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Updated: Aug 25, 2026

In Situ Ca2+ Imaging of the Enteric Nervous System
Published on: January 29, 2015
Slow excitatory metabotropic signal transmission in the enteric nervous system
1Department of Physiology and Cell Biology, College of Medicine and Public Health, The Ohio State University, Columbus, OH, USA. wood.13@osu.edu
Abstract:
Metabotropic mechanisms of excitatory signalling in enteric neurones underlie both slow synaptic transmission and paracrine transmission from enteric non-neuronal cells. The type of neurone in which signalling occurs determines the characteristics of synaptic- and paracrine-mediated slow excitatory responses. Slow excitatory responses in neurones with AH-type electrophysiological behaviour and multipolar Dogiel type II morphology are characterized by membrane depolarization associated with closure of Ca2+ -gated K+ channels that is reflected by increased neuronal input resistance. Slow excitatory responses in neurones with S-type electrophysiological behaviour and uniaxonal morphology are characterized by membrane depolarization associated with opening of cationic channels and decreased neuronal input resistance. Postreceptor signalling that involves activation of adenylate cyclase, stimulation of cAMP formation and activation protein kinase A generates excitatory responses characterized by increased neuronal input resistance in AH neurones. Postreceptor signalling that involves activation of phospholipase C, release of IP3 and diacylglycerol and activation of protein kinase C and calmodulin kinases generates excitatory responses characterized by decreased neuronal input resistance in S neurones. Slow excitatory responses that are characterized by increased neuronal input resistance are a property of AH-type neurones that function as interneurones in the neural networks of the ENS. Slow excitatory responses that are characterized by decreased neuronal input resistance are a property of S-type neurones that function either as interneurones or as musculomotor and secretomotor neurones in the neural networks of the ENS.
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