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 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 | 0.000 |
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".