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      Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

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          ABSTRACT

          Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV.

          IMPORTANCE

          Severe acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and 2003, and infected patients developed an atypical pneumonia, acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death. We identified sets of differentially expressed genes that contribute to ALI and ARDS using lethal and sublethal SARS-CoV infection models. Mathematical prioritization of our gene sets identified the urokinase and extracellular matrix remodeling pathways as the most enriched pathways. By infecting Serpine1-knockout mice, we showed that the urokinase pathway had a significant effect on both lung pathology and overall SARS-CoV pathogenesis. These results demonstrate the effective use of unbiased modeling techniques for identification of high-priority host targets that regulate disease outcomes. Similar transcriptional signatures were noted in 1918 and 2009 H1N1 influenza virus-infected mice, suggesting a common, potentially treatable mechanism in development of virus-induced ALI.

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          Most cited references 65

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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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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia.

              A previously unknown coronavirus was isolated from the sputum of a 60-year-old man who presented with acute pneumonia and subsequent renal failure with a fatal outcome in Saudi Arabia. The virus (called HCoV-EMC) replicated readily in cell culture, producing cytopathic effects of rounding, detachment, and syncytium formation. The virus represents a novel betacoronavirus species. The closest known relatives are bat coronaviruses HKU4 and HKU5. Here, the clinical data, virus isolation, and molecular identification are presented. The clinical picture was remarkably similar to that of the severe acute respiratory syndrome (SARS) outbreak in 2003 and reminds us that animal coronaviruses can cause severe disease in humans.
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                6 August 2013
                Jul-Aug 2013
                : 4
                : 4
                Affiliations
                Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA [ a ]
                Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health Sciences University, Portland, Oregon, USA [ b ]
                Department of Microbiology, School of Medicine, University of Washington, Seattle, Washington, USA [ c ]
                Oregon Clinical and Translational Research Institute, Oregon Health Sciences University, Portland, Oregon, USA [ d ]
                Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA [ e ]
                Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA [ f ]
                Washington National Primate Research Center, University of Washington, Seattle, Washington, USA [ g ]
                Author notes
                Address correspondence to Ralph S. Baric, rbaric@ 123456email.unc.edu .

                Editor Terence Dermody, Vanderbilt University School of Medicine

                Article
                mBio00271-13
                10.1128/mBio.00271-13
                3747576
                23919993
                Copyright © 2013 Gralinski et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 12
                Categories
                Research Article
                Custom metadata
                July/August 2013

                Life sciences

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