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
10.16476/j.pibb.2023.0044
- VernacularTitle:马钱子致大鼠体内毒性的研究*——“装袋”算法和16S rRNA基因测序技术在毒理学研究中的应用
- Author:
Xi-Ye WANG
1
;
Le-Er BAO
2
;
Ming-Yang JIANG
3
;
Dan LI
1
;
Mei-Rong BAI
4
Author Information
1. College of Chemistry and Materials Science, Inner Mongolia Minzu University, Tongliao 028000, China
2. Inner Mongolia Autonomous Region Drug Inspection Center, Hohhot 010000, China
3. College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China
4. Key Laboratory of Mongolian Medical Research and Development Project of Ministry of Education, Inner Mongolia Minzu University, Tongliao 028000, China
- Publication Type:Journal Article
- Keywords:
Strychnosnux-vomica L.;
toxic mechanism;
metabonomics;
intestinal flora;
bagging algorithm
- From:
Progress in Biochemistry and Biophysics
2024;51(2):404-422
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveThe 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. MethodsThe 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. ResultsThe 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. ConclusionThe toxicity mechanism of SN was revealed in vivo, which provides a scientific basis for the safe and rational clinical use of SN.
