Natural Killer Cells Regulate Pulmonary Macrophages Polarization in Host Defense Against Chlamydial Respiratory Infection
Bibliographic record
Abstract
NK cells and pulmonary macrophages both are important components of innate immunity. The interaction between NK cells and pulmonary macrophages during chlamydial infection is poorly understood. In this study, we explored the effect of NK cells on regulation of pulmonary macrophage function during chlamydial respiratory infection. We found that NK depletion led to polarization of pulmonary macrophages from M1 to M2 phenotype, and it is related to reduced miR-155 expression in lung macrophage. Using adoptive transfer approach, we found that the recipients receiving lung macrophages isolated fromC. muridarum-infected NK-cell-depleted mice exhibited an increased bacterial load and severe inflammation in the lung upon chlamydial challenge infection when compared with the recipients of lung macrophages from infected isotype control antibody treated mice. Herein, the effects of NK cells on macrophage polarization were examinedin vitro. We found that NK cells from chlamydial-infected mice (iNK) significantly induced M1 polarization compared to that from uninfected mice (uNK). Inhibition of miR-155 expression in macrophages reduced M1 polarization induced by iNK, while miR-155 over-expression enhanced it. Furthermore, neutralization of IFN-γ in the coculture system decreased the expression of miR-155 by macrophages, and resulted in weakened M1 polarization. The data indicates that NK cells promote M1 polarization through up-regulation of miR-155 in macrophages by producing IFN-γ during chlamydial infection, and NK-regulated macrophage polarization is functionally relevant to host defense against the 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) | 0.001 | 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".