Interferon α decreases expression of human scavenger receptor class BI, a possible HCV receptor in hepatocytes
Bibliographic record
Abstract
BACKGROUND: Infection with the hepatitis C virus (HCV) causes acute hepatitis. This disease has a high probability of becoming chronic and leading to cirrhosis, but a more deadly consequence is hepatocellular carcinoma. Interferon alpha (IFN alpha)-based treatment combined with ribavirin is the major therapeutic choice available for the treatment of chronic HCV infection. AIMS: The scavenger receptor class B type I (SR-BI) or its human homologue CD36 and LIMPII Analogous-1 (hSR-BI/CLA-1) has recently been shown to interact with HCV envelope glycoprotein E2, thus suggesting that it might participate in entry of the virus into host cells. This rationale underlies current interest in the potential role of IFN alpha in hSR-BI/CLA-1 expression in HepG2 cells. RESULTS: It was shown that endogenous hepatocyte expression of hSR-BI/CLA-1 was suppressed by exposure to IFN alpha. Decreased hSR-BI/CLA-1 expression in IFN alpha-treated cells was due to lower transcriptional activity of the promoter. A potential pathway for the effect of IFN alpha on hSR-BI/CLA-1 promoter activity was identified when the inhibitory action of IFN was abrogated in signal transducer and activator of transcription 1 (STAT1)/STAT2 knocked-down cells. Exposure of HepG2 cells to IFN alpha elicited a rapid phosphorylation of STAT1/STAT2, a known target of IFN alpha signalling. In addition, the mutagenesis of a STAT1/STAT2 response element in the hSR-BI/CLA-1 promoter abolished the ability of IFN alpha to suppress promoter activity. CONCLUSIONS: Together, these results indicate that the STAT1/STAT2 pathway participates in IFN alpha inhibition of hSR-BI/CLA-1 expression, and raise the possibility that lowering the expression of this gene may be of therapeutic value for treating HCV infections.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".