Tuberculosis cannot be successfully controlled unless we recognize - and act upon -- the host-pathogen interactions critical to infection outcome. The central thesis of this proposal is that the outcome of M. tuberculosis infection with respect to latency and reactivation depends on the reciprocal interplay between host and pathogen signaling networks, metabolic pathways, and genetic programs. The goal of the proposed research is to uncover and mechanistically understand how intercellular networks operating between tubercle bacillus and lung macrophage govern the transitions to/from latency at the level of genetic programs and cellular metabolism. We propose to combine i) statistical pathway analyses and ii) bottom-up and top-down modeling strategies utilizing publicly available data, data contributed by on-going research in participating laboratories, and data generated in the present program from ex vivo infection of human primary lung macrophages with M. tuberculosis. We have three specific aims.
In Aim 1, ex vivo infection data will be analyzed by statistical pathway analysis to correlate macrophage response with donor infection state (uninfected, latently infected, active disease) and relative virulence of infecting bacilli (wild type vs. attenuated). This work should reveal processes associated with latency and reactivation in host cells, generate hypotheses concerning networks and nodes critical to either outcome, or guide development of a mechanistic model for genetic cross-regulation between host and pathogen.
In Aim 2, we propose to identify candidate switch networks in the tubercle bacillus and construct mechanistic mathematical models to determine dormancy switch logic. In particular, we will test whether the network controlling the transition to dormancy results from the superposition of multiple interlinked host-induced stress-response switches coupled by complex logical gates. This work should result in predictions for conditions and mechanisms for growth arrest and dormancy- specific gene expression signatures.
In Aim 3, mathematical modeling and experimental tests will target key molecular processes mediating reciprocal macrophage-pathogen interactions at the level of lipid metabolism. Specifically, we will seek to determine whether changes in lipid metabolism occurring in the macrophage and in the tubercle bacillus form an intercellular feedback loop. Models developed by combining experimental and theoretical approaches will allow in silico simulations. These simulations will direct additional experimental perturbations to the ex vivo infection protocol that will refine the models. Understanding outcome-determining interactions between tubercle bacilli and the macrophages that carry them will have far-reaching effects on tuberculosis vaccine research, diagnostics, and therapeutics.
More than two decades of intense effort in tuberculosis research have shown that tuberculosis cannot be successfully controlled unless we recognize - and act upon -- the host-pathogen interactions that are critical to infection outcome. We hypothesize that any outcome of infection with tubercle bacilli can be viewed as the result of reciprocal, likely iterative, interaction dynamics in which host cells and bacteria change each other at cellular and molecular levels. Our program proposes to uncover and mechanistically understand the networks controlling these dynamics by combining experimental, computational and modeling approaches. Our goal is to subvert these networks to the host advantage with new vaccines and drugs targeting critical network nodes.
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