Bioinformatics Advance Access originally published online on December 11, 2008
Bioinformatics 2009 25(3):358-364; doi:10.1093/bioinformatics/btn635
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Simulation of crosstalk between small GTPase RhoA and EGFR-ERK signaling pathway via MEKK1
1Bioinformatics and Drug Design Group, Department of Pharmacy, 2Center of Computational Science and Engineering, National University of Singapore, Blk S16, Level 8, 3 Science Drive 2, 3Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, 4Department of Physics, National University of Singapore, 2 Science Drive 3, Singapore 117542, 5Cell Signaling and Developmental Biology Laboratory, Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543 and 6Shanghai Center for Bioinformation Technology, Shanghai 201203, P. R. China
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONS4 CONCLUDING REMARKSREFERENCES |
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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 overexpressed, 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: phacyz{at}nus.edu.sg
Supplementary information: Supplementary data are available at Bioinformatics online.
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONS4 CONCLUDING REMARKSREFERENCES |
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Rho GTPases such as RhoA regulate cell-cycle progression via multiple mechanisms including the promotion of sustained extracellular signal-regulated kinases (ERK) activities (Coleman et al., 2004; Etienne-Manneville and Hall, 2002; Hall, 2005; Jaffe and Hall, 2005; Wang and Zheng, 2007). 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,Yorket 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 epidermal growth factor receptor (EGFR) endocytosis by phosphorylating endophilin A1 and to prevent Akt inhibition of Raf by activating phosphatase and tensin homolog (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 MEK kinase 1 (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 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 (Yamaguchiet al. 2001) and environmental context. For instance, in specific cell types and conditions, MEKK1 has been found to not inducing ERK activation (Xuet 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. Despite the availability of a number of experimental works and mathematical models of ERK cascade, it is always important to revisit this cascade again from different perspectives 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 (ODEs) were used in our model to capture the time-dependent 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.
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2 METHODS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONS4 CONCLUDING REMARKSREFERENCES |
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2.1 Model construction and components
One of the most commonly used approaches to model biological systems is that of 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 ODEs 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 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 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.
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2.2 Collection and estimation of kinetic parameters
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 (Sasagawaet al. 2005,Schoeberlet 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 kinetical 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 SD and 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.
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2.3 Model optimization and validation
Mathematical models developed by ‘one-set-fits-all’ generic parameters may not always reproduce exact quantitative behavior in all systems, but may be able to reproduce the behavior or trend for specific systems. 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 presence or absence 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 kinetical 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.
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3 RESULTS AND DISCUSSIONS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONS4 CONCLUDING REMARKSREFERENCES |
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3.1 Validation of our simulation model against reported experimental observations
Our simulation model was validated by determining whether the simulation results are consistent with five different experimental observations. Results are shown in Supplementary Figures S2–S6 in which the time-dependent protein concentration and activity profiles are in reasonable agreement with available experimentally determined profiles. At 50 ng/ml EGF, the simulated ERK activation peaks at
5 min and decays within 50 min. This is consistent with experimental finding that treatment of 50 ng/ml and 100 ng/ml EGF in PC12 cells transiently activates ERK which peaks within 5 min and decays within 30–60 min (Sasagawaet al. 2005). Our simulation results showed that, at 50 ng/ml EGF and 0.3 µM EGFR, further elevation of EGF level sustains ERK activation, which is consistent with observations and previous simulation results (Santos et al. 2007,Schoeberlet al. 2002). The amount of simulated active RasGTP peaks at 2.5 min and quickly decays within 20 min, which is consistent with the observation that active RasGTP level in EGF-treated PC12 cells increases dramatically within 5 min and decays steeply within 10 min (Sasagawaet al. 2005). By increasing the initial concentration of MEKK1 to mimic overexpression of MEKK1, our simulation showed that increased MEKK1 level 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 non-catalytic scaffolds, such as KSR, JIP1 and 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 overexpression 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 overexpression and hyperactivity of Ras prolongs and elevates ERK activity leading to tumorigenesis (Schubbertet 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 detailed description).
3.2 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 Figures S7 and S8. One is the effect of PI3K-Akt cascade on ERK. Our simulation showed that increasing PI3K level from 0.2 µM to 3 µM 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-Raf-MEK-ERK pathways which have shown that the overall effect of the crosstalk mediator, scaffold protein GAB1, is weak (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.
3.3 Simulation of the effects of RhoA overexpression 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 overexpression 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 = 0.15 µM), 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 (Fig. 3). At reduced levels of Ras (RasGDP reduced from 0.15 µM to 0.0015 µM), RhoA elevation prolongs ERK activation by 30%, while the peak amount of active ERK is decreased by 10% (Fig. 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 (Fig. 5). Thus, our simulation results suggest it is possible to activate ERK without Ras.
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While the amount of RasGDP is high, elevation of RhoA further prolongs duration of ERK activation but significantly reduces the amplitude of active ERK (Fig. 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,Yorket al. 1998). Therefore, depending on the levels of Ras overexpression 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 overexpression 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 (Fig. 8), Ras overexpression is almost equally effective in prolonging ERK activation as that at normal RhoA level (Fig. 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 comprehensive experimental studies on the RhoA-MEKK1 crosstalk to the ERK cascade.
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3.4 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 farnesylthiosalicylic acid (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 downregulation 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.
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4 CONCLUDING REMARKS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONS4 CONCLUDING REMARKSREFERENCES |
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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). Overexpression 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 (Zhuet 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 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 the 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 (Takedaet 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.
Funding: 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); Science and Technology Commission of Shanghai Municipality of China (06PJ14072).
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Olga Troyanskaya
Received on August 22, 2008; revised on November 4, 2008; accepted on December 7, 2008
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