Visualizing tRNA-dependent mistranslation in human cells
Bibliographic record
Abstract
High-fidelity translation and a strictly accurate proteome were originally assumed as essential to life and cellular viability. Yet recent studies in bacteria and eukaryotic model organisms suggest that proteome-wide mistranslation can provide selective advantages and is tolerated in the cell at higher levels than previously thought (one error in 6.9 × 10−4 in yeast) with a limited impact on phenotype. Previously, we selected a tRNAPro containing a single mutation that induces mistranslation with alanine at proline codons in yeast. Yeast tolerate the mistranslation by inducing a heat-shock response and through the action of the proteasome. Here we found a homologous human tRNAPro (G3:U70) mutant that is not aminoacylated with proline, but is an efficient alanine acceptor. In live human cells, we visualized mistranslation using a green fluorescent protein reporter that fluoresces in response to mistranslation at proline codons. In agreement with measurements in yeast, quantitation based on the GFP reporter suggested a mistranslation rate of up to 2–5% in HEK 293 cells. Our findings suggest a stress-dependent phenomenon where mistranslation levels increased during nutrient starvation. Human cells did not mount a detectable heat-shock response and tolerated this level of mistranslation without apparent impact on cell viability. Because humans encode ∼600 tRNA genes and the natural population has greater tRNA sequence diversity than previously appreciated, our data also demonstrate a cell-based screen with the potential to elucidate mutations in tRNAs that may contribute to or alleviate 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 | 0.001 | 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".