Evolutionarily conserved A-to-I editing increases protein stability of the alternative splicing factor<i>Nova1</i>
Bibliographic record
Abstract
The structural complexity of the vertebrate brain is mirrored by its unparalleled transcriptome complexity. In particular, two post-transcriptional processes, alternative splicing and RNA editing, greatly diversify brain transcriptomes. Here we report a close connection between these two processes: we show A-to-I RNA editing in Nova1, a key brain-specific regulator of alternative splicing. Nova1 editing levels increase during embryonic development in mouse and chicken brains and show significant variation across postnatal brain regions. Evolutionary conservation of both editing and editing-associated RNA secondary structure of the Nova1 mRNA for 300 million years attests to the functional importance of Nova1 editing. Using a combination of different assays in human HEK293T cell lines, we report a novel post-translational role for this RNA editing. Whereas functional assays showed no effect of RNA editing on the regulatory splicing activity of the encoded proteins, we found evidence that edited forms exhibit reduced proteasome targeting and increased protein half-life. In addition, we found evidence for similar regulation of protein half-life by an evolutionarily conserved alternative splicing event in Nova1. These results open new venues of research on the multi-level integration of gene expression by: (1) revealing the novel role of RNA editing in regulating protein stability, and (2) establishing protein stability as a new target of multifaceted regulation.
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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".