Back

Staufen blocks autophagy in neurodegeneration

Pulst, S. M.; Paul, S.; Dansithong, W.; Gandelman, M.; Zu, T.; Ranum, L. P. W.; Figueroa, K. P.; Scoles, D. R.

2019-06-06 neuroscience
10.1101/659649 bioRxiv
Show abstract

ObjectiveThe mechanistic target of rapamycin (mTOR) kinase is one of the master coordinators of cellular stress responses, regulating metabolism, autophagy, and apoptosis. We recently reported that Staufen1 (STAU1), a stress granule (SG) protein, was overabundant in fibroblast cell lines from patients with spinocerebellar ataxia type 2 (SCA2), amyotrophic lateral sclerosis, frontotemporal degeneration, Huntingtons, Alzheimers, and Parkinsons diseases as well as animal models, and patient tissues. STAU1 overabundance is associated with mTOR hyperactivation and links SG formation with autophagy. Our objective was to determine the mechanism of mTOR regulation by STAU1. MethodsWe determined STAU1 abundance with disease- and chemical-induced cellular stressors in patient cells and animal models. We also used RNA binding assays to contextualize STAU1 interaction with MTOR mRNA. ResultsSTAU1 and mTOR were overabundant in BAC-C9orf72, ATXN2Q127, and Thy1-TDP-43 transgenic mouse models. Reducing STAU1 levels in these mice normalized mTOR levels and activity and autophagy-related marker proteins. We also saw increased STAU1 levels in HEK293 cells transfected to express C9orf72-relevant dipeptide repeats (DPRs). Conversely, DPR accumulations were not observed in cells treated by STAU1 RNAi. Overexpression of STAU1 in HEK293 cells increased mTOR levels through direct MTOR mRNA interaction, activating downstream targets and impairing autophagic flux. Targeting mTOR by rapamycin or RNAi normalized STAU1 abundance in a SCA2 cellular model. InterpretationSTAU1 interaction with mTOR drives its hyperactivation and inhibits autophagic flux in multiple models of neurodegeneration. Staufen, therefore, constitutes a novel target to modulate mTOR activity, autophagy, and for the treatment of neurodegenerative diseases.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.