This project develops a systems genetics approach using genetically defined recombinant inbred (Rl) rodent challenge models (collaborative cross mice-CC) that capture 90% of the natural genetic variation in the mouse, a unique resource capable of untangling polygenic, complex disease traits. This infection model will be coupled with genomics, proteomics, and reverse genetics and used as a platform technology to identify the virus-host interactions and susceptibility alleles that regulate highly pathogenic respiratory virus induced severe and end-stage lung disease in young and senescent animals. This platform will be used for a comparative pathogenomics approach focused on SARS-CoV, mouse-adapted influenza virus, and highly pathogenic avian influenza viruses. The goal is to compare and contrast the host susceptibility alleles and signaling circuitry that enhance pneumotropic virus replication and pathogenesis with the goal of identifying common key cellular targets that influence severe disease outcomes by diverse respiratory pathogens. To verify the importance of these alleles and signaling pathways in severe lung disease, we will model and empirically test disease outcomes using genetically defined virus mutants, siRNA knockdown techniques, CC recombinant inbred strains and select knockout animals. Finally, we evaluate the role of select common susceptibility alleles in siRNA-treated primates infected with SARS-CoV or high-path influenza viruses. Consequently, we will systematically test the hypothesis that genetic medicine and systems pathogenomics can predict disease outcomes in individuals, identify susceptibility alleles governing severe end-stage lung disease, uncover the role of specific viral genes in host signaling, and provide fundamental insights into the critical host circuitry that promotes efficient virus replication and virulence in the lung.

Public Health Relevance

impact of this work is very high, providing the first definitive studies that identify polygenetic traits associated with complex disease outcomes following acute severe respiratory tract infections in mammals. The research provides a predictive format for determining if genetic medicine can predict disease outcomes prior to infection with various respiratory viruses and potentially identifies key cell signaling pathways which regulate mild or severe disease outcomes in individuals.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
1U54AI081680-01
Application #
7676427
Study Section
Special Emphasis Panel (ZAI1-DDS-M (J2))
Project Start
2009-04-20
Project End
2014-02-28
Budget Start
2009-04-20
Budget End
2010-02-28
Support Year
1
Fiscal Year
2009
Total Cost
$747,616
Indirect Cost
Name
Oregon Health and Science University
Department
Type
DUNS #
096997515
City
Portland
State
OR
Country
United States
Zip Code
97239
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