Discovery of serum protein biomarkers in the mdx mouse model and cross-species comparison to Duchenne muscular dystrophy patients
Bibliographic record
Abstract
It is expected that serum protein biomarkers in Duchenne muscular dystrophy (DMD) will reflect disease pathogenesis, progression and aid future therapy developments. Here, we describe use of quantitative in vivo stable isotope labeling in mammals to accurately compare serum proteomes of wild-type and dystrophin-deficient mdx mice. Biomarkers identified in serum from two independent dystrophin-deficient mouse models (mdx-Δ52 and mdx-23) were concordant with those identified in sera samples of DMD patients. Of the 355 mouse sera proteins, 23 were significantly elevated and 4 significantly lower in mdx relative to wild-type mice (P-value < 0.001). Elevated proteins were mostly of muscle origin: including myofibrillar proteins (titin, myosin light chain 1/3, myomesin 3 and filamin-C), glycolytic enzymes (aldolase, phosphoglycerate mutase 2, beta enolase and glycogen phosphorylase), transport proteins (fatty acid-binding protein, myoglobin and somatic cytochrome-C) and others (creatine kinase M, malate dehydrogenase cytosolic, fibrinogen and parvalbumin). Decreased proteins, mostly of extracellular origin, included adiponectin, lumican, plasminogen and leukemia inhibitory factor receptor. Analysis of sera from 1 week to 7 months old mdx mice revealed age-dependent changes in the level of these biomarkers with most biomarkers acutely elevated at 3 weeks of age. Serum analysis of DMD patients, with ages ranging from 4 to 15 years old, confirmed elevation of 20 of the murine biomarkers in DMD, with similar age-related changes. This study provides a panel of biomarkers that reflect muscle activity and pathogenesis and should prove valuable tool to complement natural history studies and to monitor treatment efficacy in future clinical trials.
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 | 0.001 | 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".