Pathways to disease from natural variations in human cytoplasmic tRNAs
Bibliographic record
Abstract
Perfectly accurate translation of mRNA into protein is not a prerequisite for life. Resulting from errors in protein synthesis, mistranslation occurs in all cells, including human cells. The human genome encodes >600 tRNA genes, providing both the raw material for genetic variation and a buffer to ensure that resulting translation errors occur at tolerable levels. On the basis of data from the 1000 Genomes Project, we highlight the unanticipated prevalence of mistranslating tRNA variants in the human population and review studies on synthetic and natural tRNA mutations that cause mistranslation or de-regulate protein synthesis. Although mitochondrial tRNA variants are well known to drive human diseases, including developmental disorders, few studies have revealed a role for human cytoplasmic tRNA mutants in disease. In the context of the unexpectedly large number of tRNA variants in the human population, the emerging literature suggests that human diseases may be affected by natural tRNA variants that cause mistranslation or de-regulate tRNA expression and nucleotide modification. This review highlights examples relevant to genetic disorders, cancer, and neurodegeneration in which cytoplasmic tRNA variants directly cause or exacerbate disease and disease-linked phenotypes in cells, animal models, and humans. In the near future, tRNAs may be recognized as useful genetic markers to predict the onset or severity of human 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".