Differential pathology and susceptibility to MBNL loss across muscles in myotonic dystrophy mouse models
Bibliographic record
Abstract
There are 2 subtypes of myotonic dystrophy, DM1 and DM2, each caused by repeat expansion mutations. The leading pathogenic mechanism is RNA-mediated toxicity, whereby (C)CUG expansions sequester the muscleblind-like (MBNL) family of RNA binding proteins. However, key differences exist in muscle involvement patterns and histopathology between DM1 and DM2. The cause of these disparities both in how the muscles are affected within each disease and between the 2 diseases is unknown, and it is unclear if current DM mouse models recapitulate these differences or develop differential muscle susceptibility. Here, we examined the expression of disease-relevant genes across healthy human muscles from a transcriptomic atlas and collected a series of muscles from Mbnl-KO mice to evaluate characteristic histologic and molecular features of DM pathology. Our results indicate that MBNL loss discordantly affects muscles, likely through a splicing-independent mechanism, and results in a fiber atrophy profile more like DM1 than DM2. These findings point to a predominant role for MBNL loss in muscle pattern involvement in DM1, provide further evidence for additional DM2 pathomechanisms, and have important implications for muscle choice when performing analyses in new mouse models and evaluating therapeutic modalities and biomarkers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".