A comparative analysis of ADAR mutant mice reveals site-specific regulation of RNA editing
Bibliographic record
Abstract
Adenosine-to-inosine RNA editing is an essential post-transcriptional modification catalyzed by adenosine deaminase acting on RNA (ADAR)1 and ADAR2 in mammals. For numerous sites in coding sequences (CDS) and microRNAs, editing is highly conserved and has significant biological consequences, for example, by altering amino acid residues and target recognition. However, no comprehensive and quantitative studies have been undertaken to determine how specific ADARs contribute to conserved sites in vivo. Here, we amplified each RNA region with editing site(s) separately and combined these for deep sequencing. Then, we compared the editing ratios of all sites that were conserved in CDS and microRNAs 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 most of the sites showed a preference for one ADAR. In contrast, some sites, such as miR-3099-3p, showed no ADAR preference. In addition, we found that the editing ratio for several sites, such as DACT3 R/G, was up-regulated in either Adar mutant mouse strain, whereas a coordinated interplay between ADAR1 and ADAR2 was required for the efficient editing of specific sites, such as the 5-HT 2C R B site. We further created double mutant Adar1 KI Adar2 KO mice and observed viable and fertile animals with the complete absence of editing, demonstrating 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".