Mitochondrial translation termination, recycling, reinitiation, and rescue for in-frame and out-of-frame contexts
Bibliographic record
Abstract
Because mitochondria diverged from a bacterial ancestor during evolution, the mitochondrial protein synthesis system includes both mRNAs and translation factors with unique characteristics. However, the molecular mechanisms underlying translation termination, recycling, and quality control remain unclear. Here, via high-resolution mitochondrial Ribo-Seq and Disome-Seq, we reveal: the specificity of release factors for different kinds of stop codons; the role of mtRF1 in vertebrates, which do not have noncanonical stop codons in their main translons; the recycling-coupled translation of internal translons; and the rescue of mitoribosomes in the early elongation stage. mtRF1L recognizes all stop codons, whereas mtRF1 recognizes only AGA/AGG noncanonical stop codons. Additionally, mtRF1 terminates the translation of out-of-frame translons that end with AGA/AGG. We also found that mtRRF and mtIF3 are required for mitoribosome recycling on stop codons and for the reinitiation of internal translon translation. Mitoribosomes that stall at the start codons and/or at the early elongation phase are major substrates of the rescue factors ICT1, mtRF-R, and mtRES1. Moreover, HEMK1-mediated methylation of release factors enhances the termination reaction on stop codons. Our results provide insights into the mitoribosome dynamics that are associated with the completion of protein synthesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".