NK Cells Modulate Dendritic Cell (DC) Signaling Pathways and DC Recruitment in Chlamydial Infection
Bibliographic record
Abstract
Previous studies have demonstrated the significant impact of NK cells on adaptive immune responses against chlamydial infections through modulating DCs, yet the molecular mechanisms remain incompletely understood. This study investigates the role of NK cells in modulating DC signaling pathways and the recruitment of DCs during Chlamydia muridarum infection. Transcriptomic analyses revealed significant downregulation of key genes in DCs from NK-depleted mice involved in type I immunity, including IL12rb2, IL-18rap, and chemokine signaling components such as Ccl3, Ccl5, and Ccr5. Gene ontology (GO) analyses confirmed impaired chemokine–chemokine receptor interactions in DCs from NK-depleted mice. Moreover, flow cytometry analysis showed that NK-cell depletion reduced CCR5 expression on splenic and pulmonary DCs, impairing their migration toward CCL3 and CCL5. Furthermore, IFN-γ enhanced CCR5 expression on the surface of DCs, consequently promoting their migration, which was blocked by anti-IFN-γ antibodies. In vitro migration assays showed that treatment of DCs with IFN-γ increased their responsiveness to CCL3 and CCL5, the ligands of CCR5. Collectively, this study provides new insights into the indispensable role of NK cells in orchestrating DC signaling and the recruitment of DCs during chlamydial 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 | 0.000 |
| 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".