Noradrenaline Increases mEPSC Frequency in Pyramidal Cells in Layer II of Rat Barrel Cortex via Calcium Release From Presynaptic Stores

去甲肾上腺素通过突触前储存释放钙离子,增加大鼠桶状皮层第二层锥体细胞的微型兴奋性突触后电流频率

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Abstract

Somatosensory cortex is innervated by afferents originating from the locus coeruleus which typically release noradrenaline. We tested if activation of presynaptic α(1)-adrenoceptors (AR) coupled to a G(q)-mediated signaling cascade resulted in calcium (Ca(2+)) release from stores and thereby increased spontaneous transmitter release in rat barrel cortex. Adding 1-100 μM noradrenaline (NA) or 5 μM cirazoline (CO), a α(1)-AR specific agonist, to the standard artificial cerebrospinal fluid increased the frequency of miniature excitatory postsynaptic currents (mEPSC) by 64 ± 7% in 51% of pyramidal cells in layer II (responders) with no effect on the amplitude. In 42 responders, the mEPSC frequency during control was significantly smaller (39 ± 2 vs. 53 ± 4 Hz) and upon NA exposure, the input resistance (R(in)) decreased (9 ± 7%) compared to non-responders. Experiments using CO and the antagonist prazosin revealed that NA acted via binding to α(1)-ARs, which was further corroborated by simultaneously blocking β- and α(2)-ARs with propranolol and yohimbine, which did not prevent the increase in mEPSC frequency. To verify elements in the signaling cascade, both the phospholipase C inhibitor edelfosine and the membrane permeable IP(3) receptor blocker 2-APB averted the increase in mEPSC frequency. Likewise, emptying Ca(2+) stores with cyclopiazonic acid or the chelation of intracellular Ca(2+) with BAPTA-AM prevented the frequency increase, suggesting that the frequency increase was caused by presynaptic store release. When group I metabotropic glutamate receptors were activated with DHPG, co-application of NA occluded a further frequency increase suggesting that the two receptor activations may not signal independently of each other. The increased mEPSC frequency in a subset of pyramidal cells results in enhanced synaptic noise, which, together with the reduction in R(in), will affect computation in the network.

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