A Functional Role for Doscoidin Domain Receptor 1 (Ddr1) in the Regulation of Inflmmation and Fibrosis During Atherosclerotic Plaque Development
Bibliographic record
Abstract
Collagens are abundant components of the extracellular matrix in the atherosclerotic plaque. In addition to contributing to lesion volume and mechanical stability, collagens can influence the behavior of macrophages and smooth muscle cells (SMCs) and have profound effects on both inflammation and fibrosis during lesion development. The aim of this thesis was to define a functional role for the discoidin domain receptor 1 (DDR1), a collagen receptor tyrosine kinase, in murine models of atherogenesis. In our first study, using Ddr1+/+;Ldlr-/- and Ddr1-/-;Ldlr-/- mice fed a high fat diet, we identified DDR1 as a novel positive regulator of atherogenesis. Targeted deletion of DDR1 attenuated atherosclerotic plaque development by limiting inflammation and accelerating matrix accumulation and resulted in the formation of macrophage poor, matrix rich lesions. In the second study, we used bone marrow transplantation to generate chimeric mice with a deficiency of DDR1 in bone marrow derived cells and reveal a central role for macrophage DDR1 in atherogenesis. Deficiency of DDR1 in bone marrow derived cells reduced lesion size by limiting macrophage accumulation in the developing plaque. Moreover using BrdU pulse labeling, we demonstrated reduced monocyte recruitment into the early fatty streak lesions of Ddr1-/-;Ldlr-/- mice. In our third study, we again utilized bone marrow transplantation to generate mice with deficiency of DDR1 in the host derived tissues such as the vessel wall and uncovered a distinct role for DDR1 expressed on resident vessel wall smooth muscle cells in the regulation of matrix accumulation and fibrous cap formation during atherogenesis. Deficiency of DDR1 in vessel wall cells resulted in robust accumulation of collagen and elastin and resulted in the formation of larger atherosclerotic plaques, with thick fibrous caps. Taken together, these studies support a critical role for DDR1 in the development of the atherosclerotic plaque. We demonstrate that DDR1 exerts distinct and opposing effects on lesion size by regulating both monocyte recruitment and matrix accumulation. These studies underscore the importance of collagen signaling during atherogenesis, and identify DDR1 as a key transducer; providing signals that regulate both inflammation and fibrosis during atherogenesis.
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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".