Regulatory start-stop elements in 5’ untranslated regions pervasively modulate translation
Bibliographic record
Abstract
Abstract Sequence elements within the 5’ untranslated region (UTR) of eukaryotic genes, e.g. upstream open reading frames (uORFs), control translation of eukaryotic genes. We describe an element consisting of a start codon immediately followed by a stop codon which is distinct from uORFs in the lack of an elongation step. Start-stops have been described for specific cases, but their widespread impact has been overlooked. Start-stop elements occur in the 5’UTR of 1, 417 human genes and are more often occupied with a ribosome than canonical uORFs or control sequences. Start-stops efficiently halt ribosomes without evidence for accelerated RNA turnover, therefore acting as a barrier for the scanning of the small ribosomal subunit and repressing downstream translation. Our results suggest a model by which the ribosome undergoes repeated cycles of termination and partial ribosomal recycling, during which the large subunit detaches, but the 40S subunit with the Met-tRNA i Met remains associated with the mRNA to be rejoined by the 60S subunit. Start-stop elements occur in many transcription factors and signaling genes, and affect cellular fate via different routes. We investigate the start-stop element in several genes, i.e. MORF4L1 , SLC39A1 , and PSPC1 , and in more detail in ATF4 .
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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) | 0.002 | 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".