NK cells modulate T cell responses via interaction with dendritic cells in Chlamydophila pneumoniae infection
Bibliographic record
Abstract
Protective immune response to chlamydial infection is largely dependent on cell-mediated immune responses with IFN-γ production. Recent studies have shown the critical role of NK cells in bridging innate and adaptive immune responses. In this study, we investigated the effect of NK cells on T cell responses during Chlamydophila pneumoniae (Cpn) lung infection. The results showed that NK cells play a protective role in Cpn infection and influence T cell immunity largely though modulating dendritic cells (DCs) function. Specifically, we found that NK depletion significantly impaired type 1 T cell responses, but enhanced FOXP3+Treg cells and IL-10-producing CD4+T cells. The alteration of T cell responses was associated with more disease severity and higher chlamydial growth in the lung. Further analysis of DC phenotype and cytokine profile found that DCs from NK cell-depleted mice expressed lower levels of co-stimulatory molecules and produced higher levels of IL-10 than those from control IgG-treated mice. More importantly, the adoptive transfer of DCs from NK cell-depleted mice induced a much lower degree of type 1 T cell responses but a higher amount of FOXP3+ Treg cells and IL-10-producing CD4+T cells in the recipient mice than DCs from IgG-treated mice. In contrast to the strong protective effect observed in recipients of DCs from IgG-treated mice, the recipients of DCs from NK cell-depleted mice failed to be protected against Cpn infection. The data suggest that NK cells play a critical role in coordinating innate and adaptive immunity in Cpn lung infection by modulating the DC function to influence T cell responses.
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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".