Valentina Di Francesco
This project proposes an integrated, multi-disciplinary strategy for mapping the regulatory and metabolic networks of Mycobacterium tuberculosis (MTB) and the relevant state of these networks during in vitro culture and infection in the host. MTB is the causative agent of tuberculosis. Multidrug-resistant (MDR) and extensively drugresistant (XDR) tuberculosis have emerged as serious concerns throughout the world, and are classified as NIAID schedule C select agents.
This strategy integrates a combination of transcription, metabolic, proteomic, glycomic, and lipidomic profiling; high-throughput promoter mapping, bioinformatic and comparative sequence analysis; and computational modeling.
Gary K. Schoolnik, M.D. – Principal Investigator, Stanford Medical School
James Galagan, Ph.D. – Principal Investigator, Boston University/Broad Institute
D. Sherman, Ph.D. – Seattle BioMedical Research Institute (SBRI)
S. Kaufmann, Ph.D. – Max Plank Institute for Infection Biology (MPIIB)
D. B. Moody, M.D. – Brigham and Women's Hospital
Daniel Chelsky, Ph.D. – Caprion
The goal is to characterize the elements, interactions, states of the M. tuberculosis (M.tb) gene, protein, lipid, and metabolic networks during in vitro growth under conditions relevant to latency and infection. This will be accomplished by first establishing the M.tb in vitro Culturing and Sample Production Core at SBRI. Samples obtained from M.tb grown in vitro during log phase, hypoxia and re-aeration will be distributed to the High-Throughput Profiling Cores. SBRI will use microarrays to obtain expression profiles while Stanford uses a multiplex RT-PCR assay on the same RNA preparation. Expression profiling results from the microarray and RT-PCR methods will be compared and calibrated so that RT-PCR results obtained from the macrophage infection modeling Project 2 can be compared with microarray results obtained from in vitro grown bacteria. The Proteomics Core will monitor M.tb proteins produced during in vitro growth. In year 2 of this contract the Lipidomics, Glycomics and Metabolomics Cores will be established to profile M.tb produced lipids, carbohydrates, glycoproteins, and metabolic products, respectively. The Chromatin Immunoprecipitation (ChIP-Seq) Core will identify genes regulated by transcription factors identified by the expression profiling studies. The experimental protocols, analytical tools, profiling data from the study of in vitro grown M.tb and the library of epitope-tagged transcription factors employed for ChIP-Seq studies will be made publicly accessible via a systems biology web portal.
The goal is to characterize the state of both the pathogen and host molecular networks during infection of the host cell. This will be accomplished by first establishing the Host Cell Culturing and Sample Production Core at MPIIB. Profiling data will be obtained from both host and microbe using M.tb infected THP-1 macrophage cultures. Profiling data will be acquired at early, intermediate and late time points from infected macrophage cultures as well as macrophage profiles from uninfected cells. Proteomics and expression profile data will be obtained starting in year 1, with glycomics, lipidomics, metabolomics profile data starting in year 2. The Agilent host microarray assays will be performed by MPIIB. The experimental protocols, analytical tools and profiling data from the study of the THP-1 macrophage--M.tb infection model will be made publicly accessible via the TB Systems Biology Web Portal.
To develop predictive computational models of gene regulatory and metabolic networks for M. tuberculosis a Bioinformatics Core and a Centralized Database will be established for all contract generated data and provide public access through a TB Systems Biology Web Portal. This Core will develop predictive computational models of gene regulatory and metabolic networks for M. tuberculosis. ChIP-Seq data, expression data, proteomic data, and comparative genomics information will be integrated to develop and validate a model of gene regulation in Mtb. Existing algorithms will be used to infer network topology and the topology used to develop predictive models based on Boolean networks. Sequence functional annotations, metabolomic, lipidomic, glycomic, proteomic and expression data will be integrated to develop a predictive model of the metabolic network of M.tb based on steady state metabolic analysis using Flux Balance Analysis (FBA) and extensions to FBA that have been developed to predict metabolic state from expression data using FBA. Models will be developed that predict the M.tb state in vivo, i.e. within infected tissues. The gene regulatory and metabolic network models developed above will be used to make predictions and form hypothesis concerning the Mtb state in vivo.
Last Updated July 27, 2012