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Katze, Michael G.

Publications and source records attributed to Katze, Michael G..

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ antiimmune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation. We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine, tumor necrosis factor alpha (TNFα), promote pathogenesis through a parallel feed-forward circuit that promotes inflammation. These results are consistent with previous studies showing the role of over-stimulation of the inflammatory response to SARS-CoV in pathogenesis. We conclude that the critical balance between immune response and inflammation can be manipulated to improve the outcome of the infection. Further, our study provides two potential therapeutic strategies for mitigating the effects of SARS-CoV infection, and may provide insight into treatment strategies for Middle East Respiratory Syndrome Coronavirus (MERS-CoV).

network inference↗
DOE OSTI2014

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). As a result, by comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Pathogenic Influenza Viruses and Coronaviruses Utilize Similar and Contrasting Approaches To Control Interferon-Stimulated Gene Responses

The broad range and diversity of interferon-stimulated genes (ISGs) function to induce an antiviral state within the host, impeding viral pathogenesis. While successful respiratory viruses overcome individual ISG effectors, analysis of the global ISG response and subsequent viral antagonism has yet to be examined. Employing models of the human airway, transcriptomics and proteomics datasets were used to compare ISG response patterns following highly pathogenic H5N1 avian influenza (HPAI) A virus, 2009 pandemic H1N1, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome CoV (MERS-CoV) infection. The results illustrated distinct approaches utilized by each virus to antagonize the global ISG response. In addition, the data revealed that highly virulent HPAI virus and MERS-CoV induce repressive histone modifications, which downregulate expression of ISG subsets. Notably, influenza A virus NS1 appears to play a central role in this histone-mediated downregulation in highly pathogenic influenza strains. Together, the work demonstrates the existence of unique and common viral strategies for controlling the global ISG response and provides a novel avenue for viral antagonism via altered histone modifications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Advancing the High Throughput Identification of Liver Fibrosis Protein Signatures Using Multiplexed Ion Mobility Spectrometry

Rapid diagnosis of disease states using less invasive, safer, and more clinically acceptable approaches than presently employed is an imperative goal for the field of medicine. While mass spectrometry (MS)-based proteomics approaches have attempted to meet these objectives, challenges such as the enormous dynamic range of protein concentrations in clinically relevant biofluid samples coupled with the need to address human biodiversity have slowed their employment. Herein, we report on the use of a new platform that addresses these challenges by coupling technical advances in rapid gas phase multiplexed ion mobility spectrometry (IMS) separations [1, 2] with liquid chromatography (LC) and MS to dramatically increase measurement sensitivity and throughput, further enabling future MS-based clinical applications. An initial application of the LC-IMS-MS platform for the analysis of blood serum samples from stratified post-liver transplant patients with recurrent fibrosis progression illustrates its potential utility for disease characterization and use in personalized medicine [3, 4].

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

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

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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Specific Mutations in H5N1 Mainly Impact the Magnitude and Velocity of the Host Response in Mice

Influenza infection causes respiratory disease that can lead to death. The complex interplay between virus-encoded and host-specific pathogenicity regulators is not well-understood. By analyzing a collection of mouse lung samples infected by A/Vietnam/1203/2004 (H5N1; VN1203) influenza, we characterized a signature of transcripts and proteins associated with the kinetics of the host response. Using a new geometrical representation method and two criteria, we show that infection concentrations and four specific mutations in VN1203 mainly impact on the magnitude and velocity of the host response kinetics, rather than on specific sets of genes up- and down-regulated. We observed similar kinetic effects using A/California/04/2009 (H1N1)-infected samples, and we show that these effects correlate with mice morbidity and viral titer measurements. Speed and extent of changes in the host response between days 1 and 2 post-infection were attenuated for each VN1203 mutant compared to the wild-type, except for PB1-F2 deletion at a high dose, which was associated with high virulence. This indicates that the host response in that time frame is critical and that immunomodulatory therapeutics should specifically be applied during the early days post-infection.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2013

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

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.

Computational Biology↗
DOE OSTI2013

Protein Co-Expression Network Analysis (ProCoNA)

Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Environmental Molecular Sciences Laboratory↗