Disruption of aprF result in alkaline protease secretion-deficiency in Pseudomonas aeruginosa clinical isolates that enhances host inflammatory responses via Toll-like receptor 5
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that often colonizes the airways of people with cystic fibrosis (pwCF), and bronchiectasis causing chronic infection. When P. aeruginosa is confronted with various environmental conditions, it undergoes microevolution. To improve our understanding of the host-pathogen interactions occurring during chronic infections, we evaluated the host inflammatory responses to P. aeruginosa strains co-colonizing the lungs of pwCF. Differential inflammatory responses were elicited by several pairs of co-isolated P. aeruginosa strains in human bronchial epithelial cells. Characterization of these clinical isolates was initiated to find the factors explaining these divergent immune responses. Clonal relativeness of the co-isolated strains was confirmed by Multilocus Sequence Typing, and differential virulence patterns of the co-isolated strains was shown conserved across species using an in vivo Hydra vulgaris model. By comparing Single Nucleotide Variants (SNVs) of the co-isolated P. aeruginosa strains, the alkaline protease secretion protein F ( aprF) gene was identified as a modulator of host IL-8 expression. aprF mutations led to decreased proteolytic activity in culture supernatants, leading to increased levels of flagellin and subsequent activation of TLR5 on bronchial cells. We propose a mechanism linking the dysfunction of aprF to the sequestration of the alkaline protease AprA, increasing flagellin recognition by the host through TLR5 activation promoting heightened inflammatory immune responses. In addition to aprF , we report several other P. aeruginosa candidate genes that can modulate host inflammatory responses. Understanding interaction between P. aeruginosa and lung mucosa in pwCF or bronchiectasis is essential to the development of improved therapies that will mitigate the damaging chronic inflammation, thereby improving the quality of life and survival of people with these diseases. • Differential host responses are elicited by co-isolated clinical P. aeruginosa strains • Disruption of AprF results in TLR5-dependent IL-8 mRNA increase • Translocation of AprA through AprF cleaves extracellular flagellin
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".