Formation and persistence of polyglutamine aggregates in mistranslating cells
Bibliographic record
Abstract
In neurodegenerative diseases, including pathologies with well-known causative alleles, genetic factors that modify severity or age of onset are not entirely understood. We recently documented the unexpected prevalence of transfer RNA (tRNA) mutants in the human population, including variants that cause amino acid mis-incorporation. We hypothesized that a mistranslating tRNA will exacerbate toxicity and modify the molecular pathology of Huntington's disease-causing alleles. We characterized a tRNAPro mutant that mistranslates proline codons with alanine, and tRNASer mutants, including a tRNASerAGA G35A variant with a phenylalanine anticodon (tRNASerAAA) found in ∼2% of the population. The tRNAPro mutant caused synthetic toxicity with a deleterious huntingtin poly-glutamine (polyQ) allele in neuronal cells. The tRNASerAAA variant showed synthetic toxicity with proteasome inhibition but did not enhance toxicity of the huntingtin allele. Cells mistranslating phenylalanine or proline codons with serine had significantly reduced rates of protein synthesis. Mistranslating cells were slow but effective in forming insoluble polyQ aggregates, defective in protein and aggregate degradation, and resistant to the neuroprotective integrated stress response inhibitor (ISRIB). Our findings identify mistranslating tRNA variants as genetic factors that slow protein aggregation kinetics, inhibit aggregate clearance, and increase drug resistance in cellular models of neurodegenerative 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) | 0.000 | 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 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".