Analysis of Networks of Signaling Pathways

Linda J. BroadbeltChemical and Biological Engineering

Adilson MotterPhysics

Jackie JerussFeinberg School of Medicine

Lonnie SheaChemical and Biological Engineering

2

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?

3

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

4

Proposed project team

Jackie JerussBreast cancer surgeon/researcher

Lonnie SheaGene delivery/cell-based assays

Linda BroadbeltDynamic reaction modeling

Adilson MotterNetwork analysis

McCormick

Feinberg

Weinberg

5

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

6

Transcription factor activity

7

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

8

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?

9

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

10

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

11

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

12

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

13

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

14

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

15

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

16

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

17

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

11H

9ACP

8O

7C

6CH3

5H

4CR

3O

2C

1R

S-ACPS-ACP4

····3

·· ··H

H

2+ H + H

CRC

O

C

O

H CH3

1 2

65

7

10

8

9

EC 1.1.1

·· ··

11

CRC

O

C

O

CH3

1

3

4

65

7

8

9

·· ··H

11

10

00000000000ACP9

0

0

0

0

0

0

0

1

0

0

10H

0000000010H11

0000000100H10

0000000000O8

0000000000C7

0000000000CH36

0000000000H5

0000000000CR4

00000004-10O3

1000000-101C2

0000000010R1

11H

9ACP

8O

7C

6CH3

5H

4CR

3O

2C

1R

00000000000ACP9

0

0

0

0

0

0

0

1

0

0

10H

0000000010H11

0000000100H10

0000000000O8

0000000000C7

0000000000CH36

0000000000H5

0000000000CR4

00000004-10O3

1000000-101C2

0000000010R1

11H

9ACP

8O

7C

6CH3

5H

4CR

3O

2C

1R

Automated generation of signaling networks: dynamic topology

18

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

19

Proposed project team

Jackie JerussBreast cancer surgeon/researcher

Lonnie SheaGene delivery/cell-based assays

Linda BroadbeltDynamic reaction modeling

Adilson MotterNetwork analysis

McCormick

Feinberg

Weinberg

21

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.)

22

‘Single-task’ optimization reduces number of active reactions

23

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.

24

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

25

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?

26

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

27

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

28

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

29

JAK/STAT signaling pathway

Yamada, S., et al. 2006. International Immunology

30

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

31

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

32

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

33

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

34

Pre-process data - identify live cells

QuickTime™ and a decompressor

are needed to see this picture.

35

Pre-process data - compensationIL

-12R

1

IL-1

2R1

pSTAT4IL-12R2

36

IL-12 signaling - receptor expression

0 min15 min30 min1 hr2 hr3 hrControl

0.0

0.5

1.0

1.5

2.0

2.5

IL-12R2

Den

sity

-200 0 200 103

104

105

37

IL-12 signaling - receptor expression

0

50

100

150

200

250

300

350

0 60 120 180

Time (min)

IL-1

2R2

Balb/cC57BI

IL-12 receptor expression is dynamically regulated following activation

38

IL-12 signaling - pSTAT4 expression

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

-1 0 1 2 3

pSTAT4

Den

sity

0 min15 min30 min60 min120 min180 min

Population of inactive cells

39

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

40

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

41

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

42

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

43

Model of IL-12 signaling

nPP

CytRJT

CytRJT*

TYK

CytRJT* -

Adapted from Yamada, S., et al. 2006. International Immunology

44

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

45

Summary

Dynamic differences between the two inbred mouse strains

Differences are regulated through different mechanisms

Reaction pathway analysis can help discriminate between varying hypotheses

46

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

47

Acknowledgements

Prof. David J. KlinkeProf. Linda J. Broadbelt

Deepti Gupta

Funding:

NSF Graduate Research Fellowship

NIH RR016440 and NIH RR020866