Resilience to Endoplasmic Reticulum Stress Mitigates Calcium-Dependent Membrane Hyperexcitability Underlying Late Disease Onset in Spinocerebellar Ataxia Type 6

An enduring puzzle in many inherited neurological disorders is the late onset of symptoms despite expression of function-impairing mutant protein early in life. We examined the basis for onset of impairment in Spinocerebellar ataxia type 6 (SCA6), a polyglutamine ataxia with late-onset cerebellar neurodegeneration. In a mouse model of SCA6, we identified a homeostatic response that engages the unfolded protein response early in disease. This protective response provided insight into endoplasmic reticulum (ER) stress-mediated cerebellar Purkinje neuron membrane hyperexcitability as a driver of disease. Age-dependent impairment of chaperone-mediated compensation for ER stress increased calcium-dependent Purkinje neuron membrane excitability. Redundant pathways of the unfolded protein response mediate this resilience to ER stress. ER stress-related decompensation applies also to other late-onset human cerebellar ataxia. These studies elucidate a mechanism of resilience connecting aberrant proteostasis and calcium-dependent intrinsic membrane hyperexcitability to explain delayed disease onset more widely in age-dependent neurodegenerative disease. Overall design: mRNA sequencing was performed on a NovaSeq instrument (Illumina, Inc.) with ~155 million reads and 150 bp paired-end reads. 5 cerebellum samples from each genotype from each age were used. Samples were prepared individually and pooled together to reduce batch effects. The samples were then demultiplexed into fastq files and statistics were collected using FastQC (Babraham Bioinformatics). The fastq files were trimmed using Trimmomatic (Bolger et al., 2014). 150 bp reads were aligned using STAR (Dobin et al., 2013), and the percent of unique reads were calculated. Raw read counts per gene per sample were calculated using HTSeq (Anders et al., 2015). Outliers were removed based on principal component analysis and hierarchical clustering. Differential gene expression analysis was performed using DESeq2 (Love et al., 2014).