Specific branches of the proteostasis network regulate the toxicity associated with mistranslation
Bibliographic record
Abstract
All cellular functions rely on accurate protein biosynthesis. Yet, many variants of transfer RNA (tRNA) genes that induce amino acid misincorporation are found in human genomes. Mistranslation induces pleiotropic effects on proteostasis, ranging from protein misfolding to impaired protein biosynthesis and degradation. We employ Saccharomyces cerevisiae (budding yeast), a genetically and biochemically tractable model that facilitates quantitative analysis of how specific proteostasis pathways interact with mistranslating tRNAs. We tested two mistranslating tRNASer variants, one inducing proline to serine (P > S), the other arginine to serine (R > S) misincorporation. We found that P > S misincorporation impairs cellular fitness and sensitizes cells to protein misfolding to a greater extent than R > S misincorporation. Of note, we also show that, even though both tRNA variants induce misincorporation of serine, they result in the accumulation of misfolded proteins by distinct mechanisms. Specifically, R > S misincorporation reduces that association of Hsp70 with misfolded proteins, while P > S misincorporation impairs the degradation of nascent polypeptides. Our findings reveal that different mistranslating tRNASer variants impair specific branches of proteostasis and thus compromise cellular fitness by distinct mechanisms.
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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".