LasR-regulated proteases in acute vs. chronic lung infection: a double-edged sword
Bibliographic record
Abstract
Pseudomonas aeruginosa is a gram-negative opportunistic pathogen capable of causing both acute and chronic infections, particularly in individuals with compromised host defenses. The quorum sensing transcriptional activator LasR is widely recognized for its role in regulating the expression of acute virulence factors, notably several secreted proteases which cause direct host damage and subvert host immunity in acute infections. Paradoxically, lung infections caused by LasR-deficient variants, which are found in at least a third of cystic fibrosis (CF) patients with chronic P. aeruginosa infections, are associated with accelerated lung disease and increased markers of inflammation compared to infections caused by strains with a functional LasR system. While the loss of LasR function often (although not always) results in impaired production of LasR-controlled acute virulence factors, the implication of this pathoadaptation on host-pathogen interactions and chronic disease pathology is less well recognized. We recently observed that loss of LasR function in lasR variants, which results in impaired secreted protease production, led to increased expression of the membrane-bound surface adhesion molecule mICAM-1 in the airway epithelium, and increased neutrophilic inflammation. Specifically, human airway epithelial cells stimulated with lasR variants had higher mICAM-1 expression and greater neutrophil binding in vitro compared to stimulation with wild-type P. aeruginosa. In a subacute non-lethal P. aeruginosa lung infection model, lasR variant infection also induced higher mICAM-1 expression in the murine airway epithelium and was associated with increased neutrophilic pulmonary inflammation in vivo. Here, we discuss how (loss of) LasR function and LasR-regulated proteases affect host immunity, inflammation and tissue pathology in acute vs. chronic P. aeruginosa lung infection.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".