Lost in translation: How neurons cope with tRNA decoding
Bibliographic record
Abstract
Post-transcriptional tRNA modifications contribute to the decoding efficiency of tRNAs by supporting codon recognition and tRNA stability. Recent work shows that the molecular and cellular functions of tRNA modifications and tRNA-modifying-enzymes are linked to brain development and neurological disorders. Lack of these modifications affects codon recognition and decoding rate, promoting protein aggregation and translational stress response pathways with toxic consequences to the cell. In this review, we discuss the peculiarity of local translation in neurons, suggesting a role for fine-tuning of translation performed by tRNA modifications. We provide several examples of tRNA modifications involved in physiology and pathology of the nervous system, highlighting their effects on protein translation and discussing underlying mechanisms, like the unfolded protein response (UPR), ribosome quality control (RQC), and no-go mRNA decay (NGD), which could affect neuronal functions. We aim to deepen the understanding of the roles of tRNA modifications and the coordination of these modifications with the protein translation machinery in the nervous system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".