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      Multi-level inhibition of coronavirus replication by chemical ER stress

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          Abstract

          Coronaviruses (CoVs) are important human pathogens for which no specific treatment is available. Here, we provide evidence that pharmacological reprogramming of ER stress pathways can be exploited to suppress CoV replication. The ER stress inducer thapsigargin efficiently inhibits coronavirus (HCoV-229E, MERS-CoV, SARS-CoV-2) replication in different cell types including primary differentiated human bronchial epithelial cells, (partially) reverses the virus-induced translational shut-down, improves viability of infected cells and counteracts the CoV-mediated downregulation of IRE1α and the ER chaperone BiP. Proteome-wide analyses revealed specific pathways, protein networks and components that likely mediate the thapsigargin-induced antiviral state, including essential (HERPUD1) or novel (UBA6 and ZNF622) factors of ER quality control, and ER-associated protein degradation complexes. Additionally, thapsigargin blocks the CoV-induced selective autophagic flux involving p62/SQSTM1. The data show that thapsigargin hits several central mechanisms required for CoV replication, suggesting that this compound (or derivatives thereof) may be developed into broad-spectrum anti-CoV drugs.

          Abstract

          Here, Shaban et al. show that coronaviruses modulate ER stress and the unfolded protein response. The ER stress inducer thapsigargin exerts potent antiviral effects, partially reverses the virus-induced translational shut-down, reprograms the host proteome and suppresses autophagic flux, thereby inhibiting coronavirus replication at multiple levels.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              A pneumonia outbreak associated with a new coronavirus of probable bat origin

              Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats 1–4 . Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans 5–7 . Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of SARSr-CoV. In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV.
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                Author and article information

                Contributors
                john.ziebuhr@viro.med.uni-giessen.de
                michael.kracht@pharma.med.uni-giessen.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 September 2021
                20 September 2021
                2021
                : 12
                : 5536
                Affiliations
                [1 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Rudolf Buchheim Institute of Pharmacology, , Justus Liebig University, ; Giessen, Germany
                [2 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Institute of Medical Virology, , Justus Liebig University, ; Giessen, Germany
                [3 ]GRID grid.10253.35, ISNI 0000 0004 1936 9756, Mass spectrometry facility of the Department of Chemistry, , Philipps University, ; Marburg, Germany
                [4 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Institute of Medical Microbiology, , Justus Liebig University, ; Giessen, Germany
                [5 ]GRID grid.452463.2, German Center for Infection Research (DZIF), partner site Giessen-Marburg-Langen, ; Giessen, Germany
                [6 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Bioinformatics and Systems Biology, , Justus Liebig University, ; Giessen, Germany
                [7 ]GRID grid.10253.35, ISNI 0000 0004 1936 9756, Institute of Virology, , Philipps University, ; Marburg, Germany
                [8 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Department of Internal Medicine II for Pulmonary and Critical Care Medicine and Infectious Diseases, , Justus Liebig University, and Institute for Lung Health (ILH), ; Giessen, Germany
                [9 ]GRID grid.452624.3, German Center for Lung Research (DZL) and Universities of Giessen and Marburg Lung Center (UGMLC), ; Giessen, Germany
                [10 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Institute of Biochemistry, , Justus Liebig University, ; Giessen, Germany
                Author information
                http://orcid.org/0000-0002-6444-9478
                http://orcid.org/0000-0001-7623-9446
                http://orcid.org/0000-0001-8420-6281
                http://orcid.org/0000-0002-6984-7192
                http://orcid.org/0000-0002-5741-8825
                http://orcid.org/0000-0002-8501-043X
                Article
                25551
                10.1038/s41467-021-25551-1
                8452654
                34545074
                79615e34-95be-433f-bfb2-3c930077eda6
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 7 September 2020
                : 2 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 284237345
                Award ID: 268555672
                Award ID: 197785619
                Award ID: 416910386
                Award ID: 390649896
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                infection,sars-cov-2,systems virology,virus-host interactions,viral infection
                Uncategorized
                infection, sars-cov-2, systems virology, virus-host interactions, viral infection

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