Simulation of Crosstalk between Small GTPase RhoA

Bioinformatics Advance Access published December 11, 2008 Simulation of Crosstalk between Small GTPase RhoA and EGFR-ERK Signaling Pathway via MEKK1

Original paper

Simulation of Crosstalk between Small GTPase RhoA and EGFR-ERK Signaling Pathway via MEKK1 Hu Li1,6, Choong Yong Ung1,4,6, Xiao Hua Ma1,4,6, Bao Wen Li3,6, Boon Chuan Low2 Zhi Wei Cao5 and Yu Zong Chen*,1,5,6 1

Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543 2

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Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117542

Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543 5

Shanghai Center for Bioinformation Technology, Shanghai, 201203, P. R. China.

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Center of Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, Singapore 117543

ABSTRACT Motivation: Small GTPase RhoA regulates cell-cycle progression via several mechanisms. Apart from its actions via ROCK, RhoA has recently been found to activate a scaffold protein MEKK1 known to promote ERK activation. We examined whether RhoA can substantially affect ERK activity via this MEKK1-mediated crosstalk between RhoA and EGFR-ERK pathway. By extending the published EGFR-ERK simulation models represented by ordinary differential equations, we developed a simulation model that includes this crosstalk, which was validated with a number of experimental findings and published simulation results. Results: Our simulation suggested that, via this crosstalk, RhoA elevation substantially prolonged duration of ERK activation at both normal and reduced Ras levels. Our model suggests ERK may be activated in the absence of Ras. When Ras is over-expressed, RhoA elevation significantly prolongs duration of ERK activation but reduces the amount of active ERK partly due to competitive binding between ERK and RhoA to MEKK1. Our results indicated possible roles of RhoA in affecting ERK activities via MEKK1-mediated crosstalk, which seems to be supported by indications from several experimental studies that may also implicate the collective regulation of cell fate and progression of cancer and other diseases. Contact: Y.Z. Chen. Tel.: 65-6874-6877. Fax: 65-6774-6756. E-mail: [email protected]

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INTRODUCTION

Rho GTPases such as RhoA regulate cell-cycle progression via multiple mechanisms including the promotion of sustained ERK activities (Coleman, et al., 2004; Etienne-Manneville and Hall, 2002; Hall, 2005; Jaffe and Hall, 2005; Wang and Zheng, 2007). *

To whom correspondence should be addressed

The duration, magnitude and sub-cellular compartmentalization of ERK activation determine progression in diverse outcomes in normal cells (Ebisuya, et al., 2005; Murphy and Blenis, 2006), tumorigenesis (Dhillon, et al., 2007), cardiovascular disease (Budzyn, et al., 2006; Shimokawa and Rashid, 2007), and urinary bladder dysfunction (Peters, et al., 2006). For instance, sustained ERK activation causes proliferation in fibroblasts but differentiation in PC12 cells (Marshall, 1998; York, et al., 1998). Strong ERK activation causes cell cycle arrest in fibroblasts, differentiation in PC12 cells and survival in carcinoma cells, whereas weak ERK activation causes proliferation in both fibroblasts and PC12 cells and apoptosis in carcinoma cells (Murphy and Blenis, 2006). Moreover, mutations and aberrant expression of certain key proteins lead to sustained ERK activation that may promote carcinogenesis (Calipel, et al., 2003; Dhillon, et al., 2007). RhoA has been found to promote ERK activation by its interaction with Rho kinase ROCK, which helps to delay EGFR endocytosis by phosphorylating endophilin A1 and to prevent Akt inhibition of Raf by activating phosphatase PTEN that subsequently hydrolyzes Akt second messenger PIP3 (Coleman, et al., 2004; Wang and Zheng, 2007). Inhibition of Raf by Akt phosphorylation has been shown in various cell types including differentiating muscle cells (Rommel, et al., 1999) and human breast cancer cell lines (Zimmermann and Moelling, 1999), while non-inhibition of Raf by Akt phosphorylation has also been observed in certain other cell types (Kiyatkin, et al., 2006). Therefore, the effects of RhoA on ERK likely vary in different cell-types. Apart from its actions via ROCK pathway, RhoA has been found to bind and activate the kinase activity of a scaffold protein MEKK1 in HEK 293 cells (Gallagher, et al., 2004). Moreover, MEKK1 is known to phosphorylate MEK1 via its kinase activity and to recruit Ras, Raf1, and MEK1 leading to the promotion of

© 2008 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Cell Signaling and Developmental Biology Laboratory, Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543

H. Li et al.

38 initial molecular concentrations. These ODEs were then solved using the Ode45 solver of MatLab. The systems biology markup language (SBML) of our model is provided in the Supplementary Material (the Supporting Information can be found on the Bioinformatics website).

ERK activation (Dhanasekaran, et al., 2007; Gallagher, et al., 2004). In addition, MEKK1 activation of ERK has also been implicated in other cell types such as Jurkat, 3T3, A431 carcinoma, 293T, 3Y1 fibroblast cells. Therefore, at least in some circumstances, RhoA likely affects ERK activation partly via this MEKK1-mediated crosstalk between RhoA and EGFR-ERK signaling pathway. Also it is noted that this RhoA-Ras association is expected to be highly dependent on Rho GEF isoforms (Estrach, et al., 2002), cell types (Yamaguchi, et al., 2001), and environmental context. For instance, in specific cell types and conditions, MEKK1 has been found to not inducing ERK activation (Xu, et al., 1995). None-the-less, investigation of all possible effects of RhoA in different circumstances, including that of RhoA activation of ERK, is important for studying the mechanisms of the collective regulation of cell fate, survival of carcinomas (Fukui, et al., 2006; Li, et al., 2006), neuronal disorders (Mueller, et al., 2005) and progression of other diseases. Although there are some studies to explore the mechanisms of ERK activation the involvement of RhoA-MEKK1 crosstalk is not fully explored. In this study we aim to model ERK activation via RhoA-MEKK1 crosstalk. Besides there are a number of experimental works and mathematical models of ERK cascade are available, it is always important to revisit this cascade again as ERK plays crucial role in diverse biological processes. By extending the published simulation models of EGFR-ERK pathway (Brightman and Fell, 2000; Kholodenko, et al., 1999; Kiyatkin, et al., 2006; Sasagawa, et al., 2005; Schoeberl, et al., 2002; Yamada, et al., 2004), we developed a simulation model of EGFR-ERK pathway that includes the MEKK1-mediated crosstalk with RhoA. Detailed molecular interactions and the corresponding kinetic data were obtained from those used in the published simulation models and further search of literatures. As in these published models, ordinary differential equations were used in our model to capture the timedependent dynamic behavior of the concentration of proteins. We further evaluated our simulation results with a number of experimental and simulation results. The validated model was then used to study the regulation mode of RhoA in ERK activation via scaffold protein MEKK1.

The types of parameters used in our model are protein-protein interactions and catalytic activities. The published simulation studies have found that most parameters are robust and insensitive to significantly alter the overall pathway behavior (Sasagawa, et al., 2005; Schoeberl, et al., 2002). Apart from the use of the parameters of the published simulation models, additional parameters were obtained from literatures based on the widely used assumption that the parameters measured in vitro and in some cell lines are generally applicable in most cases. For those protein-protein interactions with unavailable parameters, their parameters were putatively estimated from the known parameters of the relevant interacting domain profile pairs (Singhal and Resat, 2007; Wojcik and Schachter, 2001) or other interacting protein pairs of similar sequences. As a biological network is robust and binding affinity of protein-protein interactions for proteins in similar family that mediate similar types of biochemical reactions (such as Ras and RhoA) differ within 10 fold range hence the values of kinetics parameters obtained from previous models are optimized within these ranges. To get an idea of the sensitivity of the solutions to the kinetic parameters used in current model, we did 100 random parameter sets simulations which simulate the changes in all parameters simultaneously. Sampling method was used to generate 100 different random sets of parameters falling within their ±10% ranges of the values used in current model. Figure 2 shows the maximum, minimum and mean values of these 100 different parameter sets on the active ERK relative changes. The maximum standard deviation (SD) and standard error (SE) for the 100 random parameter sets are 5.91417 and 0.59142 respectively. And the maximum SD and SE between the mean of 100 parameter sets runs and the original parameter set run in our current model are 4.02282 and 2.84457 respectively. The 100 parameter sets simulation results for active ERK and the statistical analysis for the results are provided as a Supplementary Material file (the Supporting Information can be found on the Bioinformatics website).

2.3 Model Optimization and Validation Mathematical models developed at systems levels are generally unable to reproduce exact quantitative values in all systems but are capable to produce known behavior or trend that agreed well of those systems under studied. For instance, mathematical model developed for a biological pathway from parameters obtained experimentally from one cell type can behave slightly different in another cell types. The differences of the behavior of the model in these cell types can be due to the present or absent of a crosstalk (i.e. the topology and hence the boundary of the mathematical model) and variation in values of kinetic parameters used. Hence, in this study we developed a generic model of EGFR-ERK signaling pathway with MEKK1-mediated RhoA-EGFR crosstalk to investigate the role of RhoA in regulating ERK activation. The simulated results are represented in curves of concentrations of a chemical species over time that are validated against available experimental data. If the trend or dynamics behavior of a particular reactant or product behave as the experimental data suggest, then the model is said to be optimized and can be used to analyze and predict unknown biological phenomena within the boundary of the model. If the simulation results were not in fair agreement with known experimental facts, then the definition as well as the boundary of the model has to be revisited to examine possible errors such as incorrect interaction kinetics or values of kinetics parameters. Optimized parameters obtained from previous mathematical models are not necessarily optimized in current study as the boundaries of these models are different. The cycle of optimization and validation are repeated in order to obtain simulated results that agreed well with known experimental trends.

METHODS

2.1 Model Construction and Components One of the most commonly used approaches to model biological systems is that of ordinary differential equations (ODEs). In general, a differential equation can be used to describe the chemical reaction rate that depends on the change of participating species over time. The temporal dynamic behavior of molecular species in the biological signaling pathway network can be captured by a set of coupled ODEs. Our pathway model is illustrated in Figure 1. The PI3K-Akt cascade, MEKK1-dependent and MEKK1-independent activation of Raf1-MEK1-ERK2 were included in the model. The constituent molecular interactions, their kinetic constants, and molecular concentrations are described in detail in Supplementary Table S1. The ordinary differential equations of these interactions were derived based on mass action laws with interaction rate constants defined by the forward and reverse rate constants Kf and Kb or turnover number Kcats for enzymatic reactions were derived from the published models (Kholodenko, et al., 1999; Kiyatkin, et al., 2006; Sasagawa, et al., 2005; Schoeberl, et al., 2002; Yamada, et al., 2004; Zhang, et al., 1998) or from other literatures. A set of coupled ODEs was used to describe the reaction network. Our simulation model contains 205 equations and interactions and 194 distinct molecular species, characterized by 313 kinetic parameters and

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RESULTS AND DISCUSSIONS Validation of our simulation model against reported experimental observations


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2.2 Collection and Estimation of Kinetic Parameters

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Further validation of our simulation model against published simulation studies

The validity of our simulation model was further evaluated by comparing two additional simulation results with those of published simulation studies which were not covered by the validation studies against experimental findings. The results are shown in Supplementary Figure S7 and Figure S8. One is the effect of PI3K-Akt cascade on ERK. Our simulation showed that increasing PI3K level from 0.2µM to 3µM reduces the maximum level of active ERK by 21%. Moreover, changing the PTEN level has little effect on PIP3 level. These results are consistent with the reported simulation studies of the crosstalk between PI3K-Akt and Ras-RafMEK-ERK pathways which have shown that the overall effect of the crosstalk mediator, scaffold protein GAB1, is week (Kiyatkin, et al., 2006). The second is the regulation of ERK-cascade by phosphatases. At lower levels, variation of PP2A concentrations showed little effect on the amount but substantial effect on the duration of ERK activation. At lower levels, variation of MKP3 levels showed little effect on the amount but substantial effect on

the duration of ERK activation. These results are consistent with the results of a reported simulation study showing that the duration of ERK activation is sensitive only to phosphatase reactions on MEK whereas the amplitude is most sensitive to phosphatase reactions on ERK (Mayawala, et al., 2004). It is noted that, at higher levels of both PP2A and MKP3, the amount and duration of active ERK decreases.

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Simulation of the effects of RhoA over-expression on ERK activation

RhoA elevation has been found in several carcinoma cells (Grosswendt, et al., 2007), cardiovascular disease (Budzyn, et al., 2006; Fukata, et al., 2001), and urinary bladder dysfunction (Peters, et al., 2006). High ERK levels are required for the survival of carcinoma cells (Grosswendt, et al., 2007) and RhoA has been found to promote ERK activation by multiple mechanisms (Coleman, et al., 2004; Hall, 2005; Wang and Zheng, 2007). Therefore, in addition to the effects of over-expression of Ras and other oncogenes, it is of interest to study the possible effects of RhoA elevation on ERK activation via MEKK1-mediated crosstalk and other mechanisms. Our simulation showed that, when Ras is at normal level (RasGDP=0.15µM), RhoA elevation substantially prolongs ERK activation in a dose-dependent manner, but the peaked amount of active ERK is decreased by ~26% partly due to the competition of ERK binding by MEK1 and MEKK1 (Figure 3). At reduced levels of Ras (RasGDP reduced from 0.15µM to 0.0015µM), RhoA elevation prolongs ERK activation by ~30%, while the peak amount of active ERK is decreased by ~10% (Figure 4). Interestingly, at zero Ras level (corresponding to complete knockout or inhibition), ERK activation can still be maintained (the peak amount of active ERK is nearly unchanged) by elevated RhoA via the MEKK1-mediated crosstalk, even-though ERK activation is not prolonged (Figure 5). Thus, our simulation results suggest it is possible to activate ERK without Ras. While the amount of RasGDP is high, elevation of RhoA further prolongs duration of ERK activation but significantly reduces the amplitude of active ERK (Figure 6). In some cell types such as PC12 cells, sustained number of active ERK have been found to induce differentiation, while transient ERK activation has been found to cause proliferation (Marshall, 1998; York, et al., 1998). Therefore, depending on the levels of Ras over-expression and the threshold values of active ERK for proliferation and differentiation, simultaneous elevation of RhoA and Ras may either diminish or complement the effect of Ras over-expression on cell differentiation. Here, we show that the contribution of RhoA on ERK activation appears to be attributed via MEKK1-mediated crosstalk. As shown in Figure 7, switching off this crosstalk significantly reduces the amount of active ERK while the duration of ERK activation is prolonged primarily due to ROCK-mediated RhoA activation of PTEN which subsequently delays EGFR internalization and Akt inhibition. On the other hand, in the absence of RhoA, Ras over-expression is almost equally effective in prolonging ERK activation as that at normal RhoA level (Figure 3), which is consistent with the observations that Ras is the key regulator of ERK activity (Murphy and Blenis, 2006). As most of the kinetics parameters for MEKK1-mediated reactions were based on estimation in this study we aim to capture qualitative or “semi-quantitative” dynamics of ERK activation contributed from the RhoA-MEKK1 crosstalk. However, we believe that current model can be improved with more future experimental studies on the RhoAMEKK1 crosstalk to the ERK cascade.

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Our simulation model was validated by determining whether the simulation results are consistent with five different experimental observations. Results show in Supplementary Figure S2 to Figure S6 in which the time-dependent protein concentration and activity profiles are in reasonable agreement with available experimentally determined profiles. At 50ng/ml EGF, the simulated ERK activation peaks at ~5 minutes and decays within 50 minutes. This is consistent with experimental finding that treatment of 50ng/ml and 100ng/ml EGF in PC12 cells transiently activates ERK which peaks within 5 minutes and decays within 30-60 minutes (Sasagawa, et al., 2005). Our simulation results showed that, at 50ng/ml EGF and 0.3µM EGFR, further elevation of EGF level sustains ERK activation, which is consistent with observations and previous simulation results (Santos, et al., 2007; Schoeberl, et al., 2002). The amount of simulated active RasGTP peaks at ~2.5 minutes and quickly decays within 20 minutes, which is consistent with the observation that active RasGTP level in EGF- treated PC12 cells increases dramatically within 5 minutes and decays steeply within 10 minutes (Sasagawa, et al., 2005). By increasing the initial concentration of MEKK1 to mimic over-expression of MEKK1, our simulation showed that increased MEKK1 amount helps to elevate the level of active ERK by delaying its peak time with prolonged duration of ERK activation. This result is consistent with the observation that some scaffold proteins enhance the strength of ERK signals (Ferrell, 2000; Morrison and Davis, 2003). It is noted that increasing amounts of some noncatalytic scaffolds such as KSR, JIP1, Ste5 after a certain threshold level normally lead to decreased ERK signaling due to diluted recruitment of Ras, Raf1, and MEK1 (Dard and Peter, 2006; Yoshioka, 2004). This recruitment dilution effect of MEKK1 overexpression is expected to be compensated for by the effects of MEKK1 mediated RhoA-ERK crosstalk and its kinase activity. We found that Ras over-expression increases the amount of active GTP-bound RhoA and prolongs the duration of its activation, which is consistent with the experimental finding that overexpressed active Ras promotes RhoA activation (Chen, et al., 2003; Xia and Land, 2007). Moreover, the simulated Ras overexpression leads to sustained ERK activation, which is consistent with the observations that over-expression and hyperactivity of Ras prolongs and elevates ERK activity leading to tumorigenesis (Schubbert, et al., 2007). However, the simulated RhoA overexpression reduces the level of active ERK probably due to the increased binding competition between RhoA and ERK to MEKK1 (See Supplementary Material for detail description).

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primary and secondary signal transduction roles of RhoA and other key players in different cell and tissue types (Wang and Zheng, 2007). It is of great importance to understand the mechanism of their involvement of ERK cascade and RhoA-mediated pathways in the collective promotion of cell proliferation and invasion in carcinoma cells (Faried, et al., 2006), and the mechanism and effects of anti-proliferative agents targeting these proteins and pathways (Takeda, et al., 2006). The current model can be extended into a more comprehensive regulatory model of ERK cascade when more comprehensive genomics, proteomics and metabolic data are integrated.

Experimental indications in support of the simulated effects of the MEKK1-mediated crosstalk between RhoA and EGFR-ERK pathway

It has been reported that, in some cases, Rho, ROCK and LIM kinase are required for sustained ERK activation probably via networks downstream of Rac or Cdc42 (Coleman, et al., 2004; Roovers and Assoian, 2006; Roovers, et al., 2006; Welsh, et al., 2001) as well as Ras-MAPK pathway (Croft and Olson, 2006). In general, Rho as well as Ras regulate cell-cycle progression and proliferation via multiple pathways, in cell-type specific manner (Wang and Zheng, 2007), and dictated by spatial and temporal factors (Coleman, et al., 2004). Therefore, our simulation results for the involvement of the MEKK1-mediated crosstalk on ERK activation may suggest an additional mechanism for the collective regulation of cell cycle progression and proliferation by multiple pathways, particularly in cells with elevated Rho levels. This possibility seems to be supported by a recent study showing that Ras inhibitor FTS markedly enhances RhoA level and activity, downregulates Ras, and maintains the active ERK level (Goldberg and Kloog, 2006). Although Ras inhibition generally reduces active ERK via reduced signaling in the Ras-ERK pathway, activation of ERK may be rescued and maintained via alternative routes such as that of the MEKK1-mediated RhoA-ERK crosstalk. Ras has been found to inhibit RhoA/ROCK (de Godoy, et al., 2007). As a consequence, Ras inhibition and down-regulation is expected to enhance RhoA activity, which subsequently activate ERK via the MEKK1-mediated crosstalk route to compensate for the reduced ERK activation by the reduced signaling of the Ras-ERK pathway.

ACKNOWLEDGEMENTS

REFERENCES BRIGHTMAN, F.A. AND FELL, D.A. (2000) DIFFERENTIAL FEEDBACK REGULATION OF THE MAPK CASCADE UNDERLIES THE QUANTITATIVE DIFFERENCES IN EGF AND NGF SIGNALLING IN PC12 CELLS, FEBS LETT., 482, 169-174. BUDZYN, K., MARLEY, P.D. AND SOBEY, C.G. (2006) TARGETING RHO AND RHO-KINASE IN THE TREATMENT OF CARDIOVASCULAR DISEASE, TRENDS PHARMACOL. SCI., 27, 97-104. CALIPEL, A., LEFEVRE, G., POUPONNOT, C., MOURIAUX, F., EYCHENE, A. AND MASCARELLI, F. (2003) MUTATION OF B-RAF IN HUMAN CHOROIDAL MELANOMA CELLS MEDIATES CELL PROLIFERATION AND TRANSFORMATION THROUGH THE MEK/ERK PATHWAY, J. BIOL. CHEM., 278, 42409-42418. CERESA, B.P. AND SCHMID, S.L. (2000) REGULATION OF SIGNAL TRANSDUCTION BY ENDOCYTOSIS, CURR. OPIN. CELL BIOL., 12, 204210. CHEN, J.C., ZHUANG, S., NGUYEN, T.H., BOSS, G.R. AND PILZ, R.B. (2003) ONCOGENIC RAS LEADS TO RHO ACTIVATION BY ACTIVATING THE MITOGEN-ACTIVATED PROTEIN KINASE PATHWAY AND DECREASING RHO-GTPASE-ACTIVATING PROTEIN ACTIVITY, J. BIOL. CHEM., 278, 2807-2818. COLEMAN, M.L., MARSHALL, C.J. AND OLSON, M.F. (2004) RAS AND RHO GTPASES IN G1-PHASE CELL-CYCLE REGULATION, NAT. REV. MOL. CELL BIOL., 5, 355-366. CROFT, D.R. AND OLSON, M.F. (2006) THE RHO GTPASE EFFECTOR ROCK REGULATES CYCLIN A, CYCLIN D1, AND P27KIP1 LEVELS BY DISTINCT MECHANISMS, MOL. CELL. BIOL., 26, 4612-4627. DARD, N. AND PETER, M. (2006) SCAFFOLD PROTEINS IN MAP KINASE SIGNALING: MORE THAN SIMPLE PASSIVE ACTIVATING PLATFORMS, BIOESSAYS, 28, 146-156. DE GODOY, M.A., PATEL, C.A., WALDMAN, S.A., KATSUKI, M., REGAN, R.F. AND RATTAN, S. (2007) H-RAS INHIBITS RHOA/ROCK LEADING TO A DECREASE IN THE BASAL TONE IN THE INTERNAL ANAL SPHINCTER, GASTROENTEROLOGY, 132, 1401-1409. DHANASEKARAN, D.N., KASHEF, K., LEE, C.M., XU, H. AND REDDY, E.P. (2007) SCAFFOLD PROTEINS OF MAP-KINASE MODULES, ONCOGENE, 26, 3185-3202. DHILLON, A.S., HAGAN, S., RATH, O. AND KOLCH, W. (2007) MAP KINASE SIGNALLING PATHWAYS IN CANCER, ONCOGENE, 26, 3279-3290. EBISUYA, M., KONDOH, K. AND NISHIDA, E. (2005) THE DURATION, MAGNITUDE AND COMPARTMENTALIZATION OF ERK MAP KINASE ACTIVITY: MECHANISMS FOR PROVIDING SIGNALING SPECIFICITY, J. CELL SCI., 118, 2997-3002. ESTRACH, S., SCHMIDT, S., DIRIONG, S., PENNA, A., BLANGY, A., FORT, P. AND DEBANT, A. (2002) THE HUMAN RHO-GEF TRIO AND ITS TARGET GTPASE RHOG ARE INVOLVED IN THE NGF PATHWAY, LEADING TO NEURITE OUTGROWTH, CURR. BIOL., 12, 307-312. ETIENNE-MANNEVILLE, S. AND HALL, A. (2002) RHO GTPASES IN CELL BIOLOGY, NATURE, 420, 629-635.

4 CONCLUDING REMARKS Small GTPase RhoA is well known to regulate formation of stress fibers that affect focal adhesion. Some cancerous cells had been shown to contain high levels of both active ERK and RhoA (Rosman, et al., 2008). Over-expression of RhoA had been reported to promote tumor cell migration (Joshi, et al., 2008; Kamai, et al., 2003) . In addition, RhoA had been shown to prevent apoptosis in zebrafish by activating ERK cascade (Zhu, et al., 2008). Besides, some activation mechanisms of RhoA by Ras had been reported (Chen, et al., 2003; Xia and Land, 2007). Due to the multi-faceted regulatory modes of ERK cascade little is known about how RhoA activate ERK. Scaffold protein MEKK1 is known to phosphorylate MEK1 via its kinase activity and to recruit Ras, Raf1, and MEK1 leading to the promotion of ERK activation (Dhanasekaran, et al., 2007; Gallagher, et al., 2004). On the other hand, RhoA has been found to bind and activate the kinase activity of MEKK1 in HEK 293 cells (Gallagher, et al., 2004) and activities of Ras, RhoA, and ERK had been found in many malignant cancer cells. All of these motivate us to build a mathematical model consisting of a canonical ERK cascade with RhoA-MEKK1 crosstalk as high levels. Our simulation model, validated by using a number of experimental and published simulation results, suggested that elevated level of RhoA enhance the duration and magnitude of ERK activation via the MEKK1-mediated crosstalk between RhoA and canonical EGFR-ERK pathway. The activation of ERK via this crosstalk can take place without Ras. This crosstalk thus likely acts as part of the multiple pathways that collectively regulate cell cycle progression and proliferation. Further investigation of this and other relevant cross-talks (Ceresa and Schmid, 2000; Lua and Low, 2005) will enable a more comprehensive understanding of the

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This work is supported by National University of Singapore Academic Research Fund [R-148-000-081-112/101], Ministry of Science and Technology of China [2004CB720103, 2003CB715901, and 2006AA02Z317], National Natural Science Foundation of China [30500107), Shanghai Municipal Education Commission [2000236018, 2000236016] and Science and Technology Commission of Shanghai Municipality of China [06PJ14072].

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H. Li et al.

FIGURES

Figure 3: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different RhoA concentrations and normal Ras level, using EGF as input stimulus.

Figure 2: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at 100 random different parameter sets within ±10% of the parameter set used in current model.

Figure 4: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different RhoA concentrations and reduced Ras level, using EGF as input stimulus.

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Figure 1: Pathway model used in this study. The dotted lines indicate interactions with scaffold MEKK1. a, b, c, d, e, f are binding sites in scaffold MEKK1 (see Supplementary Material for detailed description).

Figure 6: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different RhoA concentrations and elevated Ras levels, using EGF as input stimulus.

Figure 7: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different RhoA concentrations in the presence or absence of scaffold protein MEKK1, using EGF as input stimulus.

Figure 8: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different Ras concentrations in the presence (normal level) or absence of RhoA, using EGF as input stimulus.

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Figure 5: Profile of active ERK concentration (in units of percentage of initial ERK concentration) at different RhoA concentrations and zero Ras level. For comparison, the profile at normal RhoA and Ras levels is also shown (Control), using EGF as input stimulus.