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Computational Model of Ca2+ Wave Propagation in Human Retinal Pigment Epithelial ARPE-19 Cells

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Computational Model of Ca2+ Wave Propagation in Human Retinal Pigment Epithelial ARPE-19 Cells

Abstract Objective

Computational models of calcium (Ca2+) signaling have been constructed for several cell types. There are, however, no such models for retinal pigment epithelium (RPE). Our aim was to construct a Ca2+ signaling model for RPE based on our experimental data of mechanically induced Ca2+ wave in the in vitro model of RPE, the ARPE-19 monolayer. Methods

We combined six essential Ca2+ signaling components into a model: stretch-sensitive Ca2+ channels (SSCCs), P2Y2 receptors, IP3 receptors, ryanodine receptors, Ca2+ pumps, and gap junctions. The cells in our epithelial model are connected to each other to enable transport of signaling molecules. Parameterization was done by tuning the above model components so that the simulated Ca2+ waves reproduced our control experimental data and data where gap junctions were blocked. Results

Our model was able to explain Ca2+ signaling in ARPE-19 cells, and the basic mechanism was found to be as follows: 1) Cells near the stimulus site are likely to conduct Ca2+ through plasma membrane SSCCs and gap junctions conduct the Ca2+ and IP3 between cells further away. 2) Most likely the stimulated cell secretes ligand to the extracellular space where the ligand diffusion mediates the Ca2+ signal so that the ligand concentration decreases with distance. 3) The phosphorylation of the IP3 receptor defines the cell’s sensitivity to the extracellular ligand attenuating the Ca2+ signal in the distance. Conclusions

The developed model was able to simulate an array of experimental data including drug effects. Furthermore, our simulations predict that suramin may interfere ligand binding on P2Y2 receptors or accelerate P2Y2 receptor phosphorylation, which may partially be the reason for Ca2+ wave attenuation by suramin. Being the first RPE Ca2+ signaling model created based on experimental data on ARPE-19 cell line, the model offers a platform for further modeling of native RPE functions.

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