Distinct Requirements for IFNs and STAT1 in NK Cell Function
Bibliographic record
Abstract
NK cell functions were examined in mice with a targeted mutation of the STAT1 gene, an essential mediator of IFN signaling. Mice deficient in STAT1 displayed impaired basal NK cytolytic activity in vitro and were unable to reject transplanted tumors in vivo, despite the presence of normal numbers of NK cells. IL-12 enhanced NK-mediated cytolysis, but poly(I:C) did not, and a similar phenotype occurred in mice lacking IFNalpha receptors. Molecules involved in activation and lytic function of NK cells (granzyme A, granzyme B, perforin, DAP10, and DAP12) were expressed at comparable levels in both wild-type and STAT1(-/-) mice, and serine esterase activity necessary for CTL function was normal, showing that the lytic machinery was intact. NK cells with normal cytolytic activity could be derived from STAT1(-/-) bone marrow progenitors in response to IL-15 in vitro, and enhanced NK lytic activity and normal levels of IFN-gamma were produced in response to IL-12 treatment in vivo. Despite these normal responses to cytokines, STAT1(-/-) mice could not reject the NK-sensitive tumor RMA-S, even following IL-12 treatment in vivo. Whereas in vitro NK cytolysis was also reduced in mice lacking both type I and type II IFN receptors, these mice resisted tumor challenge. These results demonstrate that both IFN-alpha and IFN-gamma are required to maintain NK cell function and define a STAT1-dependent but partially IFN-independent pathway required for NK-mediated antitumor activity.
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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) | 0.000 | 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) | 0.002 | 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".