scholarly journals Uncovering the mechanism of Ge-Gen-Qin-Lian decoction for treating ulcerative colitis based on network pharmacology and molecular docking verification

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Lin Xu ◽  
Jiaqi Zhang ◽  
Yifan Wang ◽  
Zedan Zhang ◽  
Fengyun Wang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Potential Candidate ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Pharmacological Mechanism ◽  
Th17 Cell Differentiation ◽  
The Relationship

Abstract Background: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China, but the pharmacological mechanisms remain unclear. This research was designed to clarify the underlying pharmacological mechanism of GGQLD against UC. Method: In this research, a GGQLD-compound-target-UC network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein–protein interaction network was constructed to screen and evaluate hub genes and key active ingredients. Molecular docking was used to verify the activities of binding between hub targets and ingredients. Results: Finally, 83 potential therapeutic targets and 118 corresponding active ingredients were obtained by network pharmacology. Quercetin, kaempferol, wogonin, baicalein, and naringenin were identified as potential candidate ingredients. GO and KEGG enrichment analyses revealed that GGQLD had anti-inflammatory, antioxidative, and immunomodulatory effects. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (e.g., IL-6, TNF, IL-1β, CXCL8, CCL2) in the immune system and inflammation-related pathways, such as the IL-17 pathway and the Th17 cell differentiation pathway. In addition, molecular docking results demonstrated that the main active ingredient, quercetin, exhibited good affinity to hub targets. Conclusion: This research fully reflects the multicomponent and multitarget characteristics of GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.

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 Uncovering the Mechanism of Ge-Gen-Qin-Lian Decoction for Treating Ulcerative Colitis Based on Network Pharmacology and Molecular Docking Verification

2020 ◽  
Author(s):  
Lin Xu ◽  
Jiaqi Zhang ◽  
Zedan Zhang ◽  
Yifan Wang ◽  
Fengyun Wang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Network Pharmacology ◽  
Active Ingredients ◽  
Hub Genes ◽  
Pathway Analyses

Abstract Background and objective: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China while the pharmacological mechanisms still remain unclear. The present research was designed to clarify the underlying mechanism of GGQD against UC. Methods: In this research, a GGQLD-compound-target-UC (G-U) network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. GO and KEGG pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein-protein interaction network was constructed to screen and evaluate hub genes and key active ingredients, another GO and KEGG pathway analyses were subsequently performed on hub genes. Molecular docking was used to verify the activities of binding between hub targets and ingredients. Results: Finally, 83 potential therapeutic targets and 118 correspond active ingredients were obtained by network pharmacology. GO and KEGG enrichment analysis revealed that GGQLD had an effect of anti-inflammation, antioxidation, and immunomodulatory. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (eg., IL6, TNF, IL1β, CXCL8, CCL2, IL10, IL4, IL2) in immune system and inflammation-related pathways, such as IL-17 pathway and Th17 cell differentiation pathway. Besides, molecular docking results demonstrated that the main active ingredients, quercetin, exhibited good affinity to hub targets. Conclusion: This research fully reflects the characteristics of multi-component and multi-target for GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.

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 Network Pharmacology-Based Study on the Mechanism of Aloe Vera for Treating Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Jing Xie ◽  
Jun Wu ◽  
Sihui Yang ◽  
Huaijun Zhou
Keyword(s):  
Molecular Docking ◽  
Drug Targets ◽  
Aloe Vera ◽  
Beta Carotene ◽  
Network Pharmacology ◽  
Potential Mechanism ◽  
Active Ingredients ◽  
Protein Protein Interaction ◽  
Target Network ◽  
Molecule Docking

Background. Aloe vera has long been considered an anticancer herb in different parts of the world. Objective. To explore the potential mechanism of aloe vera in the treatment of cancer using network pharmacology and molecule docking approaches. Methods. The active ingredients and corresponding protein targets of aloe vera were identified from the TCMSP database. Targets related to cancer were obtained from GeneCards and OMIM databases. The anticancer targets of aloe vera were obtained by intersecting the drug targets with the disease targets, and the process was presented in the form of a Venn plot. These targets were uploaded to the String database for protein-protein interaction (PPI) analysis, and the result was visualized by Cytoscape software. Go and KEGG enrichment were used to analyze the biological process of the target proteins. Molecular docking was used to verify the relationship between the active ingredients of aloe vera and predicted targets. Results. By screening and analyzing, 8 active ingredients and 174 anticancer targets of aloe vera were obtained. The active ingredient-anticancer target network constructed by Cytoscape software indicated that quercetin, arachidonic acid, aloe-emodin, and beta-carotene, which have more than 4 gene targets, may play crucial roles. In the PPI network, AKT1, TP53, and VEGFA have the top 3 highest values. The anticancer targets of aloe vera were mainly involved in pathways in cancer, prostate cancer, bladder cancer, pancreatic cancer, and non-small-cell lung cancer and the TNF signaling pathway. The results of molecular docking suggested that the binding ability between TP53 and quercetin was the strongest. Conclusion. This study revealed the active ingredients of aloe vera and the potential mechanism underlying its anticancer effect based on network pharmacology and provided ideas for further research.

scholarly journals A Network Pharmacology-Based Study of the Mechanism of Renshen Baidu Powder in Treating Ulcerative Colitis

2021 ◽  
Author(s):  
Bo Jia ◽  
Xi-Yue Tan ◽  
Xing-Long Liu ◽  
Xin-Yun Li
Keyword(s):  
Ulcerative Colitis ◽  
Enrichment Analysis ◽  
Chronic Inflammatory Bowel Disease ◽  
Network Pharmacology ◽  
Potential Candidate ◽  
Protein Protein Interaction ◽  
Medicine Prescription ◽  
Chinese Medicine Prescription ◽  
Anti Inflammatory ◽  
Recurrent Inflammation

Abstract Ulcerative colitis (UC), one forms of chronic inflammatory bowel disease (IBD), is characterized by recurrent inflammation, and Renshen Baidu Powder (RSBDP) is often used as a traditional Chinese medicine prescription for treating of UC, but the pharmacological mechanisms remains unclear. This research aims to explore the mechanisms of RSBDP in the treatment of UC based on network pharmacology. Through multiple databases collected the compounds and targets, and constructed the network. The crossover genes enrichment analysis were performed by protein-protein interaction (PPI) construction and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, 181 compounds and 266 targets were obtained. Potential candidate ingredients mainly include phytosterols, flavonoids, and coumarins, all of which have anti-inflammatory activities. GO and KEGG enrichment analysis suggested that RSBDP have anti-inflammatory and immunomodulatory effects. The effect of RSBDP on UC might be achieved by regulating the balance of cytokines (i.e., IL-6, TNF, IL-1) and the downstream mediators (i.e., STAT1, STAT3) in the immune system. And inflammation-, immune- and hypoxic-related pathways, like TNF, Toll-like receptors and HIF-1 signaling pathway. These results provide a theoretical basis for studying the effective substances of RSBDP in the treatment of UC and their mechanism of action for further research.

scholarly journals Network Pharmacology Study on the Pharmacological Mechanism of Cinobufotalin Injection against Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yun Mao ◽  
Xi Peng ◽  
Peng Xue ◽  
Dianrong Lu ◽  
Linlu Li ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Therapeutic Targets ◽  
Target Prediction ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Protein Protein Interaction ◽  
Pharmacological Mechanism ◽  
Chinese Giant Salamander ◽  
Receptor Signaling Pathway

Cinobufotalin injection, extracted from the skin of Chinese giant salamander or black sable, has good clinical effect against lung cancer. However, owing to its complex composition, the pharmacological mechanism of cinobufotalin injection has not been fully clarified. This study aimed to explore the mechanism of action of cinobufotalin injection against lung cancer using network pharmacology and bioinformatics. Compounds of cinobufotalin injection were determined by literature retrieval, and potential therapeutic targets of cinobufotalin injection were screened from Swiss Target Prediction and STITCH databases. Lung-cancer-related genes were summarized from GeneCards, OMIM, and DrugBank databases. The pharmacological mechanism of cinobufotalin injection against lung cancer was determined by enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network was constructed. We identified 23 compounds and 506 potential therapeutic targets of cinobufotalin injection, as well as 70 genes as potential therapeutic targets of cinobufotalin injection in lung cancer by molecular docking. The antilung cancer effect of cinobufotalin injection was shown to involve cell cycle, cell proliferation, antiangiogenesis effect, and immune inflammation pathways, such as PI3K-Akt, VEGF, and the Toll-like receptor signaling pathway. In network analysis, the hub targets of cinobufotalin injection against lung cancer were identified as VEGFA, EGFR, CCND1, CASP3, and AKT1. A network diagram of “drug-compounds-target-pathway” was constructed through network pharmacology to elucidate the pharmacological mechanism of the antilung cancer effect of cinobufotalin injection, which is conducive to guiding clinical medication.

scholarly journals Mechanisms of indigo naturalis on treating ulcerative colitis explored by GEO gene chips combined with network pharmacology and molecular docking

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sizhen Gu ◽  
Yan Xue ◽  
Yang Gao ◽  
Shuyang Shen ◽  
Yuli Zhang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Signaling Pathways ◽  
Interaction Network ◽  
Oxygen Metabolism ◽  
Network Pharmacology ◽  
Signal Pathways ◽  
Active Components ◽  
Gene Chips ◽  
Indigo Naturalis

Abstract Oral administration of indigo naturalis (IN) can induce remission in ulcerative colitis (UC); however, the underlying mechanism remains unknown. The main active components and targets of IN were obtained by searching three traditional Chinese medicine network databases such as TCMSP and five Targets fishing databases such as PharmMapper. UC disease targets were obtained from three disease databases such as DrugBank,combined with four GEO gene chips. IN-UC targets were identified by matching the two. A protein–protein interaction network was constructed, and the core targets were screened according to the topological structure. GO and KEGG enrichment analysis and bioGPS localization were performed,and an Herbs-Components-Targets network, a Compound Targets-Organs location network, and a Core Targets-Signal Pathways network were established. Molecular docking technology was used to verify the main compounds-targets. Ten core active components and 184 compound targets of IN-UC, of which 43 were core targets, were enriched and analyzed by bioGPS, GO, and KEGG. The therapeutic effect of IN on UC may involve activation of systemic immunity, which is involved in the regulation of nuclear transcription, protein phosphorylation, cytokine activity, reactive oxygen metabolism, epithelial cell proliferation, and cell apoptosis through Th17 cell differentiation, the Jak-STAT and IL-17 signaling pathways, toll-like and NOD-like receptors, and other cellular and innate immune signaling pathways. The molecular mechanism underlying the effect of IN on inducing UC remission was predicted using a network pharmacology method, thereby providing a theoretical basis for further study of the effective components and mechanism of IN in the treatment of UC.

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.

scholarly journals The molecular mechanism of TiaoGanYiPi formula for treatment of chronic hepatitis B by network pharmacology and molecular docking verification

2020 ◽  
Author(s):  
Xu Cao ◽  
Xiaobin Zao ◽  
Baiquan Xue ◽  
Hening Chen ◽  
Jiaxin Zhang ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Molecular Docking ◽  
Hepatitis B ◽  
Chronic Hepatitis B ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Biological Processes ◽  
The Relationship

Abstract The Chinese herbal formula Tiao-Gan-Yi-Pi (TGYP) showed effective against Chronic Hepatitis B (CHB). In this study, we aimed to clarify the mechanisms and potential targets between TGYP and CHB through network pharmacology and molecular docking verification. The compounds of TGYP were identified in the TCMSP and CNKI databases, and their putative targets were predicted through SwissTargetPrediction and STITCH databases. The targets of CHB were obtained from the GeneCards, NCBI Gene, and DisGeNET databases. The above mentioned data were visualized using Cytoscape, and molecular docking showed the relationship between them. The expression of key targets was verified in GEO databases. Hence, we screened out 11 TGYP-related key targets for CHB included ABL1, CASP8, CCNA2, CCNB1, CDK4, CDKN1A, EP300, HIF1A, IGF1R, MAP2K1 and PGR. The key targets were predominantly enriched in the cancer, cell cycle and hepatitis B pathways and involved in the positive regulation of fibroblast proliferation, signal transduction, and negative regulation of gene expression biological processes, and expression of key target genes was related to HBV infection and liver inflammation. Through this newly constructed interaction network between TGYP and CHB, we identified active compounds and targets which could be further used for providing clinical guidance.

scholarly journals Exploring the Potential Mechanism of Xiaokui Jiedu Decoction for Ulcerative Colitis Based on Network Pharmacology and Molecular Docking

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bin Wang ◽  
Yang Liu ◽  
Jianhui Sun ◽  
Nailin Zhang ◽  
Xiaojia Zheng ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Molecular Docking ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Therapeutic Effects ◽  
Potential Mechanism ◽  
Related Gene ◽  
Active Ingredients ◽  
Colon Cells ◽  
Hub Gene

Introduction. Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC). Methods. The Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. The Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. Results. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. The hub gene targets of XJD that exert therapeutic effects on UC are RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) showed good binding activities to five of the six hub gene targets. Discussion. The active ingredients of XJD (β-sitosterol, kaempferol, formononetin, quercetin, and luteolin) may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. This may be a potential mechanism of XJD in the treatment of UC.

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