Influenza is a major public health concern around the world and determining the prognosis of an infected patient who was otherwise healthy is often a major challenge. In 2009, infections with the H1N1 strain resulted in 274,000 hospitalizations and 12,470 deaths. Risk factors for morbidity and mortality include age, co-morbid illness, such as diabetes meNitus, and lower respiratory tract disease. Viral infection is initiated in the upper ainway and, in severe cases, followed by progression to lower tract disease. In both human studies and pre-clinical animal models, several biomarkers have been associated with more severe disease, including TNF-a, IL-6, and IL-17. Host response to influenza infection is a complex trait that involves entire host-pathogen interaction networks of RNA transcripts, proteins and metabolites impacting cellular, tissue and whole organism behaviors that ultimately define both the risk and severity of infection. The complex array of these interacting factors affect entire network states that in turn increase or decrease the risk of infection or the severity of response to infection. The focus of our project is to integrate multi-scale data collected over the course of influenza infections-including system-wide transcriptomics and meta- transcriptomics, immunological response and physiological markers, along with viral diversity-in order to perform network analyses and develop computational models that predict severe disease outcome. Our goal is to leverage the power of high-dimensional, large-scale Omics data and mathematical modeling to identify risk-stratifying prognostic biomarkers that could be used in the development of point-of-care testing applicable to clinical respiratory samples to identify patients at risk for severe influenza disease. To achieve this goal, we will build predictive models from molecular interaction networks, translated to specific severity outcomes. We propose to use an age-dependent animal model (neonatal, adult and aged ferrets) and clinical human samples to collect biological measurements at multiple scales of host-virus interaction.
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