Invariant Natural Killer T Cells Promote T Cell Immunity by Modulating the Function of Lung Dendritic Cells during Chlamydia pneumoniae Infection
Bibliographic record
Abstract
In this study, we examined the effect of invariant natural killer T (iNKT) cells on the function of lung dendritic cells (LDCs) in eliciting protective immunity against Chlamydia pneumoniae (Cpn) lung infection. We employed a combination of approaches including the use of iNKT cell-deficient, Jα18-knockout (KO) mice and LDC adoptive transfer. We found that iNKT cells significantly altered the number, phenotype and cytokine profile of LDCs following infection. Furthermore, coculture of T cells with LDCs from Cpn-infected wild-type (WT) and KO mice induced type-1 and type-2 responses, respectively. More importantly, upon adoptive transfer, LDCs from Cpn-infected WT mice (WT-LDCs) conferred protective immunity, whereas LDCs from KO mice (KO-LDCs) increased the severity of disease after challenge infection. Further cytokine analyses of the lung tissues and lung-draining lymph node cells showed that KO-LDC-recipient mice exhibited a type-2 cytokine production pattern, while WT-LDC recipients exhibited a type-1 cytokine profile. Taken together, our results provide in vivo evidence that iNKT cells play a critical role in modulating LDC function to generate protective T-cell immunity, particularly in a clinically relevant intracellular bacterial infection.
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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".