RNF20-mediated transcriptional pausing and VEGFA splicing orchestrate vessel growth
Bibliographic record
Abstract
Signal-responsive gene expression is essential for vascular development, yet the mechanisms integrating signaling inputs with transcriptional activities are largely unknown. Here we show that RNF20, the primary E3 ubiquitin ligase for histone H2B, plays a multifaceted role in sprouting angiogenesis. RNF20 mediates RNA polymerase (Pol II) promoter-proximal pausing at genes highly paused in endothelial cells, involved in VEGFA signaling, stress response, cell cycle control and mRNA splicing. It also orchestrates large-scale mRNA processing events that alter the bioavailability and function of critical pro-angiogenic factors, such as VEGFA. Mechanistically, RNF20 restricts ERG-dependent Pol II pause release at highly paused genes while binding to Notch1 to promote H2B monoubiquitination at Notch target genes and Notch-dependent gene expression. This balance is crucial, as loss of Rnf20 leads to uncontrolled tip cell specification. Our findings highlight the pivotal role of RNF20 in regulating VEGF–Notch signaling circuits during vessel growth, underscoring its potential for therapeutic modulation of angiogenesis. Tetik-Elsherbiny et al. demonstrate that the E3 ubiquitin ligase RNF20 mediates RNA polymerase II promoter-proximal pausing and alternative splicing, regulating the bioavailability and signaling of pro-angiogenic factors and 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 | 0.001 |
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".