c‐Jun regulates MMP‐2 and MT1‐MMP mRNA expression in endothelium.
Bibliographic record
Abstract
Angiogenesis is the formation of new capillaries from pre‐existing ones. Proteolysis of the basement membrane and the interstitial matrix is mediated by the matrix metalloproteinases (MMPs) and is critical to the angiogenic process. We showed previously that reorganization of the actin cytoskeleton induces the expression of MMP‐2 and membrane type (MT)1‐MMP through the activation of JNK. Here we examined the involvement of the transcription factor c‐Jun, a downstream effector of JNK. We hypothesized that c‐Jun regulates the transcription of both MMP‐2 and MT1‐MMP. Treatment of skeletal muscle endothelial cells (SMEC) with small interference (si)RNA targeting c‐Jun decreased basal levels of c‐Jun protein by 40% and resulted in decreased basal MMP‐2 and MT1‐MMP mRNA expression. Activation of JNK by anisomycin increased MMP‐2 and MT1‐MMP mRNA expression and this induction was attenuated by c‐Jun siRNA treatment. Depolymerization of the actin cytoskeleton, which increases MMP‐2 protein production and activation, and both were attenuated with siRNA treatment. Vascular endothelial growth factor (VEGF) induction of MMP‐2 mRNA expression was repressed by c‐Jun siRNA. Our results point to c‐Jun as the transcription factor regulating MMP‐2 and MT1‐MMP mRNA expression downstream of JNK. Funded by NSERC and CIHR.
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 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 | 0.001 |
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".