Network Pharmacology Approach uncovering Pathways involved in targeting Hsp90 through Curcumin and Epigallocatechin to control Inflammation.

2019 ◽  
Vol 16 ◽  
Author(s):  
Umme Hani ◽  
Shivananda Kandagalla ◽  
B.S. Sharath ◽  
K Jyothsna. ◽  
H Manjunatha.
Keyword(s):  
Gene Ontology ◽  
Molecular Chaperones ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Heme Oxygenase 1 ◽  
Nrf2 Pathway ◽  
Functional Behavior ◽  
Inflammatory Proteins ◽  
Prime Target

: Hsp90 are molecular chaperones of chronic inflammatory proteins and have emerged as prime target for treatment of inflammation. Principal components from Curcuma longa and Camellia sinensis, Curcumin and EGC respectively possesses anti-inflammatory properties inhibiting cytokines responsible for inflammation. Both act on common pathways in upregulation of heme oxygenase 1 through Pkcδ-Nrf2 pathway and downregulation of Tlr4, which in turn suppress expression of Hsp90. Curcumin and EGC were also found to bind -N and -C terminal domain of Hsp90 respectively. Based on this, work was designed with network pharmacological approach. Hsp90 associated gene targets of Curcumin and EGC were collected from databases, and gene ontology studies were done. PPI were obtained from string database for specific genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Protein interaction network was constructed by cytoscape, and networks of Hsp90, Curcumin and EGC were merged to get common genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Cluego analysis was done for obtained common genes to identify functional behavior in human diseases. Main proteins involved were identified as key regulators in Pkcδ-Nrf2 and Tlr4 pathway for controlling expression of Hsp90 from Curcumin and EGC in inflammation. Docking was performed on main proteins, Hsp90, Pkcδ and Tlr4 with Curcumin and EGC, significant binding energy was obtained for docked complexes. Combinatorial effects of Curcumin and EGC were observed in Pkcδ-Nrf2 and Tlr4pathway. Present study is an attempt to unravel common pathways mediated in intervention of Curcumin and EGC for suppression of Hsp90 associated with inflammation.

scholarly journals Network Pharmacology-based Strategy for Predicting Active Ingredients and Potential Targets of Coptis Chinensis Franch in Polycystic Ovary Syndrome

2020 ◽  
Author(s):  
Jianxiong Ma ◽  
Miaoyong Ye ◽  
Ke Ma ◽  
Kang Zhou ◽  
YingYing Zhang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Chinese Medicine ◽  
Polycystic Ovary ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Coptis Chinensis ◽  
Ovary Syndrome ◽  
R Language ◽  
Bioactive Ingredients

Abstract Background: Polycystic ovary syndrome (PCOS) is a disease that causes low fertility in females. Coptis chinensis (CC) has been used to clear away heat and dampness, purify fire, and detoxify in traditional Chinese medicine (TCM). Although CC has demonstrated efficacy against PCOS in clinical practice, there is no available data regarding the bioactive ingredients, component targets, and confirmed molecular mechanism of this drug combination.Methods: A network pharmacology approach was applied to analyze the bioactive ingredients, component targets, and core signaling pathways of CC. The TCM systems pharmacology database and analysis platform (TCMSP) was used to screen effective active ingredients and targets of CC. The GeneCards, OMIM, and PharmGkb databases was utilized to screen disease targets for PCOS. R language software was used to screen common targets of drugs and diseases. Cytoscape software (version 3.7.1) was utilized to build a drug-active ingredient-disease target interaction network, and the STRING platform was utilized to construct a common target protein-protein interaction network, including 102 nodes and 221 edges. OmicShare tools was used to analysis Gene ontology (GO) biological function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) path enrichment. Schrodinger software was used to evaluate the interaction between active components and their targets and explore binding modes.Results: 14 effective active ingredients and 218 targets of CC were screened by TCMSP platform. 3517 disease targets for PCOS were obtianed by the disease database and 102 common targets of drugs and diseases were screened through R language software. Key targets of CC for the treatment of PCOS included JUN, MAPK, IL-6, CXCL8, FOS, and IL1B. A total of 123 Gene Ontology (GO) terms and 129 pathways were acquired by analyzing the enrichment of GO and KEGG. It is speculated that the AGEs/RAGE, TNF, IL-17, MAPK and HIF-1 signaling pathways are closely related to PCOS and may be the core pathways involved in PCOS. The molecular docking results showed that quercetin has a higher degree of binding to core targets (eg: IL-6, IL- 1β, MAPK, CXCL8) related to the inflammatory pathway.Conclusions: This preliminary study verified the basic pharmacological effects and mechanisms of CC, a Chinese medicine, in the treatment of PCOS. Particularly, the effect of CC on inflammation and glucose metabolism pathway was noteworthy. This study provides new insights for the systematic exploration of the mechanism of action of Chinese medicine.

scholarly journals A Network Pharmacology-Based Approach to Explore Therapeutic Mechanism of Indian Herbal Formulation Nisha Amalaki in Treating Type 2 Diabetes Mellitus

2021 ◽  
Author(s):  
Mohit Wadhawan ◽  
Varun Chhabra ◽  
Amit Katiyar ◽  
Vandna Sharma ◽  
Bharat Krushna Khuntia ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Network Pharmacology ◽  
Herbal Formulation

Abstract Nisha Amalaki (NA), an Indian herbal formulation consisting of two herbs, Curcuma longa and Emblica officinalis, has been commonly used to treat Type 2 diabetes mellitus (T2DM). However, the pharmacological mechanism of NA remains unknown. In this study, a network pharmacology-based approach was used to explore its underlying mechanism. NA phytochemicals were collected from PubChem, KNApSAcK, IMPPAT, and ChEBI databases, and their potential targets were investigated using similarity ensemble approach (Tanimoto coefficient ≥ 0.6). A protein-protein interaction network was constructed to study the interactions among the targets and clustered into separate modules using NetworkAnalyst 3.0. A significant module (P ≤ .01) was identified, and DAVID web tool was utilized for the enrichment analysis. A total of 201 phytochemicals and 262 targets of NA were selected. Forty-five nodes of the significant module were identified as potential targets of NA. The enrichment analysis exhibited 27 biological processes and 78 pathways (P ≤ .01). Out of 45, 18 nodes were associated with T2DM as probable targets of NA. The metabolite-target-pathway network revealed that anti-diabetic effect of NA is a synergy of multi-target and multi-pathway efforts via regulation of glucose, lipid metabolism, insulin resistance, β-cell survival and proliferation, inflammation, apoptosis, and cell cycle.

scholarly journals Network Pharmacology Integrated Molecular Docking Reveals the Mechanism of Anisodamine Hydrobromide Injection against Novel Coronavirus Pneumonia

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jinsong Su ◽  
Zixuan Liu ◽  
Chuan Liu ◽  
Xuanhao Li ◽  
Yi Wang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Molecular Docking ◽  
Immune System ◽  
Protein Interactions ◽  
Viral Infections ◽  
Interaction Network ◽  
Docking Studies ◽  
Network Pharmacology ◽  
Immune System Diseases ◽  
Topological Parameters

Background. The Coronavirus Disease 2019 (COVID-19) outbreak in Wuhan, China, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Anisodamine hydrobromide injection (AHI), the main ingredient of which is anisodamine, is a listed drug for improving microcirculation in China. Anisodamine can improve the condition of patients with COVID-19. Materials and Methods. Protein-protein interactions obtained from the String databases were used to construct the protein interaction network (PIN) of AHI using Cytoscape. The crucial targets of AHI PIN were screened by calculating three topological parameters. Gene ontology and pathway enrichment analyses were performed. The intersection between the AHI component proteins and angiotensin-converting enzyme 2 (ACE2) coexpression proteins was analyzed. We further investigated our predictions of crucial targets by performing molecular docking studies with anisodamine. Results. The PIN of AHI, including 172 nodes and 1454 interactions, was constructed. A total of 54 crucial targets were obtained based on topological feature calculations. The results of Gene Ontology showed that AHI could regulate cell death, cytokine-mediated signaling pathways, and immune system processes. KEGG disease pathways were mainly enriched in viral infections, cancer, and immune system diseases. Between AHI targets and ACE2 coexpression proteins, 26 common proteins were obtained. The results of molecular docking showed that anisodamine bound well to all the crucial targets. Conclusion. The network pharmacological strategy integrated molecular docking to explore the mechanism of action of AHI against COVID-19. It provides protein targets associated with COVID-19 that may be further tested as therapeutic targets of anisodamine.

scholarly journals Mechanism of Compound Houttuynia Mixture as an Anti-COVID-19 Drug Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 16 (5) ◽  
pp. 1934578X2110167
Author(s):  
Xing-Pan Wu ◽  
Tian-Shun Wang ◽  
Zi-Xin Yuan ◽  
Yan-Fang Yang ◽  
He-Zhen Wu
Keyword(s):  
Molecular Docking ◽  
Target Prediction ◽  
Interaction Network ◽  
Scientific Basis ◽  
Network Pharmacology ◽  
Chemical Components ◽  
Active Components ◽  
Discovery Studio ◽  
The Core ◽  
Pathway Annotation

Objective To explore the anti-COVID-19 active components and mechanism of Compound Houttuynia mixture by using network pharmacology and molecular docking. Methods First, the main chemical components of Compound Houttuynia mixture were obtained by using the TCMSP database and referring to relevant chemical composition literature. The components were screened for OB ≥30% and DL ≥0.18 as the threshold values. Then Swiss Target Prediction database was used to predict the target of the active components and map the targets of COVID-19 obtained through GeneCards database to obtain the gene pool of the potential target of COVID-19 resistance of the active components of Compound Houttuynia mixture. Next, DAVID database was used for GO enrichment and KEGG pathway annotation of targets function. Cytoscape 3.8.0 software was used to construct a “components-targets-pathways” network. Then String database was used to construct a “protein-protein interaction” network. Finally, the core targets, SARS-COV-2 3 Cl, ACE2 and the core active components of Compound Houttuyna Mixture were imported into the Discovery Studio 2016 Client database for molecular docking verification. Results Eighty-two active compounds, including Xylostosidine, Arctiin, ZINC12153652 and ZINC338038, were screened from Compound Houttuyniae mixture. The key targets involved 128 targets, including MAPK1, MAPK3, MAPK8, MAPK14, TP53, TNF, and IL6. The HIF-1 signaling, VEGF signaling, TNF signaling and another 127 signaling pathways associated with COVID-19 were affected ( P < 0.05). From the results of molecular docking, the binding ability between the selected active components and the core targets was strong. Conclusion Through the combination of network pharmacology and molecular docking technology, this study revealed that the therapeutic effect of Compound Houttuynia mixture on COVID-19 was realized through multiple components, multiple targets and multiple pathways, which provided a certain scientific basis of the clinical application of Compound Houttuynia mixture.

scholarly journals Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

2021 ◽  
Vol 16 (1) ◽  
pp. 1934578X2098213
Author(s):  
Xiaodong Deng ◽  
Yuhua Liang ◽  
Jianmei Hu ◽  
Yuhui Yang
Keyword(s):  
Diabetes Mellitus ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

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 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 Network Pharmacology study of Curcuma longa L.: Potential Target Proteins and their Functional Enrichment Analysis.

2020 ◽  
Author(s):  
SANGEETA KUMARI
Keyword(s):  
Signaling Pathway ◽  
Mechanism Of Action ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Target Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Objective This study’s primary goal is unraveling the mechanism of action of bioactives of Curcuma longa L. at the molecular level using protein-protein interaction network.Results We used target proteins to create protein-protein interaction network (PPIN) and identified significant node and edge attributes of PPIN. We identified the cluster of proteins in the PPIN, which were used to identify enriched pathways. . We identified closeness centrality and jaccard score as most important node and edge attribute of the PPIN respectively. The enriched pathways of various clusters were overlapped suggesting synergistic mechanism of action. The three pathways found to be common among three clusters were Gonadotropin-releasing hormone receptor pathway, Endothelin signaling pathway, and Inflammation mediated by chemokine and cytokine signaling pathway.

scholarly journals Network Pharmacology and Molecular Docking Suggest the Mechanism for Biological Activity of Rosmarinic Acid

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minglong Guan ◽  
Lan Guo ◽  
Hengli Ma ◽  
Huimei Wu ◽  
Xiaoyun Fan
Keyword(s):  
Biological Activity ◽  
Molecular Docking ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Rosmarinic Acid ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis

Rosmarinic acid (RosA) is a natural phenolic acid compound, which is mainly extracted from Labiatae and Arnebia. At present, there is no systematic analysis of its mechanism. Therefore, we used the method of network pharmacology to analyze the mechanism of RosA. In our study, PubChem database was used to search for the chemical formula and the Chemical Abstracts Service (CAS) number of RosA. Then, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to evaluate the pharmacodynamics of RosA, and the Comparative Toxicogenomics Database (CTD) was used to identify the potential target genes of RosA. In addition, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were carried out by using the web-based gene set analysis toolkit (WebGestalt). At the same time, we uploaded the targets to the STRING database to obtain the protein interaction network. Then, we carried out a molecular docking about targets and RosA. Finally, we used Cytoscape to establish a visual protein-protein interaction network and drug-target-pathway network and analyze these networks. Our data showed that RosA has good biological activity and drug utilization. There are 55 target genes that have been identified. Then, the bioinformatics analysis and network analysis found that these target genes are closely related to inflammatory response, tumor occurrence and development, and other biological processes. These results demonstrated that RosA can act on a variety of proteins and pathways to form a systematic pharmacological network, which has good value in drug development and utilization.

scholarly journals Elucidating the Mechanisms of Hugan Buzure Granule in the Treatment of Liver Fibrosis via Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yi Zhu ◽  
Ming Qiao ◽  
Jianhua Yang ◽  
Junping Hu
Keyword(s):  
Liver Fibrosis ◽  
Rna Polymerase Ii ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Docking Simulation ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Transcription Activator ◽  
Active Ingredients ◽  
Oxidoreductase Activity

Objective. To holistically explore the latent active ingredients, targets, and related mechanisms of Hugan buzure granule (HBG) in the treatment of liver fibrosis (LF) via network pharmacology. Methods. First, we collected the ingredients of HBG by referring the TCMSP server and literature and filtered the active ingredients though the criteria of oral bioavailability ≥30% and drug-likeness index ≥0.18. Second, herb-associated targets were predicted and screened based on the BATMAN-TCM and SwissTargetPrediction platforms. Candidate targets related to LF were collected from the GeneCards and OMIM databases. Furthermore, the overlapping target genes were used to construct the protein-protein interaction network and “drug-compound-target-disease” network. Third, GO and KEGG pathway analyses were carried out to illustrate the latent mechanisms of HBG in the treatment of LF. Finally, the combining activities of hub targets with active ingredients were further verified based on software AutoDock Vina. Results. A total of 25 active ingredients and 115 overlapping target genes of HBG and LF were collected. Besides, GO enrichment analysis exhibited that the overlapping target genes were involved in DNA-binding transcription activator activity, RNA polymerase II-specific, and oxidoreductase activity. Simultaneously, the key molecular mechanisms of HBG against LF were mainly involved in PI3K-AKT, MAPK, HIF-1, and NF-κB signaling pathways. Also, molecular docking simulation demonstrated that the key targets of HBG for antiliver fibrosis were IL6, CASP3, EGFR, VEGF, and MAPK. Conclusion. This work validated and predicted the underlying mechanisms of multicomponent and multitarget about HBG in treating LF and provided a scientific foundation for further research.

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