Optimal Effector Functions in Human Natural Killer Cells Rely upon Autocrine Bone Morphogenetic Protein Signaling
Bibliographic record
Abstract
Natural killer (NK) cells are critical for innate tumor immunity due to their specialized ability to recognize and kill neoplastically transformed cells. However, NK cells require a specific set of cytokine-mediated signals to achieve optimal effector function. Th1-associated cytokines promote effector functions that are inhibited by the prototypic Th2 cytokine IL4 and the TGFβ superfamily members TGFβ1 and activin-A. Interestingly, the largest subgroup of the TGFβ superfamily are the bone morphogenetic proteins (BMP), but the effects of BMP signaling on NK cell effector functions have not been evaluated. Here, we demonstrate that blood-circulating NK cells express type I and II BMP receptors, BMP-2 and BMP-6 ligands, and phosphorylated isoforms of Smad-1/-5/-8, which mediate BMP family member signaling. In opposition to the inhibitory effects of TGFβ1 or activin-A, autocrine BMP signaling was supportive to NK cell function. Mechanistic investigations in cytokine and TLR-L-activated NK cells revealed that BMP signaling optimized IFNγ and global cytokine and chemokine production, phenotypic activation and proliferation, and autologous dendritic cell activation and target cytotoxicity. Collectively, our findings identify a novel auto-activatory pathway that is essential for optimal NK cell effector function, one that might be therapeutically manipulated to help eradicate tumors. Cancer Res; 74(18); 5019-31. ©2014 AACR.
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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) | 0.002 | 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".