Systems Biology for TB

1. Systems Biology for TB Gary Schoolnik James Galagan

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

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

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

5. 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 Closed Necrotic Lesion Cavity Cavity Wall

6. Heterogeneity Prevails EvenWithin The Same LesionGranulomatous Lesion ContainingBacilli Within and Outside Host Cells Caseating Granuloma Mtb in acellular necrotic center Of granuloma Mtb In multinucleated Giant cell and Within Macrophages

7. Metabolic Adaptations Of M. tuberculosis In IFN γ-Activated Macrophages β-Oxidation of Fatty Acid Pathway Up-Regulated By Mtb In IFNγ-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)

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

9. 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

10. 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

11. in vitro Culture Sampling

12. Macrophage Culture Sampling

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

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

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

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

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

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

19. Why Clumping Is ProblematicAnd 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

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

21. 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

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

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

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

25. In Vivo Sample CoreConfronting The Sterile Prep ChallengeFor 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

26. In Vivo Sample CoreConfronting The Sterile Prep ChallengeFor The Proteomics/Glycomics Core • 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 • Sterility as determined by culture • 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

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

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

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

31. KstR Binding Motif kstR – Lipid/Cholesterol Regulator

32. MTB Complex Comparative Analysis Environmental Mycobacteria Rhodococcus Corynebactera Streptomyces

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

34. Conservation of Majority of KstR Sites Rv3515c kstR Conserved kstR Binding Sites

35. 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

36. Origins of Lipid Metabolism Pathogens Soil Russell (2007)

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

38. Far1 Far2 MTB Fatty Acid Degradation Network KstR

39. Far1 Regulon Enriched for Lipids Peter Sisk

40. Conservation of KstR -> Far1 Regulation? Thomas Abeel

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

42. Comparative Network Analysis Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq Chip-Seq KstR, Far1, Far2

43. 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

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

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

46. Year 1 Milestones V1 of Data Tracking System Completed In Progress

47. 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

48. 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