Up-regulation of a death receptor renders antiviral T cells susceptible to NK cell–mediated deletion
Bibliographic record
Abstract
Antiviral T cell responses in hepatotropic viral infections such as hepatitis B virus (HBV) are profoundly diminished and prone to apoptotic deletion. In this study, we investigate whether the large population of activated NK cells in the human liver contributes to this process. We show that in vitro removal of NK cells augments circulating CD8(+) T cell responses directed against HBV, but not against well-controlled viruses, in patients with chronic hepatitis B (CHB). We find that NK cells can rapidly eliminate HBV-specific T cells in a contact-dependent manner. CD8(+) T cells in the liver microcirculation are visualized making intimate contact with NK cells, which are the main intrahepatic lymphocytes expressing TNF-related apoptosis-inducing ligand (TRAIL) in CHB. High-level expression of the TRAIL death receptor TRAIL-R2 is found to be a hallmark of T cells exposed to the milieu of the HBV-infected liver in patients with active disease. Up-regulation of TRAIL-R2 renders T cells susceptible to caspase-8-mediated apoptosis, from which they can be partially rescued by blockade of this death receptor pathway. Our findings demonstrate that NK cells can negatively regulate antiviral immunity in chronic HBV infection and illustrate a novel mechanism of T cell tolerance in the human liver.
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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) | 0.001 | 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 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".