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Epstein-Barr virus (EBV) is a ubiquitous human pathogen, infecting up to 95% of the world’s adult population. Initial infection with EBV can cause infectious mononucleosis. The innate immune system serves as frontline defense against pathogens, such as bacteria and viruses. Natural killer (NK) cells are a part of innate immunity and can both secrete cytokines and directly target cells for lysis. NK cells express several cell surface receptors, including NKG2D, which bind multiple ligands. People with deficiencies in NK cells are often susceptible to uncontrolled infection by herpesviruses, such as Epstein-Barr virus (EBV). Infection with EBV stimulates both innate and adaptive immunity, yet the virus establishes lifelong latent infection in memory B cells. We show that the EBV oncogene EBNA1, previously known to be necessary for maintaining EBV genomes in latently infected cells, also plays an important role in suppressing NK cell responses and cell death in newly infected cells. EBNA1 does so by downregulating the NKG2D ligands ULBP1 and ULBP5 and modulating expression of c-Myc. B cells infected with a derivative of EBV that lacks EBNA1 are more susceptible to NK cell-mediated killing and show increased levels of apoptosis. Thus, EBNA1 performs a previously unappreciated role in reducing immune response and programmed cell death after EBV infection, helping infected cells avoid immune surveillance and apoptosis and thus persist for the lifetime of the host. IMPORTANCE Epstein-Barr virus (EBV) is a ubiquitous human pathogen, infecting up to 95% of the world’s adult population. Initial infection with EBV can cause infectious mononucleosis. EBV is also linked to several human malignancies, including lymphomas and carcinomas. Although infection by EBV alerts the immune system and causes an immune response, the virus persists for life in memory B cells. We show that the EBV protein EBNA1 can downregulate several components of the innate immune system linked to natural killer (NK) cells. This downregulation of NK cell activity translates to lower killing of EBV-infected cells and is likely one way that EBV escapes immune surveillance after infection. Additionally, we show that EBNA1 reduces apoptosis in newly infected B cells, allowing more of these cells to survive. Taken together, our findings uncover new functions of EBNA1 and provide insights into viral strategies to survive the initial immune response postinfection.

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.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae , Filoviridae , Flaviviridae , and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

The West African outbreak of Ebola virus (EBOV) is largely contained, but sporadic new cases continue to emerge. To assess the potential contribution of fomites to human infections with EBOV, we tested EBOV stability in human blood spotted onto Sierra Leonean banknotes and in syringe needles under hospital and environmental conditions. Under some of these conditions, EBOV remained infectious for >30 days, indicating that EBOV-contaminated items may pose a serious risk to humans.

Epstein-Barr Nuclear Antigen 1 (EBNA1) is a multifunctional protein encoded by EBV. EBNA1’s role in maintaining EBV in latently proliferating cells, by mediating EBV genome synthesis and nonrandom partitioning to daughter cells, as well as regulating viral gene transcription, is well characterized. Less understood are the roles of EBNA1 in affecting the host cell to provide selective advantages to those cells that harbor EBV. In this review we will focus on the interactions between EBNA1 and the host cell that may provide EBV-infected cells selective advantages beyond the maintenance of EBV.

Epstein Barr Virus Nuclear Antigen 1 (EBNA1) is a DNA-binding viral protein necessary both for the maintenance of the Epstein Barr virus (EBV) viral plasmid in proliferating B-cells and the expression of some latent viral genes. EBNA1 is the only viral protein that is consistently expressed among proliferating, latently-infected cells and EBV associated malignancies. While it is clear that EBNA1 serves necessary roles for the maintenance of the EBV genome and for the expression of other viral proteins in latently infected cells, I have found that EBNA1 also interacts with the host cell to provide selective advantages to those cells that harbor EBV. I used an EBNA1 Position Weighted Matrix (PWM) to identify and subsequently confirm that EBNA1 binds cellular DNA 11kb upstream of the oncogene c-Myc. c-Myc is a potent oncogene that is induced by the EBV protein EBNA2 following EBV's infection of B-cells and drives resting B-cells out of G1/G0 to become proliferating blasts. However, robust induction of an oncogene such as c-Myc comes at a cost, and recently infected B-cells also undergo a DNA Damage Response (DDR). I show that by binding this site upstream of c-Myc, EBNA1 decreases its expression, and in turn limits the induction of the DNA Damage response (DDR), and inhibits the apoptosis induced following EBV's infection of primary B-cells. We have also identified and confirmed EBNA1-binding sites within the promoter for the NKG2D ligand ULBP1 and found its expression to be decreased by EBNA1 following EBV infection. The inhibition of ULBP1, which can be induced by genotoxic stress, allows EBV-infected B-cells to limit Natural Killer cell mediated immune surveillance. I have also used to approaches to identify further cellular genes that are candidates for being regulated transcriptionally by EBNA1. These studies provide definitive evidence that EBNA1 not only binds essential elements within the viral genome but also binds sites in the human genome at which it regulates gene expression and thereby promotes efficient establishment of EBV in B lymphocytes.

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.

Innate immunity is protective against viruses, but also can facilitate pathological infection responses. Despite intensive research, our understanding of the mechanisms that regulate innate immunity in virus infection remains incomplete. Systems biology-based data-driven modeling approaches hold substantial promise toward discovery of crucial innate immune signaling regulators, yet model-derived predictions are almost completely unexplored. Here, we carried out systematic experimental validation of candidate regulators predicted by a transcriptional association network model of influenza virus-infected cells. We identified dozens of novel innate immune signaling regulators with potent effects on the replication of influenza and other viruses, and importantly, we established the biological relevance of a validated regulator in vivo. Collectively, these findings aid in clarifying mechanisms of influenza virus pathogenicity and might lead to innovative approaches for treating influenza virus disease. Similar data-driven modeling strategies may be applicable for the study of other pathogen systems or immunological disorders.