Differential Contribution of the Aryl-Hydrocarbon Receptor and Toll-Like Receptor Pathways to IL-8 Expression in Normal and Cystic Fibrosis Airway Epithelial Cells Exposed to Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa are gram-negative bacteria that frequently infect the lungs of cystic fibrosis (CF) patients. This bacterium is highly responsive to changes in its environment, resulting in the expression of a diverse array of genes that may contribute to the host inflammatory response. P. aeruginosa is well-known to induce neutrophilic inflammation via the activation of Toll-Like Receptors (TLRs). Recently, it was shown that pyocyanin, a phenazine produced by P. aeruginosa, binds to the aryl hydrocarbon receptor (AhR), leading to neutrophilic inflammation as part of the host defense response. In this study, we have investigated the contribution of the TLR and AhR signaling pathways to the expression of the neutrophil chemoattractant IL-8 in response to P. aeruginosa diffusible material. Although both pathways are involved in IL-8 synthesis, the AhR played a greater role when planktonic P. aeruginosa was grown in a media favoring phenazine synthesis. However, when P. aeruginosa was grown in a media that mimics the nutritional composition of CF sputa, both pathways contributed similarly to IL-8 synthesis. Finally, when P. aeruginosa was grown as a biofilm, the TLR pathway did not contribute to biofilm-driven IL-8 synthesis and AhR was found to only partially contribute to IL-8 synthesis, suggesting the contribution of another unknown signaling pathway. Therefore, the interaction between P. aeruginosa and airway epithelial cells is very dynamic, and sensor engagement is variable according to the adaptation of P. aeruginosa to the CF lung environment.
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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 | 0.000 |
| 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".