Matrix metalloproteinases regulate neutrophil‐endothelial cell adhesion through generation of endothelin‐1[1–32]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it