UPLC-Q/TOF MS Analysis Of GP Metabolism In RAW264.7Cell Inflammation Model Interfering By NSAIDs

RAW264.7cell line is macrophages leukemia cells in mice, glycerophospholipidplays a key role in inflammatory response, immune response and devoured reaction.This research discussed glycerophospholipid biomarkers occuring and resolvinginflammation using lipidomics methods.1.UPLC-Q/TOF MS analysis of glycerophospholipid in RAW264.7cell.An inflammation model of murine macrophage RAW264.7cell line to Kdo2-Lipid A was developed in this research, Using the analysis technology of ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry(UPLC/Q-TOF MS) in both positive and negative ESI ion mode, a rapid and sensitivemethod for analyzing the glycerophospholipid of RAW264.7cell was developed,78glycerophospholipid including22PE,49PC,4PG,1PI and2unknown were identifiedthrough MS precise molecular weight informations, MSMS cracking fragmentsinformations,compared to standard substance and references. Five glycerophospho-lipids maybe were first discovered in RAW264.7cell.2. UPLC-Q/TOF MS analysis of glycerophospholipids in different conditions’RAW264.7cell.The inflammation model of murine macrophage RAW264.7cell line to Kdo2-Lipid A was developed,then interfered by Aspirin,Brufen and Indomethacin separate-ly.This experiment was divided into5groups: the blank group, inflammation modelgroup, aspirin treatment group, ibuprofen treatment group and indomethacin treatmentgroup.These five groups were separated and analyzed using UPLC/Q-TOF MS,thenMetabolic TIC were established.3. Chemometrics analysisAll groups including the blank group, inflammation model group and NSAIDstreatment groups were analyzed by PCA and PLS-DA, meanwhile,the relationships offive groups were reflected. The blank group and inflammation model group, inflamma -tion model group and each treatment group were classified using PCLDA andPLSDA, The results showed that NSAIDs’ antiinflammatory effect was related withglycerophospholipid metabolites.Sixty–four potential biomarks that occuring andresolving inflammation were selected through t-test,Shrinke,PCLDA,VIP and PLSDAmethods.Combining with lipid databases,lipid metabolism networks andbioinformation,we know that both PC (16:0/18:1) and PE(18:0/18:1) metabolicregulation were throughout the occurrence of inflammation and resolving process,maybe the important glycerophospholipids biomarkers that occuring and resolvinginflammation, in addition, other potential lipid biomarkers still need further research.