Videos

Student Presentation: Deciphering the Gating Properties of P2X4 Receptor Channels using Markov State Models

Presenter
August 13, 2014
Abstract
Purinergic P2X receptors are a family of seven (labeled P2X1-7R) ATP-gated non-selective cation channels, ubiquitously expressed in the body. Abnormalities in them could lead to tissue inflammation and chronic pain. All members of this family are trimeric channels with three agonist binding sites that are activated and opened when occupied by ATP. The kinetics of activation (rising phase of current), desensitization (decay of current in the presence of ATP) and deactivation (decay of current after removal of ATP) are receptor-specific. The P2X4 subunit is the most widely distributed in the brain. Homomeric P2X4Rs desensitize with moderate rates, and desensitization is coupled to extensive internalization and recycling of receptors to the membrane. P2X4R is allosterically modulated by ivermectin (IVM), which increases both the ATP potency and the peak amplitude of the current (i.e., induces receptor sensitization), reduces the desensitization rate, greatly prolongs deactivation of current after ATP removal, and alters the recycling process. Many aspects of P2X4R gating have not yet been clarified and there is no comprehensive mathematical model describing its kinetics. No rationale has been provided for how IVM rescues receptors from desensitization, why it slows receptor deactivation, or why it affects receptor recycling. Using electro-physiological (current-recording) data from Stojilkovic Lab (NIH), we will be developing Markov state models and conducting systematic model comparisons and parameter optimization methods by utilizing Markov Chain Monte Carlo techniques based on Bayesian Theory, to determine the most likely model and parameter set(s) that can capture the kinetics of these receptors. The goal is to produce a model that can successfully explain the underlying mechanism of desensitization, recycling, and IVM-dependent sensitization in P2X4Rs. The parameter set(s) will be retrieved from probability distributions generated from these iterative methods.