Apolipoprotein E knockout, but not cholesteryl ester transfer protein (CETP)-associated high-density lipoprotein cholesterol (HDL-C) lowering, exacerbates muscle wasting in dysferlin-null mice
Bibliographic record
Abstract
BACKGROUND: Dysferlin-deficient limb-girdle muscular dystrophy type 2B (Dysf) mice are notorious for their mild phenotype. Raising plasma total cholesterol (CHOL) via apolipoprotein E (ApoE) knockout (KO) drastically exacerbates muscle wasting in Dysf mice. However, dysferlinopathic patients have abnormally reduced plasma high-density lipoprotein cholesterol (HDL-C) levels. The current study aimed to determine whether HDL-C lowering can exacerbate the mild phenotype of dysferlin-null mice. METHODS: Human cholesteryl ester transfer protein (CETP), a plasma lipid transfer protein not found in mice that reduces HDL-C, and/or its optimal adapter protein human apolipoprotein B (ApoB), were overexpressed in Dysf mice. Mice received a 2% cholesterol diet from 2 months of age and characterized through ambulatory and hanging functional tests, plasma analyses, and muscle histology. RESULTS: CETP/ApoB expression in Dysf mice caused reduced HDL-C (54.5%) and elevated ratio of CHOL/HDL-C (181.3%) compared to control Dysf mice in plasma, but without raising CHOL. Compared to the severe muscle pathology found in high CHOL Dysf/ApoE double knockout mice, Dysf/CETP/ApoB mice did not show significant changes in ambulation, hanging capacity, increases in damaged area, collagen deposition, or decreases in cross-sectional area and healthy myofibre coverage. CONCLUSIONS: CETP/ApoB over-expression in Dysf mice decreases HDL-C without increasing CHOL or exacerbating muscle pathology. High CHOL or nonHDL-C caused by ApoE KO, rather than low HDL-C, likely lead to rodent muscular dystrophy phenotype humanization.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".