Targeted sequencing reveals expanded genetic diversity of human transfer RNAs
Bibliographic record
Abstract
Transfer RNAs are required to translate genetic information into proteins as well as regulate other cellular processes. Nucleotide changes in tRNAs can result in loss or gain of function that impact the composition and fidelity of the proteome. Despite links between tRNA variation and disease, the importance of cytoplasmic tRNA variation has been overlooked. Using a custom capture panel, we sequenced 605 human tRNA-encoding genes from 84 individuals. We developed a bioinformatic pipeline that allows more accurate tRNA read mapping and identifies multiple polymorphisms occurring within the same variant. Our analysis identified 522 unique tRNA-encoding sequences that differed from the reference genome from 84 individuals. Each individual had ~66 tRNA variants including nine variants found in less than 5% of our sample group. Variants were identified throughout the tRNA structure with 17% predicted to enhance function. Eighteen anticodon mutants were identified including potentially mistranslating tRNAs; e.g., a tRNASer that decodes Phe codons. Similar engineered tRNA variants were previously shown to inhibit cell growth, increase apoptosis and induce the unfolded protein response in mammalian cell cultures and chick embryos. Our analysis shows that human tRNA variation has been underestimated. We conclude that the large number of tRNA genes provides a buffer enabling the emergence of variants, some of which could contribute to 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) | 0.001 | 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".