Staphylococcus aureus Leukocidin A/B (LukAB) Kills Human Monocytes via Host NLRP3 and ASC when Extracellular, but Not Intracellular
Bibliographic record
Abstract
Staphylococcus aureus infections are a growing health burden worldwide, and paramount to this bacterium's pathogenesis is the production of virulence factors, including pore-forming leukotoxins. Leukocidin A/B (LukAB) is a recently discovered toxin that kills primary human phagocytes, though the underlying mechanism of cell death is not understood. We demonstrate here that LukAB is a major contributor to the death of human monocytes. Using a variety of in vitro and ex vivo intoxication and infection models, we found that LukAB activates Caspase 1, promotes IL-1β secretion and induces necrosis in human monocytes. Using THP1 cells as a model for human monocytes, we found that the inflammasome components NLRP3 and ASC are required for LukAB-mediated IL-1β secretion and necrotic cell death. S. aureus was shown to kill human monocytes in a LukAB dependent manner under both extracellular and intracellular ex vivo infection models. Although LukAB-mediated killing of THP1 monocytes from extracellular S. aureus requires ASC, NLRP3 and the LukAB-receptor CD11b, LukAB-mediated killing from phagocytosed S. aureus is independent of ASC or NLRP3, but dependent on CD11b. Altogether, this study provides insight into the nature of LukAB-mediated killing of human monocytes. The discovery that S. aureus LukAB provokes differential host responses in a manner dependent on the cellular contact site is critical for the development of anti-infective/anti-inflammatory therapies that target the NLRP3 inflammasome.
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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".