Impairment in inflammasome signaling by the chronic Pseudomonas aeruginosa isolates from cystic fibrosis patients results in an increase in inflammatory response
Bibliographic record
Abstract
Pseudomonas aeruginosa is a common respiratory pathogen in cystic fibrosis (CF) patients which undergoes adaptations during chronic infection towards reduced virulence, which can facilitate bacterial evasion of killing by host cells. However, inflammatory cytokines are often found to be elevated in CF patients, and it is unknown how chronic P. aeruginosa infection can be paradoxically associated with both diminished virulence in vitro and increased inflammation and disease progression. Thus, we investigated the relationship between the stimulation of inflammatory cell death pathways by CF P. aeruginosa respiratory isolates and the expression of key inflammatory cytokines. We show that early respiratory isolates of P. aeruginosa from CF patients potently induce inflammasome signaling, cell death, and expression of IL-1β by macrophages, yet little expression of other inflammatory cytokines (TNF, IL-6 and IL-8). In contrast, chronic P. aeruginosa isolates induce relatively poor macrophage inflammasome signaling, cell death, and IL-1β expression but paradoxically excessive production of TNF, IL-6 and IL-8 compared to early P. aeruginosa isolates. Using various mutants of P. aeruginosa, we show that the premature cell death of macrophages caused by virulent bacteria compromises their ability to express cytokines. Contrary to the belief that chronic P. aeruginosa isolates are less pathogenic, we reveal that infections with chronic P. aeruginosa isolates result in increased cytokine induction due to their failure to induce immune cell death, which results in a relatively intense inflammation compared with early isolates.
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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".