GC-MS analysis of fatty acid metabolomics in RAW264.7 cell inflammatory model intervened by non-steroidal anti-inflammatory drugs and a preliminary study on the anti-inflammatory effects of NLRP3 signaling pathway.

Abstract

To explore the metabolomics of fatty acids and biological information of related markers in a RAW264.7 cell inflammation model. RAW264.7 macrophage inflammation model was induced by LPS, and RAW264.7 cells were treated with non-steroidal anti-inflammatory drugs (NSAIDs). The fatty acid compositions were identified by GC-MS, combined with standard product spectrum information and NIST (National Institute of Standards and Technology) database. Using chemometrics and Analysis of Variance (ANOVA), the components with VIP > 1 and P < 0.05 were selected as significant difference markers, and combined with biological methods to explore the biological significance of them. GC-MS identified 21 fatty acids in RAW264.7 cells, and screened significant difference biomarkers in each group. Among these biomarkers, C20:5 and C22:6 had significant changes in pairwise comparison among each group. Through ELISA, polymerase chain reaction (PCR) and Western Blot methods, the mRNA and protein expressions of IL-1β, NLRP3, GPR120 and β-Arrestin-2 were up-regulated after RAW264.7 cells induced by LPS and nigericin, and decreased after drug intervention. It indicated that the signal pathway centered on NLRP3 inflammasome was involved in the anti-inflammatory process of ibuprofen. It was the first time to study fatty acid metabolomics in RAW264.7 cell inflammatory model by GC-MS combined with chemometrics. The anti-inflammatory mechanism of ibuprofen was explained from NLRP3 inflammasome perspective without precedent, which enriched the research on the signal pathway of ibuprofen anti-inflammatory mechanism.