Loss of neuropeptide signalling alters temporal expression of mouse suprachiasmatic neuronal state and excitability.

Individual neurons of the hypothalamic suprachiasmatic nuclei (SCN) contain an intracellular molecular clock that drives these neurons to exhibit day‐night variation in excitability. The neuropeptide vasoactive intestinal polypeptide (VIP) and its cognate receptor, VPAC2, are synthesized by SCN neurons and this intercellular VIP‐VPAC2 receptor signal facilitates coordination of SCN neuronal activity and timekeeping. How the loss of VPAC2 receptor signalling affects the electrophysiological properties and states of SCN neurons as well as their responses to excitatory inputs is unclear. Here we used patch‐clamp electrophysiology and made recordings of SCN neurons in brain slices prepared from transgenic animals that do not express VPAC2 receptors (<italic>Vipr2</italic><italic>−/−</italic> mice) as well as animals that do (<italic>Vipr2</italic><italic>+/+</italic> mice). We report that while <italic>Vipr2</italic><italic>+/+</italic> neurons exhibit coordinated day‐night variation in their electrical state, <italic>Vipr2</italic><italic>−/−</italic> neurons lack this and instead manifest a range of states during both day and night. Further, at the population level, <italic>Vipr2</italic><italic>+/+</italic> neurons vary the membrane threshold potential at which they start to fire action potentials from day to night, while <italic>Vipr2</italic><italic>−/−</italic> neurons do not. We provide evidence that <italic>Vipr2</italic><italic>−/−</italic> neurons lack a component of voltage‐gated sodium currents that contribute to SCN neuronal excitability. Moreover, we determine that this aberrant temporal control of neuronal state and excitability alters neuronal responses to a neurochemical mimic of the light‐input pathway to the SCN. These results highlight the critical role VIP‐VPAC2 receptor signalling plays in the temporal expression of individual neuronal states as well as appropriate ensemble activity and input gating of the SCN neural network. [ABSTRACT FROM AUTHOR]