scholarly journals Network Pharmacology Prediction and Pharmacological Verification Mechanism of Yeju Jiangya Decoction on Hypertension

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ting Wang ◽  
Mao He ◽  
Yuzhong Du ◽  
Suhong Chen ◽  
Guiyuan Lv
Keyword(s):  
Nitric Oxide ◽  
Blood Pressure ◽  
Molecular Docking ◽  
Interaction Network ◽  
Reduce Blood Pressure ◽  
Network Pharmacology ◽  
Protein Protein Interaction ◽  
Induced Hypertension ◽  
Oxide Synthase ◽  
Protein Protein Interaction Network

Background. Yeju Jiangya decoction (CIF) is an herbal formula from traditional Chinese medicine (TCM) for the treatment of hypertension. Materials and Methods. Based on the analysis of network pharmacology, combined with in animal experiments, the network pharmacology was used to explore the potential proteins and mechanisms of CIF against hypertension. The bioactive compounds of CIF were screened by using the platform, and the targets of hypertension and CIF were collected. Then, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction network (PPI) core targets were carried out, and the useful proteins were found by molecular docking technology. Finally, we used N-nitro-L-arginine (L-NNA) induced hypertension model rats to confirm the effect and mechanism of CIF on hypertension. Results. 14 bioactive compounds of CIF passed the virtual screening criteria, and 178 overlapping targets were identified as core targets of CIF against hypertension. The CIF-related target network with 178 nodes and 344 edges is constructed. The topological results show that quercetin and luteolin are the key components in the network. The key targets NOS3 (nitric oxide synthase 3) and NOS2 (nitric oxide synthase 2) were screened by the protein-protein interaction network. The analysis of target protein pathway enrichment showed that the accumulation pathway is related to the vascular structure of CIF regulation of hypertension. Further verification based on molecular docking results showed that NOS3 had the good binding ability with quercetin and luteolin. On the other hand, NOS3 has an important relationship with the composition of blood vessels. Furthermore, the animal experiment indicated that after the L-NNA-induced hypertension rat model was established, CIF intervention was given by gavage for 3 weeks, and it can decrease serum concentrations of endothelin-1 (ET-1) and thromboxane B2 (TXB2), increase the expression of nitric oxide (NO) and prostacyclin 2 (PGI2), and improve renal, cardiac, and aortic lesions. At the same time, it can reduce blood pressure and shorten vertigo time. Western blot (WB) and immunohistochemistry (IHC) analyses indicated that CIF may downregulate the expression of NOS3, guanylyl cyclase-alpha 1 (GC-α1), guanylyl cyclase-alpha 2 (GC-α2), and protein kinase CGMP-dependent 1 (PRKG1). These results suggest that CIF may play an antihypertensive role by inhibiting the activation of the NOS3/PRKG1 pathway. Conclusions. The results of this study indicate that CIF has the ability to improve target organs, protect endothelial function, and reduce blood pressure and that CIF might be a potential therapeutic drug for the prevention of hypertension. It provides new insight into hypertension and the potential biological basis and mechanism for CIF clinical research.

scholarly journals Elucidating the Mechanisms of Action of Lianhua Qingwen decoction for the Treatment of COVID-19 via Systems Pharmacology Approaches

2021 ◽  
Author(s):  
Xiting Wang ◽  
Tao Lu
Keyword(s):  
Molecular Docking ◽  
Blood Circulation ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Detection Algorithm ◽  
Network Pharmacology ◽  
Systems Pharmacology ◽  
Protein Protein Interaction ◽  
Further Development ◽  
Protein Protein Interaction Network

Abstract Due to the severity of the COVID-19 epidemic, to identify a proper treatment for COVID-19 is of great significance. Traditional Chinese Medicine (TCM) has shown its great potential in the prevention and treatment of COVID-19. One of TCM decoction, Lianhua Qingwen decoction displayed promising treating efficacy. Nevertheless, the underlying molecular mechanism has not been explored for further development and treatment. Through systems pharmacology and network pharmacology approaches, we explored the potential mechanisms of Lianhua Qingwen treating COVID-19 and acting ingredients of Lianhua Qingwen decoction for COVID-19 treatment. Through this way, we generated an ingredients-targets database. We also used molecular docking to screen possible active ingredients. Also, we applied the protein-protein interaction network and detection algorithm to identify relevant protein groupings of Lianhua Qingwen. Totally, 605 ingredients and 1,089 targets were obtained. Molecular Docking analyses revealed that 35 components may be the promising acting ingredients, 7 of which were underlined according to the comprehensive analysis. Our enrichment analysis of the 7 highlighted ingredients showed relevant significant pathways that could be highly related to their potential mechanisms, e.g. oxidative stress response, inflammation, and blood circulation. In summary, this study suggests the promising mechanism of the Lianhua Qingwen decoction for COVID-19 treatment. Further experimental and clinical verifications are still needed.

scholarly journals The Highlighted Role of GAPDH and Nitric-Oxide Synthase Regulator Activity in Proton Beam Irradiated Melanoma BLM Cells

2019 ◽  
Vol 10 (5) ◽  
pp. S68-S72
Author(s):  
Mohammad-Mehdi Zadeh-Esmaeel ◽  
Shabnam Shahrokh ◽  
Mona Zamanian Azodi ◽  
Nayebali Ahmadi
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Proton Therapy ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Oxide Synthase ◽  
Protein Protein Interaction Network ◽  
Cancer Bioinformatics

Introduction: The human melanoma is a type of invasive tumor the treatment of which is challenging. To better understand the proton irradiation mechanisms as one of the widely applied therapy for this type of cancer, bioinformatics analysis of proteomics outcome could be beneficial. Methods: Protein-protein interaction network analysis of the differentially expressed proteins (DEPs) of melanoma BLM (BRO lung metastasis) cells in the treatment of 3 Gy dosage proton therapy was performed in this study via Cytoscape V.3.7.2. and its integrated plug-ins. Results: Eighteen DEPs were searched for network constructions and limited numbers of query +neighbor proteins were found central. The hub-bottlenecks (i.e. central nodes) were GAPDH, ACTB, ALB, AKT1, TP53, and EGFR. The fist mentioned proteins were from DEPs. The enrichment analysis of these elements identified nitric-oxide synthase regulator activity and the positive regulation of the norepinephrine uptake that may be the key to the mechanisms of proton therapy. Conclusion: In conclusion, the identified central nodes (EGFR, TP53, ALB, AKT1, GAPDH, and ACTB) and the related biological terms are the critical affected genes and biological terms in the irradiated melanoma cells.

Effect of magnesium supplementation on blood pressure and vascular reactivity in nitric oxide synthase inhibition-induced hypertension model

2015 ◽  
Vol 37 (8) ◽  
pp. 633-642 ◽  
Author(s):  
Filiz Basralı ◽  
Günnur Koçer ◽  
Pınar Ülker Karadamar ◽  
Seher Nasırcılar Ülker ◽  
Leyla Satı ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Blood Pressure ◽  
Nitric Oxide Synthase ◽  
Vascular Reactivity ◽  
Magnesium Supplementation ◽  
Induced Hypertension ◽  
Oxide Synthase

scholarly journals Investigating drug–target interactions in frontotemporal dementia using a network pharmacology approach

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Archana Balasubramanian ◽  
Raksha Sudarshan ◽  
Jhinuk Chatterjee
Keyword(s):  
Substance Abuse ◽  
Targeted Therapy ◽  
Drug Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Specific Drug ◽  
Protein Protein Interaction ◽  
App Gene ◽  
Protein Protein Interaction Network

Abstract Background Frontotemporal dementia (FTD) is the second most common type of dementia in individuals aged below 65 years with no current cure. Current treatment plan is the administration of multiple medications. This has the issue of causing adverse effects due to unintentional drug–drug interactions. Therefore, there exists an urgent need to propose a novel targeted therapy that can maximize the benefits of FTD-specific drugs while minimizing its associated adverse side effects. In this study, we implemented the concept of network pharmacology to understand the mechanism underlying FTD and highlight specific drug–gene and drug–drug interactions that can provide an interesting perspective in proposing a targeted therapy against FTD. Results We constructed protein–protein, drug–gene and drug–drug interaction networks to identify highly connected nodes and analysed their importance in associated enriched pathways. We also performed a historeceptomics analysis to determine tissue-specific drug interactions. Through this study, we were able to shed light on the APP gene involved in FTD. The APP gene which was previously known to cause FTD cases in a small percentage is now being extensively studied owing to new reports claiming its participation in neurodegeneration. Our findings strengthen this hypothesis as the APP gene was found to have the highest node degree and betweenness centrality in our protein–protein interaction network and formed an essential hub node between disease susceptibility genes and neuroactive ligand–receptors. Our findings also support the study of FTD being presented as a case of substance abuse. Our protein–protein interaction network highlights the target genes common to substance abuse (nicotine, morphine and cocaine addiction) and neuroactive ligand–receptor interaction pathways, therefore validating the cognitive impairment caused by substance abuse as a symptom of FTD. Conclusions Our study abandons the one-target one-drug approach and uses networks to define the disease mechanism underlying FTD. We were able to highlight important genes and pathways involved in FTD and analyse their relation with existing drugs that can provide an insight into effective medication management.

scholarly journals Potential Molecular Target Prediction and Docking Verification of Hua-Feng-Dan in Stroke Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ping Yang ◽  
Haifeng He ◽  
Shangfu Xu ◽  
Ping Liu ◽  
Xinyu Bai
Keyword(s):  
Molecular Docking ◽  
Signaling Pathways ◽  
Target Genes ◽  
Target Prediction ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Core Network ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Highly Correlated

Objective. Hua-Feng-Dan (HFD) is a Chinese medicine for stroke. This study is to predict and verify potential molecular targets and pathways of HFD against stroke using network pharmacology. Methods. The TCMSP database and TCMID were used to search for the active ingredients of HFD, and GeneCards and DrugBank databases were used to search for stroke-related target genes to construct the “component-target-disease” by Cytoscape 3.7.1, which was further filtered by MCODE to build a core network. The STRING database was used to obtain interrelationships by topology and to construct a protein-protein interaction network. GO and KEGG were carried out through DAVID Bioinformatics. Autodock 4.2 was used for molecular docking. BaseSpace was used to correlate target genes with the GEO database. Results. Based on OB ≥ 30% and DL ≥ 0.18, 42 active ingredients were extracted from HFD, and 107 associated targets were obtained. PPI network and Cytoscape analysis identified 22 key targets. GO analysis suggested 51 cellular biological processes, and KEGG suggested that 60 pathways were related to the antistroke mechanism of HFD, with p53, PI3K-Akt, and apoptosis signaling pathways being most important for HFD effects. Molecular docking verified interactions between the core target (CASP8, CASP9, MDM2, CYCS, RELA, and CCND1) and the active ingredients (beta-sitosterol, luteolin, baicalein, and wogonin). The identified gene targets were highly correlated with the GEO biosets, and the stroke-protection effects of Xuesaitong in the database were verified by identified targets. Conclusion. HFD could regulate the symptoms of stroke through signaling pathways with core targets. This work provided a bioinformatic method to clarify the antistroke mechanism of HFD, and the identified core targets could be valuable to evaluate the antistroke effects of traditional Chinese medicines.

scholarly journals A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sha Di ◽  
Lin Han ◽  
Qing Wang ◽  
Xinkui Liu ◽  
Yingying Yang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Regulation Of Blood Pressure ◽  
Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

scholarly journals A Network Pharmacology Approach to Investigating the Mechanism of Sophorae Flavescentis Radix for the Treatment of Lung Cancer

2020 ◽  
Author(s):  
Le Yu ◽  
Kangyao Yuan ◽  
Jian Zhang ◽  
Jingya Zhao ◽  
Shuchen Pei
Keyword(s):  
Lung Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Bioactive Components ◽  
Akt Signaling Pathway ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Human Cytomegalovirus Infection ◽  
Protein Protein Interaction Network

Abstract In this study, the bioactive components and predictive targets of Sophorae Flavescentis Radix were investigated by network pharmacology analysis, so as to further elucidate its potential biological mechanism in treating lung cancer. The targets corresponding to lung cancer were obtained by OMIM and Genecards. By intersecting with the targets of Sophorae Flavescentis Radix and lung cancer, the Sophorae Flavescentis Radix-lung cancer targets were obtained. Protein-protein interaction network was constructed by an online database STRING and hub genes were screened by Cytoscape 3.7.0 software. ClusterProfiler package was used to analyze Gene ontology (GO) and KEGG enrichment of the targets in R. A total of 45 bioactive components were screened from Sophorae Flavescentis Radix, corresponding to 482 Sophorae Flavescentis Radix targets and 25019 lung cancer targets. According to the GO and KEGG enrichment analysis, Sophorae Flavescentis Radix played a therapeutic role in treating lung cancer via proteoglycans lung cancer, human cytomegalovirus infection, microRNAs in cancer, PI3K-Akt signaling pathway, etc. Seven hub genes (IL6, CASP3, EGFR, VEGFA, MYC, CCND1 and ESR1) were screened by degree algorithm. In a word, the results of this study may provide novel insights into the mechanisms of Sophorae Flavescentis Radix in treatment of lung cancer.

scholarly journals Using Network Pharmacology to Explore the Mechanism of Peach Kernel-Safflower in the Treatment of Diabetic Nephropathy

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jingxue Han ◽  
Xinwei Wang ◽  
Jingyi Hou ◽  
Yu Liu ◽  
Peng Liu ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Fluid Shear Stress ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Screening Process ◽  
Protein Protein Interaction ◽  
Target Network ◽  
The Common ◽  
Relationship Of ◽  
Protein Protein Interaction Network

Objective. The mechanism of peach kernel-safflower in treating diabetic nephropathy (DN) was investigated using network pharmacology. Methods. Network pharmacology methodology was applied to screen the effective compounds of peach kernel-safflower in the SymMap and TCMSP databases. Potential targets were then screened in the ETCM, SEA, and SymMap databases to construct a compound-target network. This was followed by screening of DN targets in OMIM, Gene, and GeneCards databases. The common targets of drugs and diseases were selected for analysis in the STRING database, and the results were imported into Cytoscape 3.8.0 to construct a protein-protein interaction network. Next, GO and KEGG enrichment analyses were performed. Finally, Schrödinger molecular docking verified the reliability of the results. Results. A total of 23 effective compounds and 794 potential targets resulted from our screening process. Quercetin and luteolin were identified as the main effective ingredients in peach kernel-safflower. Furthermore, five key targets (VEGFA, IL6, TNF, AKT1, and TP53), AGE-RAGE, fluid shear stress and atherosclerosis, IL-17, and HIF-1 signaling pathways may be involved in the treatment of DN using peach kernel-safflower. Conclusions. This study embodies the complex network relationship of multicomponents, multitargets, and multipathways of peach kernel-safflower to treat DN and provides a basis for further research on its mechanism.

Virtual Screening of Potential Anti-fatigue Mechanism of Polygonati Rhizoma Based on Network Pharmacology

2020 ◽  
Vol 22 (9) ◽  
pp. 612-624 ◽  
Author(s):  
Ze-Feng Wang ◽  
Ye-Qing Hu ◽  
Qi-Guo Wu ◽  
Rui Zhang
Keyword(s):  
Virtual Screening ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Vital Role ◽  
Network Pharmacology ◽  
Pathway Network ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Protein Protein Interaction Network ◽  
Fatigue Mechanism

Background and Objective: A large number of people are facing the danger of fatigue due to the fast-paced lifestyle. Fatigue is common in some diseases, such as cancer. The mechanism of fatigue is not definite. Traditional Chinese medicine is often used for fatigue, but the potential mechanism of Polygonati Rhizoma (PR) is still not clear. This study attempts to explore the potential anti-fatigue mechanism of Polygonati Rhizoma through virtual screening based on network pharmacology. Methods: The candidate compounds of PR and the known targets of fatigue are obtained from multiple professional databases. PharmMapper Server is designed to identify potential targets for the candidate compounds. We developed a Herbal medicine-Compound-Disease-Target network and analyzed the interactions. Protein-protein interaction network is developed through the Cytoscape software and analyzed by topological methods. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment are carried out by DAVID Database. Finally, we develop Compound-Target-Pathway network to illustrate the anti-fatigue mechanism of PR. Results: This approach identified 12 active compounds and 156 candidate targets of PR. The top 10 annotation terms for GO and KEGG were obtained by enrichment analysis with 35 key targets. The interaction between E2F1 and PI3K-AKT plays a vital role in the anti-fatigue effect of PR due to this study. Conclusions: This study demonstrates that PR has multi-component, multi-target and multipathway effects.

scholarly journals Predicting the Molecular Mechanism of “Angong Niuhuang Pills” in the Treatment of COVID-19 Based on Network Pharmacology

2021 ◽  
Vol 16 (6) ◽  
pp. 1934578X2110240
Author(s):  
Peng-yu Chen ◽  
Chen Wang ◽  
Ying Zhang ◽  
Chong Yuan ◽  
Bing Yu ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Treatment Plan ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Active Components ◽  
Protein Protein Interaction ◽  
Pathway Annotation ◽  
Chinese Patent ◽  
8Th Edition

Introduction Angong Niuhuang Pills (AGNH), a Chinese patent medicine recommended in the “Diagnosis and Treatment Plan for COVID-19 (8th Edition),” may be clinically effective in treating COVID-19. The active components and signal pathways of AGNH through network pharmacology have been examined, and its potential mechanisms determined. Methods We screened the components in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) via Drug-like properties (DL) and Oral bioavailability (OB); PharmMapper and GeneCards databases were used to collect components and COVID-19 related targets; KEGG pathway annotation and GO bioinformatics analysis were based on KOBAS3.0 database; “herb-components-targets-pathways” (H-C-T-P) network and protein-protein interaction network (PPI) were constructed by Cytoscape 3.6.1 software and STRING 10.5 database; we utilized virtual molecular docking to predict the binding ability of the active components and key proteins. Results A total of 87 components and 40 targets were screened in AGNH. The molecular docking results showed that the docking scores of the top 3 active components and the targets were all greater than 90. Conclusion Through network pharmacology research, we found that moslosooflavone, oroxylin A, and salvigenin in AGNH can combine with ACE2 and 3CL, and then are involved in the MAPK and JAK-STAT signaling pathways. Finally, it is suggested that AGNH may have a role in the treatment of COVID-19.

Sign in / Sign up

Export Citation Format

Share Document