Differential modulating effect of natural killer (NK) T cells on interferon-γ production and cytotoxic function of NK cells and its relationship with NK subsets in Chlamydia muridarum infection
Bibliographic record
Abstract
Natural killer T (NKT) cells are a newly identified T-cell population with potential immunomodulatory functions. Several studies have shown modulating effects of NKT cells activated by α-galactosylceramide, a model antigen, on NK cell function. We here report a differential modulating effect of NKT cells on the interferon-γ (IFN-γ) production and cytolytic function of NK cells in a chlamydial infection model, using NKT-cell-deficient mice and antibody blocking (anti-CD1d monoclonal antibody) approaches. Our results showed that both NKT and NK cells became activated and produced IFN-γ following Chlamydia muridarum infection in vitro and in vivo. The NK cells in NKT-cell-deficient mice and CD1d-blocked mice showed decreased CD69 expression, cellular expansion and IFN-γ production but surprisingly showed increased cytolytic activity (degranulation) of immature and more mature NK cell subsets, suggesting an inhibitory role of NKT cells on NK cell killing activity. The results suggest that NKT cells preferentially promote IFN-γ production but are inhibitory for the cytotoxic function of NK cells in this infection model. Furthermore, the differential modulating effect of NKT cells on the IFN-γ production and cytotoxicity of NK cells was observed in immature and mature NK cell subsets, although it was more dramatic in the relatively mature CD11b(high) CD27(high) NK cell subset. This finding demonstrates the complexity of innate cell interactions in infection and the possible differential impact of NKT cells on the variable functional aspects of other cell(s) even in one infection setting.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".