Systems‐level interactions between insulin–EGF networks amplify mitogenic signaling
2009; Springer Nature; Volume: 5; Issue: 1 Linguagem: Inglês
10.1038/msb.2009.19
ISSN1744-4292
AutoresNikolay Borisov, Edita Aksamitiene, Anatoly Kiyatkin, Stefan Legewie, Jan Berkhout, Thomas Maiwald, Nikolai P. Kaimachnikov, Jens Timmer, Jan B. Hoek, Boris Ν. Kholodenko,
Tópico(s)Receptor Mechanisms and Signaling
ResumoArticle7 April 2009Open Access Systems-level interactions between insulin–EGF networks amplify mitogenic signaling Nikolay Borisov Nikolay Borisov Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Edita Aksamitiene Edita Aksamitiene Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Anatoly Kiyatkin Anatoly Kiyatkin Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Stefan Legewie Stefan Legewie Institute for Theoretical Biology, Humboldt University, Berlin, Germany Search for more papers by this author Jan Berkhout Jan Berkhout Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Thomas Maiwald Thomas Maiwald Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Freiburg Institute for Advanced Science, University of Freiburg, Freiburg, Germany Search for more papers by this author Nikolai P Kaimachnikov Nikolai P Kaimachnikov Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino, Russia Search for more papers by this author Jens Timmer Jens Timmer Freiburg Institute for Advanced Science, University of Freiburg, Freiburg, Germany Search for more papers by this author Jan B Hoek Jan B Hoek Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Boris N Kholodenko Corresponding Author Boris N Kholodenko Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Nikolay Borisov Nikolay Borisov Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Edita Aksamitiene Edita Aksamitiene Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Anatoly Kiyatkin Anatoly Kiyatkin Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Stefan Legewie Stefan Legewie Institute for Theoretical Biology, Humboldt University, Berlin, Germany Search for more papers by this author Jan Berkhout Jan Berkhout Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Thomas Maiwald Thomas Maiwald Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Freiburg Institute for Advanced Science, University of Freiburg, Freiburg, Germany Search for more papers by this author Nikolai P Kaimachnikov Nikolai P Kaimachnikov Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino, Russia Search for more papers by this author Jens Timmer Jens Timmer Freiburg Institute for Advanced Science, University of Freiburg, Freiburg, Germany Search for more papers by this author Jan B Hoek Jan B Hoek Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA Search for more papers by this author Boris N Kholodenko Corresponding Author Boris N Kholodenko Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Author Information Nikolay Borisov1,‡, Edita Aksamitiene1,‡, Anatoly Kiyatkin1,‡, Stefan Legewie2, Jan Berkhout1, Thomas Maiwald1,3, Nikolai P Kaimachnikov1,4, Jens Timmer3, Jan B Hoek1 and Boris N Kholodenko 1,5 1Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, Philadelphia, PA, USA 2Institute for Theoretical Biology, Humboldt University, Berlin, Germany 3Freiburg Institute for Advanced Science, University of Freiburg, Freiburg, Germany 4Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino, Russia 5UCD Conway Institute, University College Dublin, Belfield, Dublin, Ireland ‡These authors contributed equally to this work *Corresponding author. Department of Pathology, Anatomy and Cell Biology, Thomas Jefferson University, JAH, 1020 Locust Street, Philadelphia, PA 19107, USA. Tel.: +1 215 503 1614; Fax: +1 215 923 2218; E-mail: [email protected] Molecular Systems Biology (2009)5:256https://doi.org/10.1038/msb.2009.19 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP3) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP3-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues. Synopsis We present an integrated analysis of crosstalk between the insulin receptor (IR) and epidermal growth factor receptor (EGFR) signaling pathways. Our experimental and computational findings show how systems-level interactions between the EGFR and IR networks convert the insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP3) concentration into enhanced activity of the extracellular signal-regulated kinase (ERK) pathway. Physiological stimuli never act in isolation, and often cells in the body are simultaneously exposed to EGF and insulin. The EGFR and IR networks share many downstream components, yet their physiological responses to stimuli are different. In cells that express EGFR, including HEK293 cells, EGF acts as a potent activator of mitogenesis through activation of the Ras/ERK pathway. In contrast, mitogenesis and the Ras/ERK pathway are poorly activated by insulin. The main biological function of insulin is metabolic, involving the control of glucose metabolism and stimulation of protein and lipid syntheses. We show that in HEK293 cells, insulin amplifies Ras/ERK activation by low, physiological [EGF], and at saturating [EGF] the insulin effect becomes insignificant. Following 1.5- and 15-min co-stimulation with EGF plus insulin, the phospho-ERK level (which is directly related to ERK activity) is significantly larger than the sum of these levels observed for each ligand (Figure 3E, left and right panels), displaying EGF–insulin synergy. The peak ERK activity (at ∼5 min co-stimulation) does not display synergistic effects (Figure 3E, middle panel). We show that insulin–EGF crosstalk is not a consequence of extra activation of either receptor by co-stimulation with two ligands, or activation of insulin-like growth factor receptor-1 by insulin. Multiple points of crosstalk between EGFR and IR make it difficult to comprehend and predict intricate Ras/ERK signaling dynamics in a cell-dependent context, using only qualitative arguments. These dynamics depend on a variety of non-linear interactions and feedback loops. A testable computational model helps us provide insights into the key causative relationships between the input stimuli and Ras/ERK signaling and reveal specific functions of critical network nodes in generating cellular responses (Kholodenko, 2006). Our mechanistic computational model, trained by the data from HEK293 cells, suggests that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Some of the crosstalk interactions affect multiple Ras activation and deactivation routes, which involve the adaptor proteins, Grb2-associated binder-1 (GAB1) and insulin receptor substrates (IRS), and the SH2-domain containing protein tyrosine phosphatase-2 (SHP2). In the model, EGF and insulin co-stimulation increases the amount of PIP3 produced by phosphatidylinositol 3-kinase (PI3K) and further facilitates the GAB1 membrane recruitment and its subsequent tyrosine phosphorylation. An increase in the membrane-bound phospho-GAB1 promotes Grb2–SOS binding and increases [SOS] (Ras activator) in close proximity to Ras. At the same time, this gain in phospho-GAB1 also increases the amounts of RasGAP (Ras deactivator) and SHP2 bound to GAB1. Although SHP2 negatively regulates IR, EGFR, IRS, and GAB1 phosphorylation levels, it has a positive effect on Ras activation, as we showed using a specific SHP2 inhibitor, NSC-87877 (Chen et al, 2006). This positive effect is related to the formation of the GAB1–SHP2 and IRS–SHP2 complexes and subsequent dephosphorylation of multiple docking sites, involved in RasGAP binding. Simulations predict that the net result of all these interactions is an increase in positive signaling and decrease in negative signaling to Ras, which amplifies the Ras-GTP level. Additional crosstalk interactions occur at the Ras/Raf level. In the model, at any given Ras-GTP load, the simultaneous exposure to insulin plus EGF increases Raf activity, relative to insulin alone, owing to EGF-induced stimulation of tyrosine kinases, which are assumed to belong to the Src family (Wellbrock et al, 2004). We tested the model against the experiment, using kinetic data on responses to multiple perturbations, including different EGF doses, specific inhibitors and small interfering RNA (siRNA). We showed that the PI3K inhibitor wortmannin (WT) suppresses synergistic activation of the Ras/Raf/MEK/ERK pathway by insulin and EGF. The data demonstrate that the total GAB1 phosphorylation level and the concentrations of GAB1-bound Grb2, SHP2, and PI3K decrease dramatically in WT-treated cells. We conclude that the loss of insulin–EGF synergy caused by WT arises from the disruption of the GAB1–PI3K positive feedback and the loss of the GAB1-mediated membrane recruitment of signaling molecules. To get further insight into crosstalk mechanisms, we simulated the dynamics of ERK responses to EGF versus EGF plus insulin in cells with different GAB1 expression levels (Figure 5A). As expected, GAB1 suppression reduces the phospho-ERK level to a larger degree for EGF than for EGF plus insulin. Model predictions shown in Figure 5B (left panel) illustrate this phospho-ERK level with decreasing GAB1 at 1.8 min following EGF or EGF plus insulin stimulation. To test the model, HEK293 cells were transfected with targeted siRNA against GAB1 mRNA, resulting in ∼75% reduced GAB1 protein level relative to control. The experimental data corroborate in silico predictions of the larger influence of GAB1 depletion on EGF- rather than on EGF plus insulin-induced ERK phosphorylation (Figure 5C). Thus, calculations suggest that insulin endows the mitogenic EGFR pathway with increased robustness towards GAB1 downregulation. The simulations show that for EGF plus insulin stimulation, the peak level of phospho-ERK decreases only slightly with GAB1 depletion, whereas for EGF-induced ERK activation, the peak level decreases dramatically (Figure 5B, right panel). The insight provided by computational analyses goes beyond revealing a critical role of GAB1, which is only one of several key nodes of interactions between the EGFR and IR networks. In fact, insulin enhances EGF-induced ERK activation even in GAB1 knockdown cells (Figure 5C). Coincidence detection of EGF and insulin stimuli by GAB1, together with multiple positive (PI3K–PIP3–GAB1–PI3K) and negative (ERK–GAB1, ERK–SOS, mTOR–IRS) feedback loops, contributes to the control of insulin plus EGF signaling. Despite the fact that RNAi-mediated suppression of GAB1 significantly decreases EGF-induced ERK phosphorylation (Figure 5C), the downregulation of multiple network nodes is required to uncouple and completely suppress insulin plus EGF-induced Ras/ERK activation. Overall, the analysis presented here demonstrates the feasibility of using computational models to identify critical combinations of therapeutic targets and predict their effects on complex cellular responses to concurrent external cues. Introduction Cells respond to a myriad of external cues using a limited number of signaling pathways that convert multiple inputs into diverse cellular decisions. Although individual receptors and pathways have been extensively studied, it is not understood how signaling networks integrate multiple cues. The epidermal growth factor (EGF) receptor (EGFR) and the insulin receptor (IR) belong to the family of receptors with intrinsic tyrosine kinase activity (referred to as receptor tyrosine kinases, RTKs), which regulate pivotal cellular processes, including proliferation, differentiation, cell metabolism, survival, and apoptosis (Schlessinger, 2000; Taniguchi et al, 2006). The main physiological function of insulin signaling is metabolic, involving the control of glucose metabolism and stimulation of protein and lipid syntheses, whereas EGF induces proliferative responses. The EGFR and IR networks share many downstream components. Under some conditions, EGF can evoke metabolic responses, e.g., GLUT4 translocation (Ishii et al, 1994; Gogg and Smith, 2002), whereas insulin can be mitogenic, especially in cancer cells (Ish-Shalom et al, 1997; Papa et al, 1997). There is evidence that insulin can enhance EGF-stimulated extracellular signal-regulated kinase (ERK) activation, DNA synthesis, and cell proliferation responses (Crouch et al, 2000; Ediger and Toews, 2000; Chong et al, 2004), whereas growth factors, cytokines, and other hormones can negatively regulate insulin signaling (Gual et al, 2003). Yet, despite experimental evidence of crosstalk between insulin and growth factor pathways, it is unknown how combined EGF and insulin inputs are processed into integrative cellular response. This is at least in part due to the combinatorial complexity of molecular interactions and a variety of feedback and feed-forward loops, whose concerted operation is difficult to comprehend intuitively. Ligand binding and subsequent autophosphorylation of tyrosine residues on the EGFR and IR triggers mobilization of multiple adaptors, such as Src (src avian sarcoma viral oncogene homolog) homology and collagen domain protein (Shc), growth factor receptor binding protein 2 (Grb2), insulin receptor substrate family members (IRS1–6, GAB1–3), and enzymes that contain characteristic domains recognizing receptor phosphotyrosines, such as phosphatidylinositol 3-kinase (PI3K), soluble tyrosine kinase c-Src, protein tyrosine phosphatases (e.g., SHP2 and PTP1B) and others (White, 1998; Sebastian et al, 2006). Subsequently, EGF and insulin-induced signals propagate through multiple interacting branches, including the mitogen-activated protein kinase (MAPK) cascade downstream of the small membrane-anchored GTPase Ras and the PI3K/AKT cell survival pathway. The flow chart in Figure 1 shows that the same key signal transducers are activated by both EGFR and IR, either directly or indirectly. Importantly, although either receptor can stimulate both the Ras/ERK and PI3K/AKT pathways, the major routes of activation are different. IR phosphorylates IRS proteins, which are linked to the activation of Ras/ERK pathway through binding to the Grb2–SOS complex (SOS is a guanine nucleotide exchange factor for Ras), whereas EGFR activates the same pathway either by direct binding of Grb2-SOS or by binding and phosphorylation of Shc, which then recruits the Grb2–SOS complex (White, 1998; Sebastian et al, 2006). Likewise, the PI3K/AKT pathway is activated by IR via either direct or IRS-mediated recruitment of PI3K, whereas EGFR-mediated PI3K activation occurs mainly via a more intricate route that involves EGFR- and Src-induced phosphorylation of the Grb2-associated binder 1 (GAB1) (Rodrigues et al, 2000; Gu and Neel, 2003; Kiyatkin et al, 2006). Figure 1.Flow chart of signal propagation through the EGFR and IR signaling networks. Solid lines with arrows show the activation or tyrosine phosphorylation of proteins and lipids. Dotted lines represent direct protein–protein and protein–lipid interactions. Red lines with blunt ends show inhibition. Download figure Download PowerPoint There are several points of crosstalk between the EGFR and IR signaling networks (Figure 1). Following the initial activation of PI3K, the production of phosphatidylinositol-3,4,5-triphosphate (PIP3) in the plasma membrane leads to the membrane recruitment of GAB1 and IRS proteins through their pleckstrin homology (PH) domains. The membrane-recruited and subsequently tyrosine-phosphorylated GAB1 and IRS influence the Ras/ERK and PI3K/AKT pathways in multiple ways. They bind p85, the regulatory subunit of PI3K, and alleviate the intrinsic inhibition of PI3K, which further increases PIP3 production, thereby generating a positive feedback and a crosstalk point between EGFR and IR (Ogawa et al, 1998; Gu and Neel, 2003). Both GAB1 and IRS can recruit Grb2–SOS, leading to Ras activation (Myers et al, 1994; Lewitzky et al, 2001; Weng et al, 2001), or can bind the GTPase-activating protein RasGAP, which catalyzes Ras deactivation (Montagner et al, 2005). An important crosstalk point emerges because of the binding of SH2 domain-containing tyrosine protein phosphatase 2 (SHP2) to the phosphorylated GAB1 and IRS proteins, which results in both positive and negative regulations of downstream signaling (Noguchi et al, 1994; Yamauchi et al, 1995; Myers et al, 1998; Yart et al, 2001; Asante-Appiah and Kennedy, 2003). Intriguingly, although tyrosine phosphatase SHP2 negatively regulates IRS, GAB1 and PI3K/AKT signaling (Noguchi et al, 1994; Asante-Appiah and Kennedy, 2003), it positively influences ERK activity, which is partly explained by dephosphorylation of the specific sites involved in RasGAP binding (Cunnick et al, 2002; Agazie and Hayman, 2003; Montagner et al, 2005; Stoker, 2005). Downstream targets of EGFR and IR, such as ERK, GSK3 (glycogen synthase kinase 3) or mTOR (the mammalian target of rapamycin), feedback and phosphorylate GAB1 and IRS on serine/threonine residues, which disable tyrosine phosphorylation at sites engaging PI3K, Grb2, RasGAP or SHP2 (Paz et al, 1997; Gu and Neel, 2003; Johnston et al, 2003). These negative feedback loops generate additional crosstalk points between EGFR and IR. Although many mechanisms of EGFR–IR crosstalk are well characterized at the molecular level, this knowledge is insufficient to understand cellular responses to EGF plus insulin at the systems level, owing to the multitude of interpathway interactions and feedback loops. This paper brings together experimental studies of combined EGF and insulin signaling with computational modeling of the interactive EGFR and IR networks. We show that, although in HEK293 cells insulin by itself is a poor activator of ERK, it greatly enhances MAPK pathway activation by physiological EGF concentrations. The computational model elucidates the function of feedback loops and crosstalk nodes in combined EGF and insulin signaling. We demonstrate that synergistic activation of the mitogenic pathway by EGF plus insulin primarily occurs upstream of and at the Ras/Raf level. This potentiation of Ras/ERK response is initiated by insulin-induced PIP3 increase, which leads to subsequent increases in membrane recruitment of Grb2–SOS and SHP2 by GAB1 and IRS. The computational model unveils that insulin makes the mitogenic EGFR signal more robust toward GAB1 knockdown. Our results may have important ramifications for the identification of therapeutic targets aimed at eliminating insulin-induced amplification of mitogenic and survival signaling stimulated by low growth factor levels in tumor cells. Results Building a computational model of the EGF and insulin signaling networks We have developed a computational model to describe in quantitative terms how cell stimulation by EGF and insulin is linked to the activation of Ras/ERK and PI3K/AKT pathways. The current model stems from our previously developed EGFR network models that were based on in vitro and in vivo measurements of signaling kinetics. A number of EGFR signaling model predictions were validated in our own studies (Kholodenko et al, 1999; Moehren et al, 2002; Kiyatkin et al, 2006; Birtwistle et al, 2007) and, in addition, tested by other groups (Schoeberl et al, 2002; Hatakeyama et al, 2003; Resat et al, 2003; Blinov et al, 2006). This paper extends our previous models to incorporate IR signaling and regulatory processes involved in EGFR–IR crosstalk. However, we do not create a combinatorially complex in silico replica of all distinct biochemical species and interactions, which would be impractical (Borisov et al, 2005; Hlavacek et al, 2006; Birtwistle et al, 2007). Instead, we construct a basic, minimal model of the combined EGFR and IR networks. The goal of this model is to provide an insight into the mechanisms of cellular responses to combined EGF and insulin treatment that can account for our data. The model involves 78 variables for different molecular species, 111 chemical reactions (processes) and more than 200 parameters. A list of reactions, rate equations and parameter values is given in the Supplementary Table S1, and the model SBML file is provided. For many reactions, the parameter values are quantitatively consistent with the previously published values, whose derivation is fully documented in the papers by Kholodenko et al (1999), Moehren et al (2002), and Markevich et al (2004a, 2004b). For additional processes and parameters that describe multi-step processes as single reactions, Supplementary Table S1 cites the corresponding references or indicates that the parameter value was optimized using a training set of data (see Materials and methods). Below, we describe the major signaling processes that are considered and analyzed by this model. In the model, signal transduction is initiated by ligand (EGF or/and insulin) binding to their cognate receptors. This causes dimerization and autophosphorylation of EGFR, or an allosteric transition and autophosphorylation of the kinase activation loop of the predimerized IR, which leads to activation of the IR kinase and autophosphorylation of its cytoplasmic domain (De Meyts and Whittaker, 2002; Sebastian et al, 2006). The model considers that phosphorylated EGFR can directly bind Shc, Grb2–SOS, PI3K, protein phosphatase(s) and RasGAP (Jones et al, 2006; Sebastian et al, 2006). The membrane recruitment of cytoplasmic SOS is critical for the initiation of the Ras/ERK pathway by both EGFR and IR (Medema et al, 1993; Aronheim et al, 1994; Kholodenko, 2000). Interestingly, the direct recruitment of the Grb2–SOS complex by EGFR was shown to be a less effective route of Ras activation than the EGFR–Shc–Grb2–SOS mediated pathway owing to the corresponding binding affinities (Ravichandran et al, 1995; Kholodenko et al, 1999). At the membrane, SOS catalyzes the transformation of Ras-GDP into active Ras-GTP, whereas RasGAP catalyzes the reverse process of Ras deactivation. In the model, phosphorylated IR can directly associate with IRS, PI3K, phosphatase(s), and RasGAP (Staubs et al, 1994). The IRS family of major IR docking proteins consists of at least six members, IRS1-6; however, the model considers only a single 'representative' IRS protein. Importantly in the model, Src is strongly activated by EGFR and more weakly by IR (Schmelzle et al, 2006). Following initial PI3K stimulation and production of PIP3, IRS and GAB1 bind PIP3 and get phosphorylated by IR or EGFR/Src. Phosphorylated IRS and GAB1 recruit cytoplasmic proteins PI3K, Grb2–SOS, SHP2, and RasGAP to the plasma membrane, which results in additional PIP3 production and both activatory and inhibitory regulations of Ras activity (Myers et al, 1994; Ogawa et al, 1998; Gu and Neel, 2003). PIP3 is converted back to phosphatidylinositol-4,5-diphosphate (PIP2) by PTEN (phosphatase and tensin homologue) (Weng et al, 2001). Although both IRS and GAB1 have multiple tyrosine phosphorylation sites, the minimal model represents them by a single, virtual phosphorylation site (Birtwistle et al, 2007). There are some experimental data supporting this assumption and showing that binding of multiple SH2 domain-containing proteins correlates with the overall phosphorylation levels of GAB1 (Figure 4D and E below) and IRS (Goldstein et al, 2000). Where appropriate, we describe complex yet sequential multi-step processes as a single, semi-mechanistic step. As these condensed processes are sequential, our simplifications allow the reduced model to retain the original network topology. For instance, the activation of Raf by Ras includes a conformational change in Raf caused by binding to Ras-GTP, followed by the dissociation of 14-3-3 protein, dephosphorylation of inhibitory S259 and phosphorylation of activatory S338 sites (Dhillon et al, 2002). In the model, all these processes are considered as a single partial step of Raf activation. The complete Raf activation requires tyrosine phosphorylation by kinases, which are thought to belong to the Src family kinases (SFK) (Wellbrock et al, 2004). In the model, these kinases are linked to Src activity, which is differently stimulated by EGFR or IR. Likewise, in the absence of evidence for a distributed mechanism of ERK kinase (MEK) activation by Raf (Kolch, 2005), we use a simplified, one-step description, whereas a distributed mechanism of ERK activation by MEK and deactivation by MKP3 is described as a two-step process (Markevich et al, 2004a). The model also incorporates and analyzes complex feedback circuitry of the EGFR and IR networks. For instance, PIP3-dependent positive feedback circuits in the model involve GAB1–PI3K and IRS–PI3K interactions (Rodrigues et al, 2000; Johnston et al, 2003; Mattoon et al, 2004). Activated ERK inhibits SOS (Dong et al, 1996; Fucini et al, 1999), GAB1 (Lehr et al, 2004) and IRS (De Fea and Roth, 1997) by direct phosphorylation. Activated mTOR mediates multiple modes of feedback, including positive feedback to AKT and negative feedback loops to IRS (Gual et al, 2003; Sarbassov et al, 2005). Although AKT-induced inhibitory phosphorylation of Raf (Zimmermann and Moelling, 1999; Wellbrock et al, 2004) is included in the model, we assume this inhibition to be weak in HEK293 cells, as no noticeable MEK or ERK activation was detected experimentally, following inhibition of AKT activity (see Supplementary Figure S4). The current model involves many parameters that have no analogs in our previously published models. We used the experimental data that are shown in Figures 2 and 3 (excluding experiments with PI3K inhibitor) as a training data set to obtain reasonable fit between the model simulations and data by manually varying the parameter values (see Supplementary Table S1). However, when parameters were fitted, their upper and lower bounds were in agreement with experimental observations for similar reaction types. In addition, reaction rates were always constrained not to be faster than the diffusion limit. Figure 2.Dynamics of EGF or insulin-induced Ras-GTP, ERK and AKT activation, and GAB1 phosphorylation. The left panels show the time courses calculated in silico and right panels show the corresponding time courses measured in HEK293 cells stimulated with insulin (Ins, 100 nM) or EGF (0.1, 1 or 20 nM) for the indicated time intervals (min). Active GTP-bound Ras was immunoprecipitated (IP) from total cell lysates (TCL) by the agarose-conjugated Ras-binding domain (RBD) of Raf as described in Materials and methods. Proteins from Ras-IP or TCL were separated on LDS-PAGE (4–12%), transferred to nitrocellulose membrane, and immunoblotted (IB) with anti-Ras (A) or anti-phospho-ERK1/2 (T202/Y204), anti-phospho-AKT (S473) or anti-phospho-GAB1 (Y627) antibodies (B–D), respectively. The signal intensities of phosphorylated ERK1/2, AKT, or GAB1 normalized against the appropriate signal of α-tubulin protein level are expressed in arbitrary units (AU). Data shown are the mean of normalized signal intensities±s.d. from five independent experiments each performed in triplicates. Download figure Download PowerPoint Dose dependence of responses to EGF and insulin Steady-state plasma concentrations of EGF are reported to be in the range of 0.05–0.2 nM, which is much lower than the concentration range commonly used in the studies on isola
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