Modeling species-specific diacylglycerol dynamics in the RAW 264.7 macrophage

Abstract

A mathematical model of the G protein signaling pathway in RAW 264.7 macrophages downstream of P2Y(6) receptors activated by the ubiquitous signaling nucleotide uridine 5'-diphosphate is developed. The model, which is based on time-course measurements of inositol trisphosphate, cytosolic calcium, and diacylglycerol, focuses particularly on differential dynamics of multiple chemical species of diacylglycerol. When using the canonical pathway representation, the model predicted that key interactions were missing from the current network structure. Indeed, the model suggested that accurate depiction of experimental observations required an additional branch to the signaling pathway. An intracellular pool of diacylglycerol is immediately phosphorylated upon stimulation of an extracellular receptor for uridine 5'-diphosphate and subsequently used to aid replenishment of phosphatidylinositol. As a result of sensitivity analysis of the model parameters, key predictions can be made regarding which of these parameters are the most sensitive to perturbations and are therefore most responsible for output uncertainty.

Figure 1

Timecourse of stimulation with 25 µM UDP in RAW 264.7 cells. Solid black lines represent model simulations for the system of equations using the two-pool model (with Eq. (7) and Eq. (8)), whereas gray lines represent simulations using the canonical pathway for DAG (Eq. (6)). (A) 38:4 DAG response (representative of the response of most polyunsaturated fatty acid-containing DAG species). (B) 34:1 DAG response (representative of the response of most mono- and di-unsaturated fatty acid-containing DAG species). Data points in (A) and (B) contain nine replicates performed on three different experimental days, with error bars = mean ± 1 SE. Units are total change in ng over baseline levels in ~ 8×10⁶ cells. (C) IP₃ response in pmols per ~ 4.5×10⁵ cells. Points represent the average of four experiments, and error bars = mean ± 1 SE. (D) Ca²⁺ response in µM. Red curve is a representative Ca²⁺ trace taken from the UDP experiments within the AfCS single ligand screen as described in the “Intracellular free calcium assay” portion of the Methods section.

Figure 1

Timecourse of stimulation with 25 µM UDP in RAW 264.7 cells. Solid black lines represent model simulations for the system of equations using the two-pool model (with Eq. (7) and Eq. (8)), whereas gray lines represent simulations using the canonical pathway for DAG (Eq. (6)). (A) 38:4 DAG response (representative of the response of most polyunsaturated fatty acid-containing DAG species). (B) 34:1 DAG response (representative of the response of most mono- and di-unsaturated fatty acid-containing DAG species). Data points in (A) and (B) contain nine replicates performed on three different experimental days, with error bars = mean ± 1 SE. Units are total change in ng over baseline levels in ~ 8×10⁶ cells. (C) IP₃ response in pmols per ~ 4.5×10⁵ cells. Points represent the average of four experiments, and error bars = mean ± 1 SE. (D) Ca²⁺ response in µM. Red curve is a representative Ca²⁺ trace taken from the UDP experiments within the AfCS single ligand screen as described in the “Intracellular free calcium assay” portion of the Methods section.

Figure 1

Timecourse of stimulation with 25 µM UDP in RAW 264.7 cells. Solid black lines represent model simulations for the system of equations using the two-pool model (with Eq. (7) and Eq. (8)), whereas gray lines represent simulations using the canonical pathway for DAG (Eq. (6)). (A) 38:4 DAG response (representative of the response of most polyunsaturated fatty acid-containing DAG species). (B) 34:1 DAG response (representative of the response of most mono- and di-unsaturated fatty acid-containing DAG species). Data points in (A) and (B) contain nine replicates performed on three different experimental days, with error bars = mean ± 1 SE. Units are total change in ng over baseline levels in ~ 8×10⁶ cells. (C) IP₃ response in pmols per ~ 4.5×10⁵ cells. Points represent the average of four experiments, and error bars = mean ± 1 SE. (D) Ca²⁺ response in µM. Red curve is a representative Ca²⁺ trace taken from the UDP experiments within the AfCS single ligand screen as described in the “Intracellular free calcium assay” portion of the Methods section.

Figure 1

Timecourse of stimulation with 25 µM UDP in RAW 264.7 cells. Solid black lines represent model simulations for the system of equations using the two-pool model (with Eq. (7) and Eq. (8)), whereas gray lines represent simulations using the canonical pathway for DAG (Eq. (6)). (A) 38:4 DAG response (representative of the response of most polyunsaturated fatty acid-containing DAG species). (B) 34:1 DAG response (representative of the response of most mono- and di-unsaturated fatty acid-containing DAG species). Data points in (A) and (B) contain nine replicates performed on three different experimental days, with error bars = mean ± 1 SE. Units are total change in ng over baseline levels in ~ 8×10⁶ cells. (C) IP₃ response in pmols per ~ 4.5×10⁵ cells. Points represent the average of four experiments, and error bars = mean ± 1 SE. (D) Ca²⁺ response in µM. Red curve is a representative Ca²⁺ trace taken from the UDP experiments within the AfCS single ligand screen as described in the “Intracellular free calcium assay” portion of the Methods section.

Figure 2

Proposed two-pool model for DAG kinetics post-agonist stimulation with UDP. Initial production of a single species of DAG from the hydrolysis of PIP₂ in pool 1 (plasma membrane) is offset by phosphorylation of DAG by a DAG kinase in pool 2 (which we conjecture to be the Endoplasmic Reticulum) to aid in the replacement of PI. The second wave of DAG is a result of resynthesis of PIP₂, which is then hydrolyzed to form DAG and IP₃. While production of each species of DAG will follow this diagram, experimental data suggests that rates of production, degradation and transition between the pools vary between species. Abbreviations not mentioned in the text are as follows: PI4K, phosphatidylinositol 4-kinase; PI5K, phosphatidylinositol 5-kinase; DGK, DAG kinase; LPP, lipid phosphate phosphatase; IP4, inositol 4-phosphate; IP2, inositol diphosphate; IP, inositol phosphate; MAG, monoacylglycerol; TAG, triacylglycerol; GPL, glycerophospholipid; CDS, CDP-DAG synthase; PIS, phosphatidylinositol synthase; Ins, inositol.

Figure 3

Model simulations for timecourse of stimulation with 25 µM UDP in a single RAW 264.7 cell for remaining model variables. (A) Activated (solid line) and inactivated (dashed line) P2Y₆ surface receptors. (B) Total number of activated Gα·GTP. (C) Total number of PIP₂ available for hydrolysis.

Figure 3

Model simulations for timecourse of stimulation with 25 µM UDP in a single RAW 264.7 cell for remaining model variables. (A) Activated (solid line) and inactivated (dashed line) P2Y₆ surface receptors. (B) Total number of activated Gα·GTP. (C) Total number of PIP₂ available for hydrolysis.

Figure 3

Model simulations for timecourse of stimulation with 25 µM UDP in a single RAW 264.7 cell for remaining model variables. (A) Activated (solid line) and inactivated (dashed line) P2Y₆ surface receptors. (B) Total number of activated Gα·GTP. (C) Total number of PIP₂ available for hydrolysis.

Figure 4

Standardized Regression Coefficients (SRCs) corresponding to most sensitive parameters for IP₃. Most sensitive parameters are as follows: receptor rate of phosphorylation, k_p (), receptor recycling rate, k_r (), PIP₂ replenishment rate, k_rep (), and IP₃ degradation rate, k_d3 (). Inset: $R_y^2$ values for all time points are ≥ 0.8, enusuring that the SRCs for IP₃ are good measures of sensitivity.

Figure 5

Standardized Regression Coefficients (SRCs) corresponding to most sensitive parameters for DAG. Most sensitive parameters are as follows: degradation rate of DAG_p1, k_dp1 (), production rate of DAG_p2, k_ap2 (), degradation rate of DAG_p2, k_dp2 (), receptor rate of phosphorylation, k_p (), and receptor recycling rate, k_r (). Inset: Since $R_y^2$ values for all time points are ≥ 0.95, the SRCs for DAG are good measures of sensitivity.

Figure 6

Changes in 34:1 DAG response to 50% variations in (A) the rate of degradation of pool two 34:1 DAG, k_dp2, (B) the rate of activation of pool two 34:1 DAG, k_ap2, and (C) the rate of degradation of pool one 34:1 DAG, k_dp1, from their nominal values of 1.3 × 10⁻³ s⁻¹, 3.74 × 10⁻³ s⁻¹ and 1.73 × 10⁻³ s⁻¹, respectively.

Figure 6

Changes in 34:1 DAG response to 50% variations in (A) the rate of degradation of pool two 34:1 DAG, k_dp2, (B) the rate of activation of pool two 34:1 DAG, k_ap2, and (C) the rate of degradation of pool one 34:1 DAG, k_dp1, from their nominal values of 1.3 × 10⁻³ s⁻¹, 3.74 × 10⁻³ s⁻¹ and 1.73 × 10⁻³ s⁻¹, respectively.

Figure 6

Changes in 34:1 DAG response to 50% variations in (A) the rate of degradation of pool two 34:1 DAG, k_dp2, (B) the rate of activation of pool two 34:1 DAG, k_ap2, and (C) the rate of degradation of pool one 34:1 DAG, k_dp1, from their nominal values of 1.3 × 10⁻³ s⁻¹, 3.74 × 10⁻³ s⁻¹ and 1.73 × 10⁻³ s⁻¹, respectively.