Extracellular Matrix Differentially Regulates Endothelial Nitric Oxide Synthase Production in HUVECs and Human Blood Outgrowth Endothelial Progenitor Cells
Bibliographic record
Abstract
Human outgrowth endothelial progenitor cells (BOECs), derived from peripheral blood mononuclear express endothelial protein profiles (i.e. CD31, 144, and vWF). Endothelial nitric oxide synthase (eNOS) is an important regulator of vascular tone and loss of eNOS activity is a hallmark of endothelial dysfunction. In this study, we examine the expression and activity of eNOS and elucidate the cause of differential regulation of eNOS in BOECs compared to mature endothelial cells. We found that BOECs express markedly lower eNOS protein (0.34 ± 0.13), mRNA (0.29 ± 0.17) as well as activity levels (0.49 ± 0.18) when compared to HUVECs or Human Aortic Endothelial Cells. When grown on fibronectin (FN), type I collagen (Col. I), and type IV collagen (Col. IV) we found significantly decreased eNOS protein, mRNA and mRNA stability in HUVECs compared to cells on polystyrene. The matrix mediated downregulation was blocked by β1 integrin siRNA and focal adhesion kinase siRNA transfection. In addition, rho‐associated protein kinase inhibitors including fasudil and Y27632 blocked the effect of ECM on eNOS downregulation in HUVECs. In contrast, in BOECs, eNOS protein expression was unchanged by cell‐ECM interactions. Interestingly, BOECs can highly deposit ECM molecules (Col. I, FN and Laminin) that assemble to an organized mesh‐like structure whereas HUVECs only express few ECM proteins which cannot form organized structures. Blocking Col. I synthesis with siRNA significantly enhanced eNOS expression in BOECs (1.77 ± 0.41 fold increase). Taken together, our results confirm a strong matrix mediated regulation of eNOS in endothelial cells and suggest that limited eNOS expression in BOECs results from higher ECM production.
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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) | 0.001 | 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".