Venous activation of MEK/ERK drives development of arteriovenous malformation and blood flow anomalies with loss of Rasa1
Bibliographic record
Abstract
Abstract Vascular malformations develop when growth pathway signaling goes awry in the endothelial cells lining blood vessels. Arteriovenous malformations (AVMs) arise where arteries and veins abnormally connect in patients with loss of RASA1, a Ras GTPase activating protein, and, as we show here, in zebrafish rasa1 mutants. Mutant fish develop massively enlarged vessels at the connection between artery and vein in the tail vascular plexus. These AVMs progressively enlarge and become filled with slow-flowing blood and have a greater drop in pulsatility from the artery to the vein. Expression of the flow responsive transcription factor klf2a is diminished in rasa1 mutants, suggesting changes in flow velocity and pattern contribute to the progression of vessel malformations. Migration of endothelial cells is not affected in rasa1 mutants, nor is cell death or proliferation. Early developmental artery-vein patterning is also normal in rasa1 mutants, but we find that MEK/ERK signaling is ectopically activated in the vein as compared to high arterial activation seen in wildtype animals. MEK/ERK signaling inhibition prevents AVM development of rasa1 mutants, demonstrating venous MEK/ERK drives the initiation of rasa1 AVMs. Thus, rasa1 mutants show overactivation of MEK/ERK signaling causes AVM formation, altered blood flow and downstream flow responsive signaling. Summary The zebrafish model of RASA1 capillary malformation and arteriovenous malformation (CM-AVM1) develops cavernous vascular malformations driven by ectopic MEK/ERK signaling in the vein, disrupting flow and downstream mechanosensitive signaling.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".