NKG2A-mediated immune modulation of natural killer cells by <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Natural killer (NK) cells are specialized lymphocytes that help protect against viruses and cancer. However, in the context of bacterial infections, NK cells can be harmful, rather than protective. Such immune pathogenesis by NK cells has been linked to the overproduction of proinflammatory cytokines like interferon-gamma (IFN-γ). In this context, IFN-γ-deficient mice display increased survival rates in response to Staphylococcus aureus (S. aureus) infection. However, little is known about how NK cells respond to S. aureus in humans, which causes life-threatening, invasive systemic infections with high mortality rates. In this study, we found that the peripheral blood of patients with bloodstream S. aureus infection was enriched for CD57- NKG2A+ NK cells with greater cytokine-producing capacity, compared to healthy controls and those hospitalized with Escherichia coli bloodstream infections. As a possible mechanistic cause, superantigens from S. aureus promoted the expansion of CD57- NKG2A+ NK cells that produced IFN-γ through a mechanism that appears to be IL-12 independent and exhibited reduced levels of CD16 compared to unstimulated NK cells. These data suggest that S. aureus bloodstream infection in humans promotes a phenotypic shift toward CD57- NKG2A+ NK cells with greater IFN-γ-producing capacity, providing a plausible way to promote inflammation-driven disease pathogenesis.
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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) | 0.002 | 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".