Regulation of matrix metalloproteinase-9 release from IL-8-stimulated human neutrophils
Bibliographic record
Abstract
Matrix metalloproteinase-9 (MMP-9) is present in the tertiary granules of neutrophils and can be released following stimulation. We examined the signaling mechanisms that regulate interleukin-8 (IL-8)-mediated MMP-9 release from neutrophils. IL-8 activates neutrophils by interacting with two receptors: CXC chemokine receptor 1 (CXCR1) and CXCR2. Blocking CXCR1 had no effect on IL-8-mediated MMP-9 release, whereas blocking CXCR2 significantly reduced MMP-9 release. We also found that stimulating CXCR2 alone was sufficient to induce MMP-9 release. This process was independent of changes in the intracellular calcium concentration. Src-family kinases and protein kinase C (PKC) were involved in two mutually exclusive pathways regulating IL-8-mediated MMP-9 release. Inhibition of extracellular signal-regulated kinase (ERK)1/2 blocked IL-8-mediated MMP-9 release; however, inhibition of p38 mitogen-activated protein kinase had no effect on MMP-9 release. We found ERK1/2 was activated downstream of PKC, but not Src-family kinases, in this system. These data suggest that IL-8-induced MMP-9 release from neutrophils is mediated through CXCR2 and involves two distinct pathways, one involving PKC and ERK1/2 and the other involving Src-family kinases. Furthermore, our data show that the mechanisms that regulate MMP-9 release from tertiary granules are different from those that regulate primary 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 | 0.000 |
| 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".