Immunomodulation and RNA interference alter hepatitis B virus–specific CD8 T‐cell recognition of infected HepG2‐NTCP
Bibliographic record
Abstract
BACKGROUND AND AIMS: CD8 T cells are essential in controlling HBV infection. Viral control is dependent on efficient recognition of HBV-infected hepatocytes by CD8 T cells, which can induce direct lysis of infected hepatocytes. In addition, CD8 T cells produce interferon (IFN)-γ, which mediates noncytopathic viral clearance. Innate immunomodulators and HBV-targeted RNA interference (RNAi) are being developed to treat chronic hepatitis B (CHB), but may modify HBV antigen presentation and impact CD8 T-cell recognition, in addition to their primary mechanisms of action. APPROACH AND RESULTS: HBV-infected HepG2-NTCP cells were treated with tenofovir disoproxil fumarate (TDF), Toll-like receptor (TLR) 7/8 agonists, TLR7/8 conditioned media (CM) collected from immune cells, or RNAi using short interfering RNAs. The effect of these treatments on antigen presentation was measured through coculture with CD8 T cells recognizing human leukocyte antigen-A0201 restricted epitopes, HBc18-27 or HBs183-191. Cytokine profiles of TLR7/8 CM were measured using a cytometric bead array. TDF reduced viral replication, but not CD8 T-cell recognition, of infected cells. Direct exposure of infected HepG2-NTCP to TLR7/8 agonists had no impact on T-cell recognition. Exposure of infected HepG2-NTCP to TLR7/8 CM enhanced HBV-specific CD8 T-cell recognition through type 1 interferon (IFN) and IFN-γ-dependent mechanisms. RNAi rapidly suppressed HBV-DNA, HBcAg, and HBsAg expression, impairing recognition by HBV-specific CD8 T cells. CONCLUSIONS: Immunomodulation and RNAi, but not nucleos(t)ide analogues, alter the recognition of infected HepG2-NTCP by HBV-specific CD8 T cells. Understanding these changes will inform combination treatments for CHB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".