DAB2 as a biomarker and mechanistic link between lipid dysregulation and disease progression in LGMD R2
Bibliographic record
Abstract
Abstract Limb-girdle muscular dystrophy R2 (LGMD R2) is an autosomal recessive disorder caused by dysferlin deficiency, leading to progressive muscle weakness and wasting. Despite advances in understanding the mechanisms linking dysferlin loss to membrane fragility and muscle degeneration, the lack of robust clinical biomarkers has limited disease monitoring and therapeutic evaluation. Here, we identify Disabled-2 (DAB2) as a molecular and clinical biomarker for LGMD R2. Transcriptomic profiling revealed a significant upregulation of DAB2 in induced pluripotent stem cell (iPSC)-derived myotubes from patients with LGMD R2. Its expression correlated with disease severity in muscle biopsies from a cohort of 14 dysferlin-deficient individuals and in dysferlin knockout Bla/J mice, where levels increased with disease progression. Crucially, we demonstrate that DAB2 upregulation in muscle is normalized following treatment with AAV gene therapy expressing full-length dysferlin, positioning DAB2 as a dynamic biomarker for both disease monitoring and therapeutic response. Based on the role of DAB2 in lipid trafficking and the reported pathological lipid accumulation in LGMD R2, we then investigated its contribution to disease-associated lipid dysregulation. Consistent with this hypothesis, we show that high DAB2 levels paralleled lipid deposition in affected patients, iPSC-derived myotubes and mouse muscles, while siRNA- mediated DAB2 knockdown reduced lipid accumulation in LGMD R2 myotubes. Together, our findings establish DAB2 as a mechanistic link between disease severity and lipid dysregulation, and highlight its potential as a key prognostic marker, opening new avenues for precision medicine approaches in LGMD R2 and other related muscular dystrophies. Graphical abstract
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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 | 0.001 | 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 | 0.000 |
| 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".