Rhinovirus-Induced MMP-9 Expression Is Dependent on Fra-1, Which Is Modulated by Formoterol and Dexamethasone
Bibliographic record
Abstract
Matrix metalloproteinase-9 is implicated in airway inflammation and airway remodeling in asthma. We have previously confirmed that human rhinovirus-16 (HRV-16) infection increases MMP-9 expression both in vivo and in vitro. However, the role of the AP-1 sites within the MMP-9 promoter and the effect of commonly used asthma pharmacotherapies in modulating human rhinovirus (HRV)-induced MMP-9 production have not yet been elucidated. Experiments were performed in vitro in the human bronchial epithelial (HBE) cell line BEAS-2B and in primary HBE cells obtained from non-transplanted lungs. Using site-directed mutagenesis approaches, AP-1 sites were found to be necessary for HRV-induced MMP-9 promoter drive. EMSAs and supershift assays identified complexes consisting of Fos-related Ag-1 (Fra-1) in addition to other AP-1 subunits. Small interfering RNA approaches indicated that Fra-1 was induced upon HRV-16 infection in BEAS-2B cells and was necessary for MMP-9 expression in both BEAS-2B and primary HBE cells. Inhibition of MEK1/2 activity using PD98059 and U0126 reduced Fra-1 expression, DNA binding, MMP-9 promoter drive, and MMP-9 protein production. The long-acting β(2)-agonist formoterol and the glucocorticoid dexamethasone significantly reduced HRV-induced ERK phosphorylation, Fra-1, and MMP-9 expression in BEAS-2B cells. These data indicate that HRV-induced activation of the MEK/ERK MAPK pathway and Fra-1 expression are necessary for the upregulation of MMP-9 and can be modulated by two distinct but commonly used asthma pharmacotherapies. Together, these results offer insights into the mechanisms by which long-acting β(2)-agonists and glucocorticoids might reduce HRV-related asthma exacerbations.
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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 | 0.001 |
| 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".