Diminished GAD67 Terminals and Ambient GABA Inhibition Associated with Reduced Motor Neuron Recruitment Threshold in the SOD1G93A ALS mouse

Pre-symptomatic studies in mouse models of the neurodegenerative motor neuron (MN) disease, Amyotrophic Lateral Sclerosis (ALS) highlight early alterations in intrinsic and synaptic excitability and have supported an excitotoxic theory of MN death. However, a role for synaptic inhibition in disease development is not sufficiently explored among other mechanisms. Since inhibition plays a role in both regulating motor output and in neuroprotection, we examined the age-dependent anatomical changes in inhibitory presynaptic terminals on MN cell bodies using fluorescent immunohistochemistry for GAD67 (GABA) and GlyT2 (glycine) presynaptic proteins comparing ALS-vulnerable trigeminal jaw closer (JC) motor pools with the ALS-resistant extraocular (EO) MNs in the SOD1G93A mouse model for ALS. Our results indicate differential patterns of temporal changes of these terminals in vulnerable versus resilient MNs and relative differences between SOD1G93A and wild-type (WT) MNs. Notably, we found pre-symptomatic up-regulation in inhibitory terminals in the EO MNs while the vulnerable JC MNs mostly showed a decrease in inhibitory terminals. Specifically, there was a statistically significant decrease in the GAD67 somatic abuttal in the SOD1G93A JC MNs compared to WT around P12. Using in vitro patch-clamp electrophysiology, we found a parallel decrease in the ambient GABA-dependent tonic inhibition in the SOD1G93A JC MNs. While it is unclear if the two mechanisms are directly related, pharmacological blockade of specific subtype of GABAA-5 receptors suggests that tonic inhibition can control MN recruitment threshold. Furthermore, reduction in tonic GABA current as observed here in the mutant, identifies a putative molecular mechanism explaining our observations of hyperexcitable shifts in JC MN recruitment threshold in the SOD1G93A mouse. Lastly, we showcase non-parametric resampling-based bootstrap statistics for data analyses, and provide the Python code on GitHub for wider reuse.