Piceatannol Modulates Lung Epithelial Cellular Responses to Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic Gram-negative pathogen, which is the major cause of severe chronic lung infection in cystic fibrosis patients. It is also responsible for systemic infections in immunocompromised individuals and those presenting with significant pulmonary conditions in intensive care units. This microorganism has the capacity to initiate severe inflammation in infected lungs resulting in detrimental tissue damage. We have hypothesized that Syk protein tyrosine kinase mediates lung epithelial cellular responses to P. aeruginosa infection, and that a naturally occurring non-toxic Syk inhibitor piceatannol can protect infected human cells against the deleterious effects associated with this infection. We infected Syk-positive H292 or Syk-negative A549 human lung epithelial cell lines with P. aeruginosa and assessed the resulting cellular responses, i.e. production of proinflammatory cytokines, adhesion molecule expression, generation of reactive oxygen species, and apoptosis of infected cells, utilizing a multiplex bead-based immunoassay and flow cytometry. We also studied the internalization of P. aeruginosa using the gentamicin exclusion assay. We found that the piceatannol treatment significantly suppressed inflammation, oxidative stress and apoptosis in H292, but not in A549 cells implicating Syk participation in the regulation of the pathological processes induced by P. aeruginosa infection. Intriguingly, piceatannol was able to down-regulate the internalization of P. aeruginosa by both Syk-positive and Syk-negative cell lines, implying that the mechanisms of action of this compound extend beyond Syk inhibition. As piceatannol can interfere with several mechanisms of bacterial pathogenesis this natural compound deserves further study as a potential therapeutic option in P. aeruginosa 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 | 0.000 | 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".