Ribavirin potentiates interferon action by augmenting interferon-stimulated gene induction in hepatitis C virus cell culture models
Bibliographic record
Abstract
UNLABELLED: The combination of pegylated interferon (PEG-IFN) and ribavirin is the standard treatment for chronic hepatitis C. Our recent clinical study suggests that ribavirin augments the induction of interferon-stimulated genes (ISGs) in patients treated for hepatitis C virus (HCV) infection. In order to further characterize the mechanisms of action of ribavirin, we examined the effect of ribavirin treatment on ISG induction in cell culture. In addition, the effect of ribavirin on infectious HCV cell culture systems was studied. Similar to interferon (IFN)-α, ribavirin potently inhibits JFH-1 infection of Huh7.5.1 cells in a dose-dependent manner, which spans the physiological concentration of ribavirin in vivo. Microarray analysis and subsequent quantitative polymerase chain reaction assays demonstrated that ribavirin treatment resulted in the induction of a distinct set of ISGs. These ISGs, including IFN regulatory factors 7 and 9, are known to play an important role in anti-HCV responses. When ribavirin is used in conjunction with IFN-α, induction of specific ISGs is synergistic when compared with either drug applied separately. Direct up-regulation of these antiviral genes by ribavirin is mediated by a novel mechanism different from those associated with IFN signaling and intracellular double-stranded RNA sensing pathways such as RIG-I and MDA5. RNA interference studies excluded the activation of the Toll-like receptor and nuclear factor κB pathways in the action of ribavirin. CONCLUSION: Our study suggests that ribavirin, acting by way of a novel innate mechanism, potentiates the anti-HCV effect of IFN. Understanding the mechanism of action of ribavirin would be valuable in identifying novel antivirals.
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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 | 0.001 |
| 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".