Biology of ReproductionDevelopmental BiologyPeer-reviewed journal article
ADAR1 A-to-I RNA editing alters codon usage
Pavla BrachovaiD, Nehemiah S. AlvareziD, Xiaoman HongiD, Sumedha GunewardenaiD, Kailey A. VincentiD, Keith E. LathamiD, Lane K. ChristensoniD
Abstract
BackgroundFully grown mammalian oocytes and eggs are transcriptionally quiescent, and therefore have a unique RNA environment in which cellular processes depend on post-transcriptional regulation. RNA editing of adenosines into inosines (A-to-I) by adenosine deaminases acting on RNA (ADARs) is a common post-transcriptional gene regulatory mechanism, yet it has not been systematically studied in oocytes.\n\nResultsA genome-wide RNA editing analysis of transcriptionally active growing oocytes from postnatal day 12 (PND12) mice, fully grown germinal vesicle (GV) oocytes, and transcriptionally quiescent metaphase II (MII) eggs indicates an abundant amount of A-to-I editing of mRNA transcripts. Editing of mRNA was greatest in GV oocyte and MII eggs compared to the PND12 immature oocytes, this was consistent with ADAR1 levels within these cells. Compared to somatic tissues, oocytes exhibited a different pattern of RNA editing, with a high proportion of RNA edits occurring in the coding regions. These edits resulted in nucleotide substitutions that were enriched at the third nucleotide of the codon (wobble position). Codon usage can affect mRNA stability and translation efficiency.\n\nConclusionsRNA editing in mouse oocytes is distinct from RNA editing in somatic cells due to increased frequencies of coding sequence RNA edits. We provide evidence in support of a previously unreported phenomenon of selective ADAR1 editing of the codon wobble position. Editing of the wobble position has the potential to fine tune post-transcriptional gene regulation through altering codon usage. This important observation advances our current understanding of RNA editing in mammalian cells.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 15
- FWCI
- 0.58
- Field percentile
- top 38%
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