A comparative analysis among ADAR mutant mice reveals site-specific regulation of RNA editing
Bibliographic record
Abstract
ABSTRACT Adenosine-to-inosine RNA editing is an essential posttranscriptional modification catalyzed by adenosine deaminase acting on RNA (ADAR)1 and ADAR2 in mammals. For numerous sites in coding sequences (CDS) and microRNAs (miRNAs), editing is highly conserved and has significant biological consequences, for example, by altering amino acid residues and target recognition. However, technical limitations have prevented a comprehensive and quantitative study to determine how specific ADARs contribute to each site. Here, we developed a simple method in which each RNA region with an editing site was amplified separately and combined for deep sequencing. Using this method, we compared the editing ratios of all sites that were either definitely or possibly conserved in CDS and miRNAs in the cerebral cortex and spleen of wild-type mice, Adar1 E861A/E861A Ifih −/− mice expressing inactive ADAR1 (Adar1 KI) and Adar2 −/− Gria2 R/R (Adar2 KO) mice. We found that the editing ratio was frequently upregulated in either Adar mutant mouse strain. In contrast, we found that the presence of both ADAR1 and ADAR2 was required for the efficient editing of specific sites. In addition, some sites, such as miR-3099-3p, showed no preference for either ADAR. We further created double mutant Adar1 KI Adar2 KO mice and observed viable and fertile animals with complete absence of editing, suggesting that ADAR1 and ADAR2 are the sole enzymes responsible for all editing sites in vivo . Collectively, these findings indicate that editing is regulated in a site-specific manner by the different interplay between ADAR1 and ADAR2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".