Orchestration of human macrophage NLRP3 inflammasome activation by <i>Staphylococcus aureus</i> extracellular vesicles
Bibliographic record
Abstract
Release of extracellular vesicles (EVs) is a common feature among eukaryotes, archaea, and bacteria. However, the biogenesis and downstream biological effects of EVs released from gram-positive bacteria remain poorly characterized. Here, we report that EVs purified from a community-associated methicillin-resistant Staphylococcus aureus strain were internalized into human macrophages in vitro and that this process was blocked by inhibition of the dynamin-dependent endocytic pathway. Human macrophages responded to S. aureus EVs by TLR2 signaling and activation of NLRP3 inflammasomes through K + efflux, leading to the recruitment of ASC and activation of caspase-1. Cleavage of pro–interleukin (IL)-1β, pro-IL-18, and gasdermin-D by activated caspase-1 resulted in the cellular release of the mature cytokines IL-1β and IL-18 and induction of pyroptosis. Consistent with this result, a dose-dependent cytokine response was detected in the extracellular fluids of mice challenged intraperitoneally with S. aureus EVs. Pore-forming toxins associated with S. aureus EVs were critical for NLRP3-dependent caspase-1 activation of human macrophages, but not for TLR2 signaling. In contrast, EV-associated lipoproteins not only mediated TLR2 signaling to initiate the priming step of NLRP3 activation but also modulated EV biogenesis and the toxin content of EVs, resulting in alterations in IL-1β, IL-18, and caspase-1 activity. Collectively, our study describes mechanisms by which S. aureus EVs induce inflammasome activation and reveals an unexpected role of staphylococcal lipoproteins in EV biogenesis. EVs may serve as a novel secretory pathway for S. aureus to transport protected cargo in a concentrated form to host cells during infections to modulate cellular functions.
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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".