High-throughput screen of 100 000 small molecules in C9ORF72 ALS neurons identifies spliceosome modulators that mobilize G4C2 repeat RNA into nuclear export and repeat associated non-canonical translation
Bibliographic record
Abstract
An intronic G4C2 repeat expansion in the C9ORF72 gene is the major known cause for Amyotrophic Lateral Sclerosis (ALS), with current evidence for both, loss of function and pathological gain of function disease mechanisms. We screened 96 200 small molecules in C9ORF72 patient iPS neurons for modulation of nuclear G4C2 RNA foci and identified 82 validated hits, including the Brd4 inhibitor JQ1 as well as novel analogs of Spliceostatin-A, a known modulator of SF3B1, the branch point binding protein of the U2-snRNP. Spliceosome modulation by these SF3B1 targeted compounds recruits SRSF1 to nuclear G4C2 RNA, mobilizing it from RNA foci into nucleocytoplasmic export. This leads to increased repeat-associated non-canonical (RAN) translation and ultimately, enhanced cell toxicity. Our data (i) provide a new pharmacological entry point with novel as well as known, publicly available tool compounds for dissection of C9ORF72 pathobiology in C9ORF72 ALS models, (ii) allowing to differentially modulate RNA foci versus RAN translation, and (iii) suggest that therapeutic RNA foci elimination strategies warrant caution due to a potential storage function, counteracting translation into toxic dipeptide repeat polyproteins. Instead, our data support modulation of nuclear export via SRSF1 or SR protein kinases as possible targets for future pharmacological drug discovery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".