Matrix metalloproteinases regulate neutrophil‐endothelial cell adhesion through generation of endothelin‐1[1–32]
Bibliographic record
Abstract
We recently reported that matrix metalloproteinase 2 (MMP-2, gelatinase A) cleaves big endothelin 1 (ET-1), yielding the vasoactive peptide ET-1[1-32]. We tested whether ET-1[1-32] could affect the adhesion of human neutrophils to coronary artery endothelial cells (HCAEC). ET-1[1-32] rapidly down-regulated the expression of L-selectin and up-regulated expression of CD11b/CD18 on the neutrophil surface, with EC50 values of 1-3 nM. These actions of ET-1[1-32] were mediated via ETA receptors and did not require conversion of ET-1[1-32] into ET-1 by neutrophil proteases, as revealed by liquid chromatography and mass spectroscopy. Moreover, ET-1[1-32] evoked release of neutrophil gelatinase B, which cleaved big ET-1 to yield ET-1[1-32], thus revealing a positive feedback loop for ET-1[1-32] generation. Up-regulation of CD11b/CD18 expression and gelatinase release was tightly associated with activation of extracellular signal-regulated kinase (Erk). Stimulation of Erk activity was due to activation of Ras, Raf-1, and MEK (MAPK kinase). ET-1[1-32] also produced slight increases in the expression of ICAM-1 and E-selectin on HCAEC, and markedly enhanced beta2 integrin-dependent adhesion of neutrophils to activated HCAEC. These results are the first indication that gelatinolytic MMPs via cleavage of big ET-1 to yield ET-1[1-32] activate neutrophils and promote leukocyte-endothelial cell adhesion and, consequently, neutrophil trafficking into inflamed tissues.
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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".