The ADAR1 editome reveals drivers of editing-specificity for ADAR1-isoforms.

Adenosine deaminase acting on RNA ADAR1 promotes A-to-I conversion in double-stranded and structured RNAs. ADAR1 has two isoforms transcribed from different promoters: cytoplasmic ADAR1p150 is interferon-inducible while ADAR1p110 is constitutively expressed and primarily localized in the nucleus. Mutations in ADAR1 cause Aicardi - Goutières syndrome (AGS), a severe autoinflammatory disease associated with aberrant IFN production. In mice, deletion of ADAR1 or the p150 isoform leads to embryonic lethality driven by overexpression of interferon-stimulated genes. This phenotype is rescued by deletion of the cytoplasmic dsRNA-sensor MDA5 indicating that the p150 isoform is indispensable and cannot be rescued by ADAR1p110. Nevertheless, editing sites uniquely targeted by ADAR1p150 remain elusive. Here, by transfection of ADAR1 isoforms into ADAR-less mouse cells we detect isoform-specific editing patterns. Using mutated ADAR variants, we test how intracellular localization and the presence of a Z-DNA binding domain-α affect editing preferences. These data show that ZBDα only minimally contributes to p150 editing-specificity while isoform-specific editing is primarily directed by the intracellular localization of ADAR1 isoforms. Our study is complemented by RIP-seq on human cells ectopically expressing tagged-ADAR1 isoforms. Both datasets reveal enrichment of intronic editing and binding by ADAR1p110 while ADAR1p150 preferentially binds and edits 3'UTRs.

ADAR RNA editing on antisense RNAs results in apparent U-to-C base changes on overlapping sense transcripts.

Despite hundreds of RNA modifications described to date, only RNA editing results in a change in the nucleotide sequence of RNA molecules compared to the genome. In mammals, two kinds of RNA editing have been described so far, adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. Recent improvements in RNA sequencing technologies have led to the discovery of a continuously growing number of editing sites. These methods are powerful but not error-free, making routine validation of newly-described editing sites necessary. During one of these validations on DDX58 mRNA, along with A-to-I RNA editing sites, we encountered putative U-to-C editing. These U-to-C edits were present in several cell lines and appeared regulated in response to specific environmental stimuli. The same findings were also observed for the human long intergenic non-coding RNA p21 (hLincRNA-p21). A more in-depth analysis revealed that putative U-to-C edits result from A-to-I editing on overlapping antisense RNAs that are transcribed from the same loci. Such editing events, occurring on overlapping genes transcribed in opposite directions, have recently been demonstrated to be immunogenic and have been linked with autoimmune and immune-related diseases. Our findings, also confirmed by deep transcriptome data, demonstrate that such loci can be recognized simply through the presence of A-to-I and U-to-C mismatches within the same locus, reflective A-to-I editing both in the sense-oriented transcript and in the cis-natural antisense transcript (cis-NAT), implying that such clusters could be a mark of functionally relevant ADAR1 editing events.

RNA Editing Detection in HPC Infrastructures.

RNA editing by A-to-I deamination is a relevant co/posttranscriptional modification carried out by ADAR enzymes. In humans, it has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, the detection of RNA editing variants in large transcriptome sequencing experiments (RNAseq) is yet a challenging computational task. To drastically reduce computing times we have developed a novel REDItools version able to identify A-to-I events in huge amount of RNAseq data employing High Performance Computing (HPC) infrastructures.Here we show how to use REDItools v2 in HPC systems.

Databases for RNA Editing Collections.

A-to-I RNA editing in humans plays a relevant role since it can influence gene expression and increase proteome diversity. In addition, its deregulation has been linked to a variety of human diseases, including neurological disorders and cancer.In the last decade, massive transcriptome sequencing through the RNAseq technology has dramatically improved the investigation of RNA editing at single nucleotide resolution. Nowadays, different bioinformatics resources to discover and/or collect A-to-I events have been released. Hereafter, we initially provide an overview of the state-of-the-art RNA editing databases and, then, we focus on REDIportal, the largest collection of A-to-I events with more than 4.5 million sites from 2660 humans GTEx samples.

High-Throughput Sequencing to Detect DNA-RNA Changes.

The advent of deep sequencing technologies has greatly improved the study of complex eukaryotic genomes and transcriptomes, allowing the investigation of posttranscriptional molecular mechanisms as alternative splicing and RNA editing at unprecedented throughput and resolution. The most prevalent type of RNA editing in higher eukaryotes is the deamination of adenosine to inosine (A-to-I) in double-stranded RNAs. Depending on the RNA type or the RNA region involved, A-to-I RNA editing contributes to the transcriptome and proteome diversity.Hereafter, we present an easy and reproducible computational protocol for the identification of candidate RNA editing sites in humans using deep transcriptome (RNA-Seq) and genome (DNA-Seq) sequencing.

Quantifying RNA Editing in Deep Transcriptome Datasets.

Massive transcriptome sequencing through the RNAseq technology has enabled quantitative transcriptome-wide investigation of co-/post-transcriptional mechanisms such as alternative splicing and RNA editing. The latter is abundant in human transcriptomes in which million adenosines are deaminated into inosines by the ADAR enzymes. RNA editing modulates the innate immune response and its deregulation has been associated with different human diseases including autoimmune and inflammatory pathologies, neurodegenerative and psychiatric disorders, and tumors. Accurate profiling of RNA editing using deep transcriptome data is still a challenge, and the results depend strongly on processing and alignment steps taken. Accurate calling of the inosinome repertoire, however, is required to reliably quantify RNA editing and, in turn, investigate its biological and functional role across multiple samples. Using real RNAseq data, we demonstrate the impact of different bioinformatics steps on RNA editing detection and describe the main metrics to quantify its level of activity.

Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal.

RNA editing is a widespread post-transcriptional mechanism able to modify transcripts through insertions/deletions or base substitutions. It is prominent in mammals, in which millions of adenosines are deaminated to inosines by members of the ADAR family of enzymes. A-to-I RNA editing has a plethora of biological functions, but its detection in large-scale transcriptome datasets is still an unsolved computational task. To this aim, we developed REDItools, the first software package devoted to the RNA editing profiling in RNA-sequencing (RNAseq) data. It has been successfully used in human transcriptomes, proving the tissue and cell type specificity of RNA editing as well as its pervasive nature. Outcomes from large-scale REDItools analyses on human RNAseq data have been collected in our specialized REDIportal database, containing more than 4.5 million events. Here we describe in detail two bioinformatic procedures based on our computational resources, REDItools and REDIportal. In the first procedure, we outline a workflow to detect RNA editing in the human cell line NA12878, for which transcriptome and whole genome data are available. In the second procedure, we show how to identify dysregulated editing at specific recoding sites in post-mortem brain samples of Huntington disease donors. On a 64-bit computer running Linux with ≥32 GB of random-access memory (RAM), both procedures should take ~76 h, using 4 to 24 cores. Our protocols have been designed to investigate RNA editing in different organisms with available transcriptomic and/or genomic reads. Scripts to complete both procedures and a docker image are available at https://github.com/BioinfoUNIBA/REDItools.

REDIportal: a comprehensive database of A-to-I RNA editing events in humans.

RNA editing by A-to-I deamination is the prominent co-/post-transcriptional modification in humans. It is carried out by ADAR enzymes and contributes to both transcriptomic and proteomic expansion. RNA editing has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, many physiological and functional aspects of RNA editing are yet elusive. Here, we present REDIportal, available online at http://srv00.recas.ba.infn.it/atlas/, the largest and comprehensive collection of RNA editing in humans including more than 4.5 millions of A-to-I events detected in 55 body sites from thousands of RNAseq experiments. REDIportal embeds RADAR database and represents the first editing resource designed to answer functional questions, enabling the inspection and browsing of editing levels in a variety of human samples, tissues and body sites. In contrast with previous RNA editing databases, REDIportal comprises its own browser (JBrowse) that allows users to explore A-to-I changes in their genomic context, empathizing repetitive elements in which RNA editing is prominent.

Meningiomas and Proteomics: Focus on New Potential Biomarkers and Molecular Pathways.

Meningiomas are one of the most common tumors affecting the central nervous system, exhibiting a great heterogeneity in grading, treatment and molecular background. This article provides an overview of the current literature regarding the molecular aspect of meningiomas. Analysis of potential biomarkers in serum, cerebrospinal fluid (CSF) and pathological tissues was reported. Applying bioinformatic methods and matching the common proteic profile, arising from different biological samples, we highlighted the role of nine proteins, particularly related to tumorigenesis and grading of meningiomas: serpin peptidase inhibitor alpha 1, ceruloplasmin, hemopexin, albumin, C3, apolipoprotein, haptoglobin, amyloid-P-component serum and alpha-1-beta-glycoprotein. These proteins and their associated pathways, including complement and coagulation cascades, plasma lipoprotein particle remodeling and lipid metabolism could be considered possible diagnostic, prognostic biomarkers, and eventually therapeutic targets. Further investigations are needed to better characterize the role of these proteins and pathways in meningiomas. The role of new therapeutic strategies are also discussed.