Post-transcriptional regulation of Pabpn1 by the RNA binding protein HuR
Bibliographic record
Abstract
RNA processing is critical for proper spatial and temporal control of gene expression. The ubiquitous nuclear polyadenosine RNA binding protein, PABPN1, post-transcriptionally regulates multiple steps of gene expression. Mutations in the PABPN1 gene expanding an N-terminal alanine tract in the PABPN1 protein from 10 alanines to 11-18 alanines cause the muscle-specific disease oculopharyngeal muscular dystrophy (OPMD), which affects eyelid, pharynx, and proximal limb muscles. Previous work revealed that the Pabpn1 transcript is unstable, contributing to low steady-state Pabpn1 mRNA and protein levels in vivo, specifically in skeletal muscle, with even lower levels in muscles affected in OPMD. Thus, low levels of PABPN1 protein could predispose specific tissues to pathology in OPMD. However, no studies have defined the mechanisms that regulate Pabpn1 expression. Here, we define multiple cis-regulatory elements and a trans-acting factor, HuR, which regulate Pabpn1 expression specifically in mature muscle in vitro and in vivo. We exploit multiple models including C2C12 myotubes, primary muscle cells, and mice to determine that HuR decreases Pabpn1 expression. Overall, we have uncovered a mechanism in mature muscle that negatively regulates Pabpn1 expression in vitro and in vivo, which could provide insight to future studies investigating therapeutic strategies for OPMD treatment.
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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 | 0.001 |
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".