Cellular modelling: experiments and simulation to develop a physiological model of G-protein control of muscarinic K ⁺ channels in mammalian atrial cells

Abstract

The first model of G-protein-K ACh channel interaction was developed 14 years ago and then expanded to include both the receptor-G-protein cycle and G-protein-K ACh channel interaction. The G-protein-K ACh channel interaction used the Monod-WymanChangeux allosteric model with the idea that one K ACh channel is composed of four subunits, each of which binds one active G-protein subunit (G bg ). The receptor-G-protein cycle used a previous model to account for the steady-state relationship between K ACh current and intracellular guanosine-5-triphosphate at various extracellular concentrations of acetylcholine (ACh). However, simulations of the activation and deactivation of K ACh current upon ACh application or removal were much slower than experimental results. This clearly indicates some essential elements were absent from the model. We recently found that regulators of G-protein signalling are involved in the control of K ACh channel activity. They are responsible for the voltage-dependent relaxation behaviour of K ACh channels. Based on this finding, we have improved the receptor-G-protein cycle model to reproduce the relaxation behaviour. With this modification, the activation and deactivation of K ACh current in the model are much faster and now fall within physiological ranges.

The Roles of Gβγ and Gα in Gating and Regulation of GIRK Channels

“…The cooperative nature of GIRK gating is in good agreement with considerations based on structural studies: several molecules of Gβγ may be needed to produce sufficient mechanical strain to cause relative rotation of GIRK's cytoplasmic domains, which is then translated into gate opening (Whorton & MacKinnon, 2013). Based on the assumption of cooperative action of four Gβγ molecules, Kurachi and collaborators (Hosoya, Yamada, Ito, & Kurachi, 1996;Murakami, Inanobe, & Kurachi, 2013;Murakami, Suzuki, Ishii, Inanobe, & Kurachi, 2010) have developed an allosteric Monod-Wyman-Changeux-type model which adequately describes the magnitude and kinetics (but not I basal ), and the effect of RGS proteins, for the cardiac I KACh .…”

The role of G proteins in assembly and function of Kir3 inwardly rectifying potassium channels

“…79,116 play in modulating channel function. 103 However, a number of studies have demonstrated that Kir3.1 channels, expressed alone, can interact with heterotrimeric G proteins. 66,80,89 Studies of the functional stoichiometry of Gβγ with respect to channel activation indicate that up to four Gβγ subunits can bind which would lead to maximal channel opening.…”

“…66,80,89 Studies of the functional stoichiometry of Gβγ with respect to channel activation indicate that up to four Gβγ subunits can bind which would lead to maximal channel opening. 103,104 In addition, cross-linking experiments have suggested the binding of four Gβγ subunits per tetramer, attributing one Gβγ binding site per channel subunit (reviewed in ref. 41).…”

“…For example, it has been demonstrated that signaling specificity between Kir3 and Gβγ may depend on other components of the larger complex containing the GPCR as well. 68 Beyond the need for maximum binding of four Gβγ subunits to Kir3 channels, 103,104 why are there distinct classes of sites for Gβγ and Gα binding on the N-and C-termini of Kir3 channels? Until recently, the specificity of precocious (i.e., agonist-independent interactions which may occur during Kir3.1 and Kir3.2 channel trafficking or assembly) interactions between different Gβγ subunit combinations and the Kir3 channels had not been addressed.…”

The virtual physiological human: computer simulation for integrative biomedicine II

“…As has been a hallmark of previous Theme Issues, cardiac modelling remains a strong contributor to the VPH initiative. Papers in this Theme Issue range from the development of novel models of individual ion channel function in cardiac myocytes (Murakami et al 2010) to the identification of biomarkers to aid early identification of cardiac side-effects associated with pharmacological interventions (Corrias et al 2010), and to patient-specific medical device interventions (Capelli et al 2010). Of major 'cross-cutting' relevance, in this context, are access and (re-)use of data and related models (Gianni et al 2010).…”