Core C - Modeling Core

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PROJECT SUMMARY/ABSTRACT SARS-CoV-2 infection leads to different clinical outcomes that range from mild symptoms to hospitalization and sometimes death. However, biomarkers predictive of COVID-10 disease outcomes (i.e., symptoms severity, viral replication), which are key for preventive medicine, have not yet been identified. Importantly, elucidation of the molecular components that govern predictive signatures of clinical outcomes will provide important clues that will guide the development of novel therapeutic strategies. This U19 will build on a wealth of data gathered by the previous FluOMICS next-generation scientist and pursue two converging hypotheses 1) host innate immune response pathways triggered by SARS-CoV-2 infection determine the pathogenic outcome of COVID-19 disease 2) the crosstalk between SARS-CoV-2 variants and these host innate pathways results in different transcriptional and post-translational signatures contributing to the difference in disease severity and host-tropism of the viruses. The primary objective of the Modeling Core will be to use network-based modeling approaches to integrate the experimental data generated by Project 1 and Project 2, identify innate biological processes that can predict COVID-19 disease outcomes and validate them functionally in collaboration with Project 1 and Project 2. In Aim 1, the Modeling Core will integrate OMIC datasets generated in Project 1 from data from Human in vivo and mice in vivo studies and processed and analyzed by the Technology Core and the Data Management and Bioinformatics Core. The Modeling core will build host response network models that depict the viral-host interaction at play in early events of COVID-19 infection and disease, i.e., viral replication, escape from the host innate immune response in natural infection and upon vaccination and triggering of the deleterious pro-inflammatory immune response. In Aim 2, the Modeling Core will apply heuristic approaches and an iterative process with Project 1 and Project 2. This approach will help identify novel pathways downstream of host-pathogen interactions. Importantly, it will guide the implementation of gene editing of these genes and pathways in Project 2 to validate these models and improve their accuracy experimentally; It will help understand how SARS- CoV-2 variants affect the viral-host networks built-in Aim 1. In Aim 3, the Modeling Core will use experimentally identified 3D structures of viral-host interactions and an artificial intelligence model, AlphaFold, to identify virus-host interaction in the bat, the natural host of the coronaviruses, and compare them to interactions in humans to study host-tropism of the virus and infer how these changes could impact on the pathogenic outcome of the infection. Altogether the Modeling core will provide a platform that will guide the identification and experimental validation of novel druggable targets that can stop the spread of the virus in infected hosts and worldwide populations.