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3 results on '"Amy, L."'

1. A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses

Author

Gabriele Neumann, Anil K. Shukla, Athena A. Schepmoes, Laurence Josset, Bobbi Jo M. Webb-Robertson, Michael G. Katze, Chengjun Li, Yoshihiro Kawaoka, Katrina M. Waters, Amy C. Sims, Hugh D. Mitchell, Ralph S. Baric, Jean H. Chang, Thomas O. Metz, Melissa M. Matzke, Richard D. Smith, Robert A. Heegel, Susan C. Tilton, Amy L. Ellis, Amie J. Eisfeld, Nicolas Tchitchek, Jason E. McDermott, Maria L. Luna, Arndt Benecke, Pacific Northwest National Laboratory (PNNL), University of Wisconsin-Madison, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), University of Washington [Seattle], Physiopathologie des Maladies du Système Nerveux Central, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), The University of Tokyo (UTokyo), Federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH), Department of Health and Human Services [HHSN272200800060C], NIH [P41 GM103493], Mazalérat, Charlotte, Expression des Gènes et comportement adaptatifs = Molecular Genetics, Neurophysiology and Behavior (NPS-15), Neurosciences Paris Seine (NPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Neuroscience Paris Seine (NPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Proteomics, Microarrays, Viral pathogenesis, viruses, lcsh:Medicine, Disease, Virus Replication, Transcriptome, Emerging Viral Diseases, Pandemic, Genes, Regulator, lcsh:Science, Lung, Regulator gene, 0303 health sciences, Multidisciplinary, biology, Virulence, Proteomic Databases, 030302 biochemistry & molecular biology, General Medicine, Genomics, Orthomyxoviridae, 3. Good health, Severe acute respiratory syndrome-related coronavirus, Host-Pathogen Interactions, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], General Agricultural and Biological Sciences, Research Article, Computer Modeling, Computational biology, Respiratory Mucosa, Microbiology, General Biochemistry, Genetics and Molecular Biology, Virus, 03 medical and health sciences, Virology, Genetics, Animals, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Gene Networks, Biology, 030304 developmental biology, Regulatory Networks, Models, Statistical, lcsh:R, Computational Biology, Epithelial Cells, biology.organism_classification, Animal Models of Infection, Viral replication, Gene Expression Regulation, Virulence Factors and Mechanisms, Computer Science, lcsh:Q, Genome Expression Analysis, Viral Transmission and Infection
Abstract

International audience; 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 `omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

Published
2013

2. A Network Integration Approach to Predict Conserved Regulators Related to Pathogenicity of Influenza and SARS-CoV Respiratory Viruses.

Author

Mitchell, Hugh D., Eisfeld, Amie J., Sims, Amy C., McDermott, Jason E., Matzke, Melissa M., Webb-Robertson, Bobbi-Jo M., Tilton, Susan C., Tchitchek, Nicolas, Josset, Laurence, Li, Chengjun, Ellis, Amy L., Chang, Jean H., Heegel, Robert A., Luna, Maria L., Schepmoes, Athena A., Shukla, Anil K., Metz, Thomas O., Neumann, Gabriele, Benecke, Arndt G., and Smith, Richard D.

Subjects
RESPIRATORY infections, INFLUENZA viruses, SARS disease, CORONAVIRUSES, PANDEMICS, PUBLIC health, PATHOLOGY, SARS virus
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 ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models. [ABSTRACT FROM AUTHOR]

Published
2013
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3. Differences in Gastric Carcinoma Microenvironment Stratify According to EBV Infection Intensity: Implications for Possible Immune Adjuvant Therapy

Author

Strong, Michael J., Xu, Guorong, Coco, Joseph, Baribault, Carl, Vinay, Dass S., Lacey, Michelle R., Strong, Amy L., Lehman, Teresa A., Seddon, Michael B., Lin, Zhen, Concha, Monica, Baddoo, Melody, Ferris, MaryBeth, Swan, Kenneth F., Sullivan, Deborah E., Burow, Matthew E., Taylor, Christopher M., and Flemington, Erik K.

Subjects
GASTROINTESTINAL cancer, GENE expression, KILLER cells
Abstract

Epstein-Barr virus (EBV) is associated with roughly 10% of gastric carcinomas worldwide (EBVaGC). Although previous investigations provide a strong link between EBV and gastric carcinomas, these studies were performed using selected EBV gene probes. Using a cohort of gastric carcinoma RNA-seq data sets from The Cancer Genome Atlas (TCGA), we performed a quantitative and global assessment of EBV gene expression in gastric carcinomas and assessed EBV associated cellular pathway alterations. EBV transcripts were detected in 17% of samples but these samples varied significantly in EBV coverage depth. In four samples with the highest EBV coverage (hiEBVaGC – high EBV associated gastric carcinoma), transcripts from the BamHI A region comprised the majority of EBV reads. Expression of LMP2, and to a lesser extent, LMP1 were also observed as was evidence of abortive lytic replication. Analysis of cellular gene expression indicated significant immune cell infiltration and a predominant IFNG response in samples expressing high levels of EBV transcripts relative to samples expressing low or no EBV transcripts. Despite the apparent immune cell infiltration, high levels of the cytotoxic T-cell (CTL) and natural killer (NK) cell inhibitor, IDO1, was observed in the hiEBVaGCs samples suggesting an active tolerance inducing pathway in this subgroup. These results were confirmed in a separate cohort of 21 Vietnamese gastric carcinoma samples using qRT-PCR and on tissue samples using in situ hybridization and immunohistochemistry. Lastly, a panel of tumor suppressors and candidate oncogenes were expressed at lower levels in hiEBVaGC versus EBV-low and EBV-negative gastric cancers suggesting the direct regulation of tumor pathways by EBV. [ABSTRACT FROM AUTHOR]

Published
2013
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