Hyperpolarization-activated ion channels as targets for nitric oxide signalling in deep cerebellar nuclei

超极化激活离子通道作为小脑深部核中一氧化氮信号传导的靶点

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作者:Gary W Wilson, John Garthwaite

Abstract

Most biological effects of nitric oxide (NO) in the brain are mediated by guanylyl cyclase-coupled NO receptors, whose activation results in increased intracellular cGMP levels. Apart from protein kinase activation little is known about subsequent cGMP signal transduction. In optic nerve axons, hyperpolarization-activated cyclic nucleotide-modulated cation (HCN) channels, which bind cGMP or cAMP directly, were recently suggested to be a target. The aim here was to test this possibility more directly. Neurones of the rat deep cerebellar nuclei were selected for this purpose, their suitability being attested by immunocytochemistry showing that the principal neurones expressed guanylyl cyclase protein and that NO synthase-containing fibres were abundant in the neuropil. Using whole-cell voltage-clamp recording, HCN channels in the neurones were activated in response to isoprenaline and exogenous cAMP but only occasionally did they respond to NO, although exogenous cGMP was routinely effective. With the less invasive sharp microelectrode recording technique, however, exogenous NO modulated the channels reproducibly, as measured by the size of the HCN channel-mediated voltage sag following hyperpolarization. Moreover, NO also blunted the subsequent rebound depolarizing potentials, consistent with it increasing the hyperpolarization-activated current. Optimizing the whole-cell solution to improve the functioning of NO-activated guanylyl cyclase failed to restore NO sensitivity. Minimizing cellular dialysis by using the perforated-patch technique, however, was successful. The results provide evidence that HCN channels are potential downstream mediators of NO signalling in deep cerebellar nuclei neurones and suggest that the more general importance of this transduction pathway may have been overlooked previously because of unsuitable recording methods.

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