Nuclear Protein Aggregates Disrupt RNA Processing and Alter Biomechanics in a Muscle Cell Model of OPMD
Bibliographic record
Abstract
Aggregation of RNA-binding proteins (RBPs) is a hallmark of several age-related neuromuscular diseases. However, our understanding of how these aggregates drive dysfunction is often limited by the use of non-disease-relevant models. Oculopharyngeal muscular dystrophy (OPMD) is caused by a short alanine expansion mutation in the PABPN1 gene, which leads to nuclear aggregation of the protein. To investigate how these aggregates impair muscle cell function, we developed a muscle cell model with inducible expression of the pathogenic PABPN1 (A16) variant and confirmed its relevance to OPMD. Using subcellular fractionation combined with mass spectrometry and RNA sequencing, we examined the molecular consequences of nuclear PABPN1 aggregation. In the cytoplasmic fraction, we observed significant impairments in cellular metabolism and biomechanics. In the nuclear fraction, RNA metabolism was broadly disrupted, and additional RBPs were significantly enriched in insoluble aggregates. Importantly, mRNAs trapped within the aggregates were associated with impaired nuclear export and decreased translation efficiency, and the pathogenic PABPN1 variant led to reduced endogenous PABPN1 levels. Our findings support a model in which OPMD pathology arises from reduced levels of soluble PABPN1 due to nuclear aggregation and establish a mechanistic link between RBP aggregation and muscle cell dysfunction, highlighting shared pathological pathways across neuromuscular and neurodegenerative diseases.
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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".