The Important Role of Dendritic Cell (DC) in iNKT-Mediated Modulation of NK Cell Function in Chlamydia pneumoniae Lung Infection
Bibliographic record
Abstract
Chlamydia pneumoniae(Cpn) infection causes multiple acute and chronic human diseases. The role of DCs in host defense against Cpn infection has been well documented. The same is true for invariant natural killer T (iNKT) cells and NK cells, but the interaction among cells is largely unknown. In this study, we investigated the influence and mechanism of iNKT cell on the differentiation and function of NK cell inCpnlung infection and the role played by DCs in this process. We found that expansion of IFN-γ-producing NK cells quickly happened after the infection, but this response was altered in iNKT knockout (KO) mice. The expression of activation markers and the production of IFN-γby different NK subsets were significantly lower in KO mice than wild-type (WT) mice. Using in vitro DC-NK coculture and in vivo adoptive transfer approaches, we further examined the role of DCs in iNKT-mediated modulation of NK cell function. We found that NK cells expressed lower levels of activation markers and produced less IFN-γwhen they were cocultured with DCs from KO mice than WT mice. More importantly, we found that the adoptive transfer of DCs from the KO mice induced less NK cell activation and IFN-γproduction. The results provided evidence on the modulating effect of iNKT cell on NK cell function, particularly the critical role of DCs in this modulation process. The finding suggests the complexity of cellular interactions inCpnlung infection, which should be considered in designing preventive and therapeutic approaches for diseases and infections.
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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) | 0.001 | 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".