CD1d-Independent NKT Cells in β2-Microglobulin-Deficient Mice Have Hybrid Phenotype and Function of NK and T Cells
Bibliographic record
Abstract
Unlike CD1d-restricted NK1.1(+)TCRalphabeta(+) (NKT) cells, which have been extensively studied, little is known about CD1d-independent NKT cells. To characterize their functions, we analyzed NKT cells in beta(2)-microglobulin (beta(2)m)-deficient B6 mice. They are similar to NK cells and expressed NK cell receptors, including Ly49, CD94/NKG2, NKG2D, and 2B4. NKT cells were found in normal numbers in mice that are deficient in beta(2)m, MHC class II, or both. They were also found in the male HY Ag-specific TCR-transgenic mice independent of positive or negative selection in the thymus. For functional analysis of CD1d-independent NKT cells, we developed a culture system in which CD1d-independent NKT cells, but not NK, T, or most CD1d-restricted NKT cells, grew in the presence of an intermediate dose of IL-2. IL-2-activated CD1d-independent NKT cells were similar to IL-2-activated NK cells and efficiently killed the TAP-mutant murine T lymphoma line RMA-S, but not the parental RMA cells. They also killed beta(2)m-deficient Con A blasts, but not normal B6 Con A blasts, indicating that the cytotoxicity is inhibited by MHC class I on target cells. IL-2-activated NKT cells expressing transgenic TCR specific for the HY peptide presented by D(b) killed RMA-S, but not RMA, cells. They also killed RMA (H-2(b)) cells that were preincubated with the HY peptide. NKT cells from beta(2)m-deficient mice, upon CD3 cross-linking, secreted IFN-gamma and IL-2, but very little IL-4. Thus, CD1d-independent NKT cells are significantly different from CD1d-restricted NKT cells. They have hybrid phenotypes and functions of NK cells and T cells.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".