JAGGED1/NOTCH3 activation promotes aortic hypermuscularization and stenosis in elastin deficiency
Bibliographic record
Abstract
Obstructive arterial diseases, including supravalvular aortic stenosis (SVAS), atherosclerosis, and restenosis, share 2 important features: an abnormal or disrupted elastic lamellae structure and excessive smooth muscle cells (SMCs). However, the relationship between these pathological features is poorly delineated. SVAS is caused by heterozygous loss-of-function, hypomorphic, or deletion mutations in the elastin gene (ELN), and SVAS patients and elastin-mutant mice display increased arterial wall cellularity and luminal obstructions. Pharmacological treatments for SVAS are lacking, as the underlying pathobiology is inadequately defined. Herein, using human aortic vascular cells, mouse models, and aortic samples and SMCs derived from induced pluripotent stem cells of ELN-deficient patients, we demonstrated that elastin insufficiency induced epigenetic changes, upregulating the NOTCH pathway in SMCs. Specifically, reduced elastin increased levels of γ-secretase, activated NOTCH3 intracellular domain, and downstream genes. Notch3 deletion or pharmacological inhibition of γ-secretase attenuated aortic hypermuscularization and stenosis in Eln-/- mutants. Eln-/- mice expressed higher levels of NOTCH ligand JAGGED1 (JAG1) in aortic SMCs and endothelial cells (ECs). Finally, Jag1 deletion in SMCs, but not ECs, mitigated the hypermuscular and stenotic phenotype in the aorta of Eln-/- mice. Our findings reveal that NOTCH3 pathway upregulation induced pathological aortic SMC accumulation during elastin insufficiency and provide potential therapeutic targets for SVAS.
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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 | 0.001 |
| 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".