Sodium Selenide Toxicity Is Mediated by O2-Dependent DNA Breaks

Hydrogen selenide is a recurrent metabolite of selenium compounds. However, few experiments studied the direct link between this toxic agent and cell death. To address this question, we first screened a systematic collection of Saccharomyces cerevisiae haploid knockout strains for sensitivity to sodium selenide, a donor for hydrogen selenide (H2Se/HSe−/Se2−). Among the genes whose deletion caused hypresensitivity, homologous recombination and DNA damage checkpoint genes were over-represented, suggesting that DNA double-strand breaks are a dominant cause of hydrogen selenide toxicity. Consistent with this hypothesis, treatment of S. cerevisiae cells with sodium selenide triggered G2/M checkpoint activation and induced in vivo chromosome fragmentation. In vitro, sodium selenide directly induced DNA phosphodiester-bond breaks via an O2-dependent reaction. The reaction was inhibited by mannitol, a hydroxyl radical quencher, but not by superoxide dismutase or catalase, strongly suggesting the involvement of hydroxyl radicals and ruling out participations of superoxide anions or hydrogen peroxide. The •OH signature could indeed be detected by electron spin resonance upon exposure of a solution of sodium selenide to O2. Finally we showed that, in vivo, toxicity strictly depended on the presence of O2. Therefore, by combining genome-wide and biochemical approaches, we demonstrated that, in yeast cells, hydrogen selenide induces toxic DNA breaks through an O2-dependent radical-based mechanism

NG6: Integrated next generation sequencing storage and processing environment.

Chantier qualité GAInternational audienceABSTRACT: BACKGROUND: Next generation sequencing platforms are now well implanted in sequencing centres and some laboratories. Upcoming smaller scale machines such as the 454 junior from Roche or the MiSeq from Illumina will increase the number of laboratories hosting a sequencer. In such a context, it is important to provide these teams with an easily manageable environment to store and process the produced reads. RESULTS: We describe a user-friendly information system able to manage large sets of sequencing data. It includes, on one hand, a workflow environment already containing pipelines adapted to different input formats (sff, fasta, fastq and qseq), different sequencers (Roche 454, Illumina HiSeq) and various analyses (quality control, assembly, alignment, diversity studies,...) and, on the other hand, a secured web site giving access to the results. The connected user will be able to download raw and processed data and browse through the analysis result statistics. The provided workflows can easily be modified or extended and new ones can be added. Ergatis is used as a workflow building, running and monitoring system. The analyses can be run locally or in a cluster environment using Sun Grid Engine. CONCLUSIONS: NG6 is a complete information system designed to answer the needs of a sequencing platform. It provides a user-friendly interface to process, store and download high-throughput sequencing data

Prediction of SARS-CoV-2 Variant Lineages Using the S1-Encoding Region Sequence Obtained by PacBio Single-Molecule Real-Time Sequencing

International audienceThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the causal agent of the COVID-19 pandemic that emerged in late 2019. The outbreak of variants with mutations in the region encoding the spike protein S1 sub-unit that can make them more resistant to neutralizing or monoclonal antibodies is the main point of the current monitoring. This study examines the feasibility of predicting the variant lineage and monitoring the appearance of reported mutations by sequencing only the region encoding the S1 domain by Pacific Bioscience Single Molecule Real-Time sequencing (PacBio SMRT). Using the PacBio SMRT system, we successfully sequenced 186 of the 200 samples previously sequenced with the Illumina COVIDSeq (whole genome) system. PacBio SMRT detected mutations in the S1 domain that were missed by the COVIDseq system in 27/186 samples (14.5%), due to amplification failure. These missing positions included mutations that are decisive for lineage assignation, such as G142D (n = 11), N501Y (n = 6), or E484K (n = 2). The lineage of 172/186 (92.5%) samples was accurately determined by analyzing the region encoding the S1 domain with a pipeline that uses key positions in S1. Thus, the PacBio SMRT protocol is appropriate for determining virus lineages and detecting key mutations

Impact of embryonic heat exposure on bird methylome

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Preliminary results from Nanopore Q20+ sequencing

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Preliminary results from Nanopore Q20+ sequencing

International audienc

A new local score based method applied to behavior-divergent quail lines sequenced in pools precisely detects selection signatures on genes related to autism

Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans allows increasing the detection power, but haplotype-based methods require individual genotypes and are not applicable on pool-sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium, accumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal, avoiding windowing methods. This method provided results similar to haplotype-based methods on two benchmark data sets with individual genotypes. Results obtained for a divergent selection experiment on behavior in quail, where two lines were sequenced in pools, are precise and biologically coherent, while competing methods failed: our approach led to the detection of signals involving genes known to act on social responsiveness or autistic traits. This local score approach is general and can be applied to other genome-wide analyzes such as GWAS or genome scans for selection

An open-source tool to assess the carbon footprint of research

International audienceThe scrutiny over the carbon footprint of research and higher education has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies on which these estimates rely unfortunately prevents any aggregation or direct comparison. This is because carbon footprint assessments are very sensitive to key parameters (e.g., emission factors) or hypotheses (e.g., scopes). Hence, it is impossible to address fundamental questions such as: is the carbon footprint of research structurally different between disciplines? Are plane trips a major source of carbon emissions in academic research? Massive collection and curation of carbon footprint data, across a large array of research situations and disciplines, is hence an important, timely and necessary challenge to answer these questions. This paper presents a framework to collect and analyse large amounts of homogeneous research carbon emission data in a network of research entities at the national scale. It relies on an open-source web application, GES 1point5 , designed to estimate the carbon footprint of a department, research lab or team in any country of the world. Importantly, GES 1point5 is also designed to aggregate all input data and corresponding GHG emissions estimates into a comprehensive database. GES 1point5 therefore enables (i) the identification of robust local or national determinants of the carbon footprint of research and (ii) the estimation of the carbon footprint of the entire research sector at national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories in France is presented to illustrate the potential of the framework. It shows that the average emissions are 479 t CO2 e for a research lab and 3.6 t CO2 e for an average lab member (respectively 404 and 3.1 t CO2e without accounting for the indirect radiative effects of aviation), with the current scope of GES 1point5 . Availability and implementation: GES 1point5 is available online at http://labos1point5.org/ges- 1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/ labos1point5/l1p5-vuejs

Chromosomal distribution of large SVs.

<p>Histogram showing the distribution of SVs within bovine chromosomes. Deletions are shown in blue, inversions in red and tandem duplications in green.</p