| Objective: Pachymic acid(PA),a bioactive ingredient isolated from Poria cocos Wolf(PCW),is reported with potential benefits of anti-inflammatory,anti-oxidative actions.Cystitis glandularis(CG)is an inflammation of the bladder tissue resulting from pathologic changes in normal urothelial metaplasia of the bladder due to chronic irritation,including bladder infection,obstruction,and stones.It is reasoned that PA may play the potential benefits against CG,an inflammation of the bladder tissue.In this study,we aimed to apply the network pharmacology and molecular docking analyses to reveal concrete anti-CG targets and mechanisms of PA,and then the bioinformatic findings were verified by using clinical and animal samples.Methods: Firstly,screening and collecting targets of PA were completed through Swiss Target Prediction,BATMAN,and HITPICK databases,and then CG targets were screened out by using Dis Ge NET,Genecards,and Malacards databases.The intersected targets of PA and CG were identified and displayed in Venn diagram.Secondly,based on the cross targets of PA and CG,the functional protein association network was constructed by using STRING database,and the protein-protein interaction(PPI)network map was drawn by Cytoscape tool.All the core targets of PA to CG were determined by the algorithm method of Cytoscape software,and the core targets were visualized.Thirdly,the structure of PA compound was obtained from Pub Chem database,and the corresponding protein structure was obtained from PDB database.The binding ability of PA and CG core targets was identified by molecular docking method.Fourthly,R-language packages included “Cluster Profiler” “Reactome PA”“Annotation Hub” was used to perform gene ontology(GO),biological process(BP),and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis using core targets.Fifthly,the findings of GO,BP,and KEGG in core targets of PA against CG were used to construct visual graphics of drug-target-GO function-pathway-disease by using Cytoscape software and pathview package in R-language.Sixthly,4 patients with clinically diagnosed CG were selected,and CG samples were cut in situ for storage and preparation for immunofluorescence staining analysis.Seventhly,CG model of ICR mice was established,blood was collected for biochemical tests,and bladder samples were quickly separated and stored for immunostaining.Results: Firstly,the methodological data from network pharmacology approach showed that 303 and 243 reporting targets of CG and PA,and other 31 shared targets of CG and PA were identified.Secondly,subsequently,all top targets of PA against CG were screened out,including cyclooxygenase-2(PTGS2),Epidermal growth factor receptor(EGFR),tumor antigen p53(TP53),tumor necrosis factor-alpha(TNF),Interleukin-1 beta(IL1B),protooncogene c-Jun(JUN).Thirdly,Molecular docking data demonstrated that PA exerted potent bonding capacities with TNF,TP53 proteins in CG.Fourthly,in human study,the findings suggested that over-activated TNF-α expression and suppressed TP53 activation were detected in CG samples.Fifthly,in animal study,PA-treated mice showed reduced intravesical interleukin-1(IL-1),IL-6levels and lactate dehydrogenase(LDH)content,downregulated TNF-α and upregulated TP53 proteins in bladder samples.Conclusions: Firstly,taken together,our bioinformatics and experimental results have identified key anti-CG biological targets and mechanisms of PA.Secondly,more obviously,these key pharmacological targets of PA on CG have been screened out,and molecular docking proteins such as TNF-α and TP53 have been identified as the main pharmacological targets of PA against CG.The results are verified by calculation and experimental analysis. |
