Mutation-independent Proteomic Signatures of Pathological Progression in Murine Models of Duchenne Muscular Dystrophy
Bibliographic record
Abstract
The absence of the dystrophin protein in Duchenne muscular dystrophy (DMD) results in myofiber fragility and a plethora of downstream secondary pathologies. Although a variety of experimental therapies are in development, achieving effective treatments for DMD remains exceptionally challenging, not least because the pathological consequences of dystrophin loss are incompletely understood. Here we have performed proteome profiling in tibialis anterior muscles from two murine DMD models ( mdx and mdx52 ) at three ages (8, 16, and 80 weeks of age), all n = 3. High-resolution isoelectric focusing liquid chromatography-tandem MS (HiRIEF-LC–MS/MS) was used to quantify the expression of 4974 proteins across all 27 samples. The two dystrophic models were found to be highly similar, whereas multiple proteins were differentially expressed relative to WT (C57BL/6) controls at each age. Furthermore, 1795 proteins were differentially expressed when samples were pooled across ages and dystrophic strains. These included numerous proteins associated with the extracellular matrix and muscle function that have not been reported previously. Pathway analysis revealed multiple perturbed pathways and predicted upstream regulators, which together are indicative of cross-talk between inflammatory, metabolic, and muscle growth pathways ( e.g. TNF, INFγ, NF-κB, SIRT1, AMPK, PGC-1α, PPARs, ILK, and AKT/PI3K). Upregulation of CAV3, MVP and PAK1 protein expression was validated in dystrophic muscle by Western blot. Furthermore, MVP was upregulated during, but not required for, the differentiation of C2C12 myoblasts suggesting that this protein may affect muscle regeneration. This study provides novel insights into mutation-independent proteomic signatures characteristic of the dystrophic phenotype and its progression with aging.
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 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".