Abstract
Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.
Highlights
Adenosine-to-inosine (A-to-I) RNA editing catalyzed by adenosine deaminase acting on RNA (ADAR) enzymes occurs in doublestranded RNAs
The mutations were introduced both in the strand containing the editing site (“editing strand”) and in the complementary sequence involved in forming the secondary structure, which we refer to as editing complementary sequence (ECS)
We found that compensatory double mutations in NEIL1 that did not affect secondary structure resulted in only minor reduction of editing levels (Fig. 4a–c)
Summary
Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in doublestranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. How RNA editing is regulated to determine its efficiency and specificity is poorly understood Both the primary sequence and secondary structure (i.e., cis-acting regulatory elements) have been proposed to regulate ADAR editing[4,11,12,13,14,15,16]. We combine CRISPR/Cas[9] genome engineering, nextgeneration sequencing, and machine learning to decipher cis-regulatory RNA sequence and structural elements that affect ADARmediated RNA editing. We use supervised machine learning to build predictive models of substrate-specific RNA editing levels based on a variety of cis-sequence and structural features. Our integrative approach, named predicting RNA editing using sequence and structure (PREUSS), lays the foundation for developing predictive models of RNA editing
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