Systems Biology for TB Gary Schoolnik James Galagan

Natural History Latency Natural History of Tuberculosis TB Progresses As a Series of Stages Transmission Reactivation Entrance Maintenance Exit Rapid Replication In Alveolar Macrophages Silent bacillemia Innate, but not Acquired Immunity Asymptomatic Host

Natural History Latency Natural History of Tuberculosis TB Progresses As a Series of Stages Transmission Reactivation Entrance Maintenance Exit Acquired Immunity Non-replicating Persistence Bacteria within Granulomas Asymptomatic Host

Natural History Latency Natural History of Tuberculosis TB Progresses As a Series of Stages Transmission Reactivation Entrance Maintenance Exit Acquired Immunity Fails Rapid Replication, Local Spread Bacteria In Necrotic Lesions and Cavities Progressive, Symptomatic Infection

M. tuberculosis Resides In Pathologically-Different Lesions Of The Same Patient Each Lesion Type Differs With Respect To Host Cell Content, Biochemical Features, Immune Determinants Cavity Wall Closed Necrotic Lesion

Heterogeneity Prevails Even Within The Same Lesion Granulomatous Lesion Containing Bacilli Within and Outside Host Cells Caseating Granuloma Mtb in acellular necrotic center Of granuloma Mtb In multinucleated Giant cell and Within Macrophages

Metabolic Adaptations Of M. tuberculosis In IFN γ-Activated Macrophages Switch In Preferred Carbon Source From Glucose To Glycerol and Fatty Acids (Schappinger et al. JEM 198:693, 2003) And Cholesterol In IFNγ-activated Macrophages (Pandrey and Sassetti PNAS 105: 4376, 2008) β-Oxidation of Fatty Acid Pathway Up-Regulated By Mtb In IFNγ-activated Macrophages

Systems Approach to TB Metabolic Network Model Regulatory Network Model Combine genomic technology with computational methods to model TB metabolic and regulatory networks

An International Collaboration Gary Schoolnik (Stanford) RT-PCR Greg Dolganov Audrey Southwick Stefan Kaufmann (Max Planck) in vivo Sample Core Metabolomics Anca Dorhoi 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 Chris Becker (PPD) Proteomics Glycomics

Comprehensive Profiling for TB Chip-Seq SBRI/BU Transcriptomics SBRI/Stanford/ MPIIB Proteomics PPD Glycomics PPD Lipidomics BWH Metabolomics Metabolon in vitro Cultures SBRI Macrophage Cultures MPIIB Computational Regulatory and Metabolic Network Modeling Broad/BU

in vitro Culture Sampling

Macrophage Culture Sampling

Year 1 Updates Sample Production Challenges and Status Regulatory Network Reconstruction Year 1 and Year 2 Milestones

Year 1 Updates Sample Production Challenges and Status Regulatory Network Reconstruction Year 1 and Year 2 Milestones

Sample Production Cores - Status In vitro Production Core In vivo Production Core Proteomics Lipidomics/ Glycomics Metabolomics Transcriptomics Mtb Expression Profiling Host Cell Expression Profiling

In vitro Production Cores - Status In vitro Production Core In vivo Production Core Proteomics Lipidomics/ Glycomics Metabolomics Transcriptomics Mtb Expression Profiling Host Cell Expression Profiling

In Vitro Sample Core (SBRI) Bioflo 110 Fermentor Vessel and Control Unit Successfully Established In SBRI BL3 Lab Hypoxic Culture Condition Generated Technician Hired

Challenge Encountered In Vitro Sample Core (SBRI) Clumping Of M. tuberculosis During Runs In Fermentor Clumps (Bacterial Aggregates And Biofilms) Forming In Reaction Vessel

Why Clumping Is Problematic And Must Be Addressed And Resolved Sample-to-Sample Heterogeneity Single Cells and Bacteria in Aggregates May Represent Different Physiological States and Adaptations Bacteria in the Center of Aggregates May Be Oxygen Limited, Thus Adaptations During Oxygen Shift-Down May Be Spread In Time Across A Heterogeneous Culture

Addressing The Clumping Problem Increase Shear Force Physically Disperse clumps Test Different impellor types Identify Optimal Detergent 1.Not metabolized by Mtb; does not alter growth characteristics 2. Compatible with biochemical profiling (proteomics, lipidomics, metabolomics) 3. Effectively Disperses Clumps

Detergent Studies To Date Standard TB medium contains Tween80 Tween’s polymeric nature interferes with mass spec analysis n-octyl glucopyranoside (NOG) de-clumped M. smegmatis … but not M. tuberculosis, at least at the tested concentrations (<<MMC), not even with 300 RPM agitation

Detergent Studies In Progress 7H9 + NOG + 5% DMSO 7H9 + tyloxypol Three Criteria -Evaluate Growth -Monitor Aggregation State -Evaluate Compatibility With Biochemical Profiling Mass Spec Analysis 7H9 + Triton X-100 n= up to 7 m= 9 or 10 ((C15H21O(C2H4O)m)n

In vivo Production Core In vitro Production Core In vivo Production Core Proteomics Lipidomics/ Glycomics Metabolomics Transcriptomics Mtb Expression Profiling Host Cell Expression Profiling

NOTE: sterilizing step in light yellow boxes Infected THP-1 cells (Both Mtb and Host) Culture filtrate Cell pellet With Mtb chloroform and methanol mixture (2:1, V:V) 0.22 micron filter (2X) Proteomics, lipidomics & metabolomics Lipidomics & metabolomics Guanidinium thiocyanate & temperature Proteomics & Glycomics Status: In Vivo Sample Core ( MPIIB) Preparation of Mtb-Infected THP-1 Cells (Mtb + Host Profiling) Sterilizing Samples For Proteomics, Lipidomics and Metabolomics Cores

In Vivo Sample Core Confronting The Sterile Prep Challenge For The Proteomics/Glycomics Core Observation –GTC treatment + heat of an Mtb culture (lacking host cells) yields a sterile prep that produces useful proteomics data –GTC + heat of Mtb-infected THP-1 cells reduces, but does not eliminate viable Mtb; this material cannot be safely used by the proteomics/glycomics core Task –To identify a condition that produces a sterile prep (as determined by culture and the Alamar Blue assay) –Yields a prep amenable for robust proteomics/glycomics

GTC-based method: explore 3 key variables –Increase GTC volume—to--Cell Pellet volume –Increase temperature –Increase time of incubation for each temperature tested Test all variations of volume, temperature and time in parallel Monitor (1)Sterility as determined by culture (2)Quality of proteomics data Other Methods considered and rejected: Chloroform and methanol – Incompatible with proteomics analysis Gamma-Irradiation – not available at MPIIB High heat alone (45min 85 C) – Likely will result in procedure- dependent modification of proteins Paraformaldehyde – cross-links protein In Vivo Sample Core Confronting The Sterile Prep Challenge For The Proteomics/Glycomics Core

Year 1 Updates Sample Production Challenges and Status Regulatory Network Reconstruction Year 1 and Year 2 Milestones

Gene Regulatory Networks TF ChIP-Seq Expression Data/CLR TF Binding Site Prediction Literature Curation Comparative Genomics Poster: Brian Weiner & Matt Petersen

Regulon Motif Discover Genes Regulated by the same TF Assume a shared promotor TF binding sites

kstR – Lipid/Cholesterol Regulator KstR Binding Motif

MTB Complex Comparative Analysis Environmental Mycobacteria Corynebactera Rhodococcus Streptomyces

Rv3571 Conservation of KstR Binding Site Genes Sequence Conservation M. Tuberculosis H37RV Predicted kstR binding sites

Rv3515ckstR Conservation of Majority of KstR Sites Conserved kstR Binding Sites

Degrade organic compounds in soil and convert to lipid storage Degradation of polycyclic aromatic hydrocarbons (PAHs) in soil. Human smegma: neutral fats, fatty acids, sterols. Remediation of polycyclic aromatic hydrocarbon (PAH) in soil Relatives in Low Places

Origins of Lipid Metabolism Russell (2007) Pathogens Soil

Evolution of Fatty Acid Degradation Genes Size of circle = # Fad Genes Orthologs

KstR Far2 Far1 MTB Fatty Acid Degradation Network

Peter Sisk Far1 Regulon Enriched for Lipids

Thomas Abeel Conservation of KstR -> Far1 Regulation?

KstR Far2 Far1 Conserved Circuitry for Lipid Metabolism? Free Fatty Acids Cholesterol qPCR Data – Greg Dolganov

Comparative Network Analysis Chip-Seq KstR, Far1, Far2

Eflux – Combining Expression with FBA Genome-Wide Metabolic Reconstruction Algorithmically Interpret Expression Data in a Metabolic Flux Context Expression Data Colijn et al. (2009) PLoS Comput Biol Poster: Jeremy Zucker

Genome Scale Model Merged Raman et al. (2005) and McFadden (2008) models and extended Jeremy Zucker

Year 1 Updates Sample Production Challenges and Status Regulatory Network Reconstruction Year 1 and Year 2 Milestones

Year 1 Milestones CompletedIn Progress V1 of Data Tracking System

Year 2 Goals Begin Production Sample Generation Begin Production Profiling –Proteomics, glycomics, metabolomics, lipidomics, transcriptomics Scale up ChIP-Seq –Finalize tet-inducible system –Several dozen TFs Continue Regulatory and Metabolic Network Modeling

Acknowledgements TB Regulatory Network Matt Petersen Brian Weiner Abby McGuire David Sherman Tige Rustad Greg Dolganov GenomeView Browser Thomas Abeel 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 NIAID Valentina Di Francesco Karen Lacourciere Maria Giovanni