Aggregation-prone c9FTD/ALS poly(GA) RAN-translated proteins cause neurotoxicity by inducing ER stress
Bibliographic record
Abstract
The occurrence of repeat-associated non-ATG (RAN) translation, an atypical form of translation of expanded repeats that results in the synthesis of homopolymeric expansion proteins, is becoming more widely appreciated among microsatellite expansion disorders. Such disorders include amyotrophic lateral sclerosis and frontotemporal dementia caused by a hexanucleotide repeat expansion in the C9ORF72 gene (c9FTD/ALS). We and others have recently shown that this bidirectionally transcribed repeat is RAN translated, and the "c9RAN proteins" thusly produced form neuronal inclusions throughout the central nervous system of c9FTD/ALS patients. Nonetheless, the potential contribution of c9RAN proteins to disease pathogenesis remains poorly understood. In the present study, we demonstrate that poly(GA) c9RAN proteins are neurotoxic and may be implicated in the neurodegenerative processes of c9FTD/ALS. Specifically, we show that expression of poly(GA) proteins in cultured cells and primary neurons leads to the formation of soluble and insoluble high molecular weight species, as well as inclusions composed of filaments similar to those observed in c9FTD/ALS brain tissues. The expression of poly(GA) proteins is accompanied by caspase-3 activation, impaired neurite outgrowth, inhibition of proteasome activity, and evidence of endoplasmic reticulum (ER) stress. Of importance, ER stress inhibitors, salubrinal and TUDCA, provide protection against poly(GA)-induced toxicity. Taken together, our data provide compelling evidence towards establishing RAN translation as a pathogenic mechanism of c9FTD/ALS, and suggest that targeting the ER using small molecules may be a promising therapeutic approach for these devastating diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".