Control of NKT Cell Differentiation by Tissue-Specific Microenvironments
Bibliographic record
Abstract
CD1d-restricted Valpha14 NKT cells play an important role in both Th1- and Th2-type immune responses. To determine whether NKT cells develop two functionally distinct subsets that provoke different types of responses, we examined the phenotypes and cellular functions of NK1.1(+) and DX5(+) T cells. We found that both NK1.1(+) and DX5(+) T cells are CD1d-restricted Valpha14 T cells with identical Ag specificities, phenotypes, tissue locations, and functions. Similar to the NK1.1 marker, the DX5 marker (CD49b) is expressed on mature NKT cells in both NK1.1 allele-positive and allele-negative strains. However, when NK1.1(+) and DX5(+) NKT cells isolated from different tissues were compared, we found that thymic and splenic NKT cells differed not only in their cytokine profiles, but also in their phenotype and requirements for costimulatory signals. Thymic NKT cells displayed the phenotype of activated T cells and could be fully activated by TCR ligation. In contrast, splenic NKT cells displayed the phenotype of memory T cells and required a costimulatory signal for activation. Furthermore, the function and phenotype of thymic and splenic NKT cells were modulated by APCs from various tissues that expressed different levels of costimulatory molecules. Modulation of NKT cell function and differentiation may be mediated by synergic effects of costimulatory molecules on the surface of APCs. The results of the present study suggest that the costimulatory signals of tissue-specific APCs are key factors for NKT cell differentiation, and these signals cannot be replaced by anti-CD28 or anti-CD40 ligand Abs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".