17 were the potential anti-inflammatory compounds in ZN, whereas peaks 3, 5, 7, 12, and 13 were involved in the antioxidant activity. Among them, peaks 4, 5, 7, and 12 were identified as nitidine, chelerythrine, hesperidin, and oxynitidine by comparison with the standards and other references. The data in the current study achieved by double spectrum-effect analysis strategy had great importance to improve the quality standardization of ZN, and the method might be an efficiency tool for the discovery of active components in a complex system, such as TCMs.