Conservation of A-to-I RNA editing in bowhead whale and pig

RNA editing is a post-transcriptional process in which nucleotide changes are introduced into an RNA sequence, many of which can contribute to proteomic sequence variation. The most common type of RNA editing, contributing to nearly 99% of all editing events in RNA, is A-to-I (adenosine-to-inosine) editing mediated by double-stranded RNA-specific adenosine deaminase (ADAR) enzymes. A-to-I editing at ‘recoding’ sites results in non-synonymous substitutions in protein-coding sequences. Here, we present studies of the conservation of A-to-I editing in selected mRNAs between pigs, bowhead whales, humans and two shark species. All examined mRNAs–NEIL1, COG3, GRIA2, FLNA, FLNB, IGFBP7, AZIN1, BLCAP, GLI1, SON, HTR2C and ADAR2 –showed conservation of A-to-I editing of recoding sites. In addition, novel editing sites were identified in NEIL1 and GLI1 in bowhead whales. The A-to-I editing site of human NEIL1 in position 242 was conserved in the bowhead and porcine homologues. A novel editing site was discovered in Tyr244. Differential editing was detected at the two adenosines in the NEIL1 242 codon in both pig and bowhead NEIL1 mRNAs in various tissues and organs. No conservation of editing of KCNB1 and EEF1A mRNAs was seen in bowhead whales. In silico analyses revealed conservation of five adenosines in ADAR2, some of which are subject to A-to-I editing in bowheads and pigs, and conservation of a regulatory sequence in GRIA2 mRNA that is responsible for recognition of the ADAR editing enzyme.

RESIC: A Tool for Comprehensive Adenosine to Inosine RNA Editing Site Identification and Classification

Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here, we present RNA Editing Sites Identification and Classification (RESIC), an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides (1) the detection of editing sites in both repetitive and non-repetitive genomic regions; (2) the identification of hyper-edited regions; and (3) optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

A hierarchy in clusters of cephalopod mRNA editing sites

RNA editing in the form of substituting adenine to inosine (A-to-I editing) is the most frequent type of RNA editing, observed in many metazoan species. A-to-I editing sites form clusters in most studied species, and editing at clustered sites depends on editing of the adjacent sites. Although functionally important in some specific cases, A-to-I editing in most considered species is rare, the exception being soft-bodied cephalopods (coleoids), where tens of thousands of potentially important A-to-I editing sites have been identified, making coleoids an ideal object for studying of general properties and evolution of A-to-I editing sites. Here, we apply several diverse techniques to demonstrate a strong tendency of coleoid RNA editing sites to cluster along the transcript. We identify three distinct types of editing site clusters, varying in size, and describe RNA structural features and mechanisms likely underlying formation of these clusters. In particular, these observations may resolve the paradox of sequence conservation at large distances around editing sites.

An Editing-Site-Specific PCR Method for Detection and Quantification of CAO1-Edited Rice

Genome-edited plants created by genome editing technology have been approved for commercialization. Due to molecular characteristics that differ from classic genetically modified organisms (GMOs), establishing regulation-compliant analytical methods for identification and quantification of genome-edited plants has always been regarded as a challenging task. An editing-site-specific PCR method was developed based on the unique edited sequence in CAO1-edited rice plants. Test results of seven primer/probe sets indicated that this method can identify specific CAO1-edited rice from other CAO1-edited rice and wild types of rice with high specificity and sensitivity. The use of LNA (locked nucleic acid) in a probe can efficiently increase the specificity of the editing-site-specific PCR method at increased annealing temperature which can eliminate non-specific amplification of the non-target. The genome-edited ingredient content in blinded samples at the level of 0.1% to 5.0% was accurately quantified by this method on the ddPCR platform with RSD of <15% and bias in the range of ±17%, meeting the performance requirements for GMO detection method. The developed editing-site-specific PCR method presents a promising detection and quantification technique for genome-edited plants with known edited sequence.

RESIC: A tool for comprehensive adenosine to inosine RNA Editing Site Identification and Classification

Adenosine to inosine (A-to-I) RNA editing, the most prevalent type of RNA editing in metazoans, is carried out by adenosine deaminases (ADARs) in double-stranded RNA regions. Several computational approaches have been recently developed to identify A-to-I RNA editing sites from sequencing data, each addressing a particular issue. Here we present RESIC, an efficient pipeline that combines several approaches for the detection and classification of RNA editing sites. The pipeline can be used for all organisms and can use any number of RNA-sequencing datasets as input. RESIC provides 1. The detection of editing sites in both repetitive and non-repetitive genomic regions; 2. The identification of hyper-edited regions; 3. Optional exclusion of polymorphism sites to increase reliability, based on DNA, and ADAR-mutant RNA sequencing datasets, or SNP databases. We demonstrate the utility of RESIC by applying it to human, successfully overlapping and extending the list of known putative editing sites. We further tested changes in the patterns of A-to-I RNA editing, and RNA abundance of ADAR enzymes, following SARS-CoV-2 infection in human cell lines. Our results suggest that upon SARS-CoV-2 infection, compared to mock, the number of hyper editing sites is increased, and in agreement, the activity of ADAR1, which catalyzes hyper-editing, is enhanced. These results imply the involvement of A-to-I RNA editing in conceiving the unpredicted phenotype of COVID-19 disease. RESIC code is open-source and is easily extendable.

Adaptive evolution at mRNA editing sites in soft-bodied cephalopods

Background The bulk of variability in mRNA sequence arises due to mutation—change in DNA sequence which is heritable if it occurs in the germline. However, variation in mRNA can also be achieved by post-transcriptional modification including mRNA editing, changes in mRNA nucleotide sequence that mimic the effect of mutations. Such modifications are not inherited directly; however, as the processes affecting them are encoded in the genome, they have a heritable component, and therefore can be shaped by selection. In soft-bodied cephalopods, adenine-to-inosine RNA editing is very frequent, and much of it occurs at nonsynonymous sites, affecting the sequence of the encoded protein. Methods We study selection regimes at coleoid A-to-I editing sites, estimate the prevalence of positive selection, and analyze interdependencies between the editing level and contextual characteristics of editing site. Results Here, we show that mRNA editing of individual nonsynonymous sites in cephalopods originates in evolution through substitutions at regions adjacent to these sites. As such substitutions mimic the effect of the substitution at the edited site itself, we hypothesize that they are favored by selection if the inosine is selectively advantageous to adenine at the edited position. Consistent with this hypothesis, we show that edited adenines are more frequently substituted with guanine, an informational analog of inosine, in the course of evolution than their unedited counterparts, and for heavily edited adenines, these transitions are favored by positive selection. Our study shows that coleoid editing sites may enhance adaptation, which, together with recent observations on Drosophila and human editing sites, points at a general role of RNA editing in the molecular evolution of metazoans.

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

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.