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Systems Biology for TB. Gary Schoolnik James Galagan. Systems Approach to TB. Combine genomic technology with computational methods to model TB metabolic and regulatory networks. Metabolic Network Model. Regulatory Network Model. An International Collaboration.
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Systems Biology for TB Gary Schoolnik James Galagan
Systems Approach to TB Combine genomic technology with computational methods to model TB metabolic and regulatory networks Metabolic Network Model Regulatory Network Model
An International Collaboration Gary Schoolnik (Stanford) RT-PCR Greg Dolganov Audrey Southwick James Galagan (Broad, BU) ChIP-Seq Bioinf/Modeling Brian Weiner Matt Petersen Jeremy Zucker David Sherman (SBRI) in vitro sample Core Microarray Tige Rustad Kyle Minch Branch Moody (BWH) Lipidomics Lindsay Sweet Stefan Kaufmann (Max Planck) in vivo Sample Core Metabolomics Anca Dorhoi ChrisBecker (PPD) Proteomics Glycomics
in vitro Cultures SBRI Macrophage Cultures MPIIB Computational Regulatory and Metabolic Network Modeling Broad/BU Comprehensive Profiling for TB Chip-Seq SBRI/BU Transcriptomics SBRI/Stanford/ MPIIB Glycomics PPD Proteomics PPD Lipidomics BWH Metabolomics Metabolon
An In vitro Oxygen Limitation Model Progression Into and Out Of Non-Replicating Persistence Aerated Culture Early/Late Time Points Monitor Adaptation To A New State
in vitro Sampling 1 - Fermentor w/Tyloxapol Metabolomics Proteomics/ Glycomics Transcriptomics Hypoxia Reaeration 0 1 2 3 4 5 6 7 +1 +2 Days 60 90 120 Minutes Transcriptomics Bioflo 110 Fermentor Vessel and Control Unit Established In SBRI BL3 Lab Hypoxic Culture Condition Generated
Mtb-Infected J774 Macrophage CellsA Model Of Intra-phagosomal Adaptation Early Stages of M. tuberculosis—Macrophage Interaction Depicting Cell Entry Using The Same Ex vivo Macrophage Infection Model Employed By TBSysBio
Systems Approach to TB Combine genomic technology with computational methods to model TB metabolic and regulatory networks Metabolic Network Model Regulatory Network Model
Gene Regulatory Networks TF ChIP-Seq Expression Data/CLR TF Binding Site Prediction Literature Curation Comparative Genomics www.tbdb.org
Regulon Motif Discover Assume a shared promotor TF binding sites Genes Regulated by the same TF
KstR Binding Motif kstR – Lipid/Cholesterol Regulator
MTB Complex Comparative Analysis Environmental Mycobacteria Rhodococcus Corynebactera Streptomyces
Conservation of Majority of KstR Sites Rv3515c kstR Conserved kstR Binding Sites
Remediation of polycyclic aromatic hydrocarbon (PAH) in soil Human smegma: neutral fats, fatty acids, sterols. Degradation of polycyclic aromatic hydrocarbons (PAHs) in soil. Degrade organic compounds in soil and convert to lipid storage Relatives in Low Places
Origins of Lipid Metabolism Pathogens Soil Russell (2007)
Evolution of Fatty Acid Degradation Genes Size of circle = # Fad Genes Orthologs
Far1 Free Fatty Acids Cholesterol Far2 Conserved Circuitry for Lipid Metabolism? qPCR Data – Greg Dolganov Lipid Metabolism Genes KstR
Comparative Network Analysis Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq KstR, Far1, Far2
Eflux – Combining Expression with FBA Poster: Jeremy Zucker Expression Data Genome-Wide Metabolic Reconstruction Algorithmically Interpret Expression Data in a Metabolic Flux Context Colijn et al. (2009) PLoS Comput Biol
Genome Scale Model Jeremy Zucker Merged Raman et al. (2005) and McFadden (2008) models and extended
Acknowledgements TB SysBio Team Greg Dolganov David Sherman Tige Rustad Kyle Minch Louiza Dudin Stefan Kauffman Anca Dorhoi Branch Moody Lindsay Sweet Chris Becker Brian Weiner Jeremy Zucker Aaron Brandes Michael Koehrsen Audrey Southwick TB Regulatory Network Matt Petersen Brian Weiner Abby McGuire David Sherman Tige Rustad Greg Dolganov GenomeView Browser Thomas Abeel NIAID Valentina Di Francesco Karen Lacourciere Maria Giovanni