Quantitative Proteomic Analysis of Dystrophic Dog Muscle
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is caused by null mutations in the dystrophin gene, leading to progressive and unrelenting muscle loss. Although the genetic basis of DMD is well resolved, the cellular mechanisms associated with the physiopathology remain largely unknown. Increasing evidence suggests that secondary mechanisms, as the alteration of key signaling pathways, may play an important role. In order to identify reliable biomarkers and potential therapeutic targets, and taking advantage of the clinically relevant Golden Retriever Muscular Dystrophy (GRMD) dog model, a proteomic study was performed. Isotope-coded affinity tag (ICAT) profiling was used to compile quantitative changes in protein expression profiles of the vastus lateralis muscles of 4-month old GRMD vs healthy dogs. Interestingly, the set of under-expressed proteins detected appeared primarily composed of metabolic proteins, many of which have been shown to be regulated by the transcriptional peroxisome proliferator-activated receptor-gamma co-activator 1 alpha (PGC-1α). Subsequently, we were able to showed that PGC1-α expression is dramatically reduced in GRMD compared to healthy muscle. Collectively, these results provide novel insights into the molecular pathology of the clinically relevant animal model of DMD, and indicate that defective energy metabolism is a central hallmark of the disease in the canine model.
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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".