Everolimus Rescues the Phenotype of Elastin Insufficiency in Patient Induced Pluripotent Stem Cell–Derived Vascular Smooth Muscle Cells
Bibliographic record
Abstract
OBJECTIVE: Elastin gene deletion or mutation leads to arterial stenoses due to vascular smooth muscle cell (SMC) proliferation. Human induced pluripotent stem cells-derived SMCs can model the elastin insufficiency phenotype in vitro but show only partial rescue with rapamycin. Our objective was to identify drug candidates with superior efficacy in rescuing the SMC phenotype in elastin insufficiency patients. Approach and Results: SMCs generated from induced pluripotent stem cells from 5 elastin insufficiency patients with severe recurrent vascular stenoses (3 Williams syndrome and 2 elastin mutations) were phenotypically immature, hyperproliferative, poorly responsive to endothelin, and exerted reduced tension in 3-dimensional smooth muscle biowires. Elastin mRNA and protein were reduced in SMCs from patients compared to healthy control SMCs. Fourteen drug candidates were tested on patient SMCs. Of the mammalian target of rapamycin inhibitors studied, everolimus restored differentiation, rescued proliferation, and improved endothelin-induced calcium flux in all patient SMCs except one Williams syndrome. Of the calcium channel blockers, verapamil increased SMC differentiation and reduced proliferation in Williams syndrome patient cells but not in elastin mutation patients and had no effect on endothelin response. Combination treatment with everolimus and verapamil was not superior to everolimus alone. Other drug candidates had limited efficacy. CONCLUSIONS: Everolimus caused the most consistent improvement in SMC differentiation, proliferation and in SMC function in patients with both syndromic and nonsyndromic elastin insufficiency, and offers the best candidate for drug repurposing for treatment of elastin insufficiency associated vasculopathy.
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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".