Effect of Dysferlin Deficiency on Atherosclerosis and Plasma Lipoprotein Composition Under Normal and Hyperlipidemic Conditions
Bibliographic record
Abstract
Dysferlinopathies are a group of muscle disorders caused by mutations to dysferlin, a transmembrane protein involved in membrane patching events following physical damage to skeletal myofibers. We documented dysferlin expression in vascular tissues including non-muscle endothelial cells, suggesting that blood vessels may have an endogenous repair system that helps promote vascular homeostasis. To test this hypothesis, we generated dysferlin-null mice lacking apolipoprotein E (ApoE), a common model of atherosclerosis, dyslipidemia and endothelial injury when stressed with a high fat, and cholesterol-rich diet. Despite high dysferlin expression in mouse and human atheromatous plaques, loss of dysferlin did not affect atherosclerotic burden as measured in the aortic root, arch, thoracic, and abdominal aortic regions. Interestingly, we observed that dysferlin-null mice exhibit lower plasma high-density lipoprotein cholesterol (HDL-C) levels than their WT controls at all measured stages of the disease process. Western blotting revealed abundant dysferlin expression in protein extracts from mouse livers, the main regulator of plasma lipoprotein levels. Despite abnormal lipoprotein levels, Dysf/ApoE double knockout mice responded to cholesterol absorption blockade with lower total cholesterol and blunted atherosclerosis. Our study suggests that dysferlin does not protect against atherosclerosis or participate in cholesterol absorption blockade but regulates basal plasma lipoprotein composition. Dysferlinopathic patients may be dyslipidemic without greater atherosclerotic burden while remaining responsive to cholesterol absorption blockade.
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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 | 0.001 | 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".