Regulation of matrix metalloproteinase-9 (MMP-9) in TNF-stimulated neutrophils: novel pathways for tertiary granule release
Bibliographic record
Abstract
Matrix metalloproteinase-9 (MMP-9) is present in the tertiary granules of neutrophils and is rapidly released following stimulation. We examined the pathways that regulate tumor necrosis factor (TNF)-mediated MMP-9 release and found this to be dependent on the TNF receptor I. TNF rapidly activated extracellular signal-regulated kinase and p38 mitogen-activated protein kinases, but neither of these pathways was critical for MMP-9 release. Many neutrophil responses to TNF require beta2-integrin-dependent signaling and subsequent Src family kinase activation. In contrast, we found that MMP-9 release from tertiary granules was only partially affected by blocking beta2-integrin-mediated adhesion. Similarly, blocking Src family kinases with the inhibitor PP2 only attenuated TNF-induced MMP-9 release. Blocking beta2-integrin-mediated adhesion and Src family kinases did not result in additive inhibition of MMP-9 release. In contrast, inhibiting protein kinase C (PKC) with a pan-specific inhibitor blocked greater than 85% of MMP-9 release. Inhibitors against specific PKC isoforms suggested a role for PKC alpha and PKC delta in maximal MMP-9 release. These data suggest that MMP-9 release from tertiary granules uses beta2-integrin-independent signaling pathways. Furthermore, PKC isoforms play a critical role in regulating tertiary granule release.
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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 | 0.000 |
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".