High-fidelity and differential nonsense suppression in live cells and a frontotemporal dementia allele with human transfer RNAs
Bibliographic record
Abstract
Nonsense mutations generate premature termination codons (PTCs) that are responsible for 11% of genetic disease alleles. The arginine (Arg, CGA) to stop (UGA) mutation is the most common PTC. Humans encode >600 transfer RNA (tRNA) genes with many identical and similar copies. We developed a dual fluorescent reporter to quantify PTC readthrough in live cells and found single nucleotide mutations of human tRNAArg gene variants enabled differential nonsense suppression that depended on the tRNA sequence and the cell type. We investigated G36A variants of all six human tRNAArgUCG isodecoders, and only the TCG-6-1 tRNA, where G36A occurs in 0.01% of human genomes, was unable to translate nonsense codons. With tRNA sequencing, we showed that a suppressor tRNA derived from the TCG-3-1 gene was expressed 2.1-fold higher and generated 1.8-fold more nonsense suppression than a tRNA derived from the TCG-4-1 gene. In a neuroblastoma model of frontotemporal dementia, we observed >70% readthrough of progranulin R493X with a suppressor tRNA that represented 5%-18% of the total tRNAArg pool. The tRNAs outperformed aminoglycoside-induced nonsense suppression in efficacy, tolerability to the cells, and translation fidelity according to mass spectrometry. Our studies show that human nonsense suppressor tRNAs can correct genetic defects that cause disease.
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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) | 0.000 | 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) | 0.001 | 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".