Discoidin domain receptor 1-deletion ameliorates fibrosis and promotes adipose tissue beiging, brown fat activity, and increased metabolic rate in a mouse model of cardiometabolic disease
Bibliographic record
Abstract
OBJECTIVE: Discoidin domain receptor 1 (DDR1) is a collagen binding receptor tyrosine kinase implicated in atherosclerosis, fibrosis, and cancer. Our previous research showed that DDR1 could regulate smooth muscle cell trans-differentiation, fibrosis and calcification in the vascular system in cardiometabolic disease. This spectrum of activity led us to question whether DDR1 might also regulate adipose tissue fibrosis and remodeling. METHODS: We have used a diet-induced mouse model of cardiometabolic disease to determine whether DDR1 deletion impacts upon adipose tissue remodeling and metabolic dysfunction. Mice were fed a high fat diet (HFD) for 12 weeks, followed by assessment of glucose and insulin tolerance, respiration via indirect calorimetry, and brown fat activity by FDG-PET. RESULTS: mice also had reduced fibrosis, smaller adipocytes with multilocular lipid droplets, and increased UCP-1 expression characteristic of beige fat formation in subcutaneous adipose tissue. In vitro, studying C3H10T1/2 cells stimulated to differentiate, DDR1 inhibition caused a shift from white to beige adipocyte differentiation, whereas DDR1 expression was increased with TGFβ-mediated pro-fibrotic differentiation. CONCLUSION: This study is the first to identify a role for DDR1 as a driver of adipose tissue fibrosis and suppressor of beneficial beige fat 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".