Back

Gene Editing for ATXN3 Inactivation in Machado-Joseph disease: CRISPR-Cas9 as a Therapeutic Alternative to TALEN-Induced Toxicity

Lopes, S. M.; Lopes, M. M.; Oliveira, D.; Santana, M. M.; Fernandes, A. R.; Vasconcelos-Ferreira, A.; Pereira, D.; Casquinha, M.; Nobrega, C.; Matos, C. A.; Sanjana, N. E.; Hsu, P. D.; Ran, F. A.; Swiech, L.; Cong, L.; Zhang, F.; Nobre, R. J.; de Almeida, L. P.

2025-02-15 neuroscience
10.1101/2025.02.14.637261 bioRxiv
Show abstract

Machado-Joseph disease (MJD) is an autosomal dominantly-inherited neurodegenerative disorder, caused by an over-repetition of the polyglutamine-codifying region in the ATXN3 gene. Strategies based on the suppression of the deleterious gene products have demonstrated promising results in pre-clinical studies. Nonetheless, these strategies do not target the root cause of the disease. In order to prevent the downstream toxic pathways, our goal was to develop gene editing-based strategies to permanently inactivate the human ATXN3 gene. TALENs and CRISPR-Cas9 systems were designed to target exon 2 of this gene and functional characterization was performed in a human cell line. After the demonstration of TALENs and CRISPR-Cas9 efficiency on gene disruption, a sequence of each system was selected for further in vivo experiments. Although both TALENs and CRISPR-Cas9 systems led to a drastic reduction of ATXN3 aggregates in the striatum of a lentiviral-based mouse model of MJD/SCA3, only CRISPR-Cas9 system allowed the improvement of key neuropathological markers of the disease. Importantly, the administration of the engineered system in YAC-MJD84.2/84.2 mice mediated a delay in disease progression, when compared with non-treated littermates. These data provide the first in vivo evidence of the efficacy of a CRISPR-Cas9-based approach to permanently inactivate the ATXN3 gene in the brain of two mouse models of the disease, supporting its potential as a new therapeutic avenue in the context of MJD/SCA3.

Matching journals

The top 3 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.