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Systems Biology for Infectious Diseases Research

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Programs

Systems Virology - University of Washington

This project focuses on two NIAID priority pathogens: influenza virus and severe acute respiratory syndrome associated coronavirus (SAR-CoV) using a systems virology approach i.e. comprehensively analyzing and modeling the molecular and cellular events and pathogen-host interactions that occur during the course of respiratory virus infection.

The multidisciplinary research team is applying this approach to experimental systems that use highly pathogenic wild-type viruses and engineered viruses with attenuated pathogenicity due to modifications or deletions of specific viral genes. Their goal is to comprehensively analyze and model the virus-host interactions and cellular response networks induced by these viruses and to identify differences and commonalities in the host response to diverse respiratory pathogens. Findings may provide novel targets for therapeutic intervention or suggest alternative vaccine strategies.

Investigators

Michael Katze, Ph. D. - Principal Investigator, University of Washington

Ralph Baric, Ph. D. – University of North Carolina

Yoshi Kawaoka, Ph. D. – University of Wisconsin

Dick Smith, Ph. D. – Pacific Northwest National Laboratory

Shannon McWeeney, Ph. D. – Oregon Health Sciences University

Katrina Waters, Ph. D. – Pacific Northwest National Laboratories

Research Project

The program's overall hypothesis is that highly pathogenic respiratory viruses use unique and common strategies to mechanistically remodel the intracellular environment and signaling pathways of the host cell to enhance virus replication, regulate disease severity, and promote virus transmission. Using a comparative approach that includes highly pathogenic H5N1 avian influenza virus and severe acute respiratory syndrome associated coronavirus (SARS-CoV) and attenuated viruses, researchers will identify unique and common changes to cellular signaling circuitry that result in severe disease outcomes in the lung. In addition, the program will use defined knockdowns or knockouts of host genes, and high-throughput measurements to develop and systematically refine network models of the direct and indirect effects of viral genes on signaling pathways. Consequently, the program will generate a unified view of the mechanisms of virulence for highly pathogenic respiratory viruses, uncover the role of specific viral genes in host signaling, and provide fundamental insights into the viral regulation of host circuitry that promotes efficient virus replication while simultaneously regulating virulence.

Pathogens

Wild-type influenza – A/VN/1203/04

Attenuated viruses will include:

  • A/chicken/Vietnam/TY9/05.
  • Isogenic viruses that contain modifications, truncations, or deletions in key virulence determinants.

Wild-type SARS – icSARS-CoV

Attenuated viruses will include:

  • Mouse adapted SARS-CoV
  • Isogenic viruses that contain modifications, truncations, or deletions in key virulence determinants.

H1N1-SOV

Host Infection Models

The program is using Calu-3 cells, a human epithelial cell line. Cell lines are the ideal choice for computational modeling of high-throughput data for several reasons, one of which is their relative simplicity. However, using cultured cells as the sole experimental model has its drawbacks. How relevant will the obtained information be to what happens in the complex milieu of an infected animal? Parallel investigations into systems responses in cell culture and in animals, using virulent and attenuated viruses, will unequivocally determine whether cell culture responses can be used to predict in vivo outcomes; a fundamentally important question which could revolutionize live virus vaccine and therapeutic design.

Additionally, with an animal model, it is possible to measure a variety of parameters of disease (e.g., virus yields, clinical signs, gross pathology, histopathology, and time to recovery or death), and the cells or tissues examined with the program's high-throughput analyses come from their natural environment. Of course, the complexity of an animal model makes it less amenable (if not currently impossible) to use as the sole source of information for computational modeling. The program has therefore chosen to use a combination of cultured cells and animal models, with the intent of comparing, integrating, and capitalizing on the benefits of both experimental systems. The program is aware that this approach has its challenges and disadvantages. Still, however difficult this may be, the potential gains are enormous, and using a combination of experimental systems provides researchers with the best opportunity to ensure that the pathways and networks that studied have the greatest relevance to pathogenesis, as well as to translational research in arenas such as the development of antiviral therapeutics.

Modeling Strategies

Given the complexity of the systems to be modeled under this contract, researchers are utilizing a variety of modeling strategies. The majority of these can be classified into one of three major approaches: bottom-up, top-down or hybrid. In the bottom-up approach, modelers focus on the measurement and description of these complex systems to elucidate which elements are involved (i.e., which genes, proteins or metabolites are expressed or up regulated in an infection process), leading to testable hypotheses that these regulated entities are important to pathogenesis.

Top-down modeling approaches allow for the development of integrative and predictive multi-scale models of key biological processes. As much of the success garnered from this type of approach is based upon a clear problem statement within a particular scale (making it appropriate for our initial infections in epithelial cell lines), the modeling group will also rely on a hybrid approach to allow us to move beyond simplistic systems and into animal models. In the hybrid approach, bottom-up models can serve as “scaffolds” for top-down models by providing the quantitative measurements about potential interactions and sub processes to strengthen the top-down predictive modeling.

 

Last Updated March 09, 2011