A novel computational strategy to identify A-to-I RNA editing sites by RNA-Seq data: de novo detection in human spinal cord tissue

Bioinformatics Resources for RNA Editing

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or processing of noncoding RNAs (ncRNAs). In recent years, the detection of editing sites has been greatly improved through the availability of high-throughput RNA sequencing technologies. Several pipelines, employing various read mappers and variant callers with a wide range of adjustable parameters, are currently available for the detection of RNA editing events. Hereafter, we describe some of the most recent and popular tools and provide guidelines for the detection of RNA editing in massive transcriptome data.

Elucidating the editome: bioinformatics approaches for RNA editing detection

RNA editing is a widespread co/posttranscriptional mechanism affecting primary RNAs by specific nucleotide modifications, which plays relevant roles in molecular processes including regulation of gene expression and/or the processing of noncoding RNAs. In recent years, the detection of editing sites has been improved through the availability of high-throughput RNA sequencing (RNA-Seq) technologies. Accurate bioinformatics pipelines are essential for the analysis of next-generation sequencing (NGS) data to ensure the correct identification of edited sites. Several pipelines, using various read mappers and variant callers with a wide range of adjustable parameters, are available for the detection of RNA editing events. In this review, we discuss some of the most recent and popular tools and provide guidelines for RNA-Seq data generation and analysis for the detection of RNA editing in massive transcriptome data. Using simulated and real data sets, we provide an overview of their behavior, emphasizing the fact that the RNA editing detection in NGS data sets remains a challenging task.

RNA editing in plants: A comprehensive survey of bioinformatics tools and databases

RNA editing is a widespread epitranscriptomic mechanism by which primary RNAs are specifically modified through insertions/deletions or nucleotide substitutions. In plants, RNA editing occurs in organelles (plastids and mitochondria), involves the cytosine to uridine modification (rarely uridine to cytosine) within protein-coding and non-protein-coding regions of RNAs and affects organelle biogenesis, adaptation to environmental changes and signal transduction. High-throughput sequencing technologies have dramatically improved the detection of RNA editing sites at genomic scale. Consequently, different bioinformatics resources have been released to discovery and/or collect novel events. Here, we review and describe the state-of-the-art bioinformatics tools devoted to the characterization of RNA editing in plant organelles with the aim to improve our knowledge about this fascinating but yet under investigated process.

Modern Approaches for Identification of Modified Nucleotides in RNA

This review considers approaches for detection of modified monomers in the RNA structure of living organisms. Recently, some data on dynamic alterations in the pool of modifications of the key RNA species that depend on external factors affecting the cells and physiological conditions of the whole organism have been accumulated. The recent studies have presented experimental data on relationship between the mechanisms of formation of modified/minor nucleotides of RNA in mammalian cells and the development of various pathologies. The development of novel methods for detection of chemical modifications of RNA nucleotides in the cells of living organisms and accumulation of knowledge on the contribution of modified monomers to metabolism and functioning of individual RNA species establish the basis for creation of novel diagnostic and therapeutic approaches. This review includes a short description of routine methods for determination of modified nucleotides in RNA and considers in detail modern approaches that enable not only detection but also quantitative assessment of the modification level of various nucleotides in individual RNA species.

Going global: the new era of mapping modifications in RNA

The post-transcriptional modification of RNA by the addition of one or more chemical groups has been known for over 50 years. These chemical modifications, once thought to be static, are now being discovered to play key regulatory roles in gene expression. The advent of massive parallel sequencing of RNA (RNA-seq) now allows us to probe the complexity of cellular RNA and how chemically altering RNA structure expands the RNA vocabulary. Here we present an overview of the various strategies and technologies that are available to profile RNA chemical modifications at the cellular level. These strategies can be characterized as targeted and untargeted approaches: targeted strategies are developed for one single chemical modification while untargeted strategies are more broadly applicable to a range of such chemical changes. Key for all of these approaches is the ability to locate modifications within the RNA sequence. While most of these methods are built upon an RNA-Seq pipeline, alternative approaches based on mass spectrometry or conventional DNA sequencing retain value in the overall analysis process. We also look forward toward future opportunities and technologies that may expand the types of modifications that can be globally profiled. Given the ever increasing recognition that these RNA chemical modifications play important biological roles, a variety of methods, preferably orthogonal approaches, will be required to globally identify, validate and quantify RNA chemical modifications found in the transcriptome. WIREs RNA 2017, 8:e1367. doi: 10.1002/wrna.1367 For further resources related to this article, please visit the WIREs website.

Reduced levels of protein recoding by A-to-I RNA editing in Alzheimer's disease

Adenosine to inosine (A-to-I) RNA editing, catalyzed by the ADAR enzyme family, acts on dsRNA structures within pre-mRNA molecules. Editing of the coding part of the mRNA may lead to recoding, amino acid substitution in the resulting protein, possibly modifying its biochemical and biophysical properties. Altered RNA editing patterns have been observed in various neurological pathologies. Here, we present a comprehensive study of recoding by RNA editing in Alzheimer's disease (AD), the most common cause of irreversible dementia. We have used a targeted resequencing approach supplemented by a microfluidic-based high-throughput PCR coupled with next-generation sequencing to accurately quantify A-to-I RNA editing levels in a preselected set of target sites, mostly located within the coding sequence of synaptic genes. Overall, editing levels decreased in AD patients' brain tissues, mainly in the hippocampus and to a lesser degree in the temporal and frontal lobes. Differential RNA editing levels were observed in 35 target sites within 22 genes. These results may shed light on a possible association between the neurodegenerative processes typical for AD and deficient RNA editing.

The Extent of mRNA Editing Is Limited in Chicken Liver and Adipose, but Impacted by Tissular Context, Genotype, Age, and Feeding as Exemplified with a Conserved Edited Site in COG3

RNA editing is a posttranscriptional process leading to differences between genomic DNA and transcript sequences, potentially enhancing transcriptome diversity. With recent advances in high-throughput sequencing, many efforts have been made to describe mRNA editing at the transcriptome scale, especially in mammals, yielding contradictory conclusions regarding the extent of this phenomenon. We show, by detailed description of the 25 studies focusing so far on mRNA editing at the whole-transcriptome scale, that systematic sequencing artifacts are considered in most studies whereas biological replication is often neglected and multi-alignment not properly evaluated, which ultimately impairs the legitimacy of results. We recently developed a rigorous strategy to identify mRNA editing using mRNA and genomic DNA sequencing, taking into account sequencing and mapping artifacts, and biological replicates. We applied this method to screen for mRNA editing in liver and white adipose tissue from eight chickens and confirm the small extent of mRNA recoding in this species. Among the 25 unique edited sites identified, three events were previously described in mammals, attesting that this phenomenon is conserved throughout evolution. Deeper investigations on five sites revealed the impact of tissular context, genotype, age, feeding conditions, and sex on mRNA editing levels. More specifically, this analysis highlighted that the editing level at the site located on COG3 was strongly regulated by four of these factors. By comprehensively characterizing the mRNA editing landscape in chickens, our results highlight how this phenomenon is limited and suggest regulation of editing levels by various genetic and environmental factors.

Building an RNA Sequencing Transcriptome of the Central Nervous System

The composition and function of the central nervous system (CNS) is extremely complex. In addition to hundreds of subtypes of neurons, other cell types, including glia (astrocytes, oligodendrocytes, and microglia) and vascular cells (endothelial cells and pericytes) also play important roles in CNS function. Such heterogeneity makes the study of gene transcription in CNS challenging. Transcriptomic studies, namely the analyses of the expression levels and structures of all genes, are essential for interpreting the functional elements and understanding the molecular constituents of the CNS. Microarray has been a predominant method for large-scale gene expression profiling in the past. However, RNA-sequencing (RNA-Seq) technology developed in recent years has many advantages over microarrays, and has enabled building more quantitative, accurate, and comprehensive transcriptomes of the CNS and other systems. The discovery of novel genes, diverse alternative splicing events, and noncoding RNAs has remarkably expanded the complexity of gene expression profiles and will help us to understand intricate neural circuits. Here, we discuss the procedures and advantages of RNA-Seq technology in mammalian CNS transcriptome construction, and review the approaches of sample collection as well as recent progress in building RNA-Seq-based transcriptomes from tissue samples and specific cell types.

Profiling RNA editing in human tissues: towards the inosinome Atlas

Adenine to Inosine RNA editing is a widespread co- and post-transcriptional mechanism mediated by ADAR enzymes acting on double stranded RNA. It has a plethora of biological effects, appears to be particularly pervasive in humans with respect to other mammals, and is implicated in a number of diverse human pathologies. Here we present the first human inosinome atlas comprising 3,041,422 A-to-I events identified in six tissues from three healthy individuals. Matched directional total-RNA-Seq and whole genome sequence datasets were generated and analysed within a dedicated computational framework, also capable of detecting hyper-edited reads. Inosinome profiles are tissue specific and edited gene sets consistently show enrichment of genes involved in neurological disorders and cancer. Overall frequency of editing also varies, but is strongly correlated with ADAR expression levels. The inosinome database is available at: http://srv00.ibbe.cnr.it/editing/.

A-to-I RNA Editing: Current Knowledge Sources and Computational Approaches with Special Emphasis on Non-Coding RNA Molecules

RNA editing is a dynamic mechanism for gene regulation attained through the alteration of the sequence of primary RNA transcripts. A-to-I (adenosine-to-inosine) RNA editing, which is catalyzed by members of the adenosine deaminase acting on RNA (ADAR) family of enzymes, is the most common post-transcriptional modification in humans. The ADARs bind double-stranded regions and deaminate adenosine (A) into inosine (I), which in turn is interpreted by the translation and splicing machineries as guanosine (G). In recent years, this modification has been discovered to occur not only in coding RNAs but also in non-coding RNAs (ncRNA), such as microRNAs, small interfering RNAs, transfer RNAs, and long non-coding RNAs. This may have several consequences, such as the creation or disruption of microRNA/mRNA binding sites, and thus affect the biogenesis, stability, and target recognition properties of ncRNAs. The malfunction of the editing machinery is not surprisingly associated with various human diseases, such as neurodegenerative, cardiovascular, and carcinogenic diseases. Despite the enormous efforts made so far, the real biological function of this phenomenon, as well as the features of the ADAR substrate, in particular in non-coding RNAs, has still not been fully understood. In this work, we focus on the current knowledge of RNA editing on ncRNA molecules and provide a few examples of computational approaches to elucidate its biological function.

Knowledge in the Investigation of A-to-I RNA Editing Signals

RNA editing is a post-transcriptional alteration of RNA sequences that is able to affect protein structure as well as RNA and protein expression. Adenosine-to-inosine (A-to-I) RNA editing is the most frequent and common post-transcriptional modification in human, where adenosine (A) deamination produces its conversion into inosine (I), which in turn is interpreted by the translation and splicing machineries as guanosine (G). The disruption of the editing machinery has been associated to various human diseases such as cancer or neurodegenerative diseases. This biological phenomenon is catalyzed by members of the adenosine deaminase acting on RNA (ADAR) family of enzymes and occurs on dsRNA structures. Despite the enormous efforts made in the last decade, the real biological function underlying such a phenomenon, as well as ADAR's substrate features still remain unknown. In this work, we summarize the major computational aspects of predicting and understanding RNA editing events. We also investigate the detection of short motif sequences potentially characterizing RNA editing signals and the use of a logistic regression technique to model a predictor of RNA editing events. The latter, named AIRlINER, an algorithmic approach to assessment of A-to-I RNA editing sites in non-repetitive regions, is available as a web app at: http://alpha.dmi.unict.it/airliner/. Results and comparisons with the existing methods encourage our findings on both aspects.

Predicting A-to-I RNA editing by feature selection and random forest

RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and MCC (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and MCC were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.

A role of ADAR2 and RNA editing of glutamate receptors in mood disorders and schizophrenia

Background

Pre-mRNAs of 2-amino-3-(3-hydroxy-5-methyl-isoxazol-4-yl)-propanoic acid (AMPA)/kainate glutamate receptors undergo post-transcriptional modification known as RNA editing that is mediated by adenosine deaminase acting on RNA type 2 (ADAR2). This modification alters the amino acid sequence and function of the receptor. Glutamatergic signaling has been suggested to have a role in mood disorders and schizophrenia, but it is unknown whether altered RNA editing of AMPA/kainate receptors has pathophysiological significance in these mental disorders. In this study, we found that ADAR2 expression tended to be decreased in the postmortem brains of patients with schizophrenia and bipolar disorder.

Results

Decreased ADAR2 expression was significantly correlated with decreased editing of the R/G sites of AMPA receptors. In heterozygous Adar2 knockout mice (Adar2^+/− mice), editing of the R/G sites of AMPA receptors was decreased. Adar2^+/− mice showed a tendency of increased activity in the open-field test and a tendency of resistance to immobility in the forced swimming test. They also showed enhanced amphetamine-induced hyperactivity. There was no significant difference in amphetamine-induced hyperactivity between Adar2^+/− and wild type mice after the treatment with an AMPA/kainate receptor antagonist, 2,3-dihydroxy-6-nitro-7-sulfamoyl-benzo[f]quinoxaline.

Conclusions

These findings collectively suggest that altered RNA editing efficiency of AMPA receptors due to down-regulation of ADAR2 has a possible role in the pathophysiology of mental disorders.

REDItools: high-throughput RNA editing detection made easy

SUMMARY

The reliable detection of RNA editing sites from massive sequencing data remains challenging and, although several methodologies have been proposed, no computational tools have been released to date. Here, we introduce REDItools a suite of python scripts to perform high-throughput investigation of RNA editing using next-generation sequencing data.

AVAILABILITY AND IMPLEMENTATION

REDItools are in python programming language and freely available at http://code.google.com/p/reditools/.

CONTACT

ernesto.picardi@uniba.it or graziano.pesole@uniba.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.