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Record W4413140371 · doi:10.1172/jci.insight.195836

Differential pathology and susceptibility to MBNL loss across muscles in myotonic dystrophy mouse models

2025· article· en· W4413140371 on OpenAlexaff
Mackenzie L. Davenport, A.C.M. Fong, Gloria Montoya-Vazquez, Jodi L. Bubenik, Maurice S. Swanson

Bibliographic record

VenueJCI Insight · 2025
Typearticle
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsInstitute of Genetics
FundersNational Institute of Neurological Disorders and StrokeMyotonic Dystrophy Foundation
KeywordsMyotonic dystrophyMuscular dystrophyMedicineMyotoniaDifferential (mechanical device)BiologyPathologyAnatomyNeuroscienceInternal medicinePhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.293
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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