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      Seroconversion stages COVID19 into distinct pathophysiological states

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          Abstract

          COVID19 is a heterogeneous medical condition involving diverse underlying pathophysiological processes including hyperinflammation, endothelial damage, thrombotic microangiopathy, and end-organ damage. Limited knowledge about the molecular mechanisms driving these processes and lack of staging biomarkers hamper the ability to stratify patients for targeted therapeutics. We report here the results of a cross-sectional multi-omics analysis of hospitalized COVID19 patients revealing that seroconversion status associates with distinct underlying pathophysiological states. Low antibody titers associate with hyperactive T cells and NK cells, high levels of IFN alpha, gamma and lambda ligands, markers of systemic complement activation, and depletion of lymphocytes, neutrophils, and platelets. Upon seroconversion, all of these processes are attenuated, observing instead increases in B cell subsets, emergency hematopoiesis, increased D-dimer, and hypoalbuminemia. We propose that seroconversion status could potentially be used as a biosignature to stratify patients for therapeutic intervention and to inform analysis of clinical trial results in heterogenous patient populations.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                16 March 2021
                2021
                : 10
                : e65508
                Affiliations
                [1 ]Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus AuroraUnited States
                [2 ]Department of Pharmacology, University of Colorado Anschutz Medical Campus AuroraUnited States
                [3 ]Department of Pediatrics, Division of Developmental Biology, University of Colorado Anschutz Medical Campus AuroraUnited States
                [4 ]Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus AuroraUnited States
                [5 ]Data Science to Patient Value, University of Colorado Anschutz Medical Campus AuroraUnited States
                [6 ]Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus AuroraUnited States
                [7 ]Department of Biostatistics and Informatics, Colorado School of Public Health AuroraUnited States
                [8 ]Department of Emergency Medicine, University of Colorado Anschutz Medical Campus AuroraUnited States
                [9 ]Department of Pediatrics, Sections of Informatics and Data Science and Critical Care Medicine, University of Colorado Anschutz Medical Campus AuroraUnited States
                [10 ]Department of Pediatrics, Division of Allergy/Immunology, University of Colorado Anschutz Medical Campus AuroraUnited States
                University of Texas Southwestern Medical Center United States
                University of Colorado Boulder United States
                University of Texas Southwestern Medical Center United States
                Benaroya Research Institute at Virginia Mason United States
                Author information
                https://orcid.org/0000-0003-0485-3927
                https://orcid.org/0000-0003-2725-0205
                http://orcid.org/0000-0002-2436-1367
                https://orcid.org/0000-0001-9048-1941
                Article
                65508
                10.7554/eLife.65508
                7963480
                33724185
                a5d3d0b1-454d-4e15-96da-1eba8e6e1ac1
                © 2021, Galbraith et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 07 December 2020
                : 23 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: R01AI150305
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: 3R01AI150305-01S1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
                Award ID: UL1TR002535
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
                Award ID: 3UL1TR002535-03S2
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: R01HL149714
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: R01HL148151
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
                Award ID: R21HL150032
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: P30CA046934
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R35GM124939
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: RM1GM131968
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: R01AI145988
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008086, Global Down Syndrome Foundation;
                Award Recipient :
                Funded by: Anna and John J Sie Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100005508, Boettcher Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007423, Lyda Hill Foundation;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Immunology and Inflammation
                Custom metadata
                Stratification of COVID19 patients using quantitative metrics of seroconversion reveals distinct pathophysiological stages after SARS-CoV-2 infection, including key differences in immune cell types, inflammatory makers, and markers of organ function.

                Life sciences
                covid19,interferons,complement,antibodies,cytokines,sars,human
                Life sciences
                covid19, interferons, complement, antibodies, cytokines, sars, human

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