Genetic Interaction of tRNA-Dependent Mistranslation with Fused in Sarcoma Protein Aggregates
Bibliographic record
Abstract
High-fidelity protein synthesis requires properly aminoacylated transfer RNAs (tRNAs), yet diverse cell types, from bacteria to humans, show a surprising ability to tolerate errors in translation resulting from mutations in tRNAs, aminoacyl-tRNA synthetases, and other components of protein synthesis. Recently, we characterized a tRNASerAGA G35A mutant (tRNASerAAA) that occurs in 2% of the human population. The mutant tRNA decodes phenylalanine codons with serine, inhibits protein synthesis, and is defective in protein and aggregate degradation. Here, we used cell culture models to test our hypothesis that tRNA-dependent mistranslation will exacerbate toxicity caused by amyotrophic lateral sclerosis (ALS)-associated protein aggregation. Relative to wild-type tRNA, we found cells expressing tRNASerAAA showed slower but effective aggregation of the fused in sarcoma (FUS) protein. Despite reduced levels in mistranslating cells, wild-type FUS aggregates showed similar toxicity in mistranslating cells and normal cells. The aggregation kinetics of the ALS-causative FUS R521C variant were distinct and more toxic in mistranslating cells, where rapid FUS aggregation caused cells to rupture. We observed synthetic toxicity in neuroblastoma cells co-expressing the mistranslating tRNA mutant and the ALS-causative FUS R521C variant. Our data demonstrate that a naturally occurring human tRNA variant enhances cellular toxicity associated with a known causative allele for neurodegenerative disease.
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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 | 0.000 | 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) | 0.000 | 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".