Mycobacterium tuberculosis causes ~9 million new cases of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched. This project combines separate advances in systems biology and network modeling to produce an experimentally grounded and verifiable systems-level model of the host regulatory networks that affect TB progression.
Following aerosol infection, Mycobacterium tuberculosis (MTB) takes up residence within the phagocytic compartment of innate cells in the lung. Responding to cues from the bacteria, a cascade of innate regulatory networks is initiated which act to combat the bacteria directly, and also to govern the development of the subsequent adaptive immune response. This proposal tests the concept that behaviors of host and pathogen are coordinated by interwoven regulatory networks, and that the outcome of infection (bacterial containment or active disease) is the product of many network-network interactions that vary both spatially and temporally. Containment of infection occurs only when these networks drive coordinated and appropriate innate and adaptive immune responses, whereas progression to TB occurs when the immune response goes awry and is either insufficient or too robust.
Project 1 is predicated on our recent exciting observation of transcriptomic signatures that predict progression to active TB disease in humans. By integrating our human transcriptomic signatures for MTB disease progression with network models of macrophage innate immunity, we have identified nearly 200 candidate Host Regulator Genes (HRGs) of MTB infection. Leveraging our access to a vast and expanding repository of mice harboring ENU-induced incidental mutations, we will screen the HRG mouse mutants for altered MTB-induced innate and adaptive immunity in vivo. HRG mutants that alter TB disease progression will be advanced for systems analysis. After modeling, iteration and integration with Project 2 data, key predictions will be validated in human samples.
In recent years, we have contributed substantially to the infrastructure needed for systems biology, including the development of key tools for data generation, analysis and modeling. We have generated an extensive compendium of innate regulatory networks that will serve as a foundation for the MTB studies proposed here. This project combines separate advances in immunology, transcriptomics, molecular genetics, ChIP-seq, proteomics and network modeling to produce an experimentally grounded and verifiable systems-level model of the host regulatory networks that affect TB disease progression.