Study on The Toxicity of Strychnos nux-vomica L. in vivo in Rats:Application of Bagging Algorithm and 16S rRNA Gene Sequencing Technology in Toxicology Research

PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS(2024)

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摘要
Objective The traditional Chinese medicine Strychnos nux-vomica L. (SN) has the clinical effect of reducing swelling and relieving pain; however, SN is toxic due to its alkaloid components. Little is known about the endogenous metabolic changes induced by SN toxicity in rats and their potential effects on the metabolic dysregulation of intestinal microbiota. Therefore, toxicological investigation of SN is of great significance to its safety assessment. In this study, the toxic mechanisms of SN were explored using a combination of metabonomics and 16S rRNA gene sequencing. Methods The toxic dose, intensity, and target organ of SN were determined in rats using acute, cumulative, and subacute toxicity tests. UHPLC-MS was used to analyze the serum, liver, and renal samples of rats after intragastric SN administration. The decision tree and K Nearest Neighbor (KNN) model were established based on the bootstrap aggregation (bagging) algorithm to classify the omics data. After samples were extracted from rat feces, the high -throughput sequencing platform was used to analyze the 16S rRNA V3 -V4 region of bacteria. Results The bagging algorithm improved the accuracy of sample classification. Twelve biomarkers were identified, where their metabolic dysregulation may be responsible for SN toxicity in vivo. Several types of bacteria such as Bacteroidetes, Anaerostipes, Oscillospira and Bilophila, were demonstrated to be closely related to physiological indices of renal and liver function, indicating that SN-induced liver and kidney damage may be related to the disturbance of these intestinal bacteria. Conclusion The toxicity mechanism of SN was revealed in vivo, which provides a scientific basis for the safe and rational clinical use of SN.
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关键词
Strychnosnux-vomica L.,toxic mechanism,metabonomics,intestinal flora,bagging algorithm
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