<i>Listeria monocytogenes</i> infection differentially affects expression of ligands for NK cells and NK cell responses, depending on the cell type infected
Bibliographic record
Abstract
The pivotal role of NK cells in viral infection is extensively studied, whereas the role of NK cells in bacterial infection has been poorly investigated. Here, we have examined how Listeria monocytogenes (LM) affects expression of ligands for NK cell receptors and subsequent NK cell responses, depending on the type of cell infected. LM infected rat cell lines derived from different tissues were coincubated with splenic NK cells, and NK cell proliferation and IFN-γ production were measured. In addition, expression of ligands for the NK cell receptors Ly49 and NK cell receptor protein 1 (NKR-P1), MHC class I and C-type lectin-related molecules, respectively, was assessed. Infected pleural R2 cells, but not epithelium-derived colon carcinoma cell line CC531 cells, induced proliferation of NK cells. Reporter cells expressing the inhibitory NKR-P1G receptor or the activating NKR-P1F receptor were less stimulated under incubation with infected CC531 cells versus uninfected CC531 controls, suggesting that the ligand(s) in question were down-regulated by infection. Conversely, LM infection of R2 cells did not affect reporter cell stimulation compared with uninfected R2 controls. We characterized a rat monocyte cell line, termed RmW cells. In contrast to LM infected R2 cells that up-regulate MHC class I molecules, RmW cells displayed unchanged MHC class I expression following infection. In line with MHC class I expression, more NK cells produced a higher amount of IFN-γ against infected R2 cells compared with RmW cells. Together, L. monocytogenes infection may variously regulate cellular ligands for NK cells, depending on the cell type infected, affecting the outcome of NK 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 | 0.001 |
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".