Static strain stimulates expression of matrix metalloproteinase‐2 and VEGF in microvascular endothelium via JNK‐ and ERK‐dependent pathways
Bibliographic record
Abstract
VEGF and MMP protein production are both required for exercise-induced capillary growth in skeletal muscle. The underlying process by which muscle activity initiates an angiogenic response is not established, but it is known that mechanical forces such as muscle stretch are involved. We hypothesized that stretch of skeletal muscle microvascular endothelial cells induces production of MMP-2 and VEGF through a common signal pathway. Endothelial cells were grown on Bioflex plates and exposed to 10% static stretch for up to 24 h. MMP-2 protein level was measured by gelatin zymography and VEGF, MMP-2, and MT1-MMP mRNA levels were quantified by real-time quantitative PCR. ERK1/2 and JNK phosphorylation and VEGF protein levels were assessed by Western blotting. Effects of mitogen-activated protein kinases (MAPKs) (ERK1/2, JNK) and reactive oxygen species (ROS) on stretch-induced expression of MMP-2 and VEGF were tested using pharmacological inhibitors. Stretching of endothelial cells for 24 h caused significant increases in MMP-2 protein and mRNA level, but no change in MT1-MMP mRNA. While MMP-2 protein production was enhanced by H(2)O(2) in unstretched cells, ROS inhibition during stretch did not diminish MMP-2 mRNA or protein production. Inhibition of JNK suppressed stretch-induced MMP-2 protein and mRNA, but inhibition of ERK had no effect. In contrast, inhibition of ERK but not JNK attenuated the stretch-induced increase in VEGF mRNA. Our results demonstrate that differential regulation of MMP-2 and VEGF by MAPK signal pathways contribute to stretch-induced activation of microvascular endothelial cells.
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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 | 0.000 |
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".