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Biology subjects

Ashizawa, T.

Publications and source records attributed to Ashizawa, T..

2 recordsLinked to original sources

CCG*CGG interruptions in high penetrance SCA8 families increase RAN translation and protein toxicity

Spinocerebellar ataxia type 8 (SCA8), a dominantly inherited neurodegenerative disorder caused by a CTG*CAG expansion, is unusual because most individuals that carry the mutation do not develop ataxia. To understand the variable penetrance of SCA8 we studied the molecular differences between highly penetrant families and more common sporadic cases (82%) using a large cohort of SCA8 families (N=77). We show that repeat expansion mutations from individuals with two or more affected family members have CCG*CGG interruptions at a higher frequency than sporadic SCA8 cases and that the number of CCG*CGG interruptions correlates with age at onset. At the molecular level, CCG*CGG interruptions increase RNA hairpin stability and steady state levels of SCA8 RAN polyAla and polySer proteins. Additionally, the CCG*CGG interruptions, which encode arginine interruptions in the polyGln frame increase the toxicity of the resulting proteins. In summary, CCG*CGG interruptions increase polyAla and polySer RAN protein levels, polyGln protein toxicity and disease penetrance and provide novel insight into the molecular differences between SCA8 families with high vs. low disease penetrance.

genetics

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.

bioinformatics