Molecular signature of hepatitis B virus regulation by interferon‐γ in primary human hepatocytes
Bibliographic record
Abstract
AIM: A complete cure for chronic hepatitis B virus (HBV) infection requires elimination of covalently closed circular DNA; however, this remains to be clinically achieved. Interferon (IFN)-γ, a type II IFN, is produced by intrahepatic cytotoxic T lymphocytes and has non-cytolytic antiviral potential. However, the mechanism by which IFN-γ regulates HBV infection has not been fully elucidated. Thus, we developed an in vitro HBV infection assay system and analyzed the molecular signature of HBV regulation by IFN-γ. METHODS: The in vitro HBV infection assay system was established in primary human hepatocytes infected with HBV derived from the plasmid containing 1.3-mer HBV genome, and treated with IFN-γ. The antiviral effects and signaling pathways of IFN-γ were examined using microarray, and assessed by siRNA knockdown experiments of the related genes. RESULTS: IFN-γ treatment suppressed both HBV propagation and transcription as efficiently as IFN-α. Microarray analysis showed that IFN-γ stimulation induced the activation of both IFN-γ and IFN-α signaling, regulating HBV covalently closed circular DNA. HBV production was decreased by IFN-γ through Janus kinase/signal transducer and activator of transcription signaling and interferon-stimulated genes, such as 2'-5'-oligoadenylate synthase 2 and apolipoprotein B mRNA editing enzyme catalytic subunit 3G. CONCLUSIONS: IFN-γ can suppress HBV propagation and transcription in hepatocytes by activating specific intracellular signaling pathways in hepatocytes, and suggests the future application of these particular signaling pathways or genes for the complete elimination of HBV.
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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 | 0.001 |
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".