Human host defence peptide LL-37 suppresses TNFα-mediated matrix metalloproteinases MMP9 and MMP13, in human bronchial epithelial cells
Bibliographic record
Abstract
INTRODUCTION: TNFα-inducible matrix metalloproteinases play a critical role in the process of airway remodeling in respiratory inflammatory disease including asthma. The cationic host defense peptide LL-37 is elevated in the lungs during airway inflammation. However, the impact of LL-37 on TNFα-driven processes is not well understood. Here, we examined the effect of LL-37 on TNFα-mediated responses in human bronchial epithelial cells (HBECs). METHODS: We used a slow off-rate modified aptamer-based proteomics approach to define the HBEC proteome altered in response to TNFα. Abundance of selected protein candidates and signaling intermediates was examined using immunoassays, ELISA and Western blots, and mRNA abundance was examined by qRT-PCR. RESULTS: Proteomics analysis revealed that 124 proteins were significantly altered, 12 proteins were enhanced by ≥2-fold compared to unstimulated cells, in response to TNFα. MMP9 was the topmost increased protein in response to TNFα, enhanced by ∼10-fold, and MMP13 was increased by ∼3-fold, compared to unstimulated cells. Furthermore, we demonstrated that LL-37 significantly suppressed TNFα-mediated MMP9 and MMP13 in HBEC. Mechanistic data revealed that TNFα-mediated MMP9 and MMP13 production is controlled by SRC kinase and that LL-37 enhances related upstream negative regulators, namely, phospho-AKT (T308) and TNFα-mediated TNFAIP3 or A20. CONCLUSIONS: The findings of this study suggest that LL-37 may play a role in intervening in the process of airway remodeling in chronic inflammatory respiratory disease such as asthma.
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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".