Analysis of a 3′-translation enhancer in a tombusvirus: A dynamic model for RNA–RNA interactions of mRNA termini
Bibliographic record
Abstract
Tomato bushy stunt virus is a (+)-strand RNA virus that is neither 5'-capped nor 3'-polyadenylated. Translation of viral proteins is instead mediated by an RNA element, the 3'-cap-independent translational enhancer (3'CITE), which is located in its 3' untranslated region (UTR). The 3'CITE is proposed to recruit the translational machinery to the viral message, while a long-distance RNA-RNA interaction between the 3'CITE and 5' UTR is thought to deliver the 43S ribosomal subunit to the 5' end of the viral mRNA. Here we provide the first evidence that the 5' UTR and 3'CITE interact physically. Mutational analysis showed that formation of this RNA-RNA interaction in vitro correlates well with efficient translation in vivo, thus supporting its functional relevance. Other analyses of the 3'CITE confirmed an overall Y-shaped RNA secondary structure and demonstrated the importance of numerous minor structural features for efficient translation of viral mRNAs. Functional studies on the role of the 5' UTR revealed that despite the absence of a cap structure, 43S subunits load at the very 5' end and scan in a 3' direction. These results indicate that the 5'-3' RNA-RNA interaction is likely disrupted by scanning ribosomal subunits and suggest a dynamic model for the interaction of mRNA termini during active translation.
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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".