Post‐Translational Modifications of the <scp>DUX4</scp> Protein Impact Toxic Function in FSHD Cell Models
Bibliographic record
Abstract
OBJECTIVE: Facioscapulohumeral muscular dystrophy (FSHD) is caused by abnormal de-repression of the myotoxic transcription factor DUX4. Although the transcriptional targets of DUX4 are known, the regulation of DUX4 protein and the molecular consequences of this regulation are unclear. Here, we used in vitro models of FSHD to identify and characterize DUX4 post-translational modifications (PTMs) and their impact on the toxic function of DUX4. METHODS: We immunoprecipitated DUX4 protein and performed mass spectrometry to identify PTMs. We then characterized DUX4 PTMs and potential enzyme modifiers using mutagenesis, proteomics, and biochemical assays in HEK293 and human myoblast cell lines. RESULTS: We identified 17 DUX4 amino acids with PTMs, and generated 55 DUX4 mutants designed to prevent or mimic PTMs. Five mutants protected cells against DUX4-mediated toxicity and reduced the ability of DUX4 to transactivate FSHD biomarkers. These mutagenesis results suggested that DUX4 toxicity could be counteracted by serine/threonine phosphorylation and/or inhibition of arginine methylation. We therefore sought to identify modifying enzymes that could play a role in regulating DUX4 PTMs. We found several enzymes capable of modifying DUX4 protein in vitro, and confirmed that protein kinase A (PKA) and protein arginine methyltransferase (PRMT1) interact with DUX4. INTERPRETATION: These results support that DUX4 is regulated by PTMs and set a foundation for developing FSHD drug screens based mechanistically on DUX4 PTMs and modifying enzymes. ANN NEUROL 2023;94:398-413.
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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 | 0.000 | 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".