Frataxin deficiency in Friedreich's ataxia is associated with reduced levels of HAX-1, a regulator of cardiomyocyte death and survival.

Frataxin deficiency, responsible for Friedreich's ataxia (FRDA), is crucial for cell survival since it critically affects viability of neurons, pancreatic beta cells and cardiomyocytes. In FRDA, the heart is frequently affected with typical manifestation of hypertrophic cardiomyopathy, which can progress to heart failure and cause premature death. A microarray analysis performed on FRDA patient's lymphoblastoid cells stably reconstituted with frataxin, indicated HS-1-associated protein X-1 (HAX-1) as the most significantly upregulated transcript (FC = +2, P < 0.0006). quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) and western blot analysis performed on (I) HEK293 stably transfected with empty vector compared to wild-type frataxin and (II) lymphoblasts from FRDA patients show that low frataxin mRNA and protein expression correspond to reduced levels of HAX-1. Frataxin overexpression and silencing were also performed in the AC16 human cardiomyocyte cell line. HAX-1 protein levels are indeed regulated through frataxin modulation. Moreover, correlation between frataxin and HAX-1 was further evaluated in peripheral blood mononuclear cells (PBMCs) from FRDA patients and from non-related healthy controls. A regression model for frataxin which included HAX-1, group membership and group* HAX-1 interaction revealed that frataxin and HAX-1 are associated both at mRNA and protein levels. Additionally, a linked expression of FXN, HAX-1 and antioxidant defence proteins MnSOD and Nrf2 was observed both in PBMCs and AC16 cardiomyocytes. Our results suggest that HAX-1 could be considered as a potential biomarker of cardiac disease in FRDA and the evaluation of its expression might provide insights into its pathogenesis as well as improving risk stratification strategies.

AGO2 promotes telomerase activity and interaction between the telomerase components TERT and TERC.

Telomerase reverse transcriptase (TERT) and telomerase RNA component (TERC) constitute the core telomerase enzyme that maintains the length of telomeres. Telomere maintenance is affected in a broad range of cancer and degenerative disorders. Taking advantage of gain- and loss-of-function approaches, we show that Argonaute 2 (AGO2) promotes telomerase activity and stimulates the association between TERT and TERC AGO2 depletion results in shorter telomeres as well as in lower proliferation rates in vitro and in vivo We also demonstrate that AGO2 interacts with TERC and with a newly identified sRNA (terc-sRNA), arising from the H/ACA box of TERC Notably, terc-sRNA is sufficient to enhance telomerase activity when overexpressed. Analyses of sRNA-Seq datasets show that terc-sRNA is detected in primary human tissues and increases in tumors as compared to control tissues. Collectively, these data uncover a new layer of complexity in the regulation of telomerase activity by AGO2 and might lay the foundation for new therapeutic targets in tumors and telomere diseases.

HPC-REDItools: a novel HPC-aware tool for improved large scale RNA-editing analysis.

BACKGROUND: RNA editing is a widespread co-/post-transcriptional mechanism that alters primary RNA sequences through the modification of specific nucleotides and it can increase both the transcriptome and proteome diversity. The automatic detection of RNA-editing from RNA-seq data is computational intensive and limited to small data sets, thus preventing a reliable genome-wide characterisation of such process. RESULTS: In this work we introduce HPC-REDItools, an upgraded tool for accurate RNA-editing events discovery from large dataset repositories. AVAILABILITY: https://github.com/BioinfoUNIBA/REDItools2 . CONCLUSIONS: HPC-REDItools is dramatically faster than the previous version, REDItools, enabling big-data analysis by means of a MPI-based implementation and scaling almost linearly with the number of available cores.

ELIXIR-IT HPC@CINECA: high performance computing resources for the bioinformatics community.

BACKGROUND: The advent of Next Generation Sequencing (NGS) technologies and the concomitant reduction in sequencing costs allows unprecedented high throughput profiling of biological systems in a cost-efficient manner. Modern biological experiments are increasingly becoming both data and computationally intensive and the wealth of publicly available biological data is introducing bioinformatics into the "Big Data" era. For these reasons, the effective application of High Performance Computing (HPC) architectures is becoming progressively more recognized also by bioinformaticians. Here we describe HPC resources provisioning pilot programs dedicated to bioinformaticians, run by the Italian Node of ELIXIR (ELIXIR-IT) in collaboration with CINECA, the main Italian supercomputing center. RESULTS: Starting from April 2016, CINECA and ELIXIR-IT launched the pilot Call "ELIXIR-IT HPC@CINECA", offering streamlined access to HPC resources for bioinformatics. Resources are made available either through web front-ends to dedicated workflows developed at CINECA or by providing direct access to the High Performance Computing systems through a standard command-line interface tailored for bioinformatics data analysis. This allows to offer to the biomedical research community a production scale environment, continuously updated with the latest available versions of publicly available reference datasets and bioinformatic tools. Currently, 63 research projects have gained access to the HPC@CINECA program, for a total handout of ~ 8 Millions of CPU/hours and, for data storage, ~ 100 TB of permanent and ~ 300 TB of temporary space. CONCLUSIONS: Three years after the beginning of the ELIXIR-IT HPC@CINECA program, we can appreciate its impact over the Italian bioinformatics community and draw some considerations. Several Italian researchers who applied to the program have gained access to one of the top-ranking public scientific supercomputing facilities in Europe. Those investigators had the opportunity to sensibly reduce computational turnaround times in their research projects and to process massive amounts of data, pursuing research approaches that would have been otherwise difficult or impossible to undertake. Moreover, by taking advantage of the wealth of documentation and training material provided by CINECA, participants had the opportunity to improve their skills in the usage of HPC systems and be better positioned to apply to similar EU programs of greater scale, such as PRACE. To illustrate the effective usage and impact of the resources awarded by the program - in different research applications - we report five successful use cases, which have already published their findings in peer-reviewed journals.

DROPA: DRIP-seq optimized peak annotator.

BACKGROUND: R-loops are three-stranded nucleic acid structures that usually form during transcription and that may lead to gene regulation or genome instability. DRIP (DNA:RNA Immunoprecipitation)-seq techniques are widely used to map R-loops genome-wide providing insights into R-loop biology. However, annotation of DRIP-seq peaks to genes can be a tricky step, due to the lack of strand information when using the common basic DRIP technique. RESULTS: Here, we introduce DRIP-seq Optimized Peak Annotator (DROPA), a new tool for gene annotation of R-loop peaks based on gene expression information. DROPA allows a full customization of annotation options, ranging from the choice of reference datasets to gene feature definitions. DROPA allows to assign R-loop peaks to the DNA template strand in gene body with a false positive rate of less than 7%. A comparison of DROPA performance with three widely used annotation tools show that it identifies less false positive annotations than the others. CONCLUSIONS: DROPA is a fully customizable peak-annotation tool optimized for co-transcriptional DRIP-seq peaks, which allows a finest gene annotation based on gene expression information. Its output can easily be integrated into pipelines to perform downstream analyses, while useful and informative summary plots and statistical enrichment tests can be produced.

CoVaCS: a consensus variant calling system.

BACKGROUND: The advent and ongoing development of next generation sequencing technologies (NGS) has led to a rapid increase in the rate of human genome re-sequencing data, paving the way for personalized genomics and precision medicine. The body of genome resequencing data is progressively increasing underlining the need for accurate and time-effective bioinformatics systems for genotyping - a crucial prerequisite for identification of candidate causal mutations in diagnostic screens. RESULTS: Here we present CoVaCS, a fully automated, highly accurate system with a web based graphical interface for genotyping and variant annotation. Extensive tests on a gold standard benchmark data-set -the NA12878 Illumina platinum genome- confirm that call-sets based on our consensus strategy are completely in line with those attained by similar command line based approaches, and far more accurate than call-sets from any individual tool. Importantly our system exhibits better sensitivity and higher specificity than equivalent commercial software. CONCLUSIONS: CoVaCS offers optimized pipelines integrating state of the art tools for variant calling and annotation for whole genome sequencing (WGS), whole-exome sequencing (WES) and target-gene sequencing (TGS) data. The system is currently hosted at Cineca, and offers the speed of a HPC computing facility, a crucial consideration when large numbers of samples must be analysed. Importantly, all the analyses are performed automatically allowing high reproducibility of the results. As such, we believe that CoVaCS can be a valuable tool for the analysis of human genome resequencing studies. CoVaCS is available at: https://bioinformatics.cineca.it/covacs .

PeachVar-DB: A Curated Collection of Genetic Variations for the Interactive Analysis of Peach Genome Data.

Applying next-generation sequencing (NGS) technologies to species of agricultural interest has the potential to accelerate the understanding and exploration of genetic resources. The storage, availability and maintenance of huge quantities of NGS-generated data remains a major challenge. The PeachVar-DB portal, available at http://hpc-bioinformatics.cineca.it/peach, is an open-source catalog of genetic variants present in peach (Prunus persica L. Batsch) and wild-related species of Prunus genera, annotated from 146 samples publicly released on the Sequence Read Archive (SRA). We designed a user-friendly web-based interface of the database, providing search tools to retrieve single nucleotide polymorphism (SNP) and InDel variants, along with useful statistics and information. PeachVar-DB results are linked to the Genome Database for Rosaceae (GDR) and the Phytozome database to allow easy access to other external useful plant-oriented resources. In order to extend the genetic diversity covered by the PeachVar-DB further, and to allow increasingly powerful comparative analysis, we will progressively integrate newly released data.

ARGONAUTE2 cooperates with SWI/SNF complex to determine nucleosome occupancy at human Transcription Start Sites.

Argonaute (AGO) proteins have a well-established role in post-transcriptional regulation of gene expression as key component of the RNA silencing pathways. Recent evidence involves AGO proteins in mammalian nuclear processes such as transcription and splicing, though the mechanistic aspects of AGO nuclear functions remain largely elusive. Here, by SILAC-based interaction proteomics, we identify the chromatin-remodelling complex SWI/SNF as a novel AGO2 interactor in human cells. Moreover, we show that nuclear AGO2 is loaded with a novel class of Dicer-dependent short RNAs (sRNAs), that we called swiRNAs, which map nearby the Transcription Start Sites (TSSs) bound by SWI/SNF. The knock-down of AGO2 decreases nucleosome occupancy at the first nucleosome located downstream of TSSs in a swiRNA-dependent manner. Our findings indicate that in human cells AGO2 binds SWI/SNF and a novel class of sRNAs to establish nucleosome occupancy on target TSSs.

An integrated analysis of multiple datasets reveals novel gene signatures in human granulosa cells.

Granulosa cells (GCs) play crucial roles in oocyte maturation. Through gap junctions and extracellular vesicles, they mediate the exchange of molecules such as microRNAs and messenger RNAs. Different ovarian cell types exhibit unique gene expression profiles, reflecting their specialized functions and stages. By combining RNA-seq data from various cell types forming the follicle, we aimed at capturing a wide range of expression patterns, offering insights into the functional diversity and complexity of the transcriptome regulation across GCs. Herein, we performed an integrated bioinformatics analysis of RNA sequencing datasets present in public databases, with a unique and standardized workflow., By combining the data from different studies, we successfully increased the robustness and reliability of our findings and discovered novel genes, miRNAs, and signaling pathways associated with GCs function and oocyte maturation. Moreover, our results provide a valuable resource for further wet-lab research on GCs biology and their impact on oocyte development and competence.

Dissecting the Gene Expression Networks Associated with Variations in the Major Components of the Fatty Acid Semimembranosus Muscle Profile in Large White Heavy Pigs.

To date, high-throughput technology such as RNA-sequencing has been successfully applied in livestock sciences to investigate molecular networks involved in complex traits, such as meat quality. Pork quality depends on several organoleptic, technological, and nutritional characteristics, and it is also influenced by the fatty acid (FA) composition of intramuscular fat (IMF). To explore the molecular networks associated with different IMF FA compositions, the Semimembranosus muscle (SM) from two groups of Italian Large White (ILW) heavy pigs divergent for SM IMF content was investigated using transcriptome analysis. After alignment and normalization, the obtained gene counts were used to perform the Weighted Correlation Network Analysis (WGCNA package in R environment). Palmitic and palmitoleic contents showed association with the same gene modules, comprising genes significantly enriched in autophagy, mitochondrial fusion, and mitochondrial activity. Among the key genes related to these FAs, we found TEAD4, a gene regulating mitochondrial activity that seems to be a promising candidate for further studies. On the other hand, the genes comprised in the modules associated with the IMF contents of oleic, n-6, and n-3 polyunsaturated FAs (PUFAs) were significantly enriched in Mitogen-Activated Protein Kinase (MAPK) signaling, in agreement with previous studies suggesting that several MAPK players may have a primary role in regulating lipid deposition. These results give an insight into the molecular cascade associated with different IMF FA composition in ILW heavy pigs. Further studies are needed to validate the results and confirm whether some of the identified key genes may be effective candidates for pork quality.

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.

Muscle transcriptome analysis identifies genes involved in ciliogenesis and the molecular cascade associated with intramuscular fat content in Large White heavy pigs.

Intramuscular fat content (IMF) is a complex trait influencing the technological and sensorial features of meat products and determining pork quality. Thus, we aimed at analyzing through RNA-sequencing the Semimembranosus muscle transcriptome of Italian Large White pigs to study the gene networks associated with IMF deposition. Two groups of samples were used; each one was composed of six unrelated pigs with extreme and divergent IMF content (0.67 ± 0.09% in low IMF vs. 6.81 ± 1.17% in high IMF groups) that were chosen from 950 purebred individuals. Paired-end RNA sequences were aligned to Sus scrofa genome assembly 11.1 and gene counts were analyzed using WGCNA and DeSeq2 packages in R environment. Interestingly, among the 58 differentially expressed genes (DEGs), several were related to primary cilia organelles (such as Lebercilin 5 gene), in addition to the genes involved in the regulation of cell differentiation, in the control of RNA-processing, and G-protein and ERK signaling pathways. Together with cilia-related genes, we also found in high IMF pigs an over-expression of the Fibroblast Growth Factor 2 (FGF2) gene, which in other animal species was found to be a regulator of ciliogenesis. Four WGCNA gene modules resulted significantly associated with IMF deposition: grey60 (P = 0.003), darkturquoise (P = 0.022), skyblue1 (P = 0.022), and lavenderblush3 (P = 0.030). The genes in the significant modules confirmed the results obtained for the DEGs, and the analysis with "cytoHubba" indicated genes controlling RNA splicing and cell differentiation as hub genes. Among the complex molecular processes affecting muscle fat depots, genes involved in primary cilia may have an important role, and the transcriptional reprogramming observed in high IMF pigs may be related to an FGF-related molecular cascade and to ciliogenesis, which in the literature have been associated with fibro-adipogenic precursor differentiation.

MicroRNAs diagnostic and prognostic value as predictive markers for malignant mesothelioma.

Malignant mesothelioma is an aggressive tumor resistant to current therapies with a latency period ranging between 20 and 60 years, caused by inhalation of asbestos fibers, that continues to represent a social and healthcare issue. The high percentage of people exposed to asbestos for professional or environmental reasons is associated with the high biopersistence of its fibers and with its widespread use in the last century. Approximately 20-40% of men report an occupational history that might have caused the workplace exposure (criteria Helsinki, 1997). Some authors are evaluating the possible use of bioindicators as a screening and early diagnosis tool. In this regard, the use of microRNAs has been proposed as powerful diagnostic and prognostic biomarkers for many tumors and human diseases. This review focuses on the current state of knowledge on the key role of microRNAs expression as new malignant mesothelioma biomarkers, in early clinical diagnostic applications.

A gene expression atlas for different kinds of stress in the mouse brain.

Stressful experiences are part of everyday life and animals have evolved physiological and behavioral responses aimed at coping with stress and maintaining homeostasis. However, repeated or intense stress can induce maladaptive reactions leading to behavioral disorders. Adaptations in the brain, mediated by changes in gene expression, have a crucial role in the stress response. Recent years have seen a tremendous increase in studies on the transcriptional effects of stress. The input raw data are freely available from public repositories and represent a wealth of information for further global and integrative retrospective analyses. We downloaded from the Sequence Read Archive 751 samples (SRA-experiments), from 18 independent BioProjects studying the effects of different stressors on the brain transcriptome in mice. We performed a massive bioinformatics re-analysis applying a single, standardized pipeline for computing differential gene expression. This data mining allowed the identification of novel candidate stress-related genes and specific signatures associated with different stress conditions. The large amount of computational results produced was systematized in the interactive "Stress Mice Portal".

Gallop Racing Shifts Mature mRNA towards Introns: Does Exercise-Induced Stress Enhance Genome Plasticity?

Physical exercise is universally recognized as stressful. Among the "sport species", the horse is probably the most appropriate model for investigating the genomic response to stress due to the homogeneity of its genetic background. The aim of this work is to dissect the whole transcription modulation in Peripheral Blood Mononuclear Cells (PBMCs) after exercise with a time course framework focusing on unexplored regions related to introns and intergenic portions. PBMCs NGS from five 3 year old Sardinian Anglo-Arab racehorses collected at rest and after a 2000 m race was performed. Apart from differential gene expression ascertainment between the two time points the complexity of transcription for alternative transcripts was identified. Interestingly, we noted a transcription shift from the coding to the non-coding regions. We further investigated the possible causes of this phenomenon focusing on genomic repeats, using a differential expression approach and finding a strong general up-regulation of repetitive elements such as LINE. Since their modulation is also associated with the "exonization", the recruitment of repeats that act with regulatory functions, suggesting that there might be an active regulation of this transcriptional shift. Thanks to an innovative bioinformatic approach, our study could represent a model for the transcriptomic investigation of stress.

Differential Expression of Hippocampal Circular RNAs in the BTBR Mouse Model for Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition with unknown etiology. Recent experimental evidences suggest the contribution of non-coding RNAs (ncRNAs) in the pathophysiology of ASD. In this work, we aimed to investigate the expression profile of the ncRNA class of circular RNAs (circRNAs) in the hippocampus of the BTBR T + tf/J (BTBR) mouse model and age-matched C57BL/6J (B6) mice. Alongside, we analyzed BTBR hippocampal gene expression profile to evaluate possible correlations between the differential abundance of circular and linear gene products. From RNA sequencing data, we identified circRNAs highly modulated in BTBR mice. Thirteen circRNAs and their corresponding linear isoforms were validated by RT-qPCR analysis. The BTBR-regulated circCdh9 was better characterized in terms of molecular structure and expression, highlighting altered levels not only in the hippocampus, but also in the cerebellum, prefrontal cortex, and amygdala. Finally, gene expression analysis of the BTBR hippocampus pinpointed altered biological and molecular pathways relevant for the ASD phenotype. By comparison of circRNA and gene expression profiles, we identified 6 genes significantly regulated at either circRNA or mRNA gene products, suggesting low overall correlation between circRNA and host gene expression. In conclusion, our results indicate a consistent deregulation of circRNA expression in the hippocampus of BTBR mice. ASD-related circRNAs should be considered in functional studies to identify their contribution to the etiology of the disorder. In addition, as abundant and highly stable molecules, circRNAs represent interesting potential biomarkers for autism.

Characterization of Transcription Termination-Associated RNAs: New Insights into their Biogenesis, Tailing, and Expression in Primary Tumors.

Next-generation sequencing has uncovered novel classes of small RNAs (sRNAs) in eukaryotes, in addition to the well-known miRNAs, siRNAs, and piRNAs. In particular, sRNA species arise from transcription start sites (TSSs) and the transcription termination sites (TTSs) of genes. However, a detailed characterization of these new classes of sRNAs is still lacking. Here, we present a comprehensive study of sRNAs derived from TTSs of expressed genes (TTSa-RNAs) in human cell lines and primary tissues. Taking advantage of sRNA-sequencing, we show that TTSa-RNAs are present in the nuclei of human cells, are loaded onto both AGO1 and AGO2, and their biogenesis does not require DICER and AGO2 endonucleolytic activity. TTSa-RNAs display a strong bias against a G residue in the first position at 5' end, a known feature of AGO-bound sRNAs, and a peculiar oligoA tail at 3' end. AGO-bound TTSa-RNAs derive from genes involved in cell cycle progression regulation and DNA integrity checkpoints. Finally, we provide evidence that TTSa-RNAs can be detected by sRNA-Seq in primary human tissue, and their expression increases in tumor samples as compared to nontumor tissues, suggesting that in the future, TTSa-RNAs might be explored as biomarker for diagnosis or prognosis of human malignancies.

Massive NGS data analysis reveals hundreds of potential novel gene fusions in human cell lines.

Background: Gene fusions derive from chromosomal rearrangements. The resulting chimeric transcripts are often endowed with oncogenic potential. Furthermore, they serve as diagnostic tools for the clinical classification of cancer subgroups with different prognosis and, in some cases, they can provide specific drug targets. To date, many efforts have been carried out to study gene fusion events occurring in tumor samples. In recent years, the availability of a comprehensive next-generation sequencing dataset for all existing human tumor cell lines has provided the opportunity to further investigate these data in order to identify novel and still uncharacterized gene fusion events. Results: In our work, we have extensively reanalyzed 935 paired-end RNA-sequencing experiments downloaded from the Cancer Cell Line Encyclopedia repository, aiming at addressing novel putative cell-line specific gene fusion events in human malignancies. The bioinformatics analysis has been performed by the execution of four gene fusion detection algorithms. The results have been further prioritized by running a Bayesian classifier that makes an in silico validation. The collection of fusion events supported by all of the predictive software results in a robust set of ∼1,700 in silico predicted novel candidates suitable for downstream analyses. Given the huge amount of data and information produced, computational results have been systematized in a database named LiGeA. The database can be browsed through a dynamic and interactive web portal, further integrated with validated data from other well-known repositories. Taking advantage of the intuitive query forms, the users can easily access, navigate, filter, and select the putative gene fusions for further validations and studies. They can also find suitable experimental models for a given fusion of interest. Conclusions: We believe that the LiGeA resource can represent not only the first compendium of both known and putative novel gene fusion events in the catalog of all of the human malignant cell lines but it can also become a handy starting point for wet-lab biologists who wish to investigate novel cancer biomarkers and specific drug targets.

Microarray dataset of Jurkat cells following miR-93 over-expression.

The dataset presented here represents a microarray experiment of Jurkat cell line over-expressing miR-93 after lentiviral transgenic construct transduction. Three biological replicates have been performed. We further provide normalized and processed data, log2 Fold Change based ranked list and GOterms resulting table. The raw microarray data are available in the ArrayExpress database (www.ebi.ac.uk/arrayexpress) under accession number ArrayExpress: E-MTAB-4588.

microRNA-181a enhances cell proliferation in acute lymphoblastic leukemia by targeting EGR1.

Acute lymphoblastic leukemia (ALL) is an aggressive cancer that occurs in both children and adults. Starting from an integrated analysis of miRNA/mRNA expression profiles in 20 ALL patients, we identify a negative correlation between miR-181a and EGR1. Coherently, miR-181a over-expression in Jurkat T-ALL cells decreases EGR1 expression, increasing cell proliferation and enhancing the cell-cycle progression from G1 to S phase. We show that EGR1 is a new direct target of miR-181a. Our findings suggest that miR-181a behaves as an onco-miRNA in ALL by down-regulating EGR1.