Dynamic regulation of VEGF-inducible genes by an ERK-ERG-p300 transcriptional network
Bibliographic record
Abstract
The transcriptional pathways activated downstream of Vascular Endothelial Growth Factor (VEGF) signaling during angiogenesis remain incompletely characterized. By assessing the signals responsible for induction of the Notch ligand, Delta-Like 4 (DLL4) in endothelial cells we find that activation of the MAPK/ERK pathway mirrors the rapid and dynamic induction of DLL4 transcription and that this pathway is required for DLL4 expression. Furthermore, VEGF/ERK signaling induces phosphorylation and activation of the ETS transcription factor ERG, a prerequisite for DLL4 induction. Transcription of DLL4 coincides with dynamic ERG-dependent recruitment of the transcriptional co-activator p300. Genome-wide gene expression profiling identified a network of VEGF-responsive and ERG-dependent genes, and ERG ChIP-seq revealed the presence of conserved ERG-bound putative enhancer elements near these target genes. Functional experiments performed in vitro and in vivo confirm that this network of genes requires ERK, ERG, and p300 activity. Finally, genome-editing and transgenic approaches demonstrate that a highly conserved ERG-bound enhancer located upstream of HLX (a transcription factor implicated in sprouting angiogenesis) is required for its VEGF-mediated induction. Collectively, these findings elucidate a novel transcriptional pathway contributing to VEGF-dependent angiogenesis.
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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".