Type I Interferon Receptor on NK Cells Negatively Regulates Interferon-γ Production
Bibliographic record
Abstract
NK cells are a key antiviral component of the innate immune response to HSV-2, particularly through their production of IFN-γ. It is still commonly thought that type I IFN activates NK cell function; however, rather than requiring the type I IFN receptor themselves, we have previously found that type I IFN activates NK cells through an indirect mechanism involving inflammatory monocytes and IL-18. Here, we further show that direct action of type I IFN on NK cells, rather than inducing IFN-γ, negatively regulates its production during HSV-2 infection and cytokine stimulation. During infection, IFN-γ is rapidly induced from NK cells at day 2 post-infection and then immediately downregulated at day 3 post-infection. We found that this downregulation of IFN-γ release was not due to a loss of NK cells at day 3 post-infection, but negatively regulated through IFN signaling on NK cells. Absence of IFNAR on NK cells led to a significantly increased level of IFN-γ compared to WT NK cells after HSV-2 infection in vitro. Further, priming of NK cells with type I IFN was able to suppress cytokine-induced IFN-γ production from both human and mouse NK cells. We found that this immunosuppression was not mediated by IL-10. Rather, we found that type I IFN induced a significant increase in Axl expression on NK cells. Overall, our data suggests that type I IFN negatively regulates NK cell IFN-γ production through a direct mechanism in vitro and during HSV-2 infection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".