Natural Killer Cell-Derived IL-10 Prevents Liver Damage During Sustained Murine Cytomegalovirus Infection
Bibliographic record
Abstract
Natural Killer (NK) cells are lymphocytes of the innate immune response that play a vital role in controlling infections and cancer. Their pro-inflammatory role has been well established; however, less is known about the regulatory functions of NK cells, in particular, their production of the anti-inflammatory cytokine IL-10. In this study, we investigated the immunoregulatory function of NK cells during MCMV infection and demonstrated that NK cells are major producers of IL-10 during the early stage of infection. To investigate the effect of NK cell-derived IL-10, we have generated NK cell-specific IL-10-deficient mice (NKp46-Cre-Il10fl/fl) displaying no signs of age-related spontaneous inflammation, with NK cells that show no detectable IL-10 production upon in vitro stimulation. In NKp46-Cre-Il10fl/fl mice, the levels of IL-10 and IFNγ, viral burdens and T cell activation were similar between NKp46-Cre-Il10fl/fl mice and their control littermates, suggesting that NK cell-derived IL-10 is dispensable during acute MCMV infection in immunocompetent hosts. In perforin-deficient mice that show a more sustained infection, NK cells produce more sustained levels of IL-10. By crossing NKp46-Cre-Il10fl/fl mice with perforin-deficient mice, we demonstrated that NK cell-derived IL-10 regulates T cell activation, prevents liver damage, and allows for better disease outcome. Taken together, NK cell-derived IL-10 can be critical in regulating the immune response during early phases of infection and therefore protecting the host from excessive immunopathology.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".