Blood flow regulates <i>acvrl1</i> transcription via ligand-dependent Alk1 activity
Bibliographic record
Abstract
Abstract Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disease characterized by the development of arteriovenous malformations (AVMs) that can result in significant morbidity and mortality. HHT is caused primarily by mutations in bone morphogenetic protein receptors ACVRL1 /ALK1, a signaling receptor, or endoglin ( ENG ), an accessory receptor. Because overexpression of Acvrl1 prevents AVM development in both Acvrl1 and Eng null mice, enhancing ACVRL1 expression may be a promising approach to development of targeted therapies for HHT. Therefore, we sought to understand the molecular mechanism of ACVRL1 regulation. We previously demonstrated in zebrafish embryos that acvrl1 is predominantly expressed in arterial endothelial cells and that expression requires blood flow. Here, we document that flow dependence exhibits regional heterogeneity and that acvrl1 expression is rapidly restored after reinitiation of flow. Furthermore, we find that acvrl1 expression is significantly decreased in mutants that lack the circulating Alk1 ligand, Bmp10, and that BMP10 microinjection into the vasculature in the absence of flow enhances acvrl1 expression in an Alk1-dependent manner. Using a transgenic acvrl1:egfp reporter line, we find that flow and Bmp10 regulate acvrl1 at the level of transcription. Finally, we observe similar ALK1 ligand-dependent increases in ACVRL1 in human endothelial cells subjected to shear stress. These data suggest that Bmp10 acts downstream of blood flow to maintain or enhance acvrl1 expression via a positive feedback mechanism, and that ALK1 activating therapeutics may have dual functionality by increasing both ALK1 signaling flux and ACVRL1 expression.
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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) | 0.003 | 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".