Human host defence peptide LL-37 suppresses TNFα-mediated matrix metalloproteinases MMP9 and MMP13, in human bronchial epithelial cells
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it