Viral and Cellular Targets for the Varicella-Zoster Virus ORF66 Protein Kinase

Varicella-zoster virus (VZV) is a human alphaherpesvirus that causes two important diseases, chickenpox and herpes zoster, separated by a prolonged period of latent infection in which no disease occurs. VZV remains a serious public health concern despite the availability of licensed vaccines for the prevention of chickenpox during childhood and zoster in the elderly. The VZV ORF66 protein kinase is a well-established mediator of VZV tropism and pathogenesis in vivo, but information regarding the molecular mechanisms of ORF66 function is limited. ORF66 is orthologous to the alphaherpesvirus US3 kinase family, which is involved in regulating diverse aspects of the host environment. ORF66 has been implicated in nuclear exclusion of the VZV principle transcriptional regulator and tegument protein, IE62, during late-stage VZV infection; and in downmodulation of class I major histocompatibility complex surface expression, which is critical for the identification and elimination of infected cells by the adaptive immune system. However, the mechanisms underlying these processes are not well-resolved. In this work, I have significantly extended our understanding of known ORF66 functions during VZV infection, and I have established novel functions for both ORF66 and the IE62 protein. Purification of ORF66 allowed the development of an in vitro kinase assay, and I used this to prove that ORF66 directly phosphorylates IE62 adjacent to its nuclear localization signal to mediate nuclear exclusion. Additional investigations of IE62 nucleocytoplasmic trafficking revealed a novel nuclear export activity, which was multi-dimensionally regulated by intrinsic IE62 nuclear retention and phosphorylation by kinase(s) other than ORF66. With regard to the ORF66 role in the regulation of MHC-I surface presentation, I developed assays with recombinant VZV expressing enhanced green fluorescent protein-tagged ORF66 to establish that ORF66 affects the cellular environment such that early MHC-I biogenesis is delayed, resulting in a reduction in total surface MHC-I. MHC-I studies indicated highly novel ORF66 nuclear distribution patterns, and a further examination of these revealed that ORF66 may regulate nucleocapsid morphogenesis or trafficking in VZV-infected nuclei. In sum, this work represents a major contribution to VZV biology and provides insights into a multi-functional mediator of VZV persistence and pathogenesis

C57BL/6J and C57BL/6NJ Mice Are Differentially Susceptible to Inflammation-Associated Disease Caused by Influenza A Virus

Influenza viruses cause seasonal epidemics and sporadic pandemics, and are a major burden on human health. To develop better countermeasures and improve influenza disease outcomes, a clearer understanding of influenza pathogenesis is necessary. Host genetic factors have emerged as potential regulators of human influenza disease susceptibility, and in the mouse model, genetic background has been clearly linked to influenza pathogenicity. Here, we show that C57BL/6J mice are significantly more susceptible to disease caused by a 2009 pandemic H1N1 virus, an H7N9 virus, and a highly pathogenic H5N1 influenza virus compared to the closely related substrain, C57BL/6NJ. Mechanistically, influenza virus infection in C57BL/6J mice results in earlier presentation of edema, increased immune cell infiltration, higher levels of inflammatory cytokines, greater tissue damage, and delayed activation of regenerative processes in infected lung tissues compared to C57BL/6NJ mice. These differences are not dependent on virus replication levels. Six genes with known coding region differences between C57BL/6J and C57BL/6NJ strains exhibit increased transcript levels in influenza virus-infected mouse lungs, suggesting potential contributions to regulation of disease susceptibility. This work uncovers a previously unappreciated difference in disease susceptibility between the closely related C57BL/6J and C57BL/6NJ mice, which may be exploited in future studies to identify host factors and/or specific genetic elements that regulate host-dependent inflammatory mechanisms involved in influenza virus pathogenicity

Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems

<p>Abstract</p> <p>Background</p> <p>Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades.</p> <p>Results</p> <p>In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms.</p> <p>Conclusions</p> <p>This is the first demonstration of a global regulatory network modeling conserved host response between <it>in vitro </it>and <it>in vivo </it>models.</p

Folyóirat vagy gyűjteményes kötet? (Csokonai Diétai Magyar Múzsája)

BACKGROUND: The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune 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. RESULTS: 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, presumably through excessive inflammation. CONCLUSIONS: The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation

A comprehensive map of the influenza A virus replication cycle

BACKGROUND: Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets. DESCRIPTION: We constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap. CONCLUSION: The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/

The role of EGFR in influenza pathogenicity: Multiple network-based approaches to identify a key regulator of non-lethal infections

Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene’s involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections

Annotation of long non-coding RNAs expressed in Collaborative Cross founder mice in response to respiratory virus infection reveals a new class of interferon-stimulated transcripts

The outcome of respiratory virus infection is determined by a complex interplay of viral and host factors. Some potentially important host factors for the antiviral response, whose functions remain largely unexplored, are long non-coding RNAs (lncRNAs). Here we systematically inferred the regulatory functions of host lncRNAs in response to influenza A virus and severe acute respiratory syndrome coronavirus (SARS-CoV) based on their similarity in expression with genes of known function. We performed total RNA-Seq on viral-infected lungs from eight mouse strains, yielding a large data set of transcriptional responses. Overall 5,329 lncRNAs were differentially expressed after infection. Most of the lncRNAs were co-expressed with coding genes in modules enriched in genes associated with lung homeostasis pathways or immune response processes. Each lncRNA was further individually annotated using a rank-based method, enabling us to associate 5,295 lncRNAs to at least one gene set and to predict their potential cis effects. We validated the lncRNAs predicted to be interferon-stimulated by profiling mouse responses after interferon-α treatment. Altogether, these results provide a broad categorization of potential lncRNA functions and identify subsets of lncRNAs with likely key roles in respiratory virus pathogenesis. These data are fully accessible through the MOuse NOn-Code Lung interactive database (MONOCLdb)

Hypergraph models of biological networks to identify genes critical to pathogenic viral response

Background: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. Results: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. Conclusions: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses

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