Early neuroinflammatory response precedes Purkinje cell loss in the cerebellum of SCA6 knockin mouse models

Spinocerebellar ataxia type 6 (SCA6) is a dominantly inherited neurodegenerative disease caused by an expansion of a CAG repeat encoding a polyglutamine (PolyQ) tract in the Cav2.1 voltage-gated calcium channel. Pathologically, it is characterized by selective degeneration of cerebellar Purkinje cells (PCs), which are a common target for PolyQ-induced toxicity among several different SCAs. Mutant Cav2.1 confers toxicity mainly through a toxic gain-of-function mechanism, but subcellular site of expanded Cav2.1 toxicity is controversial and it remains elusive whether SCA6 shares pathogenic cascades with other SCAs. To gain insight into these problems, we studied the cerebellar gene expression patterns of young Sca6 MPI 118Q/118Q knockin (KI) mice, which express mutant Cav2.1 from endogenous locus and faithfully models human SCA6. Comparison of transcriptional changes with those of Sca1 154Q/2Q mice, a faithful KI mouse model of SCA1, revealed that transcriptional signatures in the MPI 118Q/118Q were distinct from those of Sca1 154Q/2Q. Examination of temporal profiles of candidate genes showed that upregulation of those associated with microglial activation was initiated before PC degeneration was apparent and augmented as the disease progressed. Histological analysis of the MPI 118Q/118Q cerebellum confirmed the presence of Iba-1 positive activated microglia. Moreover, predominance of M1-like pro-inflammatory microglia was observed and was concomitant with the increased expression of pro-inflammatory cytokines. These results suggest that the unique transcriptional response, which highlights upregulation of neuroinflammatory genes possibly associated with lysosomal involvement, may play a pivotal role in the pathogenesis. Modulation of innate immune system could pave the way for slowing the progression of SCA6. We used MPI 118Q/118Q and MPI 11Q/11Q mice for Sca6 KI model at six weeks old. Statistical analysis of differently expressed genes was performed using the Linear Models for Microarray Data (limma) package in R/Bioconductor.