Discoidin Domain Receptor-1 Regulates Calcific Extracellular Vesicle Release in Vascular Smooth Muscle Cell Fibrocalcific Response via Transforming Growth Factor-β Signaling
Bibliographic record
Abstract
OBJECTIVE: Collagen accumulation and calcification are major determinants of atherosclerotic plaque stability. Extracellular vesicle (EV)-derived microcalcifications in the collagen-poor fibrous cap may promote plaque rupture. In this study, we hypothesize that the collagen receptor discoidin domain receptor-1 (DDR-1) regulates collagen deposition and release of calcifying EVs by vascular smooth muscle cells (SMCs) through the transforming growth factor-β (TGF-β) pathway. APPROACH AND RESULTS: SMCs from the carotid arteries of DDR-1(-/-) mice and wild-type littermates (n=5-10 per group) were cultured in normal or calcifying media. At days 14 and 21, SMCs were harvested and EVs isolated for analysis. Compared with wild-type, DDR-1(-/-) SMCs exhibited a 4-fold increase in EV release (P<0.001) with concomitantly elevated alkaline phosphatase activity (P<0.0001) as a hallmark of EV calcifying potential. The DDR-1(-/-) phenotype was characterized by increased mineralization (Alizarin Red S and Osteosense, P<0.001 and P=0.002, respectively) and amorphous collagen deposition (P<0.001). We further identified a novel link between DDR-1 and the TGF-β pathway previously implicated in both fibrotic and calcific responses. An increase in TGF-β1 release by DDR-1(-/-) SMCs in calcifying media (P<0.001) stimulated p38 phosphorylation (P=0.02) and suppressed activation of Smad3. Inhibition of either TGF-β receptor-I or phospho-p38 reversed the fibrocalcific DDR-1(-/-) phenotype, corroborating a causal relationship between DDR-1 and TGF-β in EV-mediated vascular calcification. CONCLUSIONS: DDR-1 interacts with the TGF-β pathway to restrict calcifying EV-mediated mineralization and fibrosis by SMCs. We therefore establish a novel mechanism of cell-matrix homeostasis in atherosclerotic plaque formation.
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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 | 0.001 |
| 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".