Human Rhinovirus Infection Up-Regulates MMP-9 Production in Airway Epithelial Cells via NF-κB
Bibliographic record
Abstract
Human rhinovirus (HRV) infections up-regulate proinflammatory mediators and growth factors that are associated with exacerbations of inflammatory airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). Matrix metalloproteinase (MMP)-9 was shown to be increased in the airways of patients with asthma and COPD. We sought to determine whether HRV infection modulated the expression of MMP-9 and its highest-affinity inhibitor, the tissue inhibitor of metalloproteinase (TIMP)-1, and we explored the mechanism by which this modulation occurs. In vitro studies, using RT-PCR, ELISA, zymography, and a fluorescent activity assay, demonstrated that MMP-9 mRNA, protein, and activity were increased upon infection with HRV, whereas TIMP-1 mRNA and protein remained unchanged. These results were verified in vivo, using nasal lavage samples obtained from subjects with confirmed rhinovirus infections. Human rhinovirus infections were shown to up-regulate NF-kappaB, and NF-kappaB has also been reported to play a role in the expression of MMP-9. We therefore investigated the role of NF-kappaB in HRV-induced MMP-9 expression. Using two inhibitors of IkappaBalpha kinase beta, we observed a concentration-dependent decrease in HRV-induced MMP-9 expression. The role of NF-kappaB in HRV-induced MMP-9 expression was further confirmed using MMP-9 promoter luciferase constructs, which demonstrated that an NF-kappaB site at -620/-607 base pairs was necessary for HRV-induced MMP-9 expression. Electrophoretic mobility shift assays and supershift assays confirmed the nuclear translocation and binding of p50/p65 NF-kappaB subunits to an MMP-9-specific NF-kappaB oligonucleotide. This increase in MMP-9 may be a mechanism by which rhinovirus infections contribute to airway inflammation and, potentially, to airway remodeling.
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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) | 0.001 | 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".