Identification of Genetic Markers of Susceptibility to Intracellular Bacterial Infection Using the Collaborative Cross Mouse Model

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PROJECT SUMMARY Identification of the genetic and molecular mechanisms governing immunity against intracellular bacteria is imperative for understanding the host-pathogen-interplay and forms the basis for the development of therapeutic countermeasures. Previous attempts at increasing our understanding of this topic have relied on targeted interruption of individual genes or analysis natural genetic variability in natural populations. Herein, we propose to employ 1) animal models with pre-defined genetic variability, 2) cutting edge immunoprofiling, 3) comparative genomics, and 4) computational analyses to identify the immunological and genetic basis of sensitivity to Rickettsia infection. This approach employs the collaborative cross (CC) mice. This mouse resource involves a cohort of recombinant-inbred lines generated by randomizing the genetic diversity of existing inbred mouse resources. This pre-defined genetic diversity has significantly accelerated discovery of genetic determinants that regulate immunity against several pathogens as well as other non-infectious diseases. The CC mouse resource is distinct from other animal models as its high genetic diversity is comparable to that of human populations. Unlike in outbred animal models, each CC line reproducibly exhibits distinct phenotypes of disease susceptibility and immune profiles to pathogens. Our multi-disciplinary team will screen CC lines to establish the range of responses to the tick-borne human pathogen Rickettsia conorii. Using murine models of Rickettsia infection with well-established phenotypic difference in susceptibility to infection, we will screen initially CC mouse lines to encompass a detailed assessment of the disease phenotype (bacterial load, weight loss, body temperature, survival) and immunoprofiling of peripheral blood, spleen, and liver as relevant, representative organ. CC lines with extreme clinical and immunological phenotypes will then be selected for longitudinal in-depth immunoprofiling. Here, changes in the frequency of activated innate and antigen-specific adaptive cells, cytokine profiles in serum, and antibacterial activities of immune cells will be assessed throughout infection and disease resolution. Computational data integration and bioinformatics tools (machine learning) will be applied to establish the immune landscape of Rickettsia-specific immune responses to identify immune correlates that govern disease phenotype of each CC line. The short-term impact of the proposed work will be the identification of novel murine models that emulate differential immune responses to infection. These tools will enable researchers to test therapeutics and/or vaccines in a diverse system that, for the first time, has the potential to forecast responses in humans. Computational analysis will be performed to identify quantitative trait loci associated with disease phenotype and disease-specific immunoprofiles. This information will be the basis for the future identification of genes responsible for immunity against intracellular bacteria, which will have a significant long- term impact on our understanding of multiple rickettsial diseases.