Discoidin Domain Receptor 1 ( <i>Ddr1</i> ) Deletion Decreases Atherosclerosis by Accelerating Matrix Accumulation and Reducing Inflammation in Low-Density Lipoprotein Receptor–Deficient Mice
Bibliographic record
Abstract
Collagens are abundant within the atherosclerotic plaque, where they contribute to lesion volume and mechanical stability and influence cell signaling. The discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase that binds to collagen, is expressed in blood vessels, but evidence for a functional role during atherogenesis is incomplete. In the present study, we generated Ddr1(+/+);Ldlr(-/-) and Ddr1(-/-);Ldlr(-/-) mice and fed them an atherogenic diet for 12 or 24 weeks. Targeted deletion of Ddr1 resulted in a 50% to 60% reduction in atherosclerotic lesion area in the descending aorta at both 12 and 24 weeks. Ddr1(-/-);Ldlr(-/-) plaques exhibited accelerated deposition of fibrillar collagen and elastin at 12 weeks compared with Ddr1(+/+);Ldlr(-/-) plaques. Expression analysis of laser microdissected lesions in vivo, and of Ddr1(-/-) smooth muscle cells in vitro, revealed increased mRNA levels for procollagen alpha1(I) and alpha1(III) and tropoelastin, suggesting an enhancement of matrix synthesis in the absence of DDR1. Furthermore, whereas plaque smooth muscle cell content was unchanged, Ddr1(-/-);Ldlr(-/-) plaques had a 49% decrease in macrophage content at 12 weeks, with a concomitant reduction of in situ gelatinolytic activity. Moreover, mRNA expression of both monocyte chemoattractant protein-1 and vascular cell adhesion molecule-1 was reduced in vivo, and Ddr1(-/-);Ldlr(-/-) macrophages demonstrated impaired matrix metalloproteinase expression in vitro. These data suggest novel roles for DDR1 in macrophage recruitment and invasion during atherogenesis. In conclusion, our data support a role for DDR1 in the regulation of both inflammation and fibrosis early in plaque development. Deletion of DDR1 attenuated atherogenesis and resulted in the formation of matrix-rich plaques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".