Back

Persistence of vestibular function in the absence of glutamatergic transmission from hair cells

Mukhopadhyay, M.; Modgekar, R.; Yang-Hood, A.; Ohlemiller, K. K.; Militchin, V.; Xiao, M.; Shen, Z.; Rensing, N.; Wong, M.; Lee, S. J.; Seal, R. P.; Warchol, M. E.; Maloney, S. E.; Yuede, C. M.; Rutherford, M. A.; Pangrsic, T.

2025-10-15 neuroscience
10.1101/2025.10.15.682551 bioRxiv
Show abstract

Quantal synaptic transmission in vestibular end-organs is glutamatergic. Although genetic deletion of Slc17a8 (termed Vglut3) leads to deafness in mice, the dependence of vestibular function on VGLUT3-mediated quantal transmission is unknown. Here, we investigated the vestibular phenotype of Vglut3-/- mice at the cellular, systems, and behavioral levels. The type-II vestibular hair cells (VHCs) in Vglut3+/+mice were strongly immunoreactive for VGLUT3, while type-I VHCs showed poor immunoreactivity. In Vglut3-/- mice quantal synaptic transmission in utricular calyces was reduced in rate and amplitude by > 95%. In vivo recordings of spontaneous activity in the vestibular nerve revealed similar action potential rates and regularity in Vglut3+/+and Vglut3-/- mice, suggesting a divergent underlying mechanism compared to the silent Vglut3-/- auditory nerve. In behavioral studies, Vglut3-/- mice did not exhibit considerable sensorimotor or balance deficits. Collectively, these data support the view that non-quantal transmission is the predominant mode of neurotransmission between type I VHCs and vestibular calyceal afferent neurons. We propose that non-quantal transmission alone underlies the apparently normal vestibular nerve physiology and behavioral function in Vglut3-/- mice.

Published in Scientific Reports (predicted rank #11) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.