Targeted deletion of discoidin domain receptor 1 (Ddr1) decreases atherosclerosis, reduces inflammation and accelerates matrix accumulation in LDL receptor deficient mices
Bibliographic record
Abstract
Background: Collagens are abundant within the atherosclerotic plaque where they contribute to lesion volume, mechanical stability, and influence cellular behaviour. The discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase that binds multiple collagen subtypes, has been observed in atheromata of non-human primates, but its function during atherogenesis is unclear. Methods: To examine the role of DDR1 during atherosclerotic plaque development, we generated Ddr1+/+;Ldlr-/- and Ddr1-/-;Ldlr-/- mice and fed them an atherogenic diet for 12 or 24 weeks. Results: Targeted deletion of Ddr1 resulted in a 50-60% reduction in atherosclerotic lesion area in the descending aorta at both 12 and 24 weeks. Atherosclerotic plaques from Ddr1-/-;Ldlr-/- mice demonstrated a 49% decrease in the area occupied by macrophages at 12 weeks, however, plaque SMC content was unchanged. We also observed accelerated deposition of fibrillar collagen and elastin in Ddr1-/-;Ldlr-/- plaques with a 36% and 45% increase at 12 weeks, respectively. Ddr1-/-;Ldlr-/- mice also demonstrated an early reduction in situ gelatinolytic activity in lesions and gelatin zymography revealed reduced MMP-2 activity at 12 weeks. Finally, mRNA expression analysis of laser microdissected plaques demonstrated enhanced expression of type I collagen and elastin, and reduced collagenase expression at 12 weeks. Moreover, mRNA expression of both MCP-1 and VCAM-1 was reduced in Ddr1-/-;Ldlr-/- plaques suggesting a novel role for DDR1 in the regulation of vascular inflammation. Conclusion: Our data support a role for DDR1 as a positive regulator of atherosclerosis; capable of influencing both inflammation and matrix turnover early in plaque development, and when inhibited can result in a persistent reduction in atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".