Deep transcriptome profiling reveals limited conservation of A-to-I RNA editing in Xenopus.

BACKGROUND: Xenopus has served as a valuable model system for biomedical research over the past decades. Notably, ADAR was first detected in frog oocytes and embryos as an activity that unwinds RNA duplexes. However, the scope of A-to-I RNA editing by the ADAR enzymes in Xenopus remains underexplored. RESULTS: Here, we identify millions of editing events in Xenopus with high accuracy and systematically map the editome across developmental stages, adult organs, and species. We report diverse spatiotemporal patterns of editing with deamination activity highest in early embryogenesis before zygotic genome activation and in the ovary. Strikingly, editing events are poorly conserved across different Xenopus species. Even sites that are detected in both X. laevis and X. tropicalis show largely divergent editing levels or developmental profiles. In protein-coding regions, only a small subset of sites that are found mostly in the brain are well conserved between frogs and mammals. CONCLUSIONS: Collectively, our work provides fresh insights into ADAR activity in vertebrates and suggest that species-specific editing may play a role in each animal's unique physiology or environmental adaptation.

Direct identification of A-to-I editing sites with nanopore native RNA sequencing.

Inosine is a prevalent RNA modification in animals and is formed when an adenosine is deaminated by the ADAR family of enzymes. Traditionally, inosines are identified indirectly as variants from Illumina RNA-sequencing data because they are interpreted as guanosines by cellular machineries. However, this indirect method performs poorly in protein-coding regions where exons are typically short, in non-model organisms with sparsely annotated single-nucleotide polymorphisms, or in disease contexts where unknown DNA mutations are pervasive. Here, we show that Oxford Nanopore direct RNA sequencing can be used to identify inosine-containing sites in native transcriptomes with high accuracy. We trained convolutional neural network models to distinguish inosine from adenosine and guanosine, and to estimate the modification rate at each editing site. Furthermore, we demonstrated their utility on the transcriptomes of human, mouse and Xenopus. Our approach expands the toolkit for studying adenosine-to-inosine editing and can be further extended to investigate other RNA modifications.