Novel Role for Matrix Metalloproteinase 9 in Modulation of Cholesterol Metabolism
Bibliographic record
Abstract
Background The development of atherosclerosis is strongly linked to disorders of cholesterol metabolism. Matrix metalloproteinases ( MMP s) are dysregulated in patients and animal models with atherosclerosis. Whether systemic MMP activity influences cholesterol metabolism is unknown. Methods and Results We examined MMP ‐9–deficient ( Mmp9 −/− ) mice and found them to have abnormal lipid gene transcriptional responses to dietary cholesterol supplementation. As opposed to Mmp9 +/+ (wild‐type) mice, Mmp9 −/− mice failed to decrease the hepatic expression of sterol regulatory element binding protein 2 pathway genes, which control hepatic cholesterol biosynthesis and uptake. Furthermore, Mmp9 −/− mice failed to increase the expression of genes encoding the rate‐limiting enzymes in biliary cholesterol excretion (eg, Cyp7a and Cyp27a ). In contrast, MMP ‐9 deficiency did not impair intestinal cholesterol absorption, as shown by the 14 C‐cholesterol and 3 H‐sitostanol absorption assay. Similar to our earlier study on Mmp2 −/− mice, we observed that Mmp9 −/− mice had elevated plasma secreted phospholipase A 2 activity. Pharmacological inhibition of systemic circulating secreted phospholipase A 2 activity (with varespladib) partially normalized the hepatic transcriptional responses to dietary cholesterol in Mmp9 −/− mice. Functional studies with mice deficient in other MMP s suggested an important role for the MMP system, as a whole, in modulation of cholesterol metabolism. Conclusions Our results show that MMP ‐9 modulates cholesterol metabolism, at least in part, through a novel MMP ‐9–plasma secreted phospholipase A 2 axis that affects the hepatic transcriptional responses to dietary cholesterol. Furthermore, the data suggest that dysregulation of the MMP system can result in metabolic disorder, which could lead to atherosclerosis and coronary heart disease.
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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".