DEVELOPMENT OF A SYSTEMS TOXICOLOGY APPROACH AND ITS APPLICATION TO QUANTIFY THE BIOLOGICAL

IMPACT OF TOBACCO SMOKE IN VITRO AND IN VIVO

Philip Morris International R&D, Philip Morris Products S.A., Neuchâtel, Switzerland

Manuel Peitsch

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Organism Components

Descriptive Biology

Physiology Medicine

What is Network (Systems) Biology?

Source: Adopted from SystemsX Inspired by SystemsX.ch

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Organism Components Networks

Descriptive Biology

Systems Biology Physiology

Medicine Source: Adopted from SystemsX

Inspired by SystemsX.ch

What is Network (Systems) Biology?

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Organism Components Networks

Descriptive Biology

Systems Biology Physiology

Medicine Source: Adopted from SystemsX

Disease is a Network Phenomenon

Inspired by SystemsX.ch

What is Network (Systems) Biology?

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Biological Systems are Very Complex

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Systems Biology is Interdisciplinary: Experimental & Computational Sciences

Text Mining

Data & Information Integration & Navigation

Network Informatics

Data Analysis Mathematical Modeling

S1

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K12

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A14

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G16

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L18

G19

K20

L21

G22

K23

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D28

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E30

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D39

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40 30 20 10

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1

Mass (m/z)

% In

tens

ity

1500 2200 2900 3600 4300 5000

50

100

3876

.3

2738

.9

2324

.7

2495

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3832

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4174

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b28-D (y26)

y24 -Db24-D (y22)

y20 -D

b23

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Experimental Approaches

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Systems Toxicology Biological Network Models

Experimental Data

Computational Tools

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Quantitative Mechanism-Based Systems Impact Assessment

Hoeng J, Deehan R, Pratt D, Martin F, Sewer A, Thomson TM, Drubin DA, Waters CA, de Graaf D, and Peitsch MC. A network-based approach to quantifying the impact of biologically active substances. Drug Discov Today 17: 413-418, 2012.

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Systematic experiments across numerous test systems

Quantitative Mechanism-Based Systems Impact Assessment

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Quantitative Mechanism-Based Systems Impact Assessment .

Build and Maintain Biological Networks

Just Completed 666 nodes 1215 edges 1371 PMIDs

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• Compute Amplitudes of Perturbation for all identified Biological Networks

• Compare the Network Perturbation Amplitudes across responses between different perturbations

• Identify potential biomarkers

indicative of overall Network Perturbation State

Martin F, Thomson TM, Sewer A, Drubin DA, Mathis C, Weisensee D, Pratt D, Hoeng J, and Peitsch MC. Assessment of network perturbation amplitude by applying high-throughput data to causal biological networks. BMC Syst Biol 6: 54, 2012

Quantitative Mechanism-Based Systems Impact Assessment

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Product Biological Impact Factor is a numerical indicator of the impact of a

product (a set of substances) on the system

System 1 System 2 Prod 1 Prod 2 Prod 3 Ctrl Prod 1 Prod 2 Prod 3 Ctrl

Apoptosis 10 3 2 0 5 4 3 1

Oxidative

Stress

5 4 4 1 4 2 2 0

Proliferation

………

………

Exp BN

BIF System 1

BIF System 2

BIF System 3 BIF System 4

BIF System 5

% Apoptosis

% ER Stress

% DNA Repair % Inflammation

% Proliferation

Quantitative Mechanism-Based Systems Impact Assessment

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OECD Required Endpoints Body weight Clinical observations Clinical chemistry Hematology Respiratory physiology Organ weight Histopathology of respiratory tract organs and non-respiratory tract organs Bronchoalveolar lavage fluid

OECD plus OECD TG 412 or 413

Systems Biology Augmented Toxicology (Systems Toxicology)

Exposure for 28 or 90 days

Male and female rats

Sham Low dose Medium dose High dose

Male rats

OMICS Endpoints from organs of interest Transcriptomics - Gene expression Genomics - Methylation pattern Proteomics - Reverse protein array -2 D /MALDI Lipidomics - Lipid species and abundance

Computational analysis

Computational Network Biology, Biological Impact Factor Analysis

Subset of data e.g., Histopathology or Inflammatory data

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Results from a 28 Day Rat Inhalation Study with 3R4F

A) Differential cell count of free lung cells (neutrophils and macrophages) in BALF B) Measurement of BALF chemokines (MIP-1α, MCP-1 and MDC) C) Histopathological findings in lung parenchyma given as mean score of macrophages.

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Results from a 28 Day Rat Inhalation Study with 3R4F

Cornification in the Nasal Respiratory Epithelium

Main Bronchus (Left Lung), Goblet Cell Hyperplasia

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Experimental Design For Systems Biology Endpoints

Cryostat (20 µm sections)

Bronchial RNA

Parenchymal RNA

Left lung

RNE protein RNE RNA

Lung protein

RNE

Respiratory Epithelium of

Main Bronchus Lung Parenchyma

Laser Capture Microdissection

(LCM)

Experimental Data Production

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Experimental and Computational Workflow for Reverse Phase Protein Arrays

RPA Data Meta Data

Wrapper Significance Analysis of

Microarrays (SAM)

Usual methods, e.g. ANOVA, Linear Model,

T-test, Kruskal–Wallis (4) Х Differentially

Expressed proteins

Shapiro-Wilk Test

Extract Protein Protein spotting

Incubation with 1st and 2nd Antibody

Washing Scanning

Data analysis

Experiment

Computation

Experimental Data Production

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Reverse Phase Protein Array Results

A. Lung

Low

Med

High

Sham

B. RNE

Compute Systems Response Profiles

PCA plot in (A) lung and (B) RNE for RPA data and (C) box plot of abundance of protein P38.MAPK in RNE.

C. P38.MAPK in RNE

Sham

Low

Med

Hig

h

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Volcano Plot and Heatmap of Gene Expression Data

low med high low med high low med high

Bronchi Parenchyma RNE

Low Low Low

Med Med Med

High High High

Volcano plots representing the SRPs obtained from the three tissues, bronchi, parenchyma, and RNE, of rats exposed to low, medium and high dose of CS. As shown on the volcano plots, the systems response increased from the low to the high CS exposure level, which is also supported by the heatmap.

Compute Systems Response Profiles

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Network Perturbation Amplitudes

Westra JW, Schlage WK, Frushour BP, Gebel S, Catlett NL, Han W, Eddy SF, Hengstermann A, Matthews AL, Mathis C, Lichtner RB, Poussin C, Talikka M, Veljkovic E, Van Hooser AA, Wong B, Maria MJ, Peitsch MC, Deehan R, and Hoeng J. Construction of a computable cell proliferation network focused on non-diseased lung cells. BMC Syst Biol 5: 105, 2011. Schlage WK, Westra JW, Gebel S, Catlett NL, Mathis C, Frushour BP, Hengstermann A, Van Hooser A, Poussin C, Wong B, Lietz M, Park J, Drubin D, Veljkovic E, Peitsch MC, Hoeng J, and Deehan R. A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue. BMC Syst Biol 5: 168, 2011.

Compute Network Perturbation Amplitude

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Biological Impact Factor Calculations

Compute Product Biological Impact Factor

Bronchi RNE Parenchyma A B C Low

Med

High

Spider diagrams representing the BIF (relative to CS high) computed for the following sub-networks: 1-Cell Stress; 2- DACS/Apoptosis; 3- DACS/Autophagy; 4-DACS/DNA Damage; 5-DACS/Necroptosis; 6-DACS/Senescence; 7-IPN; 8-Cell Proliferation.

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Translational Systems Toxicology

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Basal cell

Olfactory Gland

Supporting cell

Olfactory cell

Goblet cell

Nasal epithelium

Bronchial Epithelium

Basal cell

Ciliated cell Goblet cells

Non-ciliated cell

Buccal and Gingival Mucosa

Basement membrane

Stratified squamous epithelial layer

Lamina propria

Blood vessel

Smooth muscle cells

Endothelial cells

Recapitulation of In-Vivo Biology by Organotypic Systems

“Smoke Street”

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Experimental Data Production

Primary Organotypic Culture of Human Bronchial Epithelial Cells Exposed to Whole CS

Experimental Design

TEST SUBSTANCE SHAM CIGARETTE SMOKE

Exposure Time (Min)

Post-Exposure (p-e)

Time (Hrs)

14 7 21 28 14 7 21 28

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

0.5 2 4

24 48

ENDPOINTS

Gene Expression MicroRNA MMP-1 Release Differential Cell Counts Survival

Organotypic Cultures of Human Primary Bronchial Epithelial Cells Exposed to Whole Smoke

VitroCell® Smoking Robot VC10 Dilution Chamber

Position of the aerosol inlet above the AIR-100 tissue insert

medium membrane

tissue culture well

organotypic tissue

culture insert

Exposure Chamber

37°C 24 well

plate

aerosol inlet

Upper panel: Organotypic cultures of human primary bronchial epithelial cells were directly exposed to mainstream CS using the Vitrocell™ system. Lower panel: The cells were exposed to CS during four different exposure times, then various endpoints were captured after different post-exposure times.

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Mechanistic Investigation with NPA and BIF

Compute Network Perturbation Amplitudes

14 min 0.5h 2h 4h 24h 48h

28 min 0.5h 2h 4h 24h 48h

CS versus SHAM

Cell Stress – Xenobiotic stress

Cell Stress - NFE2L2 Signalling Cell Proliferation - Cell Cycle

0

0.05

0.10

0.15

0.20

0

0.05

0.10

0.15

0.20

0

0.05

0.10

0.15

0.20

Cell Stress – Oxidative Stress

0

0.05

0.10

0.15

0.20

Cell Stress – Xenobiotic stress

Cell Stress -

NFE2L2

Signaling

Cell

Proliferation -

Cell Cycle

Cell Stress – Oxidative Stress

14 min 0.5h 2h 4h 24h 48h

28 min 0.5h 2h 4h 24h 48h

Compute Product Biological Impact Factor

CS versus SHAM

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Xenobiotic Metabolism Network

The xenobiotic metabolism (XM) network model for rat, mouse, and human respectively, which is a sub-network of Cellular Stress Network model, was built by PMI scientists in collaboration with Selventa, Cambridge, MA.

Schlage, W., J. Westra, et al. (2011). "A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue." BMC Systems Biology 5(1): 168.

● Focus on cell types found in non-diseased pulmonary and cardiovascular systems

● Focus on acute

response to physiological stressors

● Includes areas of

biology known to be modulated by smoke

Boundary Overview Network Definition

Ensure network met the research needs of PMI

728 nodes & 1379 edges

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Particulate Matter

Activation of Xenobiotic Metablism in Human and Organotypic Airway Culture Exposed to Whole Smoke

Organotypic Airway Culture Exposed to Whole Smoke vs. Sham

Smok

ers

vs. N

on-s

mok

ers

(GSE

7895

)

• Left: In vivo

• Right: In vitro

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Ackknowledgment

• Mathis C., Wagner S., Kogel U., Poussin C., Schlage W., Stinn W., Meurrens K., Weisensee D., Gebel S., Belcastro V., Xiang Y., Martin F., Sewer A., Hengstermann A., Ansari S. and Hoeng J.

Philip Morris International R&D, Philip Morris Products S.A., Neuchâtel, Switzerland

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Measurable Gene

• A network model is a collection of nodes (molecular entities) and edges

• Network models embody mechanistic knowledge

• Thus network models enable us to link short term molecular biological observations to disease process

Molecular entity

A Network Model is a Substrate for Scoring Biology

PMID: 22651900

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Method NPA Ex

perim

ent

NPA Scoring

Overview of NPA Scoring. Gene expression profiles are projected on the aggregated network model nodes. A single NPA score is then computed. Blue edges indicate causal relations, red edges indicate causal relationships between model nodes and gene expression (green = up-regulation and red = down-regulation).

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Building a Computable Biological Network

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