Differentiation of Murine NK Cells into Distinct Subsets Based on Variable Expression of the IL-12Rβ2 Subunit
Bibliographic record
Abstract
The cytokine IL-12 manifests its biological activity via interaction with a heterodimeric receptor (IL-12R) present on activated T and NK cells. The cDNAs for two IL-12R subunits have been cloned from human and mouse and designated IL-12Rbeta1 and IL-12Rbeta2. The expression of IL-12Rbeta2 on T cells is influenced by cytokines, particularly IL-4, IL-12, and IFN-gamma; however, little is known regarding regulation of IL-12R expression on NK cells. In this study we show that murine NK cells differentiate into IL-12Rbeta2(low) and IL-12Rbeta2(high) subsets after in vitro stimulation with IL-2 in the absence of exogenous polarizing cytokines. Subset development occurs gradually as NK cells expand in vitro and is generally complete by 8-12 days of culture. Once established, IL-12Rbeta2(low) and IL-12Rbeta2(high) subsets are highly stable in vitro and can be maintained for at least 20 days after FACS sorting. Formation of these NK subsets appears to be strain independent. Flow cytometric analyses demonstrate that both subsets express a number of NK-associated markers, including NK1.1, DX-5, Ly-49A, and Ly-49C, but that the Ly-49G2 class I inhibitory receptor is expressed predominantly on the IL-12Rbeta2(high) population. Both IL-12Rbeta2(low) and IL-12Rbeta2(high) NK cells respond to exogenous IL-12 by rapid production of high levels of IFN-gamma and increased lytic activity against NK-sensitive YAC-1 target cells. Analyses of cytokine gene expression by RNase protection assay indicated that similar to the recently described human NK1 subset, both IL-12Rbeta2(high) and IL-12Rbeta2(low) murine NK subsets expressed high levels of IFN-gamma, whereas neither subset expressed mRNA for the NK2-associated cytokines IL-5 and IL-13.
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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 | 0.001 |
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".