Transfer RNA supplementation rescues HARS deficiency in a humanized yeast model of Charcot-Marie-Tooth disease
Bibliographic record
Abstract
Aminoacyl-tRNA synthetases are indispensable enzymes in all cells, ensuring the correct pairing of amino acids to their cognate tRNAs to maintain translation fidelity. Autosomal dominant mutations V133F and Y330C in histidyl-tRNA synthetase (HARS) cause the genetic disorder Charcot-Marie-Tooth type 2W (CMT2W). Treatments are currently restricted to symptom relief, with no therapeutic available that targets the cause of disease. We previously found that histidine supplementation alleviated phenotypic defects in a humanized yeast model of CMT2W caused by HARS V155G and S356N that also unexpectedly exacerbated the phenotype of the two HARS mutants V133F and Y330C. Here, we show that V133F destabilizes recombinant HARS protein, which is rescued in the presence of tRNAHis. HARS V133F and Y330C cause mistranslation and cause changes to the proteome without activating the integrated stress response as validated by mass spectrometry and growth defects that persist with histidine supplementation. The growth defects and reduced translation fidelity caused by V133F and Y330C mutants were rescued by supplementation with human tRNAHis in a humanized yeast model. Our results demonstrate the feasibility of cognate tRNA as a therapeutic that rescues HARS deficiency and ameliorates toxic mistranslation generated by causative alleles for CMT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".