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      SARS-CoV-2 envelope protein regulates innate immune tolerance

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          Summary

          Severe COVID-19 often leads to secondary infections and sepsis that contribute to long hospital stays and mortality. However, our understanding of the precise immune mechanisms driving severe complications after SARS-CoV-2 infection remains incompletely understood. Here, we provide evidence that the SARS-CoV-2 envelope (E) protein initiates innate immune inflammation, via toll-like receptor 2 signaling, and establishes a sustained state of innate immune tolerance following initial activation. Monocytes in this tolerant state exhibit reduced responsiveness to secondary stimuli, releasing lower levels of cytokines and chemokines. Mice exposed to E protein before secondary lipopolysaccharide challenge show diminished pro-inflammatory cytokine expression in the lung, indicating that E protein drives this tolerant state  in vivo. These findings highlight the potential of the SARS-CoV-2 E protein to induce innate immune tolerance, contributing to long-term immune dysfunction that could lead to susceptibility to subsequent infections, and uncovers therapeutic targets aimed at restoring immune function following SARS-CoV-2 infection.

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          Highlights

          • SARS-CoV-2 envelope (E) protein activated innate immune cells through TLR2

          • E protein promoted a long-term tolerant immune state after initial activation

          • Monocytes in this tolerant state had reduced responsiveness to secondary stimuli

          • E protein priming reduced lung inflammation markers to LPS in neonatal mice

          Abstract

          Molecular biology; Immunity; Components of the immune system; Virology; Transcriptomics.

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          Most cited references76

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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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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                15 May 2024
                21 June 2024
                15 May 2024
                : 27
                : 6
                : 109975
                Affiliations
                [1 ]Genomic Medicine Center, Children’s Mercy Research Institute, Kansas City, MO, USA
                [2 ]Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, KS, USA
                [3 ]Division of Neonatology, Children’s Mercy Research Institute, Kansas City, MO, USA
                [4 ]Department of Pediatrics, University of Missouri- Kansas City, Kansas City, MO, USA
                [5 ]Department of Pediatrics, University of Kansas Medical Center, Kansas City, MO, USA
                Author notes
                []Corresponding author tcbradley@ 123456cmh.edu
                [6]

                These authors contributed equally

                [7]

                Lead contact

                Article
                S2589-0042(24)01197-0 109975
                10.1016/j.isci.2024.109975
                11140213
                38827398
                22d0b505-a3fb-4673-9ad2-fe4034205e2e
                © 2024 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 28 November 2023
                : 1 March 2024
                : 10 May 2024
                Categories
                Article

                molecular biology,immunity,components of the immune system,virology,transcriptomics

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