A chemical inducer of ribophagy limits the toxicity of ALS-related arginine-rich peptides
Porebski, B.; Lafarga, V.; Hansel, C.; Haraldsson, M.; Saez-Mas, A.; Sanchez-Mollaeda, A.; Moragon, S.; Haggblad, M.; Lidemalm, L.; Rodrigo, S.; Carreras-Puigvert, J.; Ventoso, I.; Lafarga, M.; Huhn, D.; Fernandez-Capetillo, O.
Show abstract
C9ORF72 intronic repeat expansions are the most frequent mutation found in Amyotrophic Lateral Sclerosis (ALS), producing toxic arginine-rich dipeptides (DPR) that disrupt RNA metabolism and trigger the accumulation of orphan ribosomal proteins (RP). Through a large phenotypic chemical screen, we identified "SALSa", a novel compound that mitigates DPR toxicity. Mechanistically, SALSa acts as a chemical inducer of ribophagy, a specialized form of autophagy that promotes RP clearance. Interestingly, this effect is unrelated to mTOR inhibition, the main regulator of autophagy. In contrast, this is due to an effect of the drug in ribosome biogenesis, which triggers a protective response to clear defective ribosomes. Accordingly, SALSa accumulates in nucleoli and perturbs the final steps of rRNA maturation. SALSa reduces DPR toxicity in differentiated neurons and significantly extends lifespan in a Drosophila melanogaster model of C9ORF72 ALS. These findings suggest that stimulating ribophagy could be beneficial for pathologies associated to dysfunctional ribosome biogenesis, including C9ORF72 ALS.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Drug Repurposing Screen Identifies an HRI Activating Compound that Promotes Adaptive Mitochondrial Remodeling in MFN2-deficient Cells 95%
- Signal peptide-independent secretion of keratin-19 by pancreatic cancer cells 94%
- Small mitochondrial protein NERCLIN regulates cardiolipin homeostasis and mitochondrial ultrastructure. 94%
"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.