Glutamine missense suppressor transfer RNAs inhibit polyglutamine aggregation
Bibliographic record
Abstract
Huntington's disease (HD) is caused by polyglutamine (polyQ) repeat expansions in the huntingtin gene. HD-causative polyQ alleles lead to protein aggregation, which is a prerequisite for disease. Translation fidelity modifies protein aggregation, and several studies suggest that mutating one or two glutamine (Gln) residues in polyQ reduces aggregation. Thus, we hypothesized that missense suppression of Gln codons with other amino acids will reduce polyQ aggregate formation in cells. In neuroblastoma cells, we assessed tRNA variants that misread Gln codons with serine (tRNASerC/UUG) or alanine (tRNAAlaC/UUG). The tRNAs with the CUG anticodon were more effective at suppressing the CAG repeats in polyQ, and serine and alanine mis-incorporation had differential impacts on polyQ. The expression of tRNASerCUG reduced polyQ protein production as well as both soluble and insoluble aggregate formation. In contrast, cells expressing tRNAAlaCUG selectively decreased insoluble polyQ aggregate formation by 2-fold. Mass spectrometry confirmed Ala mis-incorporation at an average level of ∼20% per Gln codon. Cells expressing the missense suppressor tRNAs showed no cytotoxic effects and no defects in growth or global protein synthesis levels. Our findings demonstrate that tRNA-dependent missense suppression of Gln codons is well tolerated in mammalian cells and significantly reduces polyQ levels and aggregates that cause HD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".