miR-122 does not impact recognition of the HCV genome by innate sensors of RNA but rather protects the 5′ end from the cellular pyrophosphatases, DOM3Z and DUSP11
Bibliographic record
Abstract
Hepatitis C virus (HCV) recruits two molecules of the liver-specific microRNA-122 (miR-122) to the 5' end of its genome. This interaction promotes viral RNA accumulation, but the precise mechanism(s) remain incompletely understood. Previous studies suggest that miR-122 is able to protect the HCV genome from 5' exonucleases (Xrn1/2), but this protection is not sufficient to account for the effect of miR-122 on HCV RNA accumulation. Thus, we investigated whether miR-122 was also able to protect the viral genome from innate sensors of RNA or cellular pyrophosphatases. We found that miR-122 does not play a protective role against recognition by PKR, RIG-I-like receptors, or IFITs 1 and 5. However, we found that knockdown of both the cellular pyrophosphatases, DOM3Z and DUSP11, was able to rescue viral RNA accumulation of subgenomic replicons in the absence of miR-122. Nevertheless, pyrophosphatase knockdown increased but did not restore viral RNA accumulation of full-length HCV RNA in miR-122 knockout cells, suggesting that miR-122 likely plays an additional role(s) in the HCV life cycle, beyond 5' end protection. Overall, our results support a model in which miR-122 stabilizes the HCV genome by shielding its 5' terminus from cellular pyrophosphatase activity and subsequent turnover by exonucleases (Xrn1/2).
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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 | 0.001 |
| 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".