Project 2 will apply systems approaches to dissect the complex problem of TB disease progression in vivo, a first for the field. We first describe an innovative screening strategy to identify the MTB genes critical for disease progression in the lung. Previously we built a DNA binding/gene expression model that allows us to predict a regulon for every MTB transcription factor, and assembled a unique collection of MTB strains in which expression of every regulator is perturbed. We will use these strains to perturb every MTB gene regulatory network during aerosol infection of mouse lungs. Once key regulators are identified, we will quantitate and characterize the changes in infected cell types and determine the specific points in disease progression where particular mutants show altered responses. We then perform detailed systems analysis of the key genes and their predicted regulons using bone marrow macrophages infected ex vivo. We will collect host and MTB transcriptomes, MTB global protein level changes and condition-specific ChlP-seq on key MTB regulators from within matched samples of infected macrophages. These data will fuel modeling of both the bacterial and host response networks, predictions from which will drive a new round of mutant evaluation, omics-scale data collection and additional modeling. Our ultimate modeling Aim, a novel integrated host/MTB network model will be tested using samples from humans, with both candidate mutant bacteria and specific host genes modulated by siRNA. 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 also made a strong start for systems analysis of MTB, producing predictive gene regulatory networks based on large-scale ChlP-seq and expression studies. This project combines separate advances in microbiology, transcriptomics, molecular genetics, ChlP-seq, proteomics and network modeling to produce an experimentally grounded and verifiable systems-level model of the MTB regulatory networks that affect disease progression.
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of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched.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 MTB regulatory networks that affect disease progression
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