Network Pharmacology-Based Identification of Potential Targets of Lonicerae japonicae Flos Acting on Anti-Inflammatory Effects
Open Access
- 20 September 2021
- journal article
- research article
- Published by Hindawi Limited in BioMed Research International
- Vol. 2021, 1-18
- https://doi.org/10.1155/2021/5507003
Abstract
Lonicerae japonicae flos (LJF) is widely used for the treatment of inflammation-related diseases in traditional Chinese medicine (TCM). To clarify the anti-inflammatory mechanism of LJF, 29 compounds with high content in LJF were selected for network pharmacology. Then, a comprehensive network pharmacology strategy was implemented, which involved compound-inflammation-target construction, protein-protein interaction (PPI) network analysis, and enrichment analysis. Finally, molecular docking and in vitro experiments were performed to verify the anti-inflammatory activity and targets of the key compound. As a result, 279 inflammation-associated proteins were identified, which are mainly involved in the AGE/RAGE signaling pathway in diabetic complications, the HIF-1 signaling pathway, the PI3K-AKT signaling pathway, and EGFR tyrosine kinase inhibitor resistance. A total of 12 compounds were linked to more than 35 targets, including apigenin, kaempferol, quercetin, luteolin, and ferulic acid. The results of molecular docking showed that AKT has the most binding activity, exhibiting certain binding activity with 10 compounds, including vanillic acid, protocatechuic acid, secologanic acid, quercetin, and luteolin; the results of qRT-PCR and WB confirmed that two key compounds, secologanic acid and luteolin, could significantly decrease the secretion of TNF-α and the AKT expression of RAW264.7 murine macrophages stimulated by LPS (lipopolysaccharide). These results demonstrate that the comprehensive strategy can serve as a universal method to illustrate the anti-inflammatory mechanisms of traditional Chinese medicine by identifying the pathways or targets.Keywords
Funding Information
- National Natural Science Foundation of China (81903750, 81872963, 2017YFC1701503, 2019JZZY011020, 2020Z02)
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