Effect of small molecule eRF3 degraders on premature termination codon readthrough
Bibliographic record
Abstract
Premature termination codon (PTC) readthrough is considered a potential treatment for genetic diseases caused by nonsense mutations. High concentrations of aminoglycosides induce low levels of PTC readthrough but also elicit severe toxicity. Identifying compounds that enhance PTC readthrough by aminoglycosides or reduce their toxicity is a continuing challenge. In humans, a binary complex of eukaryotic release factors 1 (eRF1) and 3 (eRF3a or eRF3b) mediates translation termination. They also participate in the SURF (SMG1-UPF1-eRF1-eRF3) complex assembly involved in nonsense-mediated mRNA decay (NMD). We show that PTC readthrough by aminoglycoside G418 is considerably enhanced by eRF3a and eRF3b siRNAs and cereblon E3 ligase modulators CC-885 and CC-90009, which induce proteasomal degradation of eRF3a and eRF3b. eRF3 degradation also reduces eRF1 levels and upregulates UPF1 and selectively stabilizes TP53 transcripts bearing a nonsense mutation over WT, indicating NMD suppression. CC-90009 is considerably less toxic than CC-885 and it enhances PTC readthrough in combination with aminoglycosides in mucopolysaccharidosis type I-Hurler, late infantile neuronal ceroid lipofuscinosis, Duchenne muscular dystrophy and junctional epidermolysis bullosa patient-derived cells with nonsense mutations in the IDUA, TPP1, DMD and COL17A1 genes, respectively. Combination of CC-90009 with aminoglycosides such as gentamicin or ELX-02 may have potential for PTC readthrough therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".