Endothelial Cells Require Related Transcription Enhancer Factor-1 for Cell–Cell Connections Through the Induction of Gap Junction Proteins
Bibliographic record
Abstract
OBJECTIVE: Capillary network formation represents a specialized endothelial cell function and is a prerequisite to establish a continuous vessel lumen. Formation of endothelial cell connections that form the vascular structure is regulated, at least in part, at the transcriptional level. We report here that related transcription enhancer factor-1 (RTEF-1) plays an important role in vascular structure formation. METHODS AND RESULTS: Knockdown of RTEF-1 by small interfering RNA or blockage of RTEF-1 function by the transcription enhancer activators domain decreased endothelial connections in a Matrigel assay, whereas overexpression of RTEF-1 in endothelial cells resulted in a significant increase in cell connections and aggregation. In a model of oxygen-induced retinopathy, endothelial-specific RTEF-1 overexpressing mice had enhanced angiogenic sprouting and vascular structure remodeling, resulting in the formation of a denser and more highly interconnected superficial capillary plexus. Mechanistic studies revealed that RTEF-1 induced the expression of functional gap junction proteins including connexin 43, connexin 40, and connexin 37. Blocking connexin 43 function inhibited RTEF-1-induced endothelial cell connections and aggregation. CONCLUSIONS: These findings provide novel insights into the transcriptional control of endothelial function in the coordination of cell-cell connections.
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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 | 0.001 |
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".