Human Ago2 Is Required for Efficient MicroRNA 122 Regulation of Hepatitis C Virus RNA Accumulation and Translation
Bibliographic record
Abstract
MicroRNA 122 (miR-122) increases the accumulation and translation of hepatitis C virus (HCV) RNA in infected cells through direct interactions with homologous sequences in the 5' untranslated region (UTR) of the HCV genome. Argonaute 2 (Ago2) is a component of the RNA-induced silencing complex (RISC) and mediates small interfering RNA (siRNA)-directed mRNA cleavage and microRNA translational suppression. We investigated the function of Ago2 in HCV replication to determine whether it plays a role in enhancing the synthesis and translation of HCV RNA that is associated with miR-122. siRNA-mediated depletion of Ago2 in human hepatoma cells reduced HCV RNA accumulation in transient HCV replication assays. The treatment did not adversely affect cell viability, as assessed by cell proliferation, capped translation, and interferon assays. These data are consistent with complementary roles for Ago2 and miR-122 in enhancing HCV RNA amplification. By using a transient HCV replication assay that is dependent on an exogenously provided mutant miR-122, we determined that Ago2 depletion still reduced luciferase expression and HCV RNA accumulation, independently of miR-122 biogenesis. miR-122 has previously been found to stimulate HCV translation. Similarly, Ago2 knockdown also reduced HCV translation, and its depletion reduced the ability of miR-122 to stimulate viral translation. These data suggest a direct role for Ago2 in miR-122-mediated translation. Finally, Ago2 was also necessary for efficient miR-122 enhancement of HCV RNA accumulation. These data support a model in which miR-122 functions within an Ago2-containing protein complex to augment both HCV RNA accumulation and translation.
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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 | 0.002 |
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".