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Analysis of Networks of Signaling Pathways
Linda J. BroadbeltChemical and Biological Engineering
Adilson MotterPhysics
Jackie JerussFeinberg School of Medicine
Lonnie SheaChemical and Biological Engineering
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New arsenal for fighting breast cancer
Oncologist, surgeon and pathologist work in concert to determine course of treatment Without lymph node involvement
< 1 cm: 5 year survival is 90% 1-2 cm: 5 year survival is 80%
Response to chemotherapy and survival patterns within patients of the same disease stage and prognosis differ
Can the functional integrity of specific signaling pathways help to explain these differences?
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Project overview: Patient-specific staging for breast cancer
Goal is to give patients the treatment they need for their cancer
Molecular staging based on protein or gene expression has had limited utility
Cell-based assays that report on cell activity are proposed
Correlate the transcriptional activity profile of primary tumor cells to known tumor factors, stage and grade
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Proposed project team
Jackie JerussBreast cancer surgeon/researcher
Lonnie SheaGene delivery/cell-based assays
Linda BroadbeltDynamic reaction modeling
Adilson MotterNetwork analysis
McCormick
Feinberg
Weinberg
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Transfected cell arrays Potential to combine power of the
genome with cell-based assays to provide unique information relative to “omics” approaches
Reports on cell functionality Contextualize integrity of multiple signal transduction pathways
simultaneously Determine the degree of cancer cell differentiation and normal
cellular processing in individual tumors
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Transcription factor activity
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Correlate transcriptional activity to cancer grade and stage
Use network analysis techniques to match transcriptional activity profile of cells with prognostic factors of stage, grade and hormonal status
Input
Genes
Output
Cancer grade and stage
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Intriguing questions
Do individual patients have a more benign or malignant course dependent on the presence or absence of specific signaling pathways?
Are patients who remain healthy or ultimately go into cancer remission in the face of a signaling mutation or derangement compensating through the redundancy or cooperation of other signaling pathways?
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Synthetic recoverySynthetic recovery
Compare:
1. actual metabolic fluxes with 2. metabolic fluxes that would optimize fitness
Knockout genes associated with reactions where:actual flux >> optimal flux
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E. coliTCA cyclearabinose medium
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
Example in E. coli
Predicting Synthetic Rescues in Metabolic Networks, A.E. Motter, N. Gulbahce, E. Almaas, A.-L. Barabasi, Molecular Systems Biology 4, 168 (2008)
Spontaneous Reaction Silencing in Metabolic Optimization, T. Nishikawa, N. Gulbahce, A.E. Motter, PLoS Computational Biology, to be published
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QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
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Dynamic modeling
Cell-based array data can be easily collected as a function of time
Use dynamic modeling to understand time-dependent activity of signaling pathways
Related work: examining how well the generalized JAK/STAT signaling mechanism represents the dynamics of the IL-12 activation pathway
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Model of IL-12 signaling
nPP
CytRJT
CytRJT*
TYK
CytRJT* -
Adapted from Yamada, S., et al. 2006. International Immunology
Cyt: IL-12
R: IL-12 receptor
JAK (J), TYK (T) : Janus kinase proteins
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Model of IL-12 signaling
Phase 1: Equilibration Phase 2: IL-12 signal
Stimulate system by adding IL-12 Calculate concentration profiles Compare to experimental results
IL-12 receptor Active STAT4 Normalized active STAT4
Phase 3: Parameter identifiability
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Model of IL-12 signaling0 1 2 3
0
10
20
30IL12
0 1 2 30
50
100uIL12R
0 1 2 30.012
0.013
0.014
0.015
0.016TYK2
0 1 2 30.03
0.032
0.034
0.036JAK2
0 1 2 30
20
40
60
80IL12-IL12R
0 1 2 30
0.5
1
1.5
2IL12-IL12R-TYK2
0 1 2 30
2
4
6IL12-IL12R-JAK2
0 1 2 30
0.05
0.1
0.15
0.2IL12-IL12R-TYK2-JAK2
0 1 2 30
1
2
3IL12R-TYK2
0 1 2 30
2
4
6
8IL12R-JAK2
0 1 2 30
0.05
0.1
0.15
0.2IL12R-TYK2-JAK2
0 1 2 3700
800
900
1000STAT4c
0 1 2 30
5
10
15
20STAT4ac
0 1 2 30
10
20
30STAT4Dac
0 1 2 30
200
400
600
800STAT4Dan
0 1 2 30
0.2
0.4
0.6
0.8IL12-IL12R-TYK2-JAK2-STAT4c
0 1 2 30
5
10SOCS3
0 1 2 30
0.05
0.1
0.15
0.2IL12-IL12R-TYK2-JAK2-SOCS3
0 1 2 30
20
40
60nPP
0 1 2 30
2
4
6STAT4n
0 1 2 30
50
100
150STAT4an
0 1 2 30
20
40
60nPP-STAT4Dan
0 1 2 30
2
4
6
8nPP-STAT4an
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Model of IL-12 signaling
Negative regulation via SOCS3 does not lead to a decrease in active STAT4
0 1 2 30
50
100
150
200
250
300TOTAL STAT4a
0 1 2 30
0.5
1
1.5
2
2.5
3Normalized STAT4a
Time (hours)
0 1 2 3
TOTAL IL12R
0
20
40
60
80
100
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00001000000ACP9
0
0
0
0
0
0
0
1
0
0
10H
0000000010H11
0000000100H10
0042000000O8
0120001000C7
0000001000CH36
0000001000H5
0001110010CR4
0000000410O3
1000001101CR2
0000000010R1
11H
9ACP
8O
7C
6CH3
5H
4CR
3O
2CR
1R
00001000000ACP9
0
0
0
0
0
0
0
1
0
0
10H
0000000010H11
0000000100H10
0042000000O8
0120001000C7
0000001000CH36
0000001000H5
0001110010CR4
0000000410O3
1000001101CR2
0000000010R1
11H
9ACP
8O
7C
6CH3
5H
4CR
3O
2CR
1R
+ =
00001000000ACP9
0
0
0
0
0
0
0
0
0
0
10H
0000000000H11
0000000000H10
0042000000O8
0120001000C7
0000001000CH36
0000001000H5
0001110010CR4
0000000420O3
0000001201C2
0000000010R1
11H
9ACP
8O
7C
6CH3
5H
4CR
3O
2C
1R
00001000000ACP9
0
0
0
0
0
0
0
0
0
0
10H
0000000000H11
0000000000H10
0042000000O8
0120001000C7
0000001000CH36
0000001000H5
0001110010CR4
0000000420O3
0000001201C2
0000000010R1
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6CH3
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0000000010H11
0000000100H10
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0000000000C7
0000000000CH36
0000000000H5
0000000000CR4
00000004-10O3
1000000-101C2
0000000010R1
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6CH3
5H
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3O
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1R
00000000000ACP9
0
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0
0
0
0
0
1
0
0
10H
0000000010H11
0000000100H10
0000000000O8
0000000000C7
0000000000CH36
0000000000H5
0000000000CR4
00000004-10O3
1000000-101C2
0000000010R1
11H
9ACP
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7C
6CH3
5H
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3O
2C
1R
Automated generation of signaling networks: dynamic topology
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A + B
A + B C DI.J.K L.M.N
Generation0
I.J.KL.M.N
C
Generation1
+ A + B
I.J.KL.M.N
D
Generation2
Generation of novel metabolic pathways
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Proposed project team
Jackie JerussBreast cancer surgeon/researcher
Lonnie SheaGene delivery/cell-based assays
Linda BroadbeltDynamic reaction modeling
Adilson MotterNetwork analysis
McCormick
Feinberg
Weinberg
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Constraint-based Modeling
Experimentally known that: after adaptive evolution, E. coli optimizes growth rate (Ibarra
et al. Nature 2002) - FBA after perturbations, the metabolic system moves to next
available state (Segre et al. PNAS 2002) - MOMA after several generations, growth rate converges to a new
optimal state (Sholomi et al. PNAS 2005) - FBA
Computationally: reconstructed metabolic network (Edwards & Palsson, PNAS
2000) determine the feasible metabolic steady states optimal states: flux balance analysis (FBA, Edwards & Palsson) suboptimal states: minimization of metabolic adjustment
(MOMA, Segre et al.)
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‘Single-task’ optimization reduces number of active reactions
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Of Potential Relevance for the Proposal
Explore these ideas to select against cells in competitive advantage.
Transport some of these ideas to signaling and other cell networks.
Explore the recently reconstructed genome-scale Human metabolic network to model metabolic impacts of cancer.
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Cell-mediated immune response
CD4+ T-cells Lymphocytes derived from the thymus Important in cell-mediated immunity Activated T-cells T helper cells (Th)
Recognize and bind to the its receptor Co-stimulation by surface proteins and
cytokine signaling T helper cells
Th1 produce an immune response to attack intracellular pathogens
Th2 provide a defense against extracellular pathogens
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Engineering individualized immunotherapy requires understanding the mechanistic basis for variations in disease susceptibility
Inbred mouse strains are model systems
Cell-mediated immune response
What are the dynamic differences in the Interleukin-12 receptor
signaling (IL-12) pathway in two inbred mouse strains: C57Bl/6 and
Balb/C?
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Interleukin-12 (IL-12) is a key cytokine involved in cell-mediated immunity
IL-12 initiates a cellular response through the IL-12 receptor IL-12R1: Th2 response downregulation
of 2 Lose ability to signal through IL-12 pathway
IL-12R2: Th1 response Reinforce signaling through IL-12 pathway
IL-12 signaling
O’Garra, A. 1998. Immunity
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Part of JAK/STAT family of signaling networks JAK: Janus kinase STAT: Signal Transducers of Activators of
Transcription
Signaling via the JAK/STAT pathway activation of STATs
STATs translocate to the nucleus and initiate protein synthesis
IL-12 signaling
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Regulation of JAK/STAT pathway Suppressors of cytokine signaling (SOCS)
proteins Cellular environment influences the
strength of signaling
IL-12 signaling
How well does the generalized JAK/STAT signaling mechanism
represent the dynamics of STAT4 activation via the IL-12 pathway in
naïve T helper cells?Klinke, D.J., et al. 2008. Biophysical Journal. In press
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JAK/STAT signaling pathway
Yamada, S., et al. 2006. International Immunology
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Objectives
Quantify differences in IL-12 receptor signaling in two inbred mouse strains: C57Bl/6 and Balb/C
Determine how well the generalized JAK/STAT signaling mechanism represents the dynamics IL-12 activation pathway
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Biological Question
Experimental Design
Flow Cytometry Experiment
Normalization
Quantify Proteins
1) Are measures independent?
2) Are measures specific?
Marginal PDF
Single Cell Analysis
Dynamic Changes
Clustering K-means
SOM
PCA - Factor Analysis …
Analysis•Subset/Gating•Kernel Density Estimation
•User Transform•Princomp•Factanal
Pre-processing•Data-driven Live/Dead Gating
•Background subtraction
•LinLog Transform•Compensation•Scatter Plots
Verification and Interpretation
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Differences in IL-12 signaling
Methods Isolate CD4+ T-cells from Balb/C and
C57B1/6 Activate T-cells for 44 hours
anti-CD3: nonspecific activation of T-cell receptor
anti-CD28: co-stimulation Three treatment groups
No stimulation (control) Treatment with anti-IL-4 only (control) Stimulation with anti-IL-4 and IL-12
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Differences in IL-12 signaling
Methods Frequent sampling of the cell populations Stained with markers for IL-12Rβ1, IL-
12Rβ2, and pSTAT4 Flow cytometry: measure protein
expression as a function of time R/Bioconductor: analyze results Matlab: quantify IL-12 signaling
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Pre-process data - identify live cells
QuickTime™ and a decompressor
are needed to see this picture.
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Pre-process data - compensationIL
-12R
1
IL-1
2R1
pSTAT4IL-12R2
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IL-12 signaling - receptor expression
0 min15 min30 min1 hr2 hr3 hrControl
0.0
0.5
1.0
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2.0
2.5
IL-12R2
Den
sity
-200 0 200 103
104
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IL-12 signaling - receptor expression
0
50
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350
0 60 120 180
Time (min)
IL-1
2R2
Balb/cC57BI
IL-12 receptor expression is dynamically regulated following activation
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IL-12 signaling - pSTAT4 expression
-0.5
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-1 0 1 2 3
pSTAT4
Den
sity
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Population of inactive cells
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IL-12 signaling - pSTAT4 expression
Activation of STAT4 occurs after IL-12 stimulation
-0.5
0
0.5
1
1.5
2
2.5
-1 0 1 2 3pSTAT4
Den
sity
0 min15 min30 min60 min120 min180 min
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Differences in IL-12 signaling
STAT4 decreases after initial expression - occurs to a greater extent in Balb/C
Time (min)
0
200
400
600
800
1000
1200
1400
0 60 120 180
pS
TA
T4
exp
ress
ion
Balb/cC57BI
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IL-12 signaling - normalized STAT4
Decrease in STAT4 is correlated to decrease in number of IL-12 receptors
0
0.5
1
1.5
2
2.5
0 60 120 180
Balb/cC57BI
Time (min)
No
rmal
ized
pS
TA
T4
exp
ress
ion
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Objectives
Quantify differences in IL-12 receptor signaling in two inbred mouse strains: C57Bl/6 and Balb/C IL-12R is dynamically regulated pSTAT4 increases for 30 minutes then
shows a steady decrease Decrease in pSTAT4 occurs to a greater
extent in Balb/C and is related to a decrease in IL-12R
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Model of IL-12 signaling
nPP
CytRJT
CytRJT*
TYK
CytRJT* -
Adapted from Yamada, S., et al. 2006. International Immunology
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Model of IL-12 signaling
Chizzonite, R., T., et al. 1992. Journal of Immunology
0 1 2 30
20
40
60
80IL12-IL12R
0 1 2 30
0.05
0.1
0.15
0.2IL12-IL12R-TYK2-JAK2
0 1 2 30
5
10
15
20STAT4ac
0 1 2 30
10
20
30STAT4Dac
0 1 2 30
200
400
600
800STAT4Dan
0 1 2 30
5
10SOCS3
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Summary
Dynamic differences between the two inbred mouse strains
Differences are regulated through different mechanisms
Reaction pathway analysis can help discriminate between varying hypotheses
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Future work
Incorporate other regulation mechanisms
Parameter identifiability
More experimental data SOCS3 expression Dose-response curve for IL-12R Rate of IL-12 synthesis and degradation
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Acknowledgements
Prof. David J. KlinkeProf. Linda J. Broadbelt
Deepti Gupta
Funding:
NSF Graduate Research Fellowship
NIH RR016440 and NIH RR020866