Location specific small RNA annealing to the HCV 5’ UTR promotes Hepatitis C Virus replication by favoring IRES formation and stimulating virus translation
Bibliographic record
Abstract
ABSTRACT Hepatitis C virus (HCV) genome 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) promote translation, and c) induce the canonical HCV 5’ UTR Internal Ribosome Entry Site (IRES) structure. In this report we identify the relative impact of small RNA annealing on the three functions ascribed to miR-122 and generate a mechanistic model for miR-122 promotion of HCV. First, we identified that perfectly complementary small RNAs that anneal to different locations on the HCV 5’ UTR stimulate replication with varying efficiencies and mapped the region on the HCV genome to which small RNA annealing promotes virus replication. Second, by using a panel of small RNAs that promote with varying efficiencies we link HCV replication induction with translation stimulation and 5’ UTR RNA structure modifications. However, replication promotion was not linked to genome stabilization since all small RNAs tested could stabilize the viral genome regardless of their ability to promote replication. Thus, we propose that miR-122 annealing promotes HCV replication primarily by activating the HCV IRES and stimulating translation, and that miR-122-induced HCV genome stabilization is insufficient alone but enhances virus replication. Graphical Abstract
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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) | 0.000 | 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) | 0.028 | 0.005 |
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".