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      The blood-brain barrier is dysregulated in COVID-19 and serves as a CNS entry route for SARS-CoV-2

      research-article
      1 , 2 , , 3 , 19 , 21 , 4 , 5 , 6 , 21 , 7 , 8 , 9 , 10 , 11 , 11 , 3 , 19 , 3 , 19 , 1 , 2 , 1 , 1 , 1 , 12 , 12 , 12 , 1 , 13 , 13 , 8 , 8 , 14 , 14 , 20 , 20 , 20 , 15 , 15 , 16 , 17 , 18 , 19 , 3 , 19 , 18 , 19 , 3 , 19 , 3 , 19 , 8 , 14 , 7 , 1 , 3 , 19 , ∗∗
      Stem Cell Reports
      The Author(s).
      SARS-CoV-2, COVID-19, blood-brain barrier, neurovascular unit, hiPSC, infection model

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          Abstract

          Neurological complications are common in COVID-19. Although SARS-CoV-2 has been detected in patients’ brain tissues, its entry routes and resulting consequences are not well understood. Here, we show a pronounced upregulation of interferon signaling pathways of the neurovascular unit in fatal COVID-19. By investigating the susceptibility of human induced pluripotent stem cell (hiPSC)-derived brain capillary endothelial-like cells (BCECs) to SARS-CoV-2 infection, we found that BCECs were infected and recapitulated transcriptional changes detected in vivo. While BCECs were not compromised in their paracellular tightness, we found SARS-CoV-2 in the basolateral compartment in transwell assays after apical infection, suggesting active replication and transcellular transport of virus across the blood-brain barrier (BBB) in vitro. Moreover, entry of SARS-CoV-2 into BCECs could be reduced by anti-spike-, anti-angiotensin-converting enzyme 2 (ACE2)-, and anti-neuropilin-1 (NRP1)-specific antibodies or the transmembrane protease serine subtype 2 (TMPRSS2) inhibitor nafamostat. Together, our data provide strong support for SARS-CoV-2 brain entry across the BBB resulting in increased interferon signaling.

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          Abstract

          In this article, Pless and colleagues show upregulation of IFNγ signaling in the neurovascular unit of the brain in fatal COVID-19. They show that an hiPSC-derived brain capillary endothelial cell model can be infected with SARS-CoV-2, resulting in similar expression changes, viral replication, and release while endothelial cell integrity is maintained. Infection can be prevented by antibodies or protease inhibitors.

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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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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Journal
                Stem Cell Reports
                Stem Cell Reports
                Stem Cell Reports
                The Author(s).
                2213-6711
                20 January 2022
                20 January 2022
                Affiliations
                [1 ]Institute for Neuropathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
                [2 ]Experimental Pathology Core Facility, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
                [3 ]Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Discovery Research ScreeningPort, 22525 Hamburg, Germany
                [4 ]Institute for Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
                [5 ]Bernhard Nocht Institute, Leibniz Institute for Tropical Medicine, 20359 Hamburg, Germany
                [6 ]German Center for Infection Research, Partner Site Hamburg-Borstel-Lübeck-Riems, 20359 Hamburg, Germany
                [7 ]Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
                [8 ]Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
                [9 ]Morphology and Electron Microscopy Core Facility, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
                [10 ]University Hospital Würzburg, Chair Tissue Engineering and Regenerative Medicine (TERM), 97070 Würzburg, Germany
                [11 ]Fraunhofer Institute for Silicate Research (ISC), Translational Center Regenerative Therapies (TLC-RT), 97070 Würzburg, Germany
                [12 ]Nanostring Technologies, Seattle, WA 98109, USA
                [13 ]Department for Neurosurgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
                [14 ]AIT-Austrian Institute of Technology GmbH, 1210 Wien, Austria
                [15 ]Institute of Clinical Molecular Biology, Universitätsklinikum Schleswig-Holstein, 24105 Kiel, Germany
                [16 ]Neurimmune AG, 8952 Schlieren, Switzerland
                [17 ]Zentrum für Integrative Psychiatrie (ZIP) GmbH, Universitätsklinikum Schleswig-Holstein, 24105 Kiel, Germany
                [18 ]Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), 60596 Frankfurt am Main, Germany
                [19 ]Fraunhofer Cluster of Excellence for Immune-Mediated Diseases (CIMD), 60596 Frankfurt am Main, Germany
                [20 ]University Würzburg, Institute of Virology and Immunobiology, 97078 Würzburg, Germany
                Author notes
                []Corresponding author
                [∗∗ ]Corresponding author
                [21]

                These authors contributed equally

                Article
                S2213-6711(21)00650-0
                10.1016/j.stemcr.2021.12.011
                8772030
                35063125
                4c4e32d1-32da-4612-bd51-e5cad95f9c06
                © 2021 The Author(s)

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 12 July 2021
                : 15 December 2021
                : 16 December 2021
                Categories
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                sars-cov-2,covid-19,blood-brain barrier,neurovascular unit,hipsc,infection model

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