Hornerin deposits in neuronal intranuclear inclusion disease: direct identification of proteins with compositionally biased regions in inclusions.

Neuronal intranuclear inclusion disease (NIID) is a neurodegenerative disorder, characterized by the presence of eosinophilic inclusions (NIIs) within nuclei of central and peripheral nervous system cells. This study aims to identify the components of NIIs, which have been difficult to analyze directly due to their insolubility. In order to establish a method to directly identify the components of NIIs, we first analyzed the huntingtin inclusion-rich fraction obtained from the brains of Huntington disease model mice. Although the sequence with expanded polyglutamine could not be identified by liquid-chromatography mass spectrometry, amino acid analysis revealed that glutamine of the huntingtin inclusion-rich fraction increased significantly. This is compatible with the calculated amino acid content of the transgene product. Therefore, we applied this method to analyze the NIIs of diseased human brains, which may have proteins with compositionally biased regions, and identified a serine-rich protein called hornerin. Since the analyzed NII-rich fraction was also serine-rich, we suggested hornerin as a major component of the NIIs. A specific distribution of hornerin in NIID was also investigated by Matrix-assisted laser desorption/ionization imaging mass spectrometry and immunofluorescence. Finally, we confirmed a variant of hornerin by whole-exome sequencing and DNA sequencing. This study suggests that hornerin may be related to the pathological process of this NIID, and the direct analysis of NIIs, especially by amino acid analysis using the NII-rich fractions, would contribute to a deeper understanding of the disease pathogenesis.

Proteomic analysis of heat-stable proteins revealed an increased proportion of proteins with compositionally biased regions.

Intrinsically disordered proteins (IDPs) have been in the spotlight for their unique properties, such as their lack of secondary structures and low sequence complexity. Alpha-synuclein and tau are representative disease-related IDPs with low complexity regions in their sequences, accumulating in the brains of patients with Parkinson disease and Alzheimer disease, respectively. Their heat resistance in particular was what attracted our attention. We assumed that there exist many other unidentified proteins that are resistant to heat-treatment, referred to as heat-stable proteins, which would also have low sequence complexity. In this study, we performed proteomic analysis of heat-stable proteins of mouse brains and found that proteins with compositionally biased regions are abundant in the heat-stable proteins. The proteins related to neurodegeneration are known to undergo different types of post-translational modifications (PTMs) such as phosphorylation and ubiquitination. We then investigated the heat-stability and aggregation properties of phosphorylated synuclein and tau with different phosphorylation sites. We suggest that PTMs can be important factors that determine the heat-stability and aggregation properties of a protein. IDPs identified in the heat-stable proteins of mouse brains would be candidates for the pathogenic proteins for neurodegeneration.

Non-coding RNA Neat1 and Abhd11os expressions are dysregulated in medium spiny neurons of Huntington disease model mice.

Huntington Disease (HD) is a neurodegenerative disorder caused by expanded CAG repeats in the exon1 of huntingtin gene (HTT). The mutant HTT affects the transcriptional profile of neurons by disrupting the activities of transcriptional machinery and alters expression of many genes. In this study, we identified dysregulated non-coding RNAs (ncRNAs) in medium spiny neurons of 4-week-old HD model mouse. Also, we observed the intracellular localizations of Abhd11os and Neat1 ncRNAs by ViewRNA in situ hybridization, which could provide more precise detection, suggesting that it is a useful method to investigate the expression changes of genes with low expression levels.