A network pharmacology-based investigation on the bioactive ingredients and molecular mechanisms of Gelsemium elegans Benth against colorectal cancer

Abstract Background Colorectal cancer (CRC) remains one of the leading causes of cancer-related death worldwide. Gelsemium elegans Benth (GEB) is a traditional Chinese medicine commonly used for treatment for gastrointestinal cancer, including CRC. However, the underlying active ingredients and mechanism remain unknown. This study aims to explore the active components and the functional mechanisms of GEB in treating CRC by network pharmacology-based approaches. Methods Candidate compounds of GEB were collected from the Traditional Chinese Medicine@Taiwan, Traditional Chinese Medicines Integrated Database, Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine, and published literature. Potentially active targets of compounds in GEB were retrieved from SwissTargetPrediction databases. Keywords “colorectal cancer”, “rectal cancer” and “colon cancer” were used as keywords to search for related targets of CRC from the GeneCards database, then the overlapped targets of compounds and CRC were further intersected with CRC related genes from the TCGA database. The Cytoscape was applied to construct a graph of visualized compound-target and pathway networks. Protein-protein interaction networks were constructed by using STRING database. The DAVID tool was applied to carry out Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis of final targets. Molecular docking was employed to validate the interaction between compounds and targets. AutoDockTools was used to construct docking grid box for each target. Docking and molecular dynamics simulation were performed by Autodock Vina and Gromacs software, respectively. Results Fifty-three bioactive compounds were successfully identified, corresponding to 136 targets that were screened out for the treatment of CRC. Functional enrichment analysis suggested that GEB exerted its pharmacological effects against CRC via modulating multiple pathways, such as pathways in cancer, cell cycle, and colorectal cancer. Molecular docking analysis showed that the representative compounds had good affinity with the key targets. Molecular dynamics simulation indicated that the best hit molecules formed a stable protein-ligand complex. Conclusion This network pharmacology study revealed the multiple ingredients, targets, and pathways synergistically involved in the anti-CRC effect of GEB, which will enhance our understanding of the potential molecular mechanism of GEB in treatment for CRC and lay a foundation for further experimental research.

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Based on Network Pharmacology to Explore the Molecular Mechanism of Buzhong Yiqi Decoction for the Treatment of Gastric Cancer

10.21203/rs.3.rs-1098024/v1 ◽

2021 ◽

Author(s):

Ruiping Yang ◽

Xiaojing Lin ◽

Chunhui Tao ◽

Ruixue Jiang

Keyword(s):

Gastric Cancer ◽

Molecular Dynamics ◽

Molecular Docking ◽

Signaling Pathway ◽

Molecular Mechanism ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Dynamics Simulation ◽

Network Pharmacology ◽

Active Ingredients

Abstract BackgroundBuzhong Yiqi Decoction (BZYQD) has been widely accepted as an alternative treatment for gastric cancer (GC) in China. The present study set out to determine the potential molecular mechanism of BZYQD in the treatment of GC by means of network pharmacology, molecular docking, and molecular dynamics simulation.MethodsThe potential active ingredients and targets of BZYQD were screened out through the Traditional Chinese Medicine Systems Pharmacology (TCMSP). GC-related targets were screened out through the GeneCards database, and the intersection targets of BZYQD and GC were obtained by using the Venn diagram online tool. Then, the TCM-Active Ingredient-Target network was constructed by using the Cytoscape, and the protein-protein interaction (PPI) network was constructed by using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the effective targets of BZYQD in GC were performed through the Metascape platform. Finally, the molecular docking between the compounds and the target proteins was performed by using the AutoDock Vina software. The simulation of molecular dynamics was conducted for the optimal protein-ligand complex obtained by molecular docking using the Amber18 software.ResultsA total of 150 active ingredients of BZYQD were retrieved, corresponding to 136 targets of GC. The key active ingredients were quercetin, kaempferol, nobiletin, naringenin, and formononetin. The core targets were AKT1, STAT3, TP53, MAPK1, and MAPK3. GO functional enrichment analysis showed that BZYQD treated GC by affecting various biological processes such as oxidative stress, chemical stress, lipopolysaccharide reaction, and apoptosis. KEGG pathway enrichment analysis indicated that the apoptosis signaling pathway, PI3K/Akt signaling pathway, proteoglycan in cancer, IL-17 signaling pathway, TNF signaling pathway, and HIF-1 signaling pathway were involved. Molecular docking results revealed the highest binding energy for MAPK3 and naringenin. The stable binding of MAPK3 and naringenin was also demonstrated in the molecular dynamics simulation test, with the binding free energy of -25kcal/mol.ConclusionThis study preliminarily revealed the multi-component, multi-target, and multi-pathway characteristics of BZYQD against GC, laying a scientific basis for further research on the molecular mechanism of BZYQD.

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Integrating Data Mining, Network Pharmacology, and Molecular Docking Verification to Investigate the Molecular Mechanism of Traditional Chinese Medicine Prescriptions for Treating Male Infertility

10.21203/rs.3.rs-264555/v1 ◽

2021 ◽

Author(s):

Xue Bai ◽

Yibo Tang ◽

Qiang Li ◽

Guimin Liu ◽

Dan Liu ◽

...

Keyword(s):

Data Mining ◽

Molecular Docking ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Male Infertility ◽

Signaling Pathway ◽

Molecular Mechanism ◽

Oxidant Stress ◽

Estrogen Signaling ◽

Network Pharmacology

Abstract Background: Male infertility (MI) affects almost 5% adult men worldwide, and 75% of these cases are unexplained idiopathic. There are limitations in the current treatment due to the unclear mechanism of MI, which highlight the urgent need for a more effective strategy or drug. Traditional Chinese Medicine (TCM) prescriptions have been used to treat MI for thousands of years, but their molecular mechanism is not well defined. Methods: Aiming at revealing the molecular mechanism of TCM prescriptions on MI, a comprehensive strategy integrating data mining, network pharmacology, and molecular docking verification was performed. Firstly, we collected 289 TCM prescriptions for treating MI from National Institute of TCM Constitution and Preventive Medicine for 6 years. Then, Core Chinese Materia Medica (CCMM), the crucial combination of TCM prescriptions, was obtained by the TCM Inheritance Support System from China Academy of Chinese Medical Sciences. Next, the components and targets of CCMM in TCM prescriptions and MI-related targets were collected and analyzed through network pharmacology approach.Results: The results showed that the molecular mechanism of TCM prescriptions for treating MI are regulating hormone, inhibiting apoptosis, oxidant stress and inflammatory. Estrogen signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and TNF signaling pathway are the most important signaling pathways. Molecular docking experiments were used to further validate network pharmacology results. Conclusions: This study not only discovers CCMM and the molecular mechanism of TCM prescriptions for treating MI, but may be helpful for the popularization and application of TCM treatment.

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Molecular Mechanism of Gelsemium elegans (Gardner and Champ.) Benth. Against Neuropathic Pain Based on Network Pharmacology and Experimental Evidence

Frontiers in Pharmacology ◽

10.3389/fphar.2021.792932 ◽

2022 ◽

Vol 12 ◽

Author(s):

Wancai Que ◽

Zhaoyang Wu ◽

Maohua Chen ◽

Binqing Zhang ◽

Chuihuai You ◽

...

Keyword(s):

Molecular Dynamics ◽

Neuropathic Pain ◽

Molecular Dynamics Simulation ◽

Experimental Evidence ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Dynamics Simulation ◽

Network Pharmacology ◽

Holistic View

Gelsemium elegans (Gardner and Champ.) Benth. (Gelsemiaceae) (GEB) is a toxic plant indigenous to Southeast Asia especially China, and has long been used as Chinese folk medicine for the treatment of various types of pain, including neuropathic pain (NPP). Nevertheless, limited data are available on the understanding of the interactions between ingredients-targets-pathways. The present study integrated network pharmacology and experimental evidence to decipher molecular mechanisms of GEB against NPP. The candidate ingredients of GEB were collected from the published literature and online databases. Potentially active targets of GEB were predicted using the SwissTargetPrediction database. NPP-associated targets were retrieved from GeneCards, Therapeutic Target database, and DrugBank. Then the protein-protein interaction network was constructed. The DAVID database was applied to Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis. Molecular docking was employed to validate the interaction between ingredients and targets. Subsequently, a 50 ns molecular dynamics simulation was performed to analyze the conformational stability of the protein-ligand complex. Furthermore, the potential anti-NPP mechanisms of GEB were evaluated in the rat chronic constriction injury model. A total of 47 alkaloids and 52 core targets were successfully identified for GEB in the treatment of NPP. Functional enrichment analysis showed that GEB was mainly involved in phosphorylation reactions and nitric oxide synthesis processes. It also participated in 73 pathways in the pathogenesis of NPP, including the neuroactive ligand-receptor interaction signaling pathway, calcium signaling pathway, and MAPK signaling pathway. Interestingly, 11-Hydroxyrankinidin well matched the active pockets of crucial targets, such as EGFR, JAK1, and AKT1. The 11-hydroxyrankinidin-EGFR complex was stable throughout the entire molecular dynamics simulation. Besides, the expression of EGFR and JAK1 could be regulated by koumine to achieve the anti-NPP action. These findings revealed the complex network relationship of GEB in the “multi-ingredient, multi-target, multi-pathway” mode, and explained the synergistic regulatory effect of each complex ingredient of GEB based on the holistic view of traditional Chinese medicine. The present study would provide a scientific approach and strategy for further studies of GEB in the treatment of NPP in the future.

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Study on the Mechanism of Baimai Ointment in the Treatment of Osteoarthritis Based on Network Pharmacology and Molecular Docking with Experimental Verification

Frontiers in Genetics ◽

10.3389/fgene.2021.750681 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yingyin Zhu ◽

Wanling Zhong ◽

Jing Peng ◽

Huichao Wu ◽

Shouying Du

Keyword(s):

Molecular Docking ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Signaling Pathway ◽

Molecular Mechanism ◽

Animal Experiments ◽

Tibetan Medicine ◽

Blue Staining ◽

Network Pharmacology ◽

Enzyme Linked Immunosorbent Assay

Purpose: The external preparation of the Tibetan medicine formula, Baimai ointment (BMO), has great therapeutic effects on osteoarthritis (OA). However, its molecular mechanism remains almost elusive. Here, a comprehensive strategy combining network pharmacology and molecular docking with pharmacological experiments was adopted to reveal the molecular mechanism of BMO against OA.Methods: The traditional Chinese medicine for systems pharmacology (TCMSP) database and analysis platform, traditional Chinese medicine integrated database (TCMID), GeneCards database, and DisGeNET database were used to screen the active components and targets of BMO in treating OA. A component–target (C-T) network was built with the help of Cytoscape, and the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment through STRING. Autodock Tools which was used to dock the key components and key target proteins was analyzed. Animal experiments were performed to verify the key targets of BMO. Hematoxylin–eosin and toluidine blue staining were used to observe the pathology of joints. Protein expression was determined using enzyme-linked immunosorbent assay.Results: Bioactive compounds and targets of BMO and OA were screened. The network analysis revealed that 17-β-estradiol, curcumin, licochalone A, quercetin, and glycyrrhizic acid were the candidate key components, and IL6, tumor necrosis factor (TNF), MAPK1, VEGFA, CXCL8, and IL1B were the candidate key targets in treating OA. The KEGG indicated that the TNF signaling pathway, NF-κB signaling pathway, and HIF-1 signaling pathway were the potential pathways. Molecular docking implied a strong combination between key components and key targets. The pathology and animal experiments showed BMO had great effects on OA via regulating IL6, TNF, MAPK1, VEGFA, CXCL8, and IL1B targets. These findings were consistent with the results obtained from the network pharmacology approach.Conclusion: This study preliminarily illustrated the candidate key components, key targets, and potential pathways of BMO against OA. It also provided a promising method to study the Tibetan medicine formula or external preparations.

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Molecular Mechanism of Danxiong Tongmai Granules in Treatment of Coronary Heart Disease

10.21203/rs.3.rs-784340/v1 ◽

2021 ◽

Author(s):

Jiahao Ye ◽

Ruiping Yang ◽

Zhixi Hu ◽

Lin Li ◽

Senjie Zhong ◽

...

Keyword(s):

Molecular Dynamics ◽

Coronary Heart Disease ◽

Heart Disease ◽

Molecular Docking ◽

Enrichment Analysis ◽

Dynamics Simulation ◽

Venn Diagram ◽

Network Pharmacology ◽

Ppi Network ◽

Ligand Interaction

Abstract Background: Network pharmacology has been widely adopted for mechanistic studies of Traditional Chinese Medicines (TCM). The present study uses network pharmacology to investigate the main ingredients, targets and pathways of Danxiong Tongmai Granules (DXTMG) in the treatment of coronary heart disease (CHD). We aim to validate our findings using molecular docking and molecular dynamics simulations.Methods: TCM compounds and targets were identified via searches in the BATMAN-TCM database, and the GeneCards database were used to obtain the main target genes involved in CHD, We combined disease targets with the drug targets to identify common targets, and draw a Venn diagram to visualize the results. The "TCM-compound-target" network was plotted using Cytoscape 3.7.2 software and a protein-protein interaction (PPI) network was constructed using the STRING database from which core targets were obtained. Gene ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for common drug-disease targets using R Version 4.0.4 (64 bit) software. Molecular docking of core protein-small molecule ligand interaction was modeled using AutoDock Vina software. A simulation of molecular dynamics was conducted for the optimal protein-ligand complex obtained by molecular docking using Amber18 software.Results: 162 potential targets of DXTMG involved in CHD were identified. These included INS, ALB, IL-6 and TNF according to PPI network studies. GO enrichment analysis identified a total of 3365 GO pathways, including 3049 biological process pathways (BP) concerned with the heart and circulatory system;109 cellular component (CC) pathways, including cation channels and membrane rafts and 207 molecular function (MF) pathways related to receptor ligands and activators. KEGG analysis revealed a total of 137 pathways (p<0.05), including those related to AGE-RAGE signaling associated with diabetic complications, fluid shear stress and atherosclerosis. Molecular docking revealed the highest binding energy for Neocryptotanshinone Ii (the key compound of DXTMG) and TNF. Molecular dynamics simulation indicated stable binding for TNF-Neocryptotanshinone Ii with strong hydrophobic interactions mediated predominantly by the hydrophobic residues, Leu279, Val280 and Phe278 plus hydrogen-bonding with Leu279.Conclusion: The present study reveals novel insights into the mechanism of DXTMG in treating CHD. DXTMG can influence oxidative stress、inflammation response and regulating cardiomyocytes, thereby reducing the occurrence and development of CHD.

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Network pharmacology and molecular docking study on the mechanism of colorectal cancer treatment using Xiao-Chai-Hu-Tang

PLoS ONE ◽

10.1371/journal.pone.0252508 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252508

Author(s):

Jingyun Jin ◽

Bin Chen ◽

Xiangyang Zhan ◽

Zhiyi Zhou ◽

Hui Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Molecular Docking ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Signaling Pathways ◽

Target Genes ◽

Network Pharmacology ◽

Signal Pathways ◽

Active Ingredients ◽

Molecular Docking Study

Background and objective We aimed to predict the targets and signal pathways of Xiao-Chai-Hu-Tang (XCHT) in the treatment of colorectal cancer (CRC) based on network pharmacology, just as well as to further analyze its anti-CRC material basis and mechanism of action. Methods We adopted Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and Traditional Chinese Medicine Integrated Database (TCMID) databases to screen the active ingredients and potential targets of XCHT. CRC-related targets were retrieved by analyzing published microarray data (accession number GSE110224) from the Gene Expression Omnibus (GEO) database. The common targets were used to construct the “herb-active ingredient-target” network using the Cytoscape 3.8.0 software. Next, we constructed and analyzed protein-to-protein interaction (PPI) using BisoGenet and CytoNCA plug-in in Cytoscape. We then performed Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses of target genes using the R package of clusterProfiler. Furthermore, we used the AutoDock Tools software to conduct molecular docking studies on the active ingredients and key targets to verify the network pharmacological analysis results. Results We identified a total of 71 active XCHT ingredients and 20 potential anti-CRC targets. The network analysis revealed quercetin, stigmasterol, kaempferol, baicalein, and acacetin as potential key compounds, and PTGS2, NR3C2, CA2, and MMP1 as potential key targets. The active ingredients of XCHT interacted with most CRC disease targets. We showed that XCHT’s therapeutic effect was attributed to its synergistic action (multi-compound, multi-target, and multi-pathway). Our GO enrichment analysis showed 46 GO entries, including 20 biological processes, 6 cellular components, and 20 molecular functions. We identified 11 KEGG signaling pathways, including the IL-17, TNF, Toll-like receptor, and NF-kappa B signaling pathways. Our results showed that XCHT could play a role in CRC treatment by regulating different signaling pathways. The molecular docking experiment confirmed the correlation between five core compounds (quercetin, stigmasterol, kaempferol, baicalein, and acacetin) just as well as PTGS2, NR3C2, CA2, and MMP1. Conclusion In this study, we described the potential active ingredients, possible targets, and key biological pathways responsible for the efficacy of XCHT in CRC treatment, providing a theoretical basis for further research.

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Research on Saponin Active Ingredient of Stir-Fried Dolichos Lablab L. Kernel for Treatment of Type-2 Diabetes Based on UHPLC-Q-Exactive Orbitrap MS and Network Pharmacology

10.21203/rs.3.rs-98583/v1 ◽

2020 ◽

Author(s):

Han Jun ◽

Liangzi Fang ◽

Qinfang Zheng

Keyword(s):

Type 2 Diabetes ◽

Molecular Dynamics ◽

Molecular Docking ◽

Molecular Dynamics Simulation ◽

Dynamics Simulation ◽

Network Pharmacology ◽

Dolichos Lablab ◽

Q Exactive ◽

Orbitrap Ms

Abstract BackgroundAlthough the clinical effect of stir-fried Dolichos lablab L. kernel has been approved in modern traditional Chinese medicine, existing associated studies mainly focus on its clinical studies and chemical ingredients. However, there are few studies on pharmacodynamics material basis and molecular mechanism of stirfried Dolichos lablab L. kernel in treatment of type-2 diabetes(T2DM), thus restricting the further development and utilization of stir-fried Dolichos lablab L. kernel.MethodsA qualitative analysis on saponin chemical ingredients of stir-fried Dolichos lablab L. kernel was performed using UHPLC-Q-Exactive Orbitrap MS. A total of 10 saponin ingredients were selected. Moreover, target screening, biological process and metabolism pathway analysis were accomplished by network pharmacology. Four key proteins(EGFR, IGF1, MAPK1 and PIK3R1) of type-2 diabetes were selected for molecular docking verification with saponin ingredients. Specifically, molecular dynamics simulation of ingredients which have strong bindings with proteins was conducted. ResultsIn this study, 16 saponin ingredients were identified from stir-fried Dolichos lablab L. kernel. There were 91 intersection targets and the KEGG pathway enrichment involved 20 relevant pathways. According to the molecular docking verification, saponin ingredients of stir-fried Dolichos lablab L. kernel can form stable binding with key protein targets. The molecular dynamics simulation further verifies stability and reasonability of the docking results. ConclusionsThis study provides references to identification of efficient ingredients of stir-fried Dolichos lablab L. kernel, screening of quality markers and explanation of relevant action mechanism by combining UHPLC-Q-Exactive Orbitrap MS and network pharmacology.

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The Analysis of The Application and Mechanism of Traditional Chinese Medicine in Osteoarthritis Based on Data Mining and Network Pharmacology

10.21203/rs.3.rs-113819/v1 ◽

2020 ◽

Author(s):

Jie YANG ◽

Dijin JIAO ◽

Guoguang Zhang ◽

Juntong LIU ◽

Chao QU ◽

...

Keyword(s):

Data Mining ◽

Molecular Docking ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Cell Metabolism ◽

Enrichment Analysis ◽

Network Pharmacology ◽

String Database ◽

The Core ◽

Drug Molecules

Abstract Background: Using Data Mining to retrieve the core drug of osteoarthritis in clinic, predicting the drug molecular action target through the Network Pharmacology, combining with the related targets of osteoarthritis to identify the key nodes of the interaction, exploring the pharmacological mechanism of Traditional Chinese Medicine against osteoarthritis and other possible mechanisms of actions. Methods: Pubmed, CNKI, VIP, CBM and WanFang Database was used to retrieve the commonly used therapeutic formulations for osteoarthritis patients in clinical, and screen out the core drugs through the Ancient and Modern Medical Case Cloud Platform and software Gephi, filtered out the core drug molecules and targets combined with TCMSP database and the targets of osteoarthritis in Genecard, OMIM database, impoting those datas into R project and Cytoscape to construct the intersection model of Drug molecule-osteoarthritis, carrying out PPI network and GO and KEGG enrichment analysis with String database. Vina molecular docking was implemented to draw molecular docking diagram, and the results were analyzed after comprehensive analysis. Results: The core drug pairs were identified as "Eucommiae Cortex - Achyranthis Bidentatae Radix" through correlation analysis, complex network analysis basing on the coefficient. "Eucommiae Cortex - Achyranthis Bidentatae Radix" can intervene cell behaviors through multiple pathways and regulate cell metabolism, cytokine synthesis, oxidative , cellular immunity as a consequence of topology analysis in String Database. Conclusions: "Eucommia bark - achyranthes" drug molecules can be combined with the target to produce hydrogen bond, hydrophobic function and Pi-Pi directly or indirectly affecting the corresponding targets, to participate in the regulation of osteogenesis and osteoclast proliferation, protect the extracellular matrix, inhibition of cell apoptosis and anti-inflammatory for resistance to osteoarthritis, also, providing the basis for interpretation of its action mechanism.

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