The Mechanism of Interferon Refractoriness During Hepatitis C Virus Infection and Its Reversal with a Peroxisome Proliferator-Activated Receptor α Agonist
Bibliographic record
Abstract
Patients who respond poorly to therapies for hepatitis C virus (HCV) infection display a characteristic phenotype with high basal hepatic interferon-stimulated gene (ISG) expression, but limited induction following interferon (IFN) treatment. The molecular pathways that mediate this refractory state are not known. We examined whether the AMPK activator metformin, the PPARγ agonist pioglitazone, or the PPARα agonist WY-14643 could potentiate IFN responses, reverse IFN refractoriness, and enhance viral eradication in hepatocytes. WY-14643 demonstrated the strongest antiviral synergy with IFN-α and so was tested in the context of chronic IFN activation. Cells rendered refractory to IFN by IFN-α pretreatment were resensitized by WY-14643, as demonstrated by improved STAT1 phosphorylation, promoter activation, and ISG expression. WY-14643 treatment reduced the expression of key negative regulators of IFN signaling: the AXL receptor tyrosine kinase, suppressor of cytokine signaling (SOCS) 1 and 3, which are upregulated in the IFN-refractory state. AXL is a novel regulator of IFN-α signaling that is induced by HCV infection in vitro and which may drive SOCS3 expression. Our data suggests that PPARα agonists could be a useful adjunct treatment for chronic HCV infection by reducing the expression of AXL/SOCS and increasing the sensitivity to IFN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 0.000 |
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 teacher head, 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".