Host Defence Peptide LL-37 Induces IL-6 Expression in Human Bronchial Epithelial Cells by Activation of the NF-κB Signaling Pathway
Bibliographic record
Abstract
LL-37, the only member of the cathelicidin family of cationic host defence peptides in humans, has been shown to mediate multiple immunomodulatory effects and as such is thought to be an important component of innate immune responses. A growing body of evidence indicates that LL-37 affects lung mucosal responses to pathogens through altered regulation of cell migration, proliferation, wound healing and cell apoptosis. These functions are consistent with LL-37 playing a role in regulating lung epithelial inflammatory responses; however, that role has not been clearly defined. In this report we have demonstrated that host defence peptide LL-37 induced cytokine (IL-6) and chemokine (CXCL-1/GRO-alpha and CXCL-8/IL-8) release from human bronchial epithelial cells. It was demonstrated that LL-37-mediated IL-6 release was time and dose dependent and that LL-37 up-regulated this pleiotropic cytokine at the transcriptional level. Using specific inhibitors it was shown that NF-kappaB signaling led to the LL-37-stimulated production of IL-6. LL-37 stimulation of airway epithelial cells activated NF-kappaB signaling, as demonstrated by the phosphorylation and degradation of Ikappa-Balpha, and consequent nuclear translocation of p65 and p50 NF-kappaB subunits. Furthermore this host defence peptide augmented flagellin-mediated cytokine production, indicating that LL-37 likely modulates Toll-like receptor 5-mediated responses.
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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) | 0.002 | 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".