Combined Stimulation with Interleukin-18 and Interleukin-12 Potently Induces Interleukin-8 Production by Natural Killer Cells
Bibliographic record
Abstract
The combination of interleukin (IL)-18 and IL-12 (IL-18+IL-12) potently stimulates natural killer (NK) cells, triggering an innate immune response to infections and cancers. Strategies exploiting the effects of IL-18+IL-12 have shown promise for cancer immunotherapy. However, studies have primarily characterized the NK cell response to IL-18+IL-12 in terms of interferon (IFN)-γ production, with little focus on other cytokines produced. IL-8 plays a critical role in activating and recruiting immune cells, but it also has tumor-promoting functions. IL-8 is classically produced by regulatory NK cells; however, cytotoxic NK cells do not typically produce IL-8. In this study, we uncover that stimulation with IL-18+IL-12 induces high levels of IL-8 production by ex vivo expanded and freshly isolated NK cells and NK cells in peripheral blood mononuclear cells. We further report that tumor necrosis factor (TNF)-α, produced by NK cells following IL-18+IL-12 stimulation, regulates IL-8 production. The IL-8 produced is in turn required for maximal IFN-γ and TNF-α production. These findings may have important implications for the immune response to infections and cancer immunotherapies. This study broadens our understanding of NK cell function and IL-18+IL-12 synergy by uncovering an unprecedented ability of IL-18+IL-12-activated peripheral blood NK cells to produce elevated levels of IL-8 and identifying the requirement for intermediates induced by IL-18+IL-12 for maximal cytokine production following stimulation.
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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 | 0.001 |
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".