The goal of this project is to build mathematical models of human innate immune responses to the global pathogen influenza virus A (IAV). To ensure successful replication, viral pathogens must simultaneously hijack several components of the host cell machinery while either evading or disabling innate cellular defenses. The host genetic background and subsequent viral and host signaling interactions dictate disease severity ranging from asymptomatic to mortality. Recent studies of IAV in genetically diverse murine models confirm the critical role of genotype in host response and outcome. Both molecular targets as well as key proteins involved in IAV pathogenesis could be therapeutically exploited to attenuate or prevent disease. Thus, we construct models of the molecular networks driving early innate IAV response that can be used to model genetic effects. Our experimental system is human lung epithelium, the first-line of defense against and target of IAV.
Aim 1. Genetic predictions from the gene regulatory network (GRN) governing human epithelial IAV response. GRNs describe the control of gene expression by transcription factors (TFs). We showed that integrating ATAC-seq with RNA-seq improves GRN accuracy. To construct a dynamic GRN in our heterogeneous lung tissue model, we propose scRNA-seq and scATAC-seq measurements of IAV infection and IFN? stimulation time courses. Our group recently discovered new mechanisms by which the IAV protein Ns1 drives promoter-independent transcriptional ?read-through? and alters 3D-chromatin architecture. Thus, for modeling, we also measure genomic transcription initiation and promoter-capture Hi-C. Following experimental testing and GRN refinement, we will use a deep-learning model trained on DNA sequence and epigenetic data to provide inputs that enable dynamic GRN simulations for thousands of human genotypes. We will identify genetic risk loci and molecular mechanisms driving difference in gene expression responses across individuals.
Aim 2. Model the protein-protein interactions (PPIs) and cellular signaling networks driving the innate immune response to IAV. We developed mutant influenza viruses, each encoding a FLAG-tagged viral protein, while maintaining virulence in vivo. We will use the mutant IAV to map host-virus PPIs in human lung epithelial cells and mouse lung in vivo. Integrating with diverse ?omics datasets, we will construct a molecular network model connecting virus-host PPIs through cellular signaling pathways to IAV-dependent TFs. We will test pathway reconstruction with epistasis mapping. Completion of both aims will lead to a GRN spanning virus-host PPIs and cellular signaling to TF control of gene expression in an innate-immune cell type. Our experimental-computational design is widely applicable. This model, and its future adaptation to other cells, will help identify the genetic and molecular mechanisms driving diverse human IAV responses and the network vulnerabilities to be exploited for IAV therapy.
Human response to Influenza virus infection varies dramatically between individuals, from mildly symptomatic to death. We will systematically measure and model the innate immune response to IAV at the molecular network level: from host-virus protein-protein interactions through cellular signal transduction to changes in gene expression in human cells. The resulting mathematical model will help identify network vulnerabilities to be exploited for IAV therapy and help predict differences in IAV response in the human population.