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Systems Biology for TB. Gary Schoolnik James Galagan. 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. - PowerPoint PPT Presentation
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Systems Biology for TB
Gary Schoolnik
James Galagan
Natural History
Latency
Natural History of TuberculosisTB Progresses As a Series of Stages
Transmission
ReactivationEntra
nce
Maintenance
Exit
Rapid Replication In Alveolar MacrophagesSilent bacillemia
Innate, but not Acquired ImmunityAsymptomatic Host
Natural History
Latency
Natural History of TuberculosisTB Progresses As a Series of Stages
Transmission
ReactivationEntra
nce
Maintenance
Exit
Acquired ImmunityNon-replicating PersistenceBacteria within Granulomas
Asymptomatic Host
Natural History
Latency
Natural History of TuberculosisTB Progresses As a Series of Stages
Transmission
ReactivationEntra
nce
Maintenance
Exit
Acquired Immunity FailsRapid Replication, Local Spread
Bacteria In Necrotic Lesions and CavitiesProgressive, Symptomatic Infection
M. tuberculosis Resides InPathologically-Different Lesions Of The Same Patient
Each Lesion Type Differs With Respect ToHost Cell Content, Biochemical Features, Immune
Determinants
Cavity
CavityWall
ClosedNecroticLesion
Heterogeneity Prevails EvenWithin The Same LesionGranulomatous Lesion ContainingBacilli Within and Outside Host Cells
Caseating Granuloma Mtb in acellularnecrotic centerOf granuloma
MtbIn multinucleated
Giant cell andWithin Macrophages
Metabolic Adaptations Of M. tuberculosis InIFN γ-Activated Macrophages
Switch In Preferred Carbon SourceFrom 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 AcidPathway Up-Regulated
By MtbIn IFNγ-activated
Macrophages
Systems Approach to TB
Metabolic NetworkModel
Regulatory NetworkModel
Combine genomic technology with computational methods to modelTB metabolic and regulatory networks
An International CollaborationGary Schoolnik
(Stanford)RT-PCR
Greg Dolganov
Audrey Southwick
Stefan Kaufmann (Max Planck)
in vivo Sample CoreMetabolomicsAnca Dorhoi
James Galagan (Broad, BU)ChIP-Seq
Bioinf/ModelingBrian WeinerMatt PetersenJeremy Zucker
David Sherman (SBRI)
in vitro sample CoreMicroarray Tige RustadKyle Minch
Branch Moody (BWH)
LipidomicsLindsay Sweet Chris Becker
(PPD)ProteomicsGlycomics
Comprehensive Profiling for TB
Chip-SeqSBRI/BU
TranscriptomicsSBRI/Stanford/
MPIIB
ProteomicsPPD
GlycomicsPPD
LipidomicsBWH
MetabolomicsMetabolon
in vitro CulturesSBRI
Macrophage CulturesMPIIB
Computational Regulatory and Metabolic Network ModelingBroad/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
MtbExpression
Profiling
Host CellExpressionProfiling
In vitro Production Cores - Status
In vitro Production
Core
In vivo Production
Core
Proteomics Lipidomics/Glycomics
Metabolomics Transcriptomics
MtbExpression
Profiling
Host CellExpressionProfiling
In Vitro Sample Core (SBRI)
Bioflo 110 Fermentor Vessel and Control UnitSuccessfully Established In SBRI BL3 Lab
Hypoxic Culture Condition GeneratedTechnician 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 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
Addressing The Clumping Problem
Increase Shear Force
Physically Disperse clumps
Test Different impellor types
Identify Optimal Detergent1. Not metabolized by Mtb; does not alter
growth characteristics2. 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
MtbExpression
Profiling
Host CellExpressionProfiling
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 andMetabolomics Cores
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
• GTC-based method: explore 3 key variables– Increase GTC volume—to--Cell Pellet volume– Increase temperature– Increase time of incubation for each temperature testedTest 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 CoreConfronting The Sterile Prep ChallengeFor 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-SeqExpression Data/CLRTF Binding Site Prediction
Literature CurationComparative Genomics
Poster: Brian Weiner & Matt Petersen
www.tbdb.org
Regulon Motif Discover
Genes Regulated by the same TF
Assume a shared promotorTF binding sites
kstR – Lipid/Cholesterol Regulator
KstR Binding Motif
MTB Complex
Comparative Analysis
EnvironmentalMycobacteria
Corynebactera
Rhodococcus
Streptomyces
Rv3571
Conservation of KstR Binding Site
Genes
Seq
uen
ce C
on
serv
atio
n
M. Tuberculosis H37RV
Predicted kstR binding sites
Rv3515c kstR
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 AcidsCholesterol
qPCR Data – Greg Dolganov
Comparative Network Analysis
Chip-SeqChip-SeqChip-Seq
Chip-Seq
Chip-Seq
Chip-Seq
Chip-SeqChip-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
Completed In ProgressV1 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
AcknowledgementsTB Regulatory NetworkMatt PetersenBrian WeinerAbby McGuireDavid ShermanTige RustadGreg Dolganov
GenomeView BrowserThomas Abeel
TB SysBio TeamGreg DolganovDavid ShermanTige RustadKyle MinchLouiza DudinStefan KauffmanAnca DorhoiBranch MoodyLindsay SweetChris BeckerBrian WeinerJeremy ZuckerAaron BrandesMichael Koehrsen
Audrey Southwick
NIAID
Valentina Di FrancescoKaren LacourciereMaria Giovanni