Quantifying anti-DUX4 therapy for facioscapulohumeral muscular dystrophy
Bibliographic record
Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is an inherited skeletal myopathy with no cure. Expression of the myotoxic transcription factor double homeobox 4 ( DUX4 ) is believed to underlie FSHD pathogenesis and many proposed therapies target DUX4 generation or function. Which of these therapies will be the most effective is unclear. Here, by constructing a Markov-chain-based mathematical model of DUX4-mediated myotoxity in FSHD, we interrogate various anti-DUX4 FSHD therapeutic strategies. We derive an analytical function for myonuclear life expectancy in terms of the parameters of DUX4 expression and function. In a biologically relevant parameter regime, therapeutically decreasing the DUX4 protein diffusion rate is, surprisingly, predicted to be more effective at increasing myonuclear life expectancy than reducing the rate of myonuclear apoptosis caused by the expression of DUX4-target genes. We find that targeting elements of DUX4 transcription/translation, such as mRNA stability via siRNA therapy, has a limited predicted impact on DUX4-meditated toxicity when performed in isolation. However, our model predicts a super-additive effect from combining transcription/translation targeting strategies with approaches that minimise DUX4 diffusion-mediated import into neighbouring myonuclei. Importantly, we provide a computational tool to test and inform therapeutic designs, enabling pre-clinical screening of FSHD treatment approaches.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".