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Profile image of Christian Forst

    Christian Forst, PhD

    Education

    BS, University of Vienna

    MS, University of Vienna

    PhD, University of Vienna

    Awards

    2012

    Haberecht Wildhare-Idea Research Award

    UT Southwestern Medical Center

    2006

    Distinguished Performance Award

    Los Alamos National Laboratory

    1990

    Distinguished Performance Scholarship

    University of Vienna

    1989

    Elected student participant of 1989 Nobel Laureate Conference

    Research

    Annual influenza epidemics lead to severe illness, life-threatening complications, and death, especially in high-risk groups such as young children, pregnant women, obese individuals, individuals with a compromised immune system, and indigenous populations. However, the precise mechanism of how immune cells mediate recovery in some individuals, but not others, is far from clear. We are using high-throughput genomic, proteomic, and integrated "omic" open-access datasets and resources available via the Bioinformatics Resource Centers (BRCs) in this application to decypher the heterogeneous immune respose to influenza. In particular, we are utilizing immune epitope, viral sequence, and antiviral drug information from the Influenza Research Database (IRD) and combine these data with other public information from studies of human cohorts infected with the influenza virus. Multi-omics single-cell (CITE-seq) data  provides sufficient cellular detail and serve as a “scaffold” of bulk data.

    In our view, a comprehensive and genuinely predictive model of these complex relationships can only be achieved through the systematic, integrative, and multi-dimensional OMICS approach. Host response to vaccination and influenza infection is the result of complex traits that involve a combination of host factors along with entire networks of transcripts, proteins, and metabolites. Together these responses impact cellular, tissue, and whole organism behaviors. Thus, the host responses to vaccination and infection are an emergent property of molecular networks. The goal of this integrated systems biology approach is to understand mechanisms of heterogeneous response to Influenza by determining how the interactions among biological components compare between high-risk and lower-risk populations.