@article {333, title = {Mechanisms of severe acute respiratory syndrome coronavirus-induced acute lung injury.}, journal = {MBio}, year = {2013}, abstract = {

UNLABELLED: 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.

}, keywords = {Acute Lung Injury, Animals, Blood Coagulation, Disease Models, Animal, Fibrinolysis, Gene Expression Profiling, Host-Pathogen Interactions, Lung, Mice, Mice, Inbred C57BL, Mice, Knockout, Proteome, SARS Virus, Time Factors, Urokinase-Type Plasminogen Activator}, doi = {10.1128/mBio.00271-13}, author = {Gralinski, Lisa E and Bankhead, Armand and Jeng, Sophia and Menachery, Vineet D and Proll, Sean and Belisle, Sarah E and Matzke, Melissa and Webb-Robertson, Bobbie-Jo M and Luna, Maria L and Shukla, Anil K and Ferris, Martin T and Bolles, Meagan and Chang, Jean and Aicher, Lauri and Waters, Katrina M and Smith, Richard D and Metz, Thomas O and Law, G Lynn and Katze, Michael G and McWeeney, Shannon and Baric, Ralph S} } @article {334, title = {A network integration approach to predict conserved regulators related to pathogenicity of influenza and SARS-CoV respiratory viruses.}, journal = {PLoS One}, year = {2013}, abstract = {

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel "crowd-based" approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse {\textquoteright}omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

}, doi = {10.1371/journal.pone.0069374}, author = {Mitchell, Hugh D and Eisfeld, Amie J and Sims, Amy C and McDermott, Jason E and Matzke, Melissa M and Webb-Robertson, Bobbi-Jo M and Tilton, Susan C and Tchitchek, Nicolas and Josset, Laurence and Li, Chengjun and Ellis, Amy L and Chang, Jean H and Heegel, Robert A and Luna, Maria L and Schepmoes, Athena A and Shukla, Anil K and Metz, Thomas O and Neumann, Gabriele and Benecke, Arndt G and Smith, Richard D and Baric, Ralph S and Kawaoka, Yoshihiro and Katze, Michael G and Waters, Katrina M} } @article {329, title = {Release of severe acute respiratory syndrome coronavirus nuclear import block enhances host transcription in human lung cells.}, journal = {J Virol}, year = {2013}, abstract = {

The severe acute respiratory syndrome coronavirus accessory protein ORF6 antagonizes interferon signaling by blocking karyopherin-mediated nuclear import processes. Viral nuclear import antagonists, expressed by several highly pathogenic RNA viruses, likely mediate pleiotropic effects on host gene expression, presumably interfering with transcription factors, cytokines, hormones, and/or signaling cascades that occur in response to infection. By bioinformatic and systems biology approaches, we evaluated the impact of nuclear import antagonism on host expression networks by using human lung epithelial cells infected with either wild-type virus or a mutant that does not express ORF6 protein. Microarray analysis revealed significant changes in differential gene expression, with approximately twice as many upregulated genes in the mutant virus samples by 48 h postinfection, despite identical viral titers. Our data demonstrated that ORF6 protein expression attenuates the activity of numerous karyopherin-dependent host transcription factors (VDR, CREB1, SMAD4, p53, EpasI, and Oct3/4) that are critical for establishing antiviral responses and regulating key host responses during virus infection. Results were confirmed by proteomic and chromatin immunoprecipitation assay analyses and in parallel microarray studies using infected primary human airway epithelial cell cultures. The data strongly support the hypothesis that viral antagonists of nuclear import actively manipulate host responses in specific hierarchical patterns, contributing to the viral pathogenic potential in vivo. Importantly, these studies and modeling approaches not only provide templates for evaluating virus antagonism of nuclear import processes but also can reveal candidate cellular genes and pathways that may significantly influence disease outcomes following severe acute respiratory syndrome coronavirus infection in vivo.

}, keywords = {Active Transport, Cell Nucleus, Chromatin Immunoprecipitation, Computational Biology, DNA Primers, Epithelial Cells, Gene Regulatory Networks, Humans, Lung, Microarray Analysis, Proteomics, Real-Time Polymerase Chain Reaction, SARS Virus, Signal Transduction, Systems Biology, Transcription, Genetic, Viral Regulatory and Accessory Proteins}, doi = {10.1128/JVI.02520-12}, author = {Sims, Amy C and Tilton, Susan C and Menachery, Vineet D and Gralinski, Lisa E and Sch{\"a}fer, Alexandra and Matzke, Melissa M and Webb-Robertson, Bobbie-Jo M and Chang, Jean and Luna, Maria L and Long, Casey E and Shukla, Anil K and Bankhead, Armand R and Burkett, Susan E and Zornetzer, Gregory and Tseng, Chien-Te Kent and Metz, Thomas O and Pickles, Raymond and McWeeney, Shannon and Smith, Richard D and Katze, Michael G and Waters, Katrina M and Baric, Ralph S} } @article {345, title = {Improved quality control processing of peptide-centric LC-MS proteomics data.}, journal = {Bioinformatics}, year = {2011}, abstract = {

MOTIVATION: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values.

RESULTS: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs.

AVAILABILITY: https://www.biopilot.org/docs/Software/RMD.php

CONTACT: bj@pnl.gov

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

}, keywords = {Chromatography, Liquid, Data Interpretation, Statistical, Mass Spectrometry, Peptides, Proteome, Proteomics, Quality Control, Software}, doi = {10.1093/bioinformatics/btr479}, author = {Matzke, Melissa M and Waters, Katrina M and Metz, Thomas O and Jacobs, Jon M and Sims, Amy C and Baric, Ralph S and Pounds, Joel G and Webb-Robertson, Bobbie-Jo M} }