Flagellin-mediated TLR5 activation enhances innate immune responses in healthy and diseased human airway epithelium
Bibliographic record
Abstract
Abstract Bacterial pneumonia poses a significant challenge to public health, often leading to antibiotic treatment failure. Enhancing innate immunity represents a promising adjunctive strategy to conventional antibiotic therapy. Bacterial flagellin, a Toll-like receptor 5 (TLR5) agonist, has been shown to stimulate innate immune defenses when delivered via the respiratory route, demonstrating efficacy in both preventing and treating bacterial pneumonia in murine models. This protective effect is primarily mediated through TLR5-driven activation of airway epithelial cells. This study aimed to characterize the immunomodulatory effects of flagellin on human primary respiratory epithelium. Using the MucilAir™ air-liquid interface model and RNA sequencing, we demonstrated that apical administration of flagellin induced robust immune responses in airway epithelium derived from healthy individuals, as well as patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). TLR5-mediated epithelial signaling triggered key immune-related pathways, including cytokine production, leukocyte chemotaxis, neutrophil recruitment, and antimicrobial defense, with strong commonalities across healthy and diseased airway epithelia. Furthermore, we demonstrated that flagellin effectively activated epithelial immune responses even in the presence of the bacteria Pseudomonas aeruginosa or Streptococcus pneumoniae . However, epithelial activation alone was insufficient to directly limit bacterial colonization or replication, highlighting the potential role of epithelial-immune cell interactions in achieving effective bacterial clearance. These findings support TLR5 activation as a promising therapeutic strategy to enhance host defense mechanisms and improve treatment outcomes for bacterial pneumonia in both healthy individuals and patients with COPD or CF.
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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) | 0.002 | 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".