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      Inflammasome activation in infected macrophages drives COVID-19 pathology

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          Abstract

          Severe COVID-19 is characterized by persistent lung inflammation, inflammatory cytokine production, viral RNA, and sustained interferon (IFN) response all of which are recapitulated and required for pathology in the SARS-CoV-2 infected MISTRG6-hACE2 humanized mouse model of COVID-19 with a human immune system 120 . Blocking either viral replication with Remdesivir 2123 or the downstream IFN stimulated cascade with anti-IFNAR2 in vivo in the chronic stages of disease attenuated the overactive immune-inflammatory response, especially inflammatory macrophages. Here, we show SARS-CoV-2 infection and replication in lung-resident human macrophages is a critical driver of disease. In response to infection mediated by CD16 and ACE2 receptors, human macrophages activate inflammasomes, release IL-1 and IL-18 and undergo pyroptosis thereby contributing to the hyperinflammatory state of the lungs. Inflammasome activation and its accompanying inflammatory response is necessary for lung inflammation, as inhibition of the NLRP3 inflammasome pathway reverses chronic lung pathology. Remarkably, this same blockade of inflammasome activation leads to the release of infectious virus by the infected macrophages. Thus, inflammasomes oppose host infection by SARS-CoV-2 by production of inflammatory cytokines and suicide by pyroptosis to prevent a productive viral cycle.

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          Most cited references74

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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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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                8 June 2022
                June 2022
                28 April 2022
                01 August 2022
                : 606
                : 7914
                : 585-593
                Affiliations
                [1 ]Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
                [2 ]Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
                [3 ]Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA
                [4 ]Department of Pediatrics, Harvard Medical School, Boston, MA, USA
                [5 ]Instituto René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Minas Gerais, Brazil
                [6 ]Section of Hematology, Yale Cancer Center and Department of Internal Medicine, Yale University School of Medicine, New Haven, CT
                [7 ]Laboratory of Molecular Immunology, The Rockefeller University, New York, NY, USA
                [8 ]Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA
                [9 ]Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA
                [10 ]Howard Hughes Medical Institute, The Rockefeller University, New York, NY, USA
                [11 ]Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
                [12 ]Program of Applied Mathematics, Yale University, New Haven, CT, USA
                [13 ]Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA
                Author notes

                Author contributions:

                E.S conceived the project, performed experiments, analyzed the data, and wrote the manuscript. R.Q and J.Z. performed bioinformatics analysis. C. J. performed imaging flow cytometry experiments for characterization of the NLRP3 inflammasome. E.K. prepared samples for histopathological assessment and performed all immunofluorescence staining. H. M. performed histopathological assessment of lung pathology, quantification of immunofluorescence staining and offered essential conceptual insight in interpreting lung pathology. B.I. helped establish the model in Biosafety Level 3. M.N. provided monoclonal antibodies used in the study. H.N.B helped with tissue preparation and immunofluorescence staining. S.V. provided help with IL-1 quantification protocols. Y.G.C. provided protocols and insight on dsRNA staining. J.R.B., A.H., H.S., S.H., A. I., E.M., M.N., J.L., C. W., Y.K. offered vital conceptual insight, contributed to the overall interpretation of this work, and aided in writing of the manuscript. R.A.F. co-conceived and supervised the project, helped interpret the work and supervised writing of the manuscript.

                Article
                NIHMS1809392
                10.1038/s41586-022-04802-1
                9288243
                35483404
                7ef254fd-b83a-4cd4-997e-d16a1c2a5c71

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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