Network pharmacology and GEO database-based analysis of Sini powder in the prevention of depression among shift workers

Abstract Atrial fibrillation (AF) is a common atrial arrhythmia for which there is no specific therapeutic drug. Quercetin (Que) has been used to treat cardiovascular diseases such as arrhythmias. In this study, we explored the mechanism of action of Que in AF using network pharmacology and molecular docking. The chemical structure of Que was obtained from Pubchem. TCMSP, Swiss Target Prediction, Drugbank, STITCH, Binding DB, Pharmmapper, CTD, GeneCards, DISGENET and TTD were used to obtain drug component targets and AF-related genes, and extract AF and normal tissue by GEO database differentially expressed genes by GEO database. The top targets were IL6, VEGFA, JUN, MMP9 and EGFR, and Que for AF treatment might involve the lipid and atherosclerosis pathway, the role of AGE-RAGE signaling pathway in diabetic complications, MAPK signaling pathway and IL-17 signaling pathway. In addition, molecular docking showed that Que binds strongly to key targets and is differentially expressed in AF. This study systematically elucidated the key targets of Que treatment for AF and the specific mechanisms, providing a new direction for further basic experimental exploration and clinical treatment.

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Network of “drug-target-SARS-CoV-2 Related Genes” Through Integrated Analysis of Pharmacology and Geo Database

10.21203/rs.3.rs-117894/v1 ◽

2020 ◽

Author(s):

Jin ping Hou ◽

Yong heng Wang ◽

Yu meng Chen ◽

Yi hao Chen ◽

Xiao Zhu ◽

...

Keyword(s):

Gene Therapy ◽

Molecular Docking ◽

Folic Acid ◽

Drug Target ◽

Drug Targets ◽

Interaction Mechanism ◽

Network Pharmacology ◽

Integrated Analysis ◽

Hub Genes ◽

Geo Database

Abstract BackgroundCoronavirus Disease 2019 (COVID-19) respiratory disease rapidly caused a global pandemic and social and economic disruption. The combination of Traditional Chinese medicine (TCM) and Conventional Western medicine (CWM) is more effective for COVID-19 treatment. Moreover, TCM and CWM are important data source for developing new drug targets and promote strategies treat SARS-CoV-2 infections. However, many studies have analyzed the therapeutic mechanism of CWM or TCM alone for COVID-19, it is still unclear the interaction mechanism between TCM and CWM on COVID-19.MethodsThis paper integrates network pharmacology and GEO database to mine and identify COVID-19 molecular therapeutic targets, providing potential targets and new ideas for COVID-19 gene therapy and new drug development. It includes: 1) using TCMSP, TTD, PubChem and CTD databases to analyze drug interactions and associated phenotypes for SARS-CoV-2, to correlate drug and disease interaction mechanisms to screen key drug targets; 2) using GEO database to correlate differential genes and drug targets to screen potential antiviral gene therapy targets, to construct regulatory network and key points of SARS-CoV-2 therapeutic drugs; 3) using computer simulation of molecular docking to screen virus-related proteins for new drugs. ResultsIntegrated analysis of network pharmacology discovered that baicalein, estrone and quercetin are the pivotal active ingredients in TCM and CWM. Combining drug target genes in pharmacology database and virus induced genes in GEO database, the result showed the core hub genes related to COVID-19: STAT1, IL1B, IL6, IL8, PTGS2 and NFKBIA, and these genes were significantly downregulated in A549 and NHBE cells by SARS-CoV-2 infection. Moreover, chemical interaction and molecular docking analysis of hub genes showed that folic acid might as be potential therapeutic drug for COVID-19 treatment, and SARS-CoV-2 nucleocapsid phosphoprotein was a potential drug target. The network of “drug-target-SARS-CoV-2 related genes” provide noval potential compounds and targets for further studies of SARS-CoV-2.ConclusionsIntegrated analysis of network pharmacology and big data mining provided noval potential compounds and targets for further studies of SARS-CoV-2. Our research implied folic acid and SARS-CoV-2 N as therapeutic target in TCM and CWM. Our research also suggests that targeting SARS-CoV-2 N protein is likely to be a common mechanism of TCM and CWM. On the one hand, the identification of pivotal genes provides a target for COVID-19 molecular therapy, on the other hand, it provides ideas for the analysis of interaction mechanism between virus and host.

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Genetic Chinese Medicine Network Pharmacology Research, Quercetin Improves Drug Resistance in Acute Myeloid Leukemia by Targeting CXCL10

10.21203/rs.3.rs-258594/v1 ◽

2021 ◽

Author(s):

Hu Linjun ◽

QILIANG LU ◽

YANG LIU ◽

JUNJUN ZHAO ◽

ZHI ZENG ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Acute Myeloid Leukemia ◽

Chinese Medicine ◽

Active Ingredient ◽

Myeloid Leukemia ◽

Network Pharmacology ◽

Overlapping Genes ◽

Acute Myeloid ◽

Geo Database

Abstract Background: Acute myeloid leukemia (AML) is the most common acute leukemia in adults and is a highly heterogeneous and fatal disease. At present, the main method of treatment of AML is chemotherapy, but patients who relapse often develop resistance and are not sensitive to chemotherapy. Chinese medicine network pharmacology can provide new ideas about improving AML resistance.Methods: The gene expression data of relapsed drug-resistant AML and primary AML are from Gene Expression Omnibus (GEO) database. Based on the network pharmacology of traditional Chinese medicine, the effective components and target genes of Jiedu Huayu Decoction were analyzed. we performed Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) analyses, Protein-protein interaction (PPI) network and construction on overlapping genes. We will perform prognostic analysis and gene correlation analysis of overlapping genes in GEPIA. The binding energy between the differential gene and the active ingredient of the drug was studied by molecular docking.Results: We found that quercetin, the active ingredient in Jiedu Huayu Decoction, can target CXCL10, thereby improving AML resistance.Conclusions: In this study, we found that quercetin improves drug resistance in acute myeloid leukemia by targeting CXCL10 based on the GEO database and the network pharmacology study of Chinese medicine.

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Identification of Potential Biomarkers of Depression and Network Pharmacology Approach to Investigate the Mechanism of Key Genes and Therapeutic Traditional Chinese Medicine in the Treatment of Depression

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/2165632 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Yucong Shi ◽

Dan Chen ◽

Shengsuo Ma ◽

Huachong Xu ◽

Li Deng

Keyword(s):

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Interaction Analysis ◽

Crocus Sativus ◽

Network Pharmacology ◽

Hub Genes ◽

Chinese Medicines ◽

Treatment Of Depression ◽

Key Genes ◽

Geo Database

Background. To explore the potential target of depression and the mechanism of related traditional Chinese medicine in the treatment of depression. Method. Differential gene expression in depression patients and controls was analyzed in the GEO database. Key genes for depression were obtained by searching the disease databases. The COREMINE Medical database was used to search for Chinese medicines corresponding to the key genes in the treatment of depression, and the network pharmacological analysis was performed on these Chinese medicines. Then, protein-protein interaction analysis was conducted. Prediction of gene phenotypes was based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment scores. Results. The total number of differentially expressed genes in the GEO database was 147. Combined with the GEO dataset and disease database, a total of 3533 depression-related genes were analyzed. After screening in COREMINE Medical, it was found that the top 4 traditional Chinese medicines with the highest frequency for depression were Paeonia lactiflora Pall., Crocus sativus L., Bupleurum chinense DC., and Cannabis sativa L. The compound target network consisted of 24 compounds and 138 corresponding targets, and the key targets involved PRKACA, NCOA2, PPARA, and so on. GO and KEGG analysis revealed that the most commonly used Chinese medicine could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein-protein interactions resulted in the selection of nine hub genes (ESR1, HSP90AA1, JUN, MAPK1, MAPK14, MAPK8, RB1, RELA, and TP53). In addition, a total of four ingredients (petunidin, isorhamnetin, quercetin, and luteolin) from this Chinese medicine could act on these hub genes. Conclusions. Our research revealed the complicated antidepressant mechanism of the most commonly used Chinese medicines and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formulas.

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Network Pharmacology-Based Prediction of Catalpol and Mechanisms against Stroke

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/2541316 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Jinghui Wang ◽

Meifeng Zhang ◽

Si Sun ◽

Guoran Wan ◽

Dong Wan ◽

...

Keyword(s):

Molecular Docking ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Ppi Network ◽

Pharmacological Mechanism ◽

Binding Force ◽

Strong Binding ◽

Prevention And Treatment ◽

Depth Study ◽

Geo Database

Aim. To apply the network pharmacology method to screen the target of catalpol prevention and treatment of stroke, and explore the pharmacological mechanism of Catalpol prevention and treatment of stroke. Methods. PharmMapper, GeneCards, DAVID, and other databases were used to find key targets. We selected hub protein and catalpol which were screened for molecular docking verification. Based on the results of molecular docking, the ITC was used to determine the binding coefficient between the highest scoring protein and catalpol. The GEO database and ROC curve were used to evaluate the correlation between key targets. Results. 27 key targets were obtained by mapping the predicted catalpol-related targets to the disease. Hub genes (ALB, CASP3, MAPK1 (14), MMP9, ACE, KDR, etc.) were obtained in the key target PPI network. The results of KEGG enrichment analysis showed that its signal pathway was involved in angiogenic remodeling such as VEGF, neurotrophic factors, and inflammation. The results of molecular docking showed that ACE had the highest docking score. Therefore, the ITC was used for the titration of ACE and catalpol. The results showed that catalpol had a strong binding force with ACE. Conclusion. Network pharmacology combined with molecular docking predicts key genes, proteins, and signaling pathways for catalpol in treating stroke. The strong binding force between catalpol and ACE was obtained by using ITC, and the results of molecular docking were verified to lay the foundation for further research on the effect of catalpol on ACE. ROC results showed that the AUC values of the key targets are all >0.5. This article uses network pharmacology to provide a reference for a more in-depth study of catalpol’s mechanism and experimental design.

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