Inhibition of Pathological Differentiation of Valvular Interstitial Cells by C-Type Natriuretic Peptide
Bibliographic record
Abstract
OBJECTIVE: Calcific aortic valve disease is associated with the differentiation of valvular interstitial cells (VICs) to myofibroblast and osteoblast-like cells, particularly in the fibrosa layer of the valve. Previous studies suggested that C-type natriuretic peptide (CNP) protects against calcific aortic valve disease to maintain homeostasis. We aimed to determine whether CNP inhibits VIC pathological differentiation as a mechanism to explain its protective effects. METHODS AND RESULTS: CNP expression was prominent in normal porcine aortic valves, particularly on the ventricular side, but reduced in sclerotic valves concomitant with the appearance of pathological VIC phenotypes in the fibrosa. In vitro, CNP inhibited calcified aggregate formation and bone-related transcript and protein expression by VICs grown in osteogenic conditions. Under myofibrogenic culture conditions, CNP reduced α-smooth muscle actin expression and cell-mediated gel contraction, indicating inhibition of myofibroblast differentiation. Similar to CNP, simvastatin inhibited VIC osteoblast and myofibroblast differentiation in vitro. Strikingly, simvastatin upregulated CNP expression in VICs cultured under myofibrogenic conditions, and small interfering RNA knockdown of natriuretic peptide receptor-b (a CNP receptor) significantly reduced the antifibrotic effect of simvastatin, suggesting that it acts in part via CNP/NPR-B autocrine/paracrine signaling. CONCLUSIONS: CNP inhibits myofibroblast and osteoblast differentiation of VICs and is responsible in part for inhibition of VIC myofibroblast differentiation by statins, suggesting novel mechanisms to explain the protective effect of CNP and the pleiotropic effects of statins in the aortic valve.
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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".