Omega-3 (ω-3) polyunsaturated fatty acids (PUFAs) have beneficial effects in many pathological processes, especially cardiovascular disease, and their protective eicosanoid metabolites are thought to play important roles. However, how ω-3 PUFAs affect the eicosanoid profile has not been elucidated comprehensively. Here, we systematically analyzed the eicosanoid metabolites induced by ω-3 PUFA supplementation. We developed an LC-MS/MS-based method covering 32 arachidonic acid (ARA) metabolites and 37 ω-3 PUFA-derived products. The limits of detection for eicosanoids were between 0.0625 and 1 pg and the detection specificity was optimized. We then quantified eicosanoids in mouse and human plasma and mouse aorta samples after ω-3 PUFA supplementation. Levels of EPA hydroxyl products, 4-HDoHE, 17,18-EEQ, 17,18-DiHETE, TXB2, and LXA4 were significantly changed in both mouse samples, and those of 2-series PGs, EDPs and DHA hydroxyl products were changed in aorta samples. Correlation network analysis of mouse plasma data revealed that some eicosanoids had higher connection degree or betweenness centrality score than others after ω-3 PUFA supplementation. Eicosanoids in human plasma were profiled across five time points after ω-3 PUFA supplementation. Fuzzy c-mean clustering algorithm suggested that the time curves of eicosanoid activity could be described with three kinetic patterns: sustained upregulation, short-term upregulation, and downregulation. This is the first systematic profiling of eicosanoids with ω-3 PUFA supplementation. The highly specific eicosanoid metabolomic and related data analysis methods would be powerful tools for comprehensive eicosanoid study.
Keywords: dynamic analysis; eicosanoids; metabolomics; network analysis; omega-3 polyunsaturated fatty acid; quantitative profiling.