<scp>miR</scp>‐122 Promotion of the hepatitis C virus life cycle: sound in the silence
Bibliographic record
Abstract
The unusual role for miR-122 in promoting the hepatitis C virus (HCV) life cycle was first identified in 2005, but its mechanism of action remains uncharacterized. The virus appears to use the microRNA (miRNA) in a way that is opposed to that of normal miRNAs. Instead of interacting with sequences in the 3'-untranslated region (UTR), miR-122 binds to two sites in the 5'-UTR, and instead of silencing gene expression or promoting the degradation of the viral RNA, it stabilizes the genome and potently augments the efficiency by which HCV RNA accumulates in infected cells. This review discusses the current knowledge and models for the mechanism by which miR-122 promotes the HCV life cycle. Annealing of miR-122 to the HCV genome requires particular base pairing, stimulates translation, and stabilizes the viral genome by blocking degradation by host exonucleases, but these functions are unlikely to be the whole story. We will discuss other possible functions for miR-122, the stages of the HCV life cycle at which miR-122 may influence HCV, and other related viruses that may be similarly regulated by miR-122. Despite our lack of detailed mechanistic information, antagonism of miR-122 is emerging as a powerful method to inhibit HCV infections, and unique to other HCV treatment strategies does not, thus far, appear to induce emergence of escape mutants. Used alone or in combination with other antiviral drugs, miR-122 antagonists could be useful to both inhibit the virus and provide selective pressure to inhibit the development of resistance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".