Beyond sites 1 and 2, miR-122 target sites in the HCV genome have negligible contributions to HCV RNA accumulation in cell culture
Bibliographic record
Abstract
Hepatitis C virus (HCV) recruits two molecules of the liver-specific microRNA-122 (miR-122) to two adjacent sites (S1 and S2) located at the 5' end of the viral RNA genome. This interaction promotes HCV RNA accumulation by stabilising the viral RNA and resulting in alteration of the secondary structure of the viral genome. In addition to S1 and S2, the HCV genome contains several other putative miR-122 binding sites, one in the IRES region, three in the NS5B coding region, and one in the 3' UTR. We investigated and compared the relative contributions of the S1, S2, IRES, NS5B (NS5B.1, 2 and 3) and 3' UTR sites on protein expression, viral RNA accumulation, and infectious particle production by mutational analysis and supplementation with compensatory mutant miR-122 molecules. We found that mutations predicted to alter miR-122 binding at the IRES and NS5B.2 sites lead to reductions in HCV core protein expression and viral RNA accumulation; with a concomitant decrease in viral particle production for the NS5B.2 mutant. However, supplementation of miR-122 molecules with compensatory mutations did not rescue these site mutants to wild-type levels, suggesting that mutation of these sequences likely disrupts an additional interaction important to the HCV life cycle, beyond direct interactions with miR-122. Thus, S1 and S2 play a predominant role in viral RNA accumulation, while miR-122 interactions with the IRES, NS5B and 3' UTR regions have negligible contributions to viral protein expression, viral RNA accumulation, and infectious particle production.
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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) | 0.001 | 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) | 0.002 | 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".