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Heterogeneous glutamatergic receptor mRNA expression across phrenic motor neurons in rats.

Sabhya RanaGary C SieckCarlos B Mantilla
Published in: Journal of neurochemistry (2019)
The diaphragm muscle comprises various types of motor units that are recruited in an orderly fashion governed by the intrinsic electrophysiological properties (membrane capacitance as a function of somal surface area) of phrenic motor neurons (PhMNs). Glutamate is the main excitatory neurotransmitter at PhMNs and acts primarily via fast acting AMPA and N-methyl-D-aspartic acid (NMDA) receptors. Differences in receptor expression may also contribute to motor unit recruitment order. We used single cell, multiplex fluorescence in situ hybridization to determine glutamatergic receptor mRNA expression across PhMNs based on their somal surface area. In adult male and female rats (n = 9) PhMNs were retrogradely labeled for analyses (n = 453 neurons). Differences in the total number and density of mRNA transcripts were evident across PhMNs grouped into tertiles according to somal surface area. A ~ 25% higher density of AMPA (Gria2) and NMDA (Grin1) mRNA expression was evident in PhMNs in the lower tertile compared to the upper tertile. These smaller PhMNs likely comprise type S motor units that are recruited first to accomplish lower force, ventilatory behaviors. In contrast, larger PhMNs with lower volume densities of AMPA and NMDA mRNA expression presumably comprise type FInt and FF motor units that are recruited during higher force, expulsive behaviors. Furthermore, there was a significantly higher cytosolic NMDA mRNA expression in small PhMNs suggesting a more important role for NMDA-mediated glutamatergic neurotransmission at smaller PhMNs. These results are consistent with the observed order of motor unit recruitment and suggest a role for glutamatergic receptors in support of this orderly recruitment. Cover Image for this issue: doi: 10.1111/jnc.14747.
Keyphrases
  • single cell
  • spinal cord
  • magnetic resonance
  • computed tomography
  • deep learning
  • skeletal muscle
  • binding protein
  • machine learning
  • mechanical ventilation
  • contrast enhanced
  • positron emission tomography
  • energy transfer