Location specific annealing of miR-122 and other small RNAs defines an Hepatitis C Virus 5′ UTR regulatory element with distinct impacts on virus translation and genome stability
Bibliographic record
Abstract
Hepatitis C virus (HCV) replication requires annealing of a liver specific small-RNA, miR-122 to 2 sites on 5' untranslated region (UTR). Annealing has been reported to (a) stabilize the genome, (b) stimulate translation and (c) promote the formation of translationally active Internal Ribosome Entry Site (IRES) RNA structure. In this report, we map the RNA element to which small RNA annealing promotes HCV to nucleotides 1-44 and identify the relative impact of small RNA annealing on virus translation promotion and genome stabilization. We mapped the optimal region on the HCV genome to which small RNA annealing promotes virus replication to nucleotides 19-37 and found the efficiency of viral RNA accumulation decreased as annealing moved away from this region. Then, by using a panel of small RNAs that promote replication with varying efficiencies we link the efficiency of lifecycle promotion with translation stimulation. By contrast small RNA annealing stabilized the viral genome even if they did not promote virus replication. Thus, we propose that miR-122 annealing promotes HCV replication by annealing to an RNA element that activates the HCV IRES and stimulates translation, and that miR-122 induced HCV genome stabilization is insufficient alone but enhances virus replication.
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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".