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      Network Pharmacology Combined with Molecular Docking and Experimental Verification Reveals the Bioactive Components and Potential Targets of Danlong Dingchuan Decoction against Asthma

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          Abstract

          Background

          Danlong Dingchuan Decoction has a definite effect in the clinical treatment of asthma. This study aimed to explore the material and molecular biological basis of Danlong Dingchuan Decoction in treating asthma through network pharmacology combined with animal experiments.

          Materials and Methods

          First, the chemical constituents of Danlong Dingchuan Decoction were screened from the Traditional Chinese Medicine Systematic Pharmacology Analysis Platform (TCMSP) and the Traditional Chinese Medicine and Chemical Composition Database. Literature reports on asthma targets were obtained from the Online Mendelian Inheritance in Man (OMIM), Therapeutic Targets Database (TTD), and other databases. Then, the protein-protein interaction network was constructed according to the matching results of Danlong Dingchuan Decoction and asthma targets. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Finally, the interaction between the active compounds of Danlong Dingchuan Decoction and key targets was simulated using molecular docking. In animal experiments, ovalbumin was used to induce asthma in mice. After treating the mice by oral gavage administration of Danlong Dingchuan Decoction, the expression levels of tumor necrosis factor- α (TNF- α) and interleukin-1 β (IL-1 β) were detected in the lung tissue of the mice by enzyme-linked immunosorbent assay kit, whereas TLR4 mRNA expression was detected by quantitative reverse transcription-polymerase chain reaction.

          Results

          A total of 247 active compounds and 155 potential targets were obtained. Enrichment analysis showed that quercetin, xanthine, lysine, kaempferol, ß-sitosterol, and four other active compounds were the main components of Danlong Dingchuan Decoction; IL-6, TNF, CXCL8, VEGFA, MAPK3, IL-10, PTGS2, IL-1 β, IL-4, and TLR4 were the potential targets for therapy. KEGG analysis showed that the cAMP signaling pathway, cGMP-PKG signaling pathway, NF- κB signaling pathway, and PI3K-Akt signaling pathway might play an important role in treating asthma. Molecular docking analysis showed that quercetin combined well with TNF, CXCL8, and TLR4. Animal experiments showed that Danlong Dingchuan Decoction effectively reduced the expression levels of TNF- α, IL-4, TGF- β1, IL-6, IL-8, and IL-1 β in the lung tissue of asthmatic mice and inhibited TLR4 mRNA expression.

          Conclusions

          Danlong Dingchuan Decoction may act on key targets (such as IL-6, TNF, CXCL8, VEGFA, and MAPK3) with key active ingredients (such as quercetin, xanthine, lysine, kaempferol, and ß-sitosterol) to reduce the expression levels of IL-4, IL-6, IL-8, and other Th2 cytokines. This may be the mechanism by which Danlong Dingchuan Decoction reduces airway inflammation and treats asthma mediated by Th2 cytokines.

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          Most cited references46

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

            We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique. (c) 2009 Wiley Periodicals, Inc.
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              Network analyses in systems pharmacology.

              Systems pharmacology is an emerging area of pharmacology which utilizes network analysis of drug action as one of its approaches. By considering drug actions and side effects in the context of the regulatory networks within which the drug targets and disease gene products function, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. Systems pharmacology can provide new approaches for drug discovery for complex diseases. The integrated approach used in systems pharmacology can allow for drug action to be considered in the context of the whole genome. Network-based studies are becoming an increasingly important tool in understanding the relationships between drug action and disease susceptibility genes. This review discusses how analysis of biological networks has contributed to the genesis of systems pharmacology and how these studies have improved global understanding of drug targets, suggested new targets and approaches for therapeutics, and provided a deeper understanding of the effects of drugs. Taken together, these types of analyses can lead to new therapeutic options while improving the safety and efficacy of existing medications.
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                Author and article information

                Contributors
                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi
                1741-427X
                1741-4288
                2022
                10 February 2022
                10 February 2022
                : 2022
                : 7895271
                Affiliations
                1Clinical Medical College, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, China
                2Chengdu Xinjin District Hospital of Traditional Chinese Medicine, Chengdu, 611430, China
                3Suining First People's Hospital, Suining, 629000, China
                4Zigong Hospital of Traditional Chinese Medicine, Zigong 643000, China
                5Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610072, China
                Author notes

                Academic Editor: Guy Cohen

                Author information
                https://orcid.org/0000-0003-3838-978X
                https://orcid.org/0000-0002-3133-9221
                https://orcid.org/0000-0001-7576-8856
                https://orcid.org/0000-0002-7192-5532
                https://orcid.org/0000-0002-7466-9490
                https://orcid.org/0000-0002-0287-3169
                https://orcid.org/0000-0003-4731-6793
                Article
                10.1155/2022/7895271
                8853800
                d4e0a163-90b0-437d-b8c3-6378fa583450
                Copyright © 2022 Beibei Xue et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 September 2021
                : 13 January 2022
                : 17 January 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81574027
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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