TNFα Augments Cytokine-Induced NK Cell IFNγ Production through TNFR2
Bibliographic record
Abstract
NK cells play a central role in innate immunity, acting directly through cell-mediated cytotoxicity and by secreting cytokines. TNFα activation of TNFR2 enhances NK cell cytotoxicity, but its effects on the other essential function of NK cells - cytokine production, for which IFNγ is paramount - are poorly defined. We identify the expression of both TNFα receptors on human peripheral blood NK cells (TNFR2 > TNFR1) and show that TNFα significantly augments IFNγ production from IL-2-/IL-12-treated NK cells in vitro, an effect mimicked by a TNFR2 agonistic antibody. TNFα also enhanced murine NK cell IFNγ production via TNFR2 in vitro. In a mouse model characterized by the hepatic recruitment and activation of NK cells, TNFR2 also regulated NK cell IFNγ production in vivo. Specifically, in this model, after activation of an innate immune response, hepatic numbers of TNFR2-expressing and IFNγ-producing NK cells were both significantly increased; however, the frequency of IFNγ-producing hepatic NK cells was significantly reduced in TNFR2-deficient mice. We delineate an important role for TNFα, acting through TNFR2, in augmenting cytokine-induced NK cell IFNγ production in vivo and in vitro, an effect with significant potential implications for the regulation of innate and adaptive immune responses.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".