Mitochondrial and Nuclear DNA Variants in Amyotrophic Lateral Sclerosis: Enrichment in the Mitochondrial Control Region and Sirtuin Pathway Genes in Spinal Cord Tissue.

Amyotrophic Lateral Sclerosis (ALS) is a progressive disease with prevalent mitochondrial dysfunctions affecting both upper and lower motor neurons in the motor cortex, brainstem, and spinal cord. Despite mitochondria having their own genome (mtDNA), in humans, most mitochondrial genes are encoded by the nuclear genome (nDNA). Our study aimed to simultaneously screen for nDNA and mtDNA genomes to assess for specific variant enrichment in ALS compared to control tissues. Here, we analysed whole exome (WES) and whole genome (WGS) sequencing data from spinal cord tissues, respectively, of 6 and 12 human donors. A total of 31,257 and 301,241 variants in nuclear-encoded mitochondrial genes were identified from WES and WGS, respectively, while mtDNA reads accounted for 73 and 332 variants. Despite technical differences, both datasets consistently revealed a specific enrichment of variants in the mitochondrial Control Region (CR) and in several of these genes directly associated with mitochondrial dynamics or with Sirtuin pathway genes within ALS tissues. Overall, our data support the hypothesis of a variant burden in specific genes, highlighting potential actionable targets for therapeutic interventions in ALS.

Structural and Comparative Analyses of Insects Suggest the Presence of an Ultra-Conserved Regulatory Element of the Genes Encoding Vacuolar-Type ATPase Subunits and Assembly Factors.

Gene and genome comparison represent an invaluable tool to identify evolutionarily conserved sequences with possible functional significance. In this work, we have analyzed orthologous genes encoding subunits and assembly factors of the V-ATPase complex, an important enzymatic complex of the vacuolar and lysosomal compartments of the eukaryotic cell with storage and recycling functions, respectively, as well as the main pump in the plasma membrane that energizes the epithelial transport in insects. This study involves 70 insect species belonging to eight insect orders. We highlighted the conservation of a short sequence in the genes encoding subunits of the V-ATPase complex and their assembly factors analyzed with respect to their exon-intron organization of those genes. This study offers the possibility to study ultra-conserved regulatory elements under an evolutionary perspective, with the aim of expanding our knowledge on the regulation of complex gene networks at the basis of organellar biogenesis and cellular organization.

Dysregulation of testis mRNA expression levels in hatchery-produced vs wild greater amberjack Seriola dumerili.

Reproductive dysfunctions have been recently documented in male greater amberjack Seriola dumerili caught from the wild and reared in captivity. In the present study, we compared testis transcriptome in wild fish (WILD), hatchery-produced fish with apparently normal spermatogenesis (Normal Farmed; NormalF) and hatchery-produced fish with evident reproductive dysfunction (Dysfunctional Farmed; DysF). Gene expression analysis identified 2157, 1985 and 74 differentially expressed genes (DEGs) in DysF vs WILD, NormalF vs DysF and NormalF vs WILD comparisons, respectively. In DysF, a dysregulation of several interconnected biological processes, including cell assembly, steroidogenesis and apoptosis was found. Gene enrichment of progesterone-mediated oocyte maturation, oocyte meiosis and cell cycle pathways were identified in the DysF vs NormalF comparison. Most of the DEGs involved in the enriched pathways were downregulated in DysF. The comparison of NormalF vs WILD showed that most of the DEGs were downregulated in NormalF, including a gene that encodes for a regulatory protein with a protective role in apoptosis regulation (ptpn6), indicating that spermatogenesis was dysfunctional also in the apparently "normal" hatchery-produced fish. Hence, rearing of male greater amberjack in captivity, from eggs produced by captive breeders, did not prevent the appearance of reproductive dysfunctions, and these dysfunctions involved several biological processes and metabolic pathways.

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.

UTRdb 2.0: a comprehensive, expert curated catalog of eukaryotic mRNAs untranslated regions.

The 5' and 3' untranslated regions of eukaryotic mRNAs (UTRs) play crucial roles in the post-transcriptional regulation of gene expression through the modulation of nucleo-cytoplasmic mRNA transport, translation efficiency, subcellular localization, and message stability. Since 1996, we have developed and maintained UTRdb, a specialized database of UTR sequences. Here we present UTRdb 2.0, a major update of UTRdb featuring an extensive collection of eukaryotic 5' and 3' UTR sequences, including over 26 million entries from over 6 million genes and 573 species, enriched with a curated set of functional annotations. Annotations include CAGE tags and polyA signals to label the completeness of 5' and 3'UTRs, respectively. In addition, uORFs and IRES are annotated in 5'UTRs as well as experimentally validated miRNA targets in 3'UTRs. Further annotations include evolutionarily conserved blocks, Rfam motifs, ADAR-mediated RNA editing events, and m6A modifications. A web interface allowing a flexible selection and retrieval of specific subsets of UTRs, selected according to a combination of criteria, has been implemented which also provides comprehensive download facilities. UTRdb 2.0 is accessible at http://utrdb.cloud.ba.infn.it/utrdb/.

A Machine Learning Approach to Parkinson's Disease Blood Transcriptomics.

The increased incidence and the significant health burden associated with Parkinson's disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.

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.

A primer on machine learning techniques for genomic applications.

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

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.

REDIportal: millions of novel A-to-I RNA editing events from thousands of RNAseq experiments.

RNA editing is a relevant epitranscriptome phenomenon able to increase the transcriptome and proteome diversity of eukaryotic organisms. ADAR mediated RNA editing is widespread in humans in which millions of A-to-I changes modify thousands of primary transcripts. RNA editing has pivotal roles in the regulation of gene expression or modulation of the innate immune response or functioning of several neurotransmitter receptors. Massive transcriptome sequencing has fostered the research in this field. Nonetheless, different aspects of the RNA editing biology are still unknown and need to be elucidated. To support the study of A-to-I RNA editing we have updated our REDIportal catalogue raising its content to about 16 millions of events detected in 9642 human RNAseq samples from the GTEx project by using a dedicated pipeline based on the HPC version of the REDItools software. REDIportal now allows searches at sample level, provides overviews of RNA editing profiles per each RNAseq experiment, implements a Gene View module to look at individual events in their genic context and hosts the CLAIRE database. Starting from this novel version, REDIportal will start collecting non-human RNA editing changes for comparative genomics investigations. The database is freely available at http://srv00.recas.ba.infn.it/atlas/index.html.

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.

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.

REDIdb 3.0: A Comprehensive Collection of RNA Editing Events in Plant Organellar Genomes.

RNA editing is an important epigenetic mechanism by which genome-encoded transcripts are modified by substitutions, insertions and/or deletions. It was first discovered in kinetoplastid protozoa followed by its reporting in a wide range of organisms. In plants, RNA editing occurs mostly by cytidine (C) to uridine (U) conversion in translated regions of organelle mRNAs and tends to modify affected codons restoring evolutionary conserved aminoacid residues. RNA editing has also been described in non-protein coding regions such as group II introns and structural RNAs. Despite its impact on organellar transcriptome and proteome complexity, current primary databases still do not provide a specific field for RNA editing events. To overcome these limitations, we developed REDIdb a specialized database for RNA editing modifications in plant organelles. Hereafter we describe its third release containing more than 26,000 events in a completely novel web interface to accommodate RNA editing in its genomics, biological and evolutionary context through whole genome maps and multiple sequence alignments. REDIdb is freely available at http://srv00.recas.ba.infn.it/redidb/index.html.

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.