Dysferlin deficiency alters lipid metabolism and remodels the skeletal muscle lipidome in mice
Bibliographic record
Abstract
Defects in the gene coding for dysferlin, a membrane-associated protein, affect many tissues, including skeletal muscles, with a resultant myopathy called dysferlinopathy. Dysferlinopathy manifests postgrowth with a progressive loss of skeletal muscle function, early intramyocellular lipid accumulation, and a striking later replacement of selective muscles by adipocytes. To better understand the changes underpinning this disease, we assessed whole-body energy homeostasis, skeletal muscle fatty acid metabolism, lipolysis in adipose tissue, and the skeletal muscle lipidome using young adult dysferlin-deficient male BLAJ mice and age-matched C57Bl/6J WT mice. BLAJ mice had increased lean mass and reduced fat mass associated with increased physical activity and increased adipose tissue lipolysis. Skeletal muscle fatty acid metabolism was remodeled in BLAJ mice, characterized by a partitioning of fatty acids toward storage rather than oxidation. Lipidomic analysis identified marked changes in almost all lipid classes examined in the skeletal muscle of BLAJ mice, including sphingolipids, phospholipids, cholesterol, and most glycerolipids but, surprisingly, not triacylglycerol. These observations indicate that an early manifestation of dysferlin deficiency is the reprogramming of skeletal muscle and adipose tissue lipid metabolism, which is likely to contribute to the progressive adverse histopathology in dysferlinopathies.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".