X-linked muscular dystrophy in a Labrador Retriever strain: phenotypic and molecular characterization.
Bibliographic record
Abstract
Abstract Background Canine models of Duchenne muscular dystrophy (DMD) are valuable to evaluate therapies because they faithfully reproduce the human disease. Several cases of dystrophinopathies have been described in canines, but the GRMD (Golden Retriever Muscular Dystrophy) model remains the one used in most preclinical studies. Methods We report a new spontaneous dystrophinopathy in a Labrador retriever strain, named LRMD (Labrador Retriever Muscular Dystrophy), for which a colony was established. Fourteen LRMD dogs were followed-up and compared to the GRMD standard. Results The clinical features of the GRMD disease were found in LRMD dogs, and the functional tests provided data roughly overlapping those measured in GRMD dogs, with similar inter-individual heterogeneity. Molecular techniques including RNA-sequencing allowed to map and identify the LRMD causal mutation, consisting in a 2.2-Mb inversion disrupting the DMD gene within its intron 20, and involving TMEM47 gene. In skeletal muscles, the Dp71 isoform was ectopically expressed as a probable consequence of the mutation. We found no evidence of polymorphism in the two LTBP4 and Jagged1 modifier genes that would explain the observed inter-individual variability. Conclusions This study provides a full comparative description of a new spontaneous canine dystrophinopathy, that we demonstrate is phenotypically equivalent to the GRMD model. We report a novel large DNA mutation within the DMD gene and provide evidence that LRMD is a relevant model to pinpoint additional DMD modifier genes.
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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".