9p21.3 Coronary Artery Disease Risk Variants Disrupt TEAD Transcription Factor–Dependent Transforming Growth Factor β Regulation of p16 Expression in Human Aortic Smooth Muscle Cells
Bibliographic record
Abstract
BACKGROUND: The mechanism whereby the 9p21.3 locus confers risk for coronary artery disease remains incompletely understood. Risk alleles are associated with reduced expression of the cell cycle suppressor genes CDKN2A (p16 and p14) and CDKN2B (p15) and increased vascular smooth muscle cell proliferation. We asked whether risk alleles disrupt transcription factor binding to account for this effect. METHODS AND RESULTS: A bioinformatic screen was used to predict which of 59 single nucleotide polymorphisms at the 9p21.3 locus disrupt (or create) transcription factor binding sites. Electrophoretic mobility shift and luciferase reporter assays examined the binding and functionality of the predicted regulatory sequences. Primary human aortic smooth muscle cells (HAoSMCs) were genotyped for 9p21.3, and HAoSMCs homozygous for the risk allele showed reduced p15 and p16 levels and increased proliferation. rs10811656 and rs4977757 disrupted functional TEF-1 TEC1 AbaA domain (TEAD) transcription factor binding sites. TEAD3 and TEAD4 overexpression induced p16 in HAoSMCs homozygous for the nonrisk allele, but not for the risk allele. Transforming growth factor β, known to activate p16 and also to interact with TEAD factors, failed to induce p16 or to inhibit proliferation of HAoSMCs homozygous for the risk allele. Knockdown of TEAD3 blocked transforming growth factor β-induced p16 mRNA and protein expression, and dual knockdown of TEAD3 and TEAD4 markedly reduced p16 expression in heterozygous HAoSMCs. CONCLUSIONS: Here, we identify a novel mechanism whereby sequences at the 9p21.3 risk locus disrupt TEAD factor binding and TEAD3-dependent transforming growth factor β induction of p16 in HAoSMCs. This mechanism accounts, in part, for the 9p21.3 coronary artery disease risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".