Loss of TIMP4 (Tissue Inhibitor of Metalloproteinase 4) Promotes Atherosclerotic Plaque Deposition in the Abdominal Aorta Despite Suppressed Plasma Cholesterol Levels
Bibliographic record
Abstract
Objective: Atherosclerosis is accumulation of lipids and extracellular matrix in the arterial wall. TIMPs (tissue inhibitor of metalloproteinases) can impact plaque deposition by regulating ECM (extracellular matrix) turnover. TIMP4 also influences lipid metabolism and smooth muscle cell (SMC) proliferation. We investigated the role of TIMP4 in atherosclerosis. Approach and Results: Mice lacking low-density lipoprotein receptor ( Ldlr −/− ) and Timp4 ( Timp4 −/− / Ldlr −/− ) were fed high-fat diet (HFD) or regular laboratory diet. After 3 or 6 months, HFD-fed male and female Timp4 −/− / Ldlr −/− mice exhibited higher plaque density in the abdominal aorta (but not in aortic valves, arch, thoracic aorta) compared with Ldlr −/− mice. Although plasma lipid and cholesterol levels were lower in Timp4 −/− / Ldlr −/− -HFD, cholesterol content in the abdominal aorta was higher along with elevated inflammatory cytokines, MMP (matrix metalloproteinase) activities, CD68 + /calponin + macrophage-like SMCs in Timp4 −/− / Ldlr −/− -HFD compared with Ldlr −/− -HFD mice. In vitro, oxidized LDL (low-density lipoprotein) markedly increased CD68 expression, reduced SMC markers, increased lipid uptake, and reduced cholesterol efflux protein ABCA1 (ATP-binding cassette transporter A1) in Timp4 −/− / Ldlr −/− compared with Ldlr −/− primary SMCs from abdominal, but not thoracic aorta. TIMP4 expression in the abdominal aorta (in vivo) and its corresponding SMCs (in vitro) was ≈2-fold higher than in the thoracic aorta and SMCs; TIMP4 levels decreased following HFD. Timp4 -deficiency in bone marrow–derived macrophages did not alter their foam cell formation capacity. Conclusions: TIMP4 protects against plaque deposition in the abdominal aorta independent of plasma cholesterol levels. TIMP4 prevents proteolytic degradation of ABCA1 in SMCs, hindering cholesterol accumulation and transdifferentiation to macrophage-like foam cells, representing a novel negative regulator of 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".