Computational Modeling of Competitive Metabolism between ω3- and ω6-Polyunsaturated Fatty Acids in Inflammatory Macrophages.

Abstract

Arachidonic acid (AA), a representative ω6-polyunsaturated fatty acid (PUFA), is a precursor of 2-series prostaglandins (PGs) that play important roles in inflammation, pain, fever, and related disorders including cardiovascular diseases. Eating fish or supplementation with the ω3-PUFAs such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) is widely assumed to be beneficial in preventing cardiovascular diseases. A proposed mechanism for a cardio-protective role of ω3-PUFAs assumes competition between AA and ω3-PUFAs for cyclooxygenases (COX), leading to reduced production of 2-series PGs. In this study, we have used a systems biology approach to integrate existing knowledge and novel high-throughput data that facilitates a quantitative understanding of the molecular mechanism of ω3- and ω6-PUFA metabolism in mammalian cells. We have developed a quantitative computational model of the competitive metabolism of AA and EPA via the COX pathway through a two-step matrix-based approach to estimate the rate constants. This model was developed by using lipidomic data sets that were experimentally obtained from EPA-supplemented ATP-stimulated RAW264.7 macrophages. The resulting model fits the experimental data well for all metabolites and demonstrates that the integrated metabolic and signaling networks and the experimental data are consistent with one another. The robustness of the model was validated through parametric sensitivity and uncertainty analysis. We also validated the model by predicting the results from other independent experiments involving AA- and DHA-supplemented ATP-stimulated RAW264.7 cells using the parameters estimated with EPA. Furthermore, we showed that the higher affinity of EPA binding to COX compared with AA was able to inhibit AA metabolism effectively. Thus, our model captures the essential features of competitive metabolism of ω3- and ω6-PUFAs.