Pyocyanin Production by <i>Pseudomonas aeruginosa</i> Induces Neutrophil Apoptosis and Impairs Neutrophil-Mediated Host Defenses In Vivo
Bibliographic record
Abstract
Clearance of neutrophils from inflamed sites is critical for resolution of inflammation, but pathogen-driven neutrophil apoptosis can impair host defenses. We previously showed that pyocyanin, a phenazine toxic metabolite produced by Pseudomonas aeruginosa, accelerates neutrophil apoptosis in vitro. We compared wild-type and pyocyanin-deficient strains of P. aeruginosa in a murine model of acute pneumonia. Intratracheal instillation of either strain of P. aeruginosa caused a rapid increase in bronchoalveolar lavage neutrophil counts up to 18 h after infection. In wild-type infection, neutrophil numbers then declined steadily, whereas neutrophil numbers increased up to 48 h in mice infected with pyocyanin-deficient P. aeruginosa. In keeping with these differences, pyocyanin production was associated with reduced bacterial clearance from the lungs. Neutrophil apoptosis was increased in mice infected with wild-type compared with the phenazine-deficient strain or two further strains that lack pyocyanin production, but produce other phenazines. Concentrations of potent neutrophil chemokines (MIP-2, KC) and cytokines (IL-6, IL-1beta) were significantly lower in wild-type compared with phenazine-deficient strain-infected mice at 18 h. We conclude that pyocyanin production by P. aeruginosa suppresses the acute inflammatory response by pathogen-driven acceleration of neutrophil apoptosis and by reducing local inflammation, and that this is advantageous for bacterial survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".