Influenza A virus is a major human respiratory pathogen, and available vaccines and antivirals are of limited efficacy. In order to identify novel targets for therapeutic intervention during influenza virus infection, we have assembled an interdisciplinary team that uses a highly integrated systems level approach to identify and validate key genes/networks involved in virus pathogenesis. The overarching theme of our multidisciplinary proposal ?FluOMICS: The NEXT Generation? is to obtain multiple OMICS-based systems level measurements and integrate them using modeling approaches and machine learning algorithms to identify and validate 1) host-virus networks that modulate influenza A virus disease severity, 2) biomarkers in blood that reflect the activation states of these networks and 3) novel host targets for therapeutic interventions. Our underlying main hypothesis is that host networks involved in viral replication and early host responses regulate disease outcomes and represent targets for therapeutic intervention. The proposed studies leverage on our previous collaborations that generated global datasets and models that predict severity of disease caused by three influenza virus strains with different levels of virulence. While our previous studies gave many novel insights in influenza pathogenesis, they likely provide a narrow window on the determinants of disease severity in humans. Thus, it is necessary expand beyond the specific virus strains that were used to study pathogenesis, and explore a broader context of viral and host perturbations linked to clinical outcomes. In order to identify clinically relevant networks involved in influenza virus pathogenesis we propose to integrate into predictive and comprehensive models global responses during influenza virus infection in three systems 1) human blood from a human cohort of patients with documented influenza virus infection and diverse clinical outcomes (Project 1); 2) mouse blood and tissues from experimentally infected animals under a variety of conditions and perturbations resulting in diverse disease outcomes (Project 1) and 3) relevant primary human cells (Project 2). Samples will be processed and send to the Technology Core for global transcriptomics, proteomics and metabolomics analysis. OMICS data sets will be integrated and compared by the Modeling Core to generate network models of disease, uncover blood biomarkers and identify key drivers as targets for therapeutic intervention. Predicted network regulators will be used as a source for subsequent iterative rounds of perturbations to refine existing and to identify new network disease models. Data and models will be managed and disseminated by the Data Management and Bioinformatics Core. We expect that these studies will uncover and validate novel pathogenic networks, blood biomarkers associated with them, and specific therapeutic targets to revert pathogenic networks. In summary, our modeling approaches will find correlates and associations between diverse experimental systems that will help us define human blood biomarkers, and link them to in vivo and ex vivo signatures for both companion diagnostics and personalized therapies.
We propose a systematic approach (FluOMICS) to generate predictive models of influenza virus pathogenesis which will a) allow us to identify biomarkers for predicting disease outcome, and b) provide avenues to explore for new, host-directed, therapeutic interventions.
|Beyleveld, Grant; Chin, Daniel J; Moreno Del Olmo, Elena et al. (2018) Nucleolar Relocalization of RBM14 by Influenza A Virus NS1 Protein. mSphere 3:|