Modeling and Rescue of the Vascular Phenotype of Williams-Beuren Syndrome in Patient Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Elastin haploinsufficiency in Williams-Beuren syndrome (WBS) leads to increased vascular smooth muscle cell (SMC) proliferation and stenoses. Our objective was to generate a human induced pluripotent stem (hiPS) cell model for in vitro assessment of the WBS phenotype and to test the ability of candidate agents to rescue the phenotype. hiPS cells were reprogrammed from skin fibroblasts of a WBS patient with aortic and pulmonary stenosis and healthy control BJ fibroblasts using four-factor retrovirus reprogramming and were differentiated into SMCs. Differentiated SMCs were treated with synthetic elastin-binding protein ligand 2 (EBPL2) (20 μg/ml) or the antiproliferative drug rapamycin (100 nM) for 5 days. We generated four WBS induced pluripotent stem (iPS) cell lines that expressed pluripotency genes and differentiated into all three germ layers. Directed differentiation of BJ iPS cells yielded an 85%-92% pure SMC population that expressed differentiated SMC markers, were functionally contractile, and formed tube-like structures on three-dimensional gel assay. Unlike BJ iPS cells, WBS iPS cells generated immature SMCs that were highly proliferative, showed lower expression of differentiated SMC markers, reduced response to the vasoactive agonists, carbachol and endothelin-1, impaired vascular tube formation, and reduced calcium flux. EBPL2 partially rescued and rapamycin fully rescued the abnormal SMC phenotype by decreasing the smooth muscle proliferation rate and enhancing differentiation and tube formation. WBS iPS cell-derived SMCs demonstrate an immature proliferative phenotype with reduced functional and contractile properties, thereby recapitulating the human disease phenotype. The ability of rapamycin to rescue the phenotype provides an attractive therapeutic candidate for patients with WBS and vascular stenoses.
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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 | 0.000 |
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".