SARS-CoV adaptations through a Systems Biology Lens (SYBIL)

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Previous to the COVID-19 pandemic, we assembled, under the same U19 mechanism, a multidisciplinary team to obtain multiple OMICS-based systems level measurements and integrate them using modeling approaches and machine learning algorithms to identify and validate host-virus networks that modulate influenza A virus disease severity, biomarkers in blood that reflect the activation states of these networks and novel host targets for therapeutic interventions. As a result, we have identified important host factors and pathways involved in antiviral response and virus replication and disease, leading to potential treatments. The sudden onset of the COVID-19 pandemic in 2020 prompted us to redirect our systems biology efforts to study SARS-CoV-2 infections. We now propose to continue these studies under this U19 renewal application entitled SARS-CoV adaptations through a Systems Biology Lens (SYBIL). Our underlying main hypothesis is that host networks involved in SARS-CoV replication and early host responses regulate disease outcomes and represent targets for therapeutic intervention. As these viruses have a zoonotic origin in bats, we will also explore virus-host networks involved in human adaptation and responsible for changing the tropism from the original bat host to humans. In order to identify clinically relevant networks involved in SARS-CoV replication, pathogenesis and host tropism we propose to integrate into predictive and comprehensive models global responses during infection in three systems 1) human blood and respiratory samples from human cohorts with documented SARS-CoV infection and diverse clinical outcomes (Project 1); 2) blood and tissues from experimentally infected animals under a variety of conditions and perturbations resulting in diverse disease outcomes (Project 1) and 3) human and bat cells subjected to viral protein expression, genetic screens and infection with diverse SARS-CoVs, including primary cells derived from iPSC precursors (Project 2). Samples will be processed and send to the Technology Core for global transcriptomics, epigenetics, proteomics and functional genomics analysis. OMICS data sets will be integrated and compared by the Modeling Core to generate specific hypothesis and network models of disease, uncover blood biomarkers and identify host and virus key drivers of viral replication, host response, disease outcome and host tropism. Predicted network regulators will be used as a source for subsequent iterative rounds of perturbations to refine existing and to identify new network models. Data and models will be managed and disseminated by the Data Management and Bioinformatics Core. We expect that these studies will uncover novel virus-host pathogenic networks, blood biomarkers associated with them, determinants of human adaptation and specific therapeutic targets. In summary, our modeling approaches will find associations between diverse experimental systems that will help us define determinants associated with SARS-CoV bat-human host jumping COVID-19 severity, and link them to in vivo and ex vivo signatures for risk assessment, companion diagnostics and personalized therapies.