<i>CDKN2B</i> Regulates <i>TGF</i> β Signaling and Smooth Muscle Cell Investment of Hypoxic Neovessels
Bibliographic record
Abstract
RATIONALE: Genetic variation at the chromosome 9p21 cardiovascular risk locus has been associated with peripheral artery disease, but its mechanism remains unknown. OBJECTIVE: To determine whether this association is secondary to an increase in atherosclerosis, or it is the result of a separate angiogenesis-related mechanism. METHODS AND RESULTS: Quantitative evaluation of human vascular samples revealed that carriers of the 9p21 risk allele possess a significantly higher burden of immature intraplaque microvessels than carriers of the ancestral allele, irrespective of lesion size or patient comorbidity. To determine whether aberrant angiogenesis also occurs under nonatherosclerotic conditions, we performed femoral artery ligation surgery in mice lacking the 9p21 candidate gene, Cdkn2b. These animals developed advanced hindlimb ischemia and digital autoamputation, secondary to a defect in the capacity of the Cdkn2b-deficient smooth muscle cell to support the developing neovessel. Microarray studies identified impaired transforming growth factor β (TGFβ) signaling in cultured cyclin-dependent kinase inhibitor 2B (CDKN2B)-deficient cells, as well as TGFβ1 upregulation in the vasculature of 9p21 risk allele carriers. Molecular signaling studies indicated that loss of CDKN2B impairs the expression of the inhibitory factor, SMAD-7, which promotes downstream TGFβ activation. Ultimately, this manifests in the upregulation of a poorly studied effector molecule, TGFβ1-induced-1, which is a TGFβ-rheostat known to have antagonistic effects on the endothelial cell and smooth muscle cell. Dual knockdown studies confirmed the reversibility of the proposed mechanism, in vitro. CONCLUSIONS: These results suggest that loss of CDKN2B may not only promote cardiovascular disease through the development of atherosclerosis but may also impair TGFβ signaling and hypoxic neovessel maturation.
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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".