TRPV1 (transient receptor potential vanilloid subtype 1) receptors play a central role in nociception and inflammatory pain. Interestingly, both agonists and antagonists of TRPV1 have been tested therapeutically, suggesting that the regulation of TRPV1 activity is a complex balance between pro- and anti-pain signaling states. Understanding the complex regulation of these receptors, including their integration with other cellular signaling networks, requires mathematical modeling. In this study, we developed a mechanistic mathematical model of the regulation of the different subunits of TRPV1 channels in sensory neurons. Using this model, we investigated mechanisms by which inflammatory mediators in damaged tissues might sensitize TRPV1 to stimuli. We also investigated acute capsaicin-induced calcium-dependent desensitization of TRPV1 channels. Building upon previous literature from our laboratory and others, we postulated a four-state allosteric TRPV1 channel model. We assumed the activation and deactivation states of the channel were local minima in a complex energy landscape. Ion/Ligand binding, phosphorylation and dephosphorylation events along with external inputs such as temperature or applied voltage modulated the activation energy associated with the transition between discrete channel states. We then embedded this channel model into a larger continuum model of the regulation of TRPV1 channels by intracellular ion binding and phosphorylation/dephosphorylation. We extended our previous work on ATP- induced Ca2+ dynamics in peripheral nerve endings to include the TRPV1 model along with the ATP-induced PKC pathway, PGE2-induced PKC/PKA-dependent signaling, and the Ca2+-dependent CaMKII and calcineurin pathways. The aggregate model recapitulated and ultimately predicted features of TRPV1 channels including their combined responses to temperature, voltage and capsaicin binding. Sensitivity analysis of the aggregate model suggested that cellular signaling mechanism were more important than TRPV1 channel kinetics where Ca2+ levels were the most critical influence on TRPV1 modulation. Taken together, although limited to small part of the overall architecture of TRPV1-related pain signaling, the mathematical model presented here could be a useful starting point for further investigation of the many factors controlling TRPV1 activity and pain.
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