Post on 02-Jan-2016
Predicting ErbB Network Signaling Dynamics
Marc R Birtwistle16 Sept 2011
Georgia Health Sciences University
Dept. of BiostatisticsResearch Forum
• Background– ErbB signaling network– Why we build mathematical models to help us
understand signal transduction
• How Cells Generate and Interpret Ligand-Specific Spatiotemporal Signaling in the ErbB Network
• Cell-to-Cell Variability in Protein Expression and ErbB Signaling
Outline
ErbB Signaling Network
Yarden and Sliwkowski, Nat. Rev. Mol. Cell Biol., 2; 127-137 (2001)
ErbB Receptors and Targeted Pharmaceuticals for Cancer Treatment
4
Citri and Yarden, Nat. Rev. Mol. Cell Biol., 7; 505-516 (2006)
• Sometimes they’re successful, but many times they’re not.
• Why? How can we do better?
5
Our Hypothesis: The Collective Network Behavior is Most Important
Oda et al., Mol. Sys. Biol., 1; (2005)
Example: A Negative Feedback Amplifier Inherently Resists Perturbations Within the Feedback Loop
MEK
ERK
Proliferation, Migration, etc
BXB-ER
4-OHT
Without Feedback
U0126
With Feedback
Sturm, O, Orton, R, Grindlay, J, Birtwistle, MR, Vyshemirsky, V, Gilbert, D, Calder, M, Pitt, A, Kholodenko, B and Kolch, W. The mammalian MAPK/ERK pathway exhibits properties of a negative feedback amplifier. Sci Signal 3(153), (2010)
U0126
EGF
Ras
Raf
MEK
ERK
Proliferation, Migration, etc
ErbB1
• Need to know:– Magnitude of Effects on D
A strong; C weak D upA weak; C strong D down
– Dynamics of Interactions with DA slow; C fast D down then upA fast; C slow D up then down
– Localization with DA local; C distant D upA distant; C local D down
Qualitative Knowledge is Not Enough to Predict Outcomes
7
A Mathematical Model Helps Us Keep Track of Quantitative, Spatiotemporal Aspects of ErbB Signaling
Modeling the ErbB Signaling Network in the MCF-7 Breast Cancer Cell Line
Where does the specificity come from?
MCF-7 breast adenocarcinoma cells; HRG induces differentiation (lipid droplet accumulation); EGF induces only proliferation
Adapted from Yarden and Sliwkowski, Nat Rev Mol Cell Biol 2001
Specificity Can Emerge from the Spatiotemporal Dynamics of Signaling
Adapted from Marshall, Cell, 1995
PC-12 Cells
Transient
Sustained
How does the network generate distinct signaling dynamics?
How does the network interpret different dynamics?
MCF-7 cells; HRG induces differentiation (lipid droplet accumulation); EGF induces only proliferation
Birtwistle et al., Ligand-depednent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol, 2007
What is Controlling ErbB Signaling Dynamics?
Transient
Sustained
Constructing a Model of Short-Term ErbB Signaling
Birtwistle et al., Mol Syst Biol, 2007
Biological Knowledge
Wiring Diagram
Kinetic Scheme
•An ordinary differential equation (ODE) model describing signaling from EGF and HRG to ERK and Akt over a 30 minute time course•117 species (ODEs)•96 net reactions (combined forward and reverse)
Data
Generating Hypotheses—Sensitivity Analysis
Birtwistle et al., Mol Syst Biol, 2007
Perturb Parameters for Negative Regulatory Processes—Simulate Effects on Signaling Dynamics
PTP-1BERK to Receptors
ERK to SOS
ERK to Gab
Receptor to RasGAP
Receptor Trafficking
Simulated ppERK dynamics-10-fold ErbB2 overexpression
Quantitatively corroborated by proteomic data of Wolf-Yadlin et al. (2006) and Kumar et al. (2007); 10 and 30 min after EGF stimulation, ERK activation is 1.15 to 2-fold higher in ErbB2-overexpressing human mammary epithelial cells
How Does This Receptor and
Negative Feedback Control Hypothesis
Apply to the Classical PC-12 Cell
System?
von Kriegsheim, Baiocchi, Birtwistle et al., Cell fate decisions are specified by the ERK interactome Nat Cell Biol, 2009
Ras vs. Rap1 Activation?
15
York et al. (1998) observed that both NGF and EGF induce transient Ras activation but only NGF induced sustained Rap1 activation. The hypothesis therefore was that this
sustained Rap1 activation leads to sustained NGF-induced ERK activation.
Using FRET-based reporters for Ras and Rap1 activity, however, Mochizuki et al. (2001) showed that both Ras and Rap1 activation are sustained in response to NGF
Dominant negative Ras blocks EGF and NGF-induced ERK activation…
…without affecting Rap1 activation
von Kriegsheim, Baiocchi, Birtwistle et al., Nat Cell Biol, 2009
Ligand-Dependent Positive Feedback?
16
Santos et al. (2007) found that NGF induces positive feedback from ERK to Ras, whereas EGF induces negative feedback from ERK to Ras. This led to the hypothesis
that the NGF-induced positive feedback is what sustains ERK activity
Test: Stimulate PC-12 cells with NGF in the presence of U0126 (inhibitor of ERK activation) to break the positive feedback, and then measure upstream Ras and MEK activation.
If positive feedback is responsible for sustained ERK activity, then Ras and MEK activation should be transient with U0126.
Mechanisms for generating transient vs. sustained ERK signaling in PC-12 cells remain unclear…
von Kriegsheim, Baiocchi, Birtwistle et al., Nat Cell Biol, 2009
A Quantitative, Interaction Proteomics Approach Identifies New Players
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Neurofibromin-1 (NF1) Phosphoprotein enriched in astrocytes 15 (PEA-15)
•The predominant RasGAP in PC12 cells
•Displays transient vs. sustained dissociation from Ras, which is may be due to ERK phosphorylation of NF1
•A cytoplasmic anchor for ERK when unphosphorylated
•Phosphorylated by either ERK dependent mechanisms (S104) or by Akt directly (S116), and then dissociates from ERK, allowing ERK to enter the nucleus
•Potential positive feedback from ERK to Ras •Positive crosstalk from the Akt pathway
von Kriegsheim, Baiocchi, Birtwistle et al., Nat Cell Biol, 2009
Unfortunately these players don’t reveal a mechanisms for generating transient vs. sustained ERK signaling
A Systems-Level View of EGF and NGF Signaling in PC-12 Cells
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•PKC and RKIP control ERK dynamics (Santos et al., 2007).
•PKC phosphorylates and inactivates RKIP (Corbit et al., 2003).
•PKC is activated via the PLC pathway, which also displays transient vs. sustained behavior:
•PLC signaling is predominantly effective at the plasma membrane where its substrate PIP(4,5)2 is present (Haugh et al., 2002; Di Paolo et al., 2006).
•NGF signaling is shifted towards the plasma membrane by the p75 neurotrophin (NTR) receptor (Saxena et al., 2005), whereas EGF signaling is shifted towards the cell interior due to rapid ligand induced-internalization.
•Active PLC induces Ras activation through the GRP family of guanine exchange factors in PC-12 cells (as phorbol esters) (Buday et al., 2008; Brose et al., 2002)
•Distinct from the canonical SOS pathway which is subject to negative feedback.
von Kriegsheim, Baiocchi, Birtwistle et al., Nat Cell Biol, 2009
A Systems-Level View of EGF and NGF Signaling in PC-12 Cells
19
•Describes signaling from EGF and NGF to ERK over a 30 minute time course in PC-12 cells
•System of 35 ordinary differential equations based on standard chemical reaction kinetics rates
•73 kinetic parameters whose values are constrained by our own data and those of Sasagawa et al. (2005) and Santos et al. (2007).
Based on this model, the main difference between NGF and EGF signaling (sustained vs transient) is receptor localization
Experimentally Testing the Receptor Localization Hypothesis
20
•TGF activates EGFR, but dissociates from EGFR in the endosomes causing receptor recycling. TGF should therefore sustain ERK signaling relative to EGF
•Cbl activity is needed for EGFR internalization, and the Cbl-70Z mutant is an inactive dominant negative. Therefore 70Z should sustain EGF-induced ppERK.
•Knock-down of PLC should make NGF-induced signaling more transient.
von Kriegsheim, Baiocchi, Birtwistle et al., Nat Cell Biol, 2009
MCF-7 cells; HRG induces differentiation (lipid droplet accumulation); EGF induces only proliferation
Nakakuki*, Birtwistle* et al., Ligand-specific c-Fos responses emerge from the spatiotemporal control of ErbB network dynamics. Cell, 2010
How Are Different ERK Dynamics Interpreted?
Transient
Sustained
c-Fos Acts as a Sensor for the ERK Activation Dynamics
Nakakuki*, Birtwistle*, et al., Cell, 2010
Adapted from Murphy et al., Nat Cell Biol 2002
Why does sustained ERK signaling cause transient c-
fos mRNA expression?
How robust are the all-or-nothing pc-Fos responses?
Building a Mechanistic Model
ODE model based on mass-action and Michaelis-Menten kinetics
Parameter estimation with a genetic algorithm
Nakakuki*, Birtwistle*, et al., Cell, 2010
Model Predicts that DUSPs Control the c-fos mRNA Kinetics
EGF HRG
Nakakuki*, Birtwistle*, et al., Cell, 2010
DUSP Knockdown Doesn’t Significantly Affect HRG-induced c-fos mRNA Kinetics
Nakakuki*, Birtwistle*, et al., Cell, 2010
EGFHRG
What’s wrong with the current model?
Hypothesis: c-Fos induces or perhaps is its own transcriptional repressor
Nakakuki*, Birtwistle*, et al., Cell, 2010
Evidence for the Refined Model
EGF HRG
Nakakuki*, Birtwistle*, et al., Cell, 2010
How Robust is This System?The “Core” Model
Training: 10 nM
Validation: 1, 0.1 nM
Nakakuki*, Birtwistle*, et al., Cell, 2010
How Robust is This System?
Cascade of coherent feedforward loops
Robustness to Input Disturbances Robustness to Internal Perturbations
Robustness: Sum over all inverse, absolute parameter sensitivity coefficients for the time-integrated pc-Fos response.
Inner CFL Intact
Inner CFL Broken
Nakakuki*, Birtwistle*, et al., Cell, 2010
Integral Negative Feedback
How General is the Core Model?Predicting the responses in PC-12 cells
Predicting the responses to EGF+PMA in MCF-7 cells
Nakakuki*, Birtwistle*, et al., Cell, 2010
Quantifying the Transient vs. Sustained Paradigm
Approximating the ppERK dynamics
A master relationship between the transience of ppERK signaling and the integrated pc-Fos
response
Nakakuki*, Birtwistle*, et al., Cell, 2010
• Generation:– Ligand-specific, spatiotemporal signaling patterns are
generated by negative feedback coupled with receptor trafficking.
• Interpretation:– Transcriptional circuits consisting of integral negative
feedback and cascades of coherent feedforward loops provide robust interpretation of transient vs. sustained signaling dynamics.
Conclusions-I
Cell-to-Cell Variability in Protein Expression and ErbB Signaling
Cell-to-Cell Heterogeneity in ErbB Network Signaling and Phenotypes
It is widely thought that cell-to-cell variability in protein expression levels plays a major role
Sigal et al., 2006Spudich et al., 1976McAdams and Arkin, 1997to name a few…
Burst-like gene expression in mammalian cells
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Raj et al., Stochastic mRNA Synthesis in Mammalian Cells, PLoS Biology, 2006
Large cell-to-cell variability in mRNA levels of the YFP reporter gene
Variability seems to arise from changes in local chromatin structure
Cell-to-cell variability in protein expression in response to a topoisomerase-1 inhibitor
(camptothecin)
Cohen et al., Dynamic Proteomics of Individual Cancer Cells in Response to a Drug, Science, 2008
Library of cell lines expressing YFP-tagged proteins under control of the endogenous
promoter and automated microscopy methods
Large cell-to-cell variability Divergent
responses
Cell-to-cell variability in DDX5 can partially determine whether a cell lives or dies in response to the topo-1 inhibitor
• What kind of probability distribution characterizes this cell-to-cell variability in protein expression?
• What are the implications for ErbB signaling dynamics?
Questions
37
The “Standard” Gene Expression Model
38
•Stochastic simulations using the Gibson and Bruck method in Copasi
•Six parameters characterizing the model were combinatorially varied to make 6400 total combinations
•Each parameter combination was simulated 707 times to get an approximate steady-state protein abundance distribution
Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.
Results are well-modeled by a gamma distribution
39
SSE=0.24 SSE=0.66
SSE=0.12 SSE=0.21
Fre
qu
en
cy
Fre
qu
en
cy
SSE
Protein Abundance [#]
gamma lognormal Weibull
Fre
qu
en
cy
SS
E
)()(
/)1(
obsk
obs
Nk
k
eNNf
obs
obsobs
Gamma pdf:
Gamma distribution describes data better than lognormal or Weibull distributions
Some Flow Cytometry Measurements Compared to the Gamma Distribution
Model
40
LS174 cells
A2780 cells
MEFs with inducible K-Ras downregulation
Total K-Ras [AU]
Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.
Predictions of the Gamma Distribution Model: Noise Scaling with the Mean
41
kobsobs;2 kobs obs 2;
1
kobs
CV 0.5
1
kobs
Tet-regulated systems and RNAi should affect obs but leave kobs constant (Raj et al., 2006)
Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.
Comparison to Flow Cytometry Experiments
42
Inducible RKIP Downregulation by shRNA in LS174 cells
Inducible WT K-Ras downregulation in MEFs without endogenous Ras
Inducible K-Ras V12 downregulation in in MEFs without endogenous Ras
Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.
Cell-to-Cell Heterogeneity in EGF-induced ERK Signaling
Birtwistle et al., Mixed Analog-Digital Responses Determine Single Cell and Population Switches in MAPK Signaling. Under Revision
EGF-induced ERK activation in HEK293 cell populations measured by flow cytometry
Cell-to-cell variability in total ERK levels doesn’t affect bimodality
What Kind of ERK Cascade Might Account for the Observed Behavior?
Raf pRaf
RasGTP
v1
v2
MEK pMEK
v3
v6
ppMEK
v4
v5
ERK pERK
v7
v10
ppERK
v8
v9
v11
Feedback• positive—PF (Fa=5)• negative—NF (Fa=0.5)• none—US (Fa=1)
• Model adapted from Markevich et al., Mol Syst Biol, 2006
• Topology changed by varying the feedback strength (Fa)
• Total protein levels (RasGTP, Raf, MEK, ERK) sampled from a gamma distribution
• RasGTP dynamics estimated from experimental data
1. Distributions of active ERK display bimodal/shouldering behavior with increasing EGF dosea. Characteristic of bistable or
ultrasensitive dose-responsepositive or no feedback
2. The ERK-on population mean exhibits analog behavior at shorter times, but effective digital behavior at longer times and becomes smaller as time progressesa. Characteristic of negative feedback
Birtwistle et al., Mixed Analog-Digital Responses Determine Single Cell and Population Switches in MAPK Signaling. Under Revision
Comparing Model Simulations with the Observed Population ResponsesData Model
Birtwistle et al., Mixed Analog-Digital Responses Determine Single Cell and Population Switches in MAPK Signaling. Under Revision
All three models give rise to bimodal/shouldering behavior
Only negative feedback model shows proper dose-response
DataModel
Ongoing Testing of the Modeling Predictions
Negative Feedback should be dominant over the investigated time scales
Desensitization of RasGTP levels should underly the mixed analog-digital behavior of the ERK-on population mean
Birtwistle et al., Mixed Analog-Digital Responses Determine Single Cell and Population Switches in MAPK Signaling. Under Revision
DataModel
• Cell-to-cell variability in protein expression seems to be well-described by a gamma distribution
• Bimodal population responses of ERK activation can emerge from a negative feedback system combined with cell-to-cell variability in protein expression
Conclusions-II
• Systems Biology Ireland– Kolch Group– Kholodenko Group (at
Thomas Jefferson University as well)
– von Kriegsheim Group
Acknowledgements
48
• Ogunnaike Group at the University of Delaware, USA
• Hatekeyama Group at RIKEN, Yokohama, Japan
• Funding and Partners– Marie Curie
International Incoming Fellowship
– EMBO Long-term fellowship
EXTRA
Protein Expression: A simple bursting model
50
11 bN
2121deg bebN tk
32)(
132deg21deg bebebN tkttk
1t 2t
3t
1b
2b
3b
1
1deg
1
1deg
1
1
k
jj
k
jj
tk
i
k
iii
k
i
tk
ik
e
b
ebN
•Limit as k∞ will give us the desired distribution, which we denote f(Nss)•Nss is a sum of identically distributed, yet non-identically weighted exponential random variablesNot identically gamma; closed-form solution unknown
/1
)(;1
~ BeBfExpB
Run Some Simulations…stochastic sampling of burst sizes and between-burst intervals
Calculate Nss in 10,000 “cells” for different values of , kdeg, and
51
Nss: Random variable; protein abundance directly after an expression burst: Mean number of proteins produced per expression burstkdeg: First-order protein degradation constant: Mean waiting time between expression bursts
Simulation Results are Well-Modeled by the Gamma Distribution
N ss ~Gam(keff , eff ); f (N ss )N ss
(keff 1)e Nss / eff
eff keff (keff )
Observed Protein Abundance Distribution
53
Nobs
t
Nss
t
tobs
obstkssobs eNN deg
Observed protein abundance, Nobs, is an explicit function of two random variables:
Nss: Number of proteins after an expression burst; gamma
tobs: Time of observation after an expression burst; uniform between 0 and t
Run Some Simulations…stochastic sampling of burst sizes, between-burst intervals, and
observation time
Calculate Nobs in 10,000 “cells” for different values of and kdeg
54
Nobs: Random variable; observed protein abundance: Mean number of proteins produced per expression burstkdeg: First-order protein degradation constant: Mean waiting time between expression bursts
The Gamma Distribution Again
55
56
What about a more realistic gene expression model?
More Knockdown Experiments
57
A2780 cells
MDA-MB-237 and SW480 cells; Lapan et al., 2008
Fold Mean Expression Change = 0.57
Fold Mean Expression Change = 0.61
Fold Mean Expression Change = 0.71
What Does This Mean for Knockdown Experiments?
58
What Does This Mean for Knockdown Experiments?
59
Dox [ng/mL]R
ela
tiv
e R
KIP
A
bu
nd
an
ce
LS174 colon carcinoma cells with tet-inducible RKIP downregulation by shRNA expression
Assay RKIP levels by flow cytomtery
Can We Get a “Good” Knockdown?
60
Endogenous Ras-less MEFs with engineered tet-off WT K-Ras expressionK-Ras levels measured by flow cytometry
Some Fluorescence Microscopy Measurements Compared to the
Gamma Distribution Model
61
Raj et al., 2006, CHOs stably expressing CFP or YFP
Lapan et al., 2008, PTEN data from MDA-MB-231 cells; STAT3 data from SW480
Predictions of the Gamma Distribution Model: Noise Scaling with the Mean
62
2
2kobs obs
2
kobs obs
obs
Based on modeling, protein half-life and burst frequency affect kobs. So far we’ve have had trouble experimentally manipulating these
cleanly. Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.
Data from yeast support this scaling behavior
63
Log scale makes inverse proportionality trend appear linear
43 GFP tagged protein proteins under 11 different environmental conditions in S. cerevisiae (Bar-Even et al, 2006)
Birtwistle, MR, Kholodenko, BN, Kolch, W. Mammalian Protein Expression Noise: The Gamma Distribution and Implications for Overexpression and Knockdown Experiments. To be submitted.