小桶
系统药理学
计算生物学
对接(动物)
自动停靠
活性成分
交互网络
计算机科学
人工智能
机器学习
药理学
化学
基因本体论
生物
生物化学
医学
基因
生物信息学
基因表达
护理部
药品
作者
Meiling Jin,Wenwen Ren,Weiguang Zhang,Linchang Liu,Zhiwei Yin,Diangeng Li
摘要
This study aimed to explore the underlying mechanisms of Shenyankangfu tablet (SYKFT) in the treatment of glomerulonephritis (GN) based on network pharmacology, machine learning, molecular docking, and experimental validation.The active ingredients and potential targets of SYKFT were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, the targets of GN were obtained through GeneCards, etc. Perl and Cytoscape were used to construct an herb-active ingredient-target network. Then, the clusterProfiler package of R was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We also used the STRING platform and Cytoscape to construct a protein-protein interaction (PPI) network, as well as the SwissTargetPrediction server to predict the target protein of the core active ingredient based on machine-learning model. Molecular-docking analysis was further performed using AutoDock Vina and Pymol. Finally, we verified the effect of SYKFT on GN in vivo.A total of 154 active ingredients and 255 targets in SYKFT were screened, and 135 targets were identified to be related to GN. GO enrichment analysis indicated that biological processes were primarily associated with oxidative stress and cell proliferation. KEGG pathway analysis showed that these targets were involved mostly in infection-related and GN-related pathways. PPI network analysis identified 13 core targets of SYKFT. Results of machine-learning model suggested that STAT3 and AKT1 may be the key target. Results of molecular docking suggested that the main active components of SYKFT can be combined with various target proteins. In vivo experiments confirmed that SYKFT may alleviate renal pathological injury by regulating core genes, thereby reducing urinary protein.This study demonstrated for the first time the multicomponent, multitarget, and multipathway characteristics of SYKFT for GN treatment.
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