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Study of the mechanism of Baizhu-Huangqi-Zhishi Formula in the treatment of constipation in Parkinson disease based on network pharmacology and its experimental verification |
BAO Zhongming1,2 WEN Daiwei3 LU Pingang4 HUANG Wubiao1 TANG Guowen1 |
1.Department of Encephalopathy, Traditional Chinese Medicine Hospital of Wuzhou City, Guangxi Zhuang Autonomous Region, Wuzhou 543002, China; 2.Department of Neurology, the Sixth Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi Zhuang Autonomous Region, Wuzhou 543002, China;
3.Department of Spleen and Stomach, Traditional Chinese Medicine Hospital of Wuzhou City, Guangxi Zhuang Autonomous Region, Wuzhou 543002, China; 4.Department of Encephalopathy, Traditional Chinese Medicine Hospital of Wuzhou City, Guangxi Zhuang Autonomous Region, Wuzhou 543002, China |
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Abstract Objective To explore the mechanism of Baizhu-Huangqi-Zhishi Formula in the treatment of constipation in Parkinson disease based on network pharmacology and to conduct its experimental verification. Methods The main active components of Rhizoma Atractylodis Macrocephalae, Radix Astragali seu Hedysari and Fructus Aurantii Immaturus were screened by network pharmacology, and the functional analysis and signal pathway analysis were carried out to analyze the molecular mechanism of the active compound-target network in treating constipation in Parkinson disease. Twenty healthy male SD rats aged six weeks were selected, and 15 of them were double injected with 6-hydroxydopamine to establish the model of Parkinson disease. After success, the constipation model of Parkinson disease was established by gavage with Loperamide 3 mg/kg. After successful modeling, the rats were divided into model group, positive control group and experimental group according to random number table method, with five rats in each group and other five rats as the normal group. The experimental group was given 10 ml/kg concentrated solution of Baizhu-Huangqi-Zhishi Formula by gavage. The positive control group was given Metobar 50 mg/kg and polyethylene glycol 1 mg/kg by gavage. The normal group and the model group were given the same amount of normal saline, once a day, for two weeks. Two weeks later, the gene and protein expressions of mitogen-activation protein kinase 8 (MAPK8), interleukin-6 (IL-6), MYC, and cysteine aspartic acid specific protease 3 (caspase 3) in colon of the four groups were detected by quantitative real-time PCR (qRT-PCR) and Western blot. Results Through database and literature screening, a total of 50 active molecules were obtained from Rhizoma Atractylodis Macrocephalae, Radix Astragali seu Hedysari and Fructus Aurantii Immaturus, and 116 action targets were identified, 85 of which were constipation in Parkinson disease. The main nodes of protein-protein interaction network were MAPK8, epidermal growth factor receptor, Cyclin D1, IL-6, MYC, and vascular endothelial growth factor A, etc. Functional analysis and signal pathway analysis showed that Baizhu-Huangqi-Zhishi Formula was involved in constipation in Parkinson disease mainly through biological processes such as ubiquitin-like protein ligase binding and ubiquitin protein ligase binding. At the same time, it may be possible to treat constipation in Parkinson disease through PI3K-Akt, MAPK, neurodegeneration-multiple diseases and other signaling pathways. The results of qRT-PCR and Western blot showed that the expression levels of MAPK8 gene and protein in the model group were lower than those in the normal group, and the expression levels of IL-6, MYC, caspase 3 gene and protein in the model group were higher than those in the normal group. The expression levels of MAPK8 gene and protein in the experimental group were higher than those in the model group, and the expression levels of IL-6, MYC, caspase 3 gene and protein in the experimental group were lower than that in the model group, with statistical significance (all P < 0.05). Conclusion Baizhu-Huangqi-Zhishi Formula can achieve the therapeutic effect constipation in Parkinson disease well through multiple targets and multiple pathways.
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