Generation of an Inhibitory NK Cell Subset by TGF-β1/IL-15 Polarization
Bibliographic record
Abstract
NK cells have been shown to exhibit inflammatory and immunoregulatory functions in a variety of healthy and diseased settings. In the context of chronic viral infection and cancer, distinct NK cell populations that inhibit adaptive immune responses have been observed. To understand how these cells arise and further characterize their immunosuppressive role, we examined in vitro conditions that could polarize human NK cells into an inhibitory subset. TGF-β1 has been shown to induce regulatory T cells in vitro and in vivo; we therefore investigated if TGF-β1 could also induce immunosuppressive NK-like cells. First, we found that TGF-β1/IL-15, but not IL-15 alone, induced CD103+CD49a+ NK-like cells from peripheral blood NK cells, which expressed markers previously associated with inhibitory CD56+ innate lymphoid cells, including high expression of GITR and CD101. Moreover, supernatant from ascites collected from patients with ovarian carcinoma also induced CD103+CD49a+ NK-like cells in vitro in a TGF-β-dependent manner. Interestingly, TGF-β1/IL-15-induced CD103+CD56+ NK-like cells suppressed autologous CD4+ T cells in vitro by reducing absolute number, proliferation, and expression of activation marker CD25. Collectively, these findings provide new insight into how NK cells may acquire an inhibitory phenotype in TGF-β1-rich environments.
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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 | 0.000 |
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 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".