Regulation of A-to-I RNA editing and stop codon recoding to control selenoprotein expression during skeletal myogenesis
Bibliographic record
Abstract
Abstract Selenoprotein N (SELENON), a selenocysteine (Sec)-containing protein with high reductive activity, maintains redox homeostasis, thereby contributing to skeletal muscle differentiation and function. Loss-of-function mutations in SELENON cause severe neuromuscular disorders. In the early-to-middle stage of myoblast differentiation, SELENON maintains redox homeostasis and modulates endoplasmic reticulum (ER) Ca 2+ concentration, resulting in a gradual reduction from the middle-to-late stages due to unknown mechanisms. The present study describes post-transcriptional mechanisms that regulate SELENON expression during myoblast differentiation. Part of an Alu element in the second intron of SELENON pre-mRNA is frequently exonized during splicing, resulting in an aberrant mRNA that is degraded by nonsense-mediated mRNA decay (NMD). In the middle stage of myoblast differentiation, ADAR1-mediated A-to-I RNA editing occurs in the U1 snRNA binding site at 5′ splice site, preventing Alu exonization and producing mature mRNA. In the middle-to-late stage of myoblast differentiation, the level of Sec-charged tRNA Sec decreases due to downregulation of essential recoding factors for Sec insertion, thereby generating a premature termination codon in SELENON mRNA, which is targeted by NMD.
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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".