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      Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses

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          Abstract

          SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 ( n = 93), other viral ( n = 41) or non-viral ( n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

          Abstract

          Here, the authors provide upper airway gene expression data from patients with COVID-19 and other viral and non-viral acute respiratory illnesses. They find attenuated activation of innate immune and pro-inflammatory pathways in COVID-19 as compared to other viral infections, which may contribute to its propensity for pre-symptomatic transmission.

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          Most cited references45

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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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            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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Contributors
                chaz.langelier@ucsf.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                17 November 2020
                17 November 2020
                2020
                : 11
                : 5854
                Affiliations
                [1 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Division of Infectious Diseases, , University of California, ; San Francisco, CA USA
                [2 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Division of Pulmonary and Critical Care Medicine, , University of California, ; San Francisco, CA USA
                [3 ]Chan Zuckerberg Biohub, San Francisco, CA USA
                [4 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Biochemistry and Biophysics, , University of California, ; San Francisco, CA USA
                [5 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Division of Hospital Medicine, , University of California, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0002-7299-808X
                http://orcid.org/0000-0003-2412-756X
                http://orcid.org/0000-0001-5953-0004
                http://orcid.org/0000-0002-4349-8131
                http://orcid.org/0000-0002-6562-4004
                http://orcid.org/0000-0001-7141-5420
                http://orcid.org/0000-0002-6708-4646
                Article
                19587
                10.1038/s41467-020-19587-y
                7673985
                33203890
                14031fbb-9a19-4de1-9479-b40fc637bf30
                © The Author(s) 2020

                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
                : 22 May 2020
                : 16 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: 1K23HL138461-01A1
                Award Recipient :
                Funded by: Chan Zuckerberg Biohub Chan Zuckerberg Initiative
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                gene regulation in immune cells,sars-cov-2,molecular medicine
                Uncategorized
                gene regulation in immune cells, sars-cov-2, molecular medicine

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