Induction of the Plasminogen Activator System by Mechanical Stimulation of Human Bronchial Epithelial Cells
Bibliographic record
Abstract
Mechanical stimulation of the airway epithelium, as would occur during bronchoconstriction, is a potent stimulus and can activate profibrotic pathways. We used DNA microarray technology to examine gene expression in compressed normal human bronchial epithelial cells (NHBE). Compressive stress applied continuously over an 8-h period to NHBE cells led to the upregulation of several families of genes, including a family of plasminogen-related genes that were previously not known to be regulated in this system. Real-time PCR demonstrated a peak increase in gene expression of 8.0-fold for urokinase plasminogen activator (uPA), 16.2-fold for urokinase plasminogen activator receptor (uPAR), 4.2-fold for plasminogen activator inhibitor-1 (PAI-1), and 3.9-fold for tissue plasminogen activator (tPA). Compressive stress also increased uPA protein levels in the cell lysates (112.0 versus 82.0 ng/ml, P = 0.0004), and increased uPA (4.7 versus 3.3 ng/ml, P = 0.02), uPAR (1.3 versus 0.86 ng/ml, P = 0.007), and PAI-1 (50 versus 36 ng/ml, P = 0.006) protein levels in cell culture media. Functional studies demonstrated increased urokinase-dependent plasmin generation in compression-stimulated cells (0.0090 versus 0.0033 OD/min, P = 0.03). In addition, compression led to increased activation of matrix metalloproteinase (MMP)-9 and MMP-2 in a urokinase-dependent manner. In postmortem human lung tissue, we observed an increase in epithelial uPA and uPAR immunostaining in the airways of two patients who died in status asthmaticus compared with minimal immunoreactivity noted in airways from seven lung donors without asthma. Together these observations suggest an integrated response of airway epithelial cells to mechanical stimulation, acting through the plasminogen-activating system to modify the airway microenvironment.
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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) | 0.003 | 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".