The Interactions of Multiple Cytokines Control NK Cell Maturation
Bibliographic record
Abstract
Although NK cells are well known for their cytotoxic functions, they also produce an array of immunoregulatory cytokines and chemokines. During an immune response, NK cells are exposed to complex combinations of cytokines that influence their differentiation and function. In this study, we have examined the phenotypic and functional consequences of exposing mouse NK cells to IL-4, IL-12, IL-15, IL-18, and IL-21 and found that although all factors induced signs of maturation, characterized by decreased proliferation and IFN-γ secretion, distinct combinations induced unique cytokine secretion profiles. In contrast, the immunosuppressive factors IL-10 and TGF-β had little direct effect on NK cell effector functions. Sustained IL-18 signals resulted in IL-13 and GM-CSF production, whereas IL-12 and IL-21 induced IL-10 and TNF-α. Surprisingly, with the exception of IL-21, all cytokines suppressed cytotoxic function of NK cells at the expense of endogenous cytokine production suggesting that "helper-type" NK cells were generated. The cytokine signals also profoundly altered the cell surface phenotype of the NK cells-a striking example being the downregulation of the activating receptor NKG2D by IL-4 that resulted in decreased NKG2D-dependent killing. IL-4 exposure also modulated NKG2D expression in vivo suggesting it is functionally important during immune responses. This study highlights the plasticity of NK cell differentiation and suggests that the relative abundance of cytokines at sites of inflammation will lead to diverse outcomes in terms of NK cell phenotype and interaction with the immune system.
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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 | 0.001 | 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) | 0.001 | 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".