PubMed 15004209
Automatically associated channels: KChIP2 , Kv1.4 , Kv11.1 , Kv3.1 , Kv4.3 , Slo1
Title: Activation properties of Kv4.3 channels: time, voltage and [K+]o dependence.
Authors: Shimin Wang, Vladimir E Bondarenko, Yujie Qu, Michael J Morales, Randall L Rasmusson, Harold C Strauss
Journal, date & volume: J. Physiol. (Lond.), 2004 Jun 15 , 557, 705-17
PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/15004209
Abstract
Rapidly inactivating, voltage-dependent K(+) currents play important roles in both neurones and cardiac myocytes. Kv4 channels form the basis of these currents in many neurones and cardiac myocytes and their mechanism of inactivation appears to differ significantly from that reported for Shaker and Kv1.4 channels. In most channel gating models, inactivation is coupled to the kinetics of activation. Hence, there is a need for a rigorous model based on comprehensive experimental data on Kv4.3 channel activation. To develop a gating model of Kv4.3 channel activation, we studied the properties of Kv4.3 channels in Xenopus oocytes, without endogenous KChIP2 ancillary subunits, using the perforated cut-open oocyte voltage clamp and two-electrode voltage clamp techniques. We obtained high-frequency resolution measurements of the activation and deactivation properties of Kv4.3 channels. Activation was sigmoid and well described by a fourth power exponential function. The voltage dependence of the activation time constants was best described by a biexponential function corresponding to at least two different equivalent charges for activation. Deactivation kinetics are voltage dependent and monoexponential. In contrast to other voltage-sensitive K(+) channels such as HERG and Shaker, we found that elevated extracellular [K(+)] modulated the activation process by slowing deactivation and stabilizing the open state. Using these data we developed a model with five closed states and voltage-dependent transitions between the first four closed states coupled to a voltage-insensitive transition between the final closed (partially activated) state and the open state. Our model closely simulates steady-state and kinetic activation and deactivation data.