Research on the potential mechanism of Chuanxiong Rhizoma on treating Diabetic Nephropathy based on network pharmacology
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Background: Chuanxiong Rhizoma is one of the traditional Chinese medicines which have been used for years in the treatment of diabetic nephropathy (DN).However, the mechanism of Chuanxiong Rhizoma in DN has not yet been fully understood.Methods: We performed network pharmacology to construct target proteins interaction network of Chuanxiong Rhizoma.Active ingredients were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.DRUGBANK database was used to predict target proteins of Chuanxiong Rhizoma.Gene ontology (GO) biological process analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed for functional prediction of the target proteins.Molecular docking was applied for evaluating the drug interactions between hub targets and active ingredients.Results: Twenty-eight target genes fished by 6 active ingredients of Chuanxiong Rhizoma were obtained in the study.The top 10 significant GO analyses and 6 KEGG pathways were enriched for genomic analysis.We also acquired 1366 differentially expressed genes associated with DN from GSE30528 dataset, including five target genes: KCNH2, NCOA1, KDR, NR3C2 and ADRB2.Molecular docking analysis successfully combined KCNH2, NCOA1, KDR and ADRB2 to Myricanone with docking scores from 4.61 to 6.28.NR3C2 also displayed good docking scores with Wallichilide and Sitosterol (8.13 and 8.34, respectively), revealing good binding forces to active compounds of Chuanxiong Rhizoma.Conclusions: Chuanxiong Rhizoma might take part in the treatment of DN through pathways associated with steroid hormone, estrogen, thyroid hormone and IL-17.KCNH2, NCOA1, KDR, ADRB2 and NR3C2 were proved to be the hub targets, which were closely related to corresponding active ingredients of Chuanxiong Rhizoma.Keywords:
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In recent years, an increasing number of patients have had diabetes and cancer simultaneously; thus, it is crucial for physicians to select hypoglycemic drugs with the lowest risk of inducing cancer. Gliclazide is a widely used sulfonylurea hypoglycemic drug, but its cancer risk remains controversial. Here, we explored the primary targets of gliclazide and its associated genes by querying an available database to construct a biological network. By using DrugBank and STRING, we found two primary targets of gliclazide and 50 gliclazide-associated genes, which were then enrolled for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using WebGestalt. From this analysis, we obtained the top 15 KEGG pathways. Accurate analysis of these KEGG pathways revealed that two pathways, one linked to bladder cancer and the other linked to the phosphoinositide 3-kinase-AKT signaling pathway, are functionally associated with gliclazide, and from these we identified four overlapping genes. Finally, genomic analysis using cBioPortal showed that genomic alterations of these four overlapping genes predict poor prognosis for patients with bladder cancer. In conclusion, gliclazide should be used with caution as a hypoglycemic drug for diabetic patients with cancer, especially bladder cancer. In addition, this study provides a functional network analysis to flexibly explore drug interaction systems and estimate their safety.
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Purpose: Prescriptions of Han-Shi-Yu-Fei (HSYF), Han-Shi-Zu-Fei (HSZF), and Yi-Du-Bi-Fei (YDBF) were effective in treating COVID-19. Based on network pharmacology and molecular docking, overlapping Traditional Chinese medicines (TCMs), their active components, and core targets were explored in this study. Methods: First, the overlapping TCMs and their active components were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) by evaluating Oral Bioactivity (OB) and Drug Likeness (DL). The overlapping targets of potential components and COVID-19 were collected by SwissTargetPrediction, Gene Cards, and Venn 2.1.0 databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were analyzed via DAVID6.8.1 database. Through comprehensive analysis of the “prescriptions-TCMs-components” (P-T-C), “components-targets-pathways” (C-T-P) and “protein–protein interaction” (PPI) networks constructed by Cytoscape 3.7.1 software, the active components and core targets were obtained. Finally, the binding energies of these components with ACE2 and SARS-CoV-2 3CL were analyzed by AutDockTools-1.5.6 and PyMOL software. Results: In all, five overlapping TCMs, 40 potential active components, and 47 candidate targets were obtained and analyzed in these prescriptions. There were 288 GO entries ( P < 0.05), including 211 biological process (BP), 40 cell composition (CC), and 37 molecular function (MF) entries. Most of the 105 KEGG pathways ( P < 0.05) were involved with viral infection and inflammation. Through “PPI” and “C-T-P” networks, the core targets (EGFR, PTGS2, CDK2, GSK3B, PIK3R1, and MAPK3) and active components (Q27134551, acanthoside B, neohesperidin, and irisolidone) with high degrees were obtained. Molecular docking results showed that the above-mentioned four components could inhibit the binding of ACE2 and SARS-COV-2 3CL to protect against COVID-19. Conclusion: In this study, the active components and core targets of three prescriptions in the treatment of COVID-19 were elaborated by network pharmacology and molecular docking, providing a reference for their applications.
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Objective: The relationships of ‘ingredient-target-pathway’ and hypoglycemic effect of Scutellariae Radix (SD) in the treatment of diabetes were explored using network pharmacology, molecular docking and animal experiments. Methods: SD and its targets were identified using network analysis followed by experimental validation. First, the Tcmsp and Drugbank databases were mined for the targets of SD and diabetes, and the intersection target genes were screened. The key targets and enriched pathways were examined by Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. SD main active components, target genes and KEGG networks were created by Cytoscape software. The correspondence between SD components and targets was verified by molecular docking. Finally, animal experiments were carried out to confirm the hypoglycemic effect of SD. Results: The 22 intersection targets were confirmed for the hypoglycemic effect of SD. GO analysis showed that 18 biological processes, 9 cellular components and 15 molecular functions were identified (P ≤ 0.01). Eighteen related signaling pathways were identified by KEGG analysis (P ≤ 0.05). Molecular docking results indicated that the targets of diabetes bound strongly to the main components of SD. Animal experiments showed that SD could decrease the blood sugar of diabetic rats to normal level. Conclusion: The present study explored the potential targets and signaling pathways of SD on diabetes. The results may help to illustrate the hypoglycemic mechanism (s) of SD.
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Objective. We systematically analyzed the mechanism of plant-derived drugs alleviating cancer pain in our hospital through network pharmacology, so as to provide the possibility of further application of traditional Chinese medicine in the treatment of cancer pain. Methods. We used TCMSP, ETCM, and TCMID databases to mine the active ingredients of plant-derived drugs. We combined OMIM, GeneCards, and DrugBank databases to mine and match the common targets of plant-derived drugs for cancer pain. We used the STRING platform and Cytoscape software to analyze and screen out the core targets. We used GO and KEGG methods to analyze the biological processes, molecular functions, cellular composition, and signaling pathways involved in the reduction of cancer pain by plant-derived drugs. Results. We found 153 active ingredients from botanical drugs by TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TCMSP), ETCM (The Encyclopedia of Traditional Chinese Medicine), and TCMID (Traditional Chinese Medicine Integrated Database) databases, covering 341 protein targets in human body. Combined with OMIM (Online Mendelian Inheritance in Man), GeneCards, and DrugBank databases, we excavated and matched 141 targets of plant-derived drugs and cancerous pain diseases. Through the analysis of the STRING platform and Cytoscape software, 19 core targets including TNF, MAPK1, JUN, and IL-6 were screened out. Go and KEGG enrichment showed that plant-derived drugs alleviated cancer pain processes involving 193 biological processes, 47 molecular functions, 22 cell components, and 118 signaling pathways. By screening genes involved in KEGG signaling pathway, it was found that plant-derived drugs were mainly associated with PI3K-Akt signaling pathway, tumor necrosis factor signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway, and HIF-1 signaling pathway in alleviating cancer pain. Conclusion. These results indicate that botanical drugs can positively affect the expression of inflammatory factors and apoptotic factors in the process of treatment and relief of cancer pain, which is expected to have a potential therapeutic effect on the relief of cancer pain.
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Abstract [Background] Resveratrol is a polyphenol present abundantly in lots of traditional Chinese medicines, which has been shown to have beneficial effects on neurological diseases. However, the molecular mechanisms of resveratrol on traumatic brain injury have not been systematically studied yet. In this study, we elucidated the pharmacological mechanisms of resveratrol in treating traumatic brain injury by using a network pharmacology method. [Methods] The pharmacokinetics properties of resveratrol were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). The putative targets of resveratrol were obtained from TCMSP, BATMAN-TCM, SuperPred, PharmMapper, SwissTargetPrediction, DrugBank, and a literature search. The targets related to traumatic brain injury were obtained from TTD, DrugBank, CTD, GeneCards, OMIM, MalaCards, and a literature search. The STRING database and the Cytoscape 3.8.0 software were used to build the protein-protein interaction (PPI) network. The Metascape database was used to obtain the gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment information. Finally, The AutoDockTools 1.5.6 and PyMOL 2.4.0 software were employed for molecular docking analyses, and Discovery Studio 2020 was used for interaction analyses. [Results] A total of 165 overlapping targets involved in resveratrol intervention in traumatic brain injury were determined. The GO function analysis indicated that the targets are including the positive regulation of transferase activity, the positive regulation of cell migration, reactive oxygen species metabolism, the wound response, and so on. The KEGG pathway analysis identified the following enriched pathways: the AGE-RAGE signalling pathway, the FoxO signalling pathway, insulin resistance, complement and coagulation cascades, the HIF-1 signalling pathway, and so on. According to the PPI network analysis, INS, IGF1, TNF, TP53, ALB, IL6, SRC, STAT3, VEGFA, and MMP9 were identified as hub target genes, in which IL6, MMP9, INS, and SRC showed a good binding affinity with resveratrol in molecular docking. [Conclusions] Resveratrol may target multiple genes and multiple pathways to reduce brain damage after traumatic brain injury.
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Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection; however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.
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This study aims at investigating the potential targets and functional mechanisms of Scutellariae Radix-Coptidis Rhizoma (QLYD) against atherosclerosis (AS) through network pharmacology, molecular docking, bioinformatic analysis and experimental validation.The compositions of QLYD were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and literature, where the main active components of QLYD and corresponding targets were identified. The potential therapeutic targets of AS were excavated using the OMIM database, DrugBank database, DisGeNET database, CTD database and GEO datasets. The protein-protein interaction (PPI) network of common targets was constructed and visualized by Cytoscape 3.7.2 software. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed to analyze the function of core targets in the PPI network. Molecular docking was carried out using AutoDockTools, AutoDock Vina, and PyMOL software to verify the correlation between the main components of QLYD and the core targets. Mouse AS model was established and the results of network pharmacology were verified by in vivo experiments.Totally 49 active components and 225 corresponding targets of QLYD were obtained, where 68 common targets were identified by intersecting with AS-related targets. Five hub genes including IL6, VEGFA, AKT1, TNF, and IL1B were screened from the PPI network. GO functional analysis reported that these targets had associations mainly with cellular response to oxidative stress, regulation of inflammatory response, epithelial cell apoptotic process, and blood coagulation. KEGG pathway analysis demonstrated that these targets were correlated to AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, IL-17 signaling pathway, MAPK signaling pathway, and NF-kappa B signaling pathway. Results of molecular docking indicated good binding affinity of QLYD to FOS, AKT1, and TNF. Animal experiments showed that QLYD could inhibit inflammation, improve blood lipid levels and reduce plaque area in AS mice to prevent and treat AS.QLYD may exert anti-inflammatory and anti-oxidative stress effects through multi-component, multi-target and multi-pathway to treat AS.
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Objective: Erchen Decoction (ECD), a well-known traditional Chinese medicine, exerts metabolism-regulatory, immunoregulation, and anti-tumor effects. However, the action and pharmacological mechanism of ECD remain largely unclear. In the present study, we explored the effects and mechanisms of ECD in the treatment of CRC using network pharmacology, molecular docking, and systematic experimental validation. Methods: The active components of ECD were obtained from the TCMSP database and the potential targets of them were annotated by the STRING database. The CRC-related targets were identified from different databases (OMIM, DisGeNet, GeneCards, and DrugBank). The interactive targets of ECD and CRC were screened and the protein-protein interaction (PPI) networks were constructed. Then, the hub interactive targets were calculated and visualized from the PPI network using the Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. In addition, the molecular docking was performed. Finally, systematic in vitro, in vivo and molecular biology experiments were performed to further explore the anti-tumor effects and underlying mechanisms of ECD in CRC. Results: A total of 116 active components and 246 targets of ECD were predicted based on the component-target network analysis. 2406 CRC-related targets were obtained from different databases and 140 intersective targets were identified between ECD and CRC. 12 hub molecules (STAT3, JUN, MAPK3, TP53, MAPK1, RELA, FOS, ESR1, IL6, MAPK14, MYC, and CDKN1A) were finally screened from PPI network. GO and KEGG pathway enrichment analyses demonstrated that the biological discrepancy was mainly focused on the tumorigenesis-, immune-, and mechanism-related pathways. Based on the experimental validation, ECD could suppress the proliferation of CRC cells by inhibiting cell cycle and promoting cell apoptosis. In addition, ECD could inhibit tumor growth in mice. Finally, the results of molecular biology experiments suggested ECD could regulate the transcriptional levels of several hub molecules during the development of CRC, including MAPKs, PPARs, TP53, and STATs. Conclusion: This study revealed the potential pharmacodynamic material basis and underlying molecular mechanisms of ECD in the treatment of CRC, providing a novel insight for us to find more effective anti-CRC drugs.
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Objective. YuPingFeng Granules (YPFGs) is an herbal formula clinically used in China for more than 100 years to treat pneumonia. Nevertheless, the mechanism of YPFG in pneumonia treatment has not been established. This network pharmacology-based strategy has been performed to elucidate active compounds as well as mechanisms of YPFG in pneumonia treatment. Methods. First, active compounds of YPFG were identified in the traditional Chinese medicine systems pharmacology (TCMSP) database, and then the targets related to the active compounds were obtained from TCMSP and Swiss Target Prediction databases. Next, using DisGeNET, DrugBank, and GeneCards databases, we got therapeutic targets of pneumonia and common targets between pneumonia targets and YPFG. After that, a protein-protein interaction (PPI) network of pneumonia composed of common targets was built to analyze the interactions among these targets, which focused on screening for hub targets by topology. Then, online software and the ClusterProfiler package were utilized for the enrichment analysis of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) data. Finally, the visualization software of Autodock was used for molecular docking among the hub target proteins. Results. 10 hub genes were selected by comparing the GO and KEGG functions of pneumonia targets with those of the common targets of YPFG and pneumonia. By using molecular docking technology, a total of 3 active ingredients have been verified as being able to combine closely with 6 hub targets and contribute to their therapeutic effects. Conclusion. This research explored the multigene pharmacological mechanism of action of YPFG against pneumonia through network pharmacology. The findings present new ideas for studying the mechanism of action of Chinese medicine against pneumonia caused by bacteria.
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