Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Abstract

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs (dsRNAs). How the RNA sequence and structure (i.e., the cis-regulation) determine the editing efficiency and specificity is poorly understood, despite a compelling need towards functional understanding of known editing events and transcriptome engineering of desired adenosines. We developed a CRISPR/Cas9-mediated saturation mutagenesis approach to generate comprehensive libraries of point mutations near an editing site and its editing complementary sequence (ECS) at the endogenous genomic locus. We used machine learning to integrate diverse RNA sequence features and computationally predicted structures to model editing levels measured by deep sequencing and identified cis-regulatory features of RNA editing. As proof-of-concept, we applied this integrative approach to three editing substrates. Our models explained over 70% of variation in editing levels. The models indicate that RNA sequence and structure features synergistically determine the editing levels.Our integrative approach can be broadly applied to any editing site towards the goal of deciphering the RNA editing code. It also provides guidance for designing and screening of antisense RNA sequences that form dsRNA duplex with the target transcript for ADAR-mediated transcriptome engineering.

Deciphering the principles of the RNA editing code via large-scale systematic probing

“…However, some reports indicate that dsRNA with a few mismatches could be a better substrate than perfect double strands (Br€ ummer et al, 2017;Solomon et al, 2017) and that RNA tertiary structure may play a role as well (Reenan, 2005;Tian et al, 2011). Attempts to characterize the effect of structural variations on editing levels have been partially successful for individual sites, but could not provide a comprehensive, globally applicable model (Liu et al, 2019). Overall, the known sequence and structural features, even when combined, fail to explain or predict the highly complex editing landscapes, suggesting the involvement of more remote sequence and/or structural elements in dictating editing state and editing levels.…”

The predictive value of double-stranded RNA for A-to-I editing detection

“…Indeed, Liu et al . exploited the predicted secondary structure in their analysis and reported that RNA structure is one of the key features for predicting A-to-I editing sites (Liu et al ., 2019). Nonetheless, the double-stranded nature of edited transcripts is not widely used for editing prediction, with examples of incorporating such knowledge into predictive models being rare to non-existent.…”