Systems Biology for TB

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