JNK as a positive regulator of angiogenic potential in endothelial cells
Bibliographic record
Abstract
Angiogenesis (the growth of new capillaries) occurs in adults in response to physiological stimuli such as wound healing and exercise. The mitogen-activated protein kinase c-jun N-terminal kinase (JNK) has a controversial role in the process of angiogenesis, with previous evidence supporting JNK as both a positive and negative regulator of blood vessel growth. The purpose of this study was to clarify the role of JNK in the angiogenesis process. Phosphorylated JNK was observed in cultured endothelial cells, and levels were constant regardless of extracellular matrix composition. Using SP600125, inhibition of JNK attenuated sprout growth in 3D capillary sprout cultures. Inhibition of JNK reduced endothelial cell proliferation and migration in vitro. JNK inhibition and siRNA knockdown of c-jun (a downstream target of JNK) decreased protein levels of the transcription factor Egr-1, a regulator of genes involved in proliferation and migration. Matrix metalloproteinase-2 (MMP-2) production, and activity also, was reduced in sprout cultures treated with SP600125. c-Jun silencing decreased both MMP-2 and membrane type-1 (MT1)-MMP mRNA in endothelial cells, implicating both JNK and c-jun as activators of proteolysis. Taken together, these results provide evidence that JNK and its downstream target c-jun positively regulate angiogenesis via activation of endothelial cell proliferation, migration and proteolysis.
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) | 0.001 | 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".