Matrix Metalloproteinase Enhances Big-Endothelin-1 Constriction in Mesenteric Vessels of Pregnant Rats With Reduced Uterine Blood Flow
Bibliographic record
Abstract
Preeclampsia is a leading cause of maternal and fetal morbidity/mortality; however, the pathophysiological mechanisms are unclear. Vascular endothelial dysfunction in preeclampsia has been partially attributed to changes in endothelin-1 (ET-1). Several enzymes, including matrix metalloproteinases (MMPs; particularly MMP-2), cleave the inactive precursor big ET-1 (bET-1) to active ET-1. Notably, expression levels of MMP-2 have been shown to be on the increase in women who subsequently develop preeclampsia. We hypothesized that the increased MMP-2 expression leads to increased bET-1 conversion, thereby increasing vasoconstriction in preeclampsia. A reduced uteroplacental perfusion pressure (RUPP) model of preeclampsia in the rat was used to assess mesenteric artery vascular function. Responses to bET-1 (3-310 nmol/L) and ET-1 (1-200 nmol/L) were studied in the presence or absence of inhibitors of enzymes known to cleave bET-1. Vascular contractility in response to bET-1 was greater in RUPP than Sham (P<0.001), whereas neither responses to ET-1 nor maximal contractility to high potassium salt solution (123.70 mmol/L) were different. MMP inhibition with GM6001 (30 μmol/L) significantly decreased responses to bET-1 in RUPP (P<0.001) but not Sham-operated rats. Interestingly, combined treatment with GM6001 and L-NG-nitroarginine methyl ester (100 μmol/L) revealed a NO modulation of MMPs that was reduced in RUPP. In summary, we found increased vascular contractility to bET-1 in the RUPP model of preeclampsia that was likely attributable to upstream enzymatic pathways. These data are consistent with a greater contribution of MMP to cleavage of bET-1 to ET-1 ex vivo in RUPP, suggesting that this enzyme may be partially responsible for increased bET-1-induced contractility.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".