Anti-inflammatory effect and mechanism of active parts of Artemisia mongolica in LPS-induced Raw264.7 cells based on network pharmacology analysis.

Ethnopharmacological relevance: Artemisia mongolica is well known for its use in folk medicine, it is commonly used to alleviate a variety of diseases associated with inflammation, such as laryngitis, tonsillitis, headaches and hepatitis in northwest China. However, its anti-inflammatory mechanism is still unknown. Aim of the study: The most potential anti-inflammatory part (AMPA) was identified by screening individual parts of A. Mongolica. After the network pharmacological analysis, the anti-inflammation effects and molecular mechanisms of AMPA were evaluated in RAW264.7 cells induced by LPS. Materials and methods: AMPA was chosen as the most anti-inflammatory of the A. Mongolica, as measured by the effect of each part of the A. Mongolica on NO and COX-2. The chemical composition of AMPA was identified using HPLC-Q-TOF-MS/MS, and targets of bioactive chemicals and targets related to inflammation were found using open-source databases. The "Compound-targets" network and PPI network were established by combining compounds and overlapped targets, and targets in the PPI networks were analyzed by GO and KEGG enrichment. The RAW26.7 cells induced by LPS were used as a model of inflammation examination. MTT assay was performed to assess the cytotoxicity of AMPA on LPS-induced RAW264.7 cells. The level of NO was measured by the Griess method while the inflammatory factors were detected by ELISA. The protein expression levels of iNOS, COX-2, MAPK, NF-kappa B signaling pathway and AMPK/Nrf2-related proteins were determined by Western blot. The results of nuclear translocation of p65 and Nrf2 were analyzed by immunofluorescence assay. Results: A total of 18 compounds with potential bioactivity were identified, and after intersecting 640 compoundpredicted targets and 1608 inflammation targets, the compounds and intersected targets were utilized to structure "compound-target" and PPI networks. Among AMPA, AM6, AM7, AM11, AM8 and AM1 compounds were essential in the "compound-targets" network, meanwhile, TNF, RELA, MAPK1, NOS2, PRKAG, and PTGS2 targets play important roles in the PPI network. The top 10 terms and pathways were obtained based on GO and KEGG. The cell experiments show that 50 mu g/mL was the maximum concentration of AMPA without cytotoxicity in the LPS-induced RAW264.7 cell model. When compared with the LPS group, AMPA treatment not only effectively suppressed the generation of NO, TNF-alpha, IL-6, PGE2, IL-1 beta and MCP-1 in LPS-induced RAW264.7 cells, but also down-regulated the expression of COX-2, iNOS and the protein levels p-ERK, p-p38, p-I kappa B-alpha and p-p65, inhibited the nuclear translocation of p65. Furthermore, the expression levels of p-LKB1, p-AMPK, Nrf2 and HO-1 proteins were up-regulated and Nrf2 nuclear translocation was promoted. Conclusion: AMPA should be considered an anti-inflammatory agent for the results of network pharmacology and in vitro, which could inhibit the MAPK pathway and NF-kappa B pathway and activate the AMPK/Nrf2 pathway in LPS-stimulated RAW264.7 cells.