Back

Increased Karyopherin Alpha Levels Attenuate Mutant Ataxin-1-Induced Neurodegeneration

Ruff, E. K.; Timperman, D. L.; Amador, A. A.; Aguirre-Lamus, I.; de Haro, M.; Al-Ramahi, I.

2023-07-14 neuroscience
10.1101/2023.07.12.548780 bioRxiv
Show abstract

Neurodegenerative diseases are characterized by the abnormal accumulation of disease-driving proteins. Emerging evidence suggests that nucleocytoplasmic transport (NCT) components play a critical role in neurodegeneration. This study investigates the effects of modulating the levels of different karyopherins on mutant Ataxin-1 (mATXN1)-induced neurodegeneration in Spinocerebellar Ataxia Type 1 (SCA1) Drosophila and cell models. Our findings reveal that ATXN1 [82Q] interacts with KPNAs in the nucleus and cytoplasm of neurons. Increasing KPNA levels ameliorates ATXN1 [82Q]-induced neurodegeneration and progressive neuronal dysfunction. Surprisingly, elevated KPNA levels did not increase nuclear mATXN1, instead, mechanistic analyses demonstrate that KPNA retains mATXN1 in the cytoplasm, reducing its nuclear accumulation. Moreover, higher KPNA leads to a decrease in soluble oligomeric mATXN1. Interestingly, inhibition of a different karyopherin, KPNB1, elevated KPNA levels and reduced nuclear mATXN1 in human neuronal precursor cells. Consistently, knockdown of KPNB1 attenuates ATXN1 [82Q]-induced neurodegeneration and reduces its nuclear aggregation in Drosophila. These results indicate that KPNAs may act as chaperones for mutant ATXN1, preventing its nuclear translocation and reducing its pathological effects. Importantly, they also constitute a proof of principle that retaining mATXN1 in the cytoplasm represents an attractive and viable therapeutic option. Given the dysregulation of karyopherins in many neurodegenerative diseases and their emerging role as chaperones, the results presented here may extend beyond SCA1 into other disorders like Alzheimers or Parkinsons disease.

Matching journals

The top 8 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.