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.

Figure 1. Computational simulation of the COX-dependent eicosanoid profiles in EPA-supplemented ATP-stimulated RAW264.7 cells

(A) The chemical structures of AA, EPA and DHA. (B) Experimental conditions. AA and EPA were supplemented in RAW264.7 cell culture media for 24 hr before stimulation. Then, cells were stimulated with ATP and the culture media were collected at the indicated time points to measure the eicosanoid levels by liquid chromatography-tandem mass spectrometry (LC-MS). (C) Simplified AA and EPA metabolic pathways via the COX pathway. The measured and non-measured metabolites are given in black and gray letters, respectively. Arrows indicate the enzymatic and non-enzymatic reactions and the Ø symbol represents additional metabolic pathways including degradation. (D) The experimental data (Exp) for EPA and non-supplement represent means ± SEM. The simulation results (Fit) are shown as red and blue curves for EPA supplemented and non-supplemented data, respectively.

Figure 2. Parametric sensitivity analysis and leave-one-metabolite-out analysis

(A) Slope of the sensitivity curves are shown as heat maps. Sensitivity of KCOX:EPA and EPA was found in the range of −4 – 0.5. (B) The simulation results of leave-one-metabolite (PGD₂)-out are shown as orange and light blue curves for EPA supplemented and non-supplemented data, respectively. The blue and red lines are simulation results obtained from Fig. 1D. (C) The estimated parameters by leave-one-metabolite (PGD₂)-out methods are compared with optimized parameters.

Figure 3. Computational prediction of COX-dependent eicosanoid profile in AA and DHA-supplemented ATP-stimulated RAW264.7

(A) The experimental data (Exp) for non-supplemented and AA-supplemented cases represent means ± SEM. The simulation results (Fit) are shown as green and blue curves for AA-supplemented and non-supplemented, respectively. (B) The experimental data (Exp) for non-supplemented and DHA-supplemented cases represent means ± SEM. The simulation results (Fit) are shown as brown and blue curves for DHA-supplemented and non-supplemented cases, respectively.

Figure 4. Computational simulation of COX activities

(A) The COX specific activities for AA were simulated with increasing concentrations of AA in the presence of EPA. (B) The COX specific activities for EPA were simulated with increasing concentrations of EPA in the presence of AA.