Detection of a-to-i rna editing in sars-cov-2

ADAR1-mediated deamination of adenosines in long double-stranded RNAs plays an important role in modulating the innate immune response. However, recent investigations based on metatranscriptomic samples of COVID-19 patients and SARS-COV-2-infected Vero cells have recovered contrasting findings. Using RNAseq data from time course experiments of infected human cell lines and transcriptome data from Vero cells and clinical samples, we prove that A-to-G changes observed in SARS-COV-2 genomes represent genuine RNA editing events, likely mediated by ADAR1. While the A-to-I editing rate is generally low, changes are distributed along the entire viral genome, are overrepresented in exonic regions, and are (in the majority of cases) nonsynonymous. The impact of RNA editing on virus–host interactions could be relevant to identify potential targets for therapeutic interventions

The cell line A-to-I RNA editing catalogue

Adenosine-to-inosine (A-to-I) RNA editing is a common post transcriptional modification. It has a critical role in protecting against false activation of innate immunity by endogenous double stranded RNAs and has been associated with various regulatory processes and diseases such as autoimmune and cardiovascular diseases as well as cancer. In addition, the endogenous A-to-I editing machinery has been recently harnessed for RNA engineering. The study of RNA editing in humans relies heavily on the usage of cell lines as an important and commonly-used research tool. In particular, manipulations of the editing enzymes and their targets are often developed using cell line platforms. However, RNA editing in cell lines behaves very differently than in normal and diseased tissues, and most cell lines exhibit low editing levels, requiring over-expression of the enzymes. Here, we explore the A-to-I RNA editing landscape across over 1000 human cell lines types and show that for almost every editing target of interest a suitable cell line that mimics normal tissue condition may be found. We provide CLAIRE, a searchable catalogue of RNA editing levels across cell lines available at http://srv00.recas.ba.infn.it/atlas/claire.html, to facilitate rational choice of appropriate cell lines for future work on A-to-I RNA editing

High number of circulating CD34+ cells in patients with myelophthisis.

Hematopoietic Stem Cells High number of circulating CD34+ cells in patients with myelophthisis Six patients with bone marrow micrometastases from solid cancers presented with increased numbers of circulating CD34+ cells; the CD34+ cell counts were very high in some cases. By contrast, no patient with metastatic cancer without bone marrow involvement showed raised numbers of circulating hemopoietic progenitors. haematologica 2005; 90:976-977 (http:/

Investigating human mitochondrial genomes in single cells

Mitochondria host multiple copies of their own small circular genome that has been extensively studied to trace the evolution of the modern eukaryotic cell and discover important mutations linked to inherited diseases. Whole genome and exome sequencing have enabled the study of mtDNA in a large number of samples and experimental conditions at single nucleotide resolution, allowing the deciphering of the relationship between inherited mutations and phenotypes and the identification of acquired mtDNA mutations in classical mitochondrial diseases as well as in chronic disorders, ageing and cancer. By applying an ad hoc computational pipeline based on our MToolBox software, we reconstructed mtDNA genomes in single cells using whole genome and exome sequencing data obtained by different amplification methodologies (eWGA, DOP-PCR, MALBAC, MDA) as well as data from single cell Assay for Transposase Accessible Chromatin with high-throughput sequencing (scATAC-seq) in which mtDNA sequences are expected as a byproduct of the technology. We show that assembled mtDNAs, with the exception of those reconstructed by MALBAC and DOP-PCR methods, are quite uniform and suitable for genomic investigations, enabling the study of various biological processes related to cellular heterogeneity such as tumor evolution, neural somatic mosaicism and embryonic development

Refusing to Endorse. A must Explanation for Pejoratives.

In her analysis of pejoratives, Eva Picardi rejects a too sharp separation between descriptive and expressive content. I reconstruct some of her arguments, endorsing Eva’s criticism of Williamson’s analysis of Dummett and developing a suggestion by Manuel Garcia Carpintero on a speech act analysis of pejoratives. Eva’s main concern is accounting for our instinctive refusal to endorse an assertion containing pejoratives because it suggests a picture of reality we do not share. Her stance might be further developed claiming that uses of pejoratives not only suggest, but also promote a wrong picture of reality. Our refusal to endorse implies rejecting not only a wrong picture of reality but also a call for participation to what that picture promotes

Measurement of spleen volume by ultrasound scanning in patients with thrombocytosis: a prospective study.

Spleen size was assessed in 73 patients with thrombocytosis and in 15 healthy subjects, comparing palpation with ultra-sonography (US) measurement of longitudinal diameter and volume. Intraobserver and interobserver variability for volume on US, checked in 12 patients, was very low. Correlation between spleen volume measured by US and that measured by computed tomography was excellent. Splenomegaly was detected by palpation in 25% of patients, by US assessment of longitudinal diameter in 33%, and by US assessment of volume in 52%. After diagnostic work-up, 54 patients had a diagnosis of essential thrombocythemia (ET), 4 of idiopathic myelofibrosis (IMF), and 15 of secondary thrombocytosis (ST). Spleen volume in patients with ST was in the normal range (138 ± 47 mL) and was significantly lower than that in patients with ET or IMF (370 ± 210 mL; P < .001). Thus, US-measured volume was the most sensitive method for identifying nonpalpable splenomegaly in patients with primary myeloproliferative diseases, and it may help in distinguishing these diseases from reactive disorder

Evaluation of the Interplay between the ADAR Editome and Immunotherapy in Melanoma.

RNA editing is a highly conserved posttranscriptional mechanism that contributes to transcriptome diversity. In mammals, it includes nucleobase deaminations that convert cytidine (C) into uridine (U) and adenosine (A) into inosine (I). Evidence from cancer studies indicates that RNA-editing enzymes promote certain mechanisms of tumorigenesis. On the other hand, recoding editing in mRNA can generate mutations in proteins that can participate in the Major Histocompatibility Complex (MHC) ligandome and can therefore be recognized by the adaptive immune system. Anti-cancer treatment based on the administration of immune checkpoint inhibitors enhance these natural anti-cancer immune responses. Based on RNA-Seq datasets, we evaluated the editome of melanoma cell lines generated from patients pre- and post-immunotherapy with immune checkpoint inhibitors. Our results reveal a differential editing in Arthrobacter luteus (Alu) sequences between samples pre-therapy and relapses during therapy with immune checkpoint inhibitors. These data pave the way towards the development of new diagnostics and therapies targeted to editing that could help in preventing relapses during immunotherapies

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

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

A decade of movement ecology

Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. Recent advances in tracking technology have enabled fundamental questions about movement to be tackled, leading to the development of the movement ecology framework (MEF), considered a milestone in the field [1]. The MEF introduced an integrative theory of organismal movement, linking internal state, motion capacity and navigation capacity to external factors. Here, a decade later, we investigated the current state of research in the field. Using a text mining approach on >8000 peer-reviewed papers in movement ecology, we explored the main research topics, evaluated the impact of the MEF, and assessed changes in the use of technological devices, software and statistical methods. The number of publications has increased considerably and there have been major technological changes in the past decade (i.e.~increased use of GPS devices, accelerometers and video cameras, and a convergence towards R), yet we found that research focuses on the same questions, specifically, on the effect of environmental factors on movement and behavior. In practice, it appears that movement ecology research does not reflect the MEF. We call on researchers to transform the field from technology-driven to embrace interdisciplinary collaboration, in order to reveal key processes underlying movement (e.g.~navigation), as well as evolutionary, physiological and life-history consequences of particular strategies