Báo cáo khoa học: A systems biological approach suggests that transcriptional feedback regulation by dual-specificity phosphatase 6 shapes extracellular signal-related kinase activity in RAS-transformed fibroblasts pptx

A systems biological approach suggests that transcriptional feedback regulation by dual-specificity phosphatase 6 shapes extracellular signal-related kinase activity in RAS-transformed fibroblasts Nils Blu ¨ thgen 1,2 , Stefan Legewie 1 , Szymon M. Kielbasa 3 , Anja Schramme 2 , Oleg Tchernitsa 2 , Jana Keil 2 , Andrea Solf 2 , Martin Vingron 3 , Reinhold Scha ¨ fer 2 , Hanspeter Herzel 1 and Christine Sers 2 1 Institute for Theoretical Biology, Humboldt University, Berlin, Germany 2 Laboratory of Molecular Tumor Pathology, Charite ´ , Universita ¨ tsmedizin Berlin, Germany 3 Computational Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany The mitogen-activated protein kinase cascade (MAPK) activating extracellular signal-related kinase ERK1 and ERK2 controls crucial cell fate decisions such as differ- entiation, proliferation and malignant transformation. Quantitative differences in signal strength or signal duration result in specific cell fates, e.g. either prolifer- ation or differentiation [1]. The activity of ERK1,2 is regulated through a balance of stimulation through Keywords dual-specificity phosphatase; mathematical modelling; mitogen activated protein kinase; transcriptional feed-back Correspondence N. Blu ¨ thgen, Institute of Pathology, Universita ¨ tsmedizin Charite ´ , FORSYS junior group, Chariteplatz 1, D-10117 Berlin Fax: +49 30 450 536 909 Tel: +49 30 450 536 134 E-mail: nils.bluethgen@charite.de Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/data- base/bluthgen/index.html free of charge (Received 10 July 2008, revised 8 November 2008, accepted 8 December 2008) doi:10.1111/j.1742-4658.2008.06846.x Mitogen-activated protein kinase (MAPK) signaling determines crucial cell fate decisions in most cell types, and mediates cellular transformation in many types of cancer. The activity of MAPK is controlled by reversible phosphorylation, and the quantitative characteristics of MAPK activation determine the cellular response. Many systems biological studies have analyzed the activation kinetics and the dose–response behavior of the MAPK signaling pathway. Here we investigate how the pathway activity is controlled by transcriptional feedback loops. Initially, we predict that MAPK signaling regulates phosphatases, by integrating promoter sequence data and ontology-based classification of gene function. From this, we deduce that MAPK signaling might be controlled by transcriptional nega- tive feedback regulation via dual-specificity phosphatases (DUSPs), and implement a mathematical model to further test this hypothesis. Using time-resolved measurements of pathway activity and gene expression, we employ a model selection approach, and select DUSP6 as a highly likely candidate for shaping the activity of the MAPK pathway during cellular transformation caused by oncogenic RAS. Two predictions from the model were confirmed: first, feedback regulation requires that DUSP6 mRNA and protein are unstable; and second, the activation kinetics of MAPK are ultrasensitive. Taken together, an integrated systems biological approach reveals that transcriptional negative feedback controls the kinetics and the extent of MAPK activation under both physiological and pathological conditions. Abbreviations CREB, cAMP response element binding protein; DUSP, dual-specificity phosphatase; ERK, extracellular signal-related kinase; FDR, false discovery rate; IPTG, isopropyl-thio-b- D-galactoside; IR, inducible RAS; MAPK, mitogen-activated protein kinase; MEK, mitogen-activated protein kinase ⁄ extracellular signal-related kinase kinase; PDGF, platelet-derived growth factor; siRNA, small intefering RNA; SRF, serum response factor. 1024 FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS upstream kinases [MAPK ⁄ ERK (MEK)1,2] and inhibi- tory actions, namely dephosphorylation through spe- cific phosphatases. Most experimental and theoretical approaches have focused on the biochemical mecha- nisms and on the spatiotemporal ordering mediating ERK1,2 activation. These approaches lead to the assumption that simply inhibiting MEK1,2 or ERK1,2 using therapeutic small-molecule inhibitors would be sufficient to suppress pathway activation and thereby reverse downstream biological responses such as immune function, mitogenesis or even malignant cell growth. However, many inhibitors directly targeting MEK or upstream kinases have produced unpredict- able cellular and clinical responses [2]. The MAPK signaling network has been investigated by mathematical modeling for more than a decade [3–5]. The input–output relationships of the MAPK cascade have been intensively studied by mechanistic modeling. Studies in Xenopus oocyctes have suggested that the cascade-like structure and double phosphory- lation of MEK and ERK give rise to a nonlinear sigmoidal response [6]. Moreover, the influence of post-translational feedback loops on the dynamic behavior of this signaling cascade has been unveiled, and it was found that there are positive and negative feedbacks, depending on the cellular context [7]. Posi- tive feedback has been shown to increase the sensitivity of the stimulus–response relationships. It may even cause bistability, where the state of signaling may depend on whether the pathway has been stimulated earlier [8]. In contrast, negative MAPK feedback allows the MAPK cascade to return to lower activity, even if upstream signaling persists, and therefore to adapt to prolonged extracellular stimulation [9]. If the signaling pathway is very sensitive, negative feedback can bring about oscillations. This has been postulated by Kholodenko for MAPK signaling [10], and was recently observed experimentally [11]. So far, the consequences of transcriptional feedbacks in MAPK signaling have not been addressed in detail. Currently, the majority of biological information on negative regulation of MEK ⁄ ERK signaling is derived from studies on mouse, chicken and zebrafish develop- ment [12–15]; the relevance in adult animals is less clear. These studies revealed an essential role for the ERK-specific dual-specificity phosphatase DUSP6 in development, and showed that it acts downstream of the fibroblast growth factor receptor to inhibit the ERK response. Previous mathematical models were focused on the control of ERK activation by hormones at short time scales of < 60 min, and the concentrations of the proteins were assumed to be con- stant and independent of transcriptional changes. However, many important cell fate decisions and cellu- lar transformations are slow processes that require long-term MAPK activation and subsequent altera- tions in gene expression [16,17]. Downstream of ERK, numerous transcription factors become activated in sequential transcriptional cascades. It is believed that distinct combinations of transcription factors give rise to a specific cellular response [18,19]. In attempts to predict the transcription factors that are functionally involved in certain ERK-dependent processes, even sophisticated methods, including combinatorial approaches or the analysis of phylogenetic conserva- tion of potential regulatory sites, have proven unsatis- factory [20]. Therefore, the transcriptional response was only rarely taken into account in modeling approaches addressing ERK1,2 signaling, and the role of individual transcription factors targeted through MEK ⁄ ERK signaling was not included. Here we aim at identifying feedback adaptation mechanisms within the MEK ⁄ ERK signaling cascade by first scanning MAPK target genes for potential functions in MAPK signaling. Candidate transcrip- tional feedback loops are then further analyzed using a semiquantitative mathematical model of MAPK signal- ing that incorporates changes in the transcriptome. This approach allows us to identify transcriptional feedback loops that may be important in cellular trans- formation and for cell fate decisions. The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem. sun.ac.za/database/bluthgen/index.html free of charge. Results Transcription factors downstream of ERK are predicted regulators of phosphatase function The genome-wide prediction of target genes of a partic- ular transcription factor is far from being reliable [21]. Therefore, we developed and validated a function-ori- ented approach to predict target genes responding to ERK activation [22]. Instead of screening promoter sequences for transcription factor binding sites and pre- dicting target genes directly, we asked which cellular functions are regulated by a specific transcription factor or combinations thereof. Once a set of defined tran- scription factors was identified, transcriptional targets were predicted and further tested for enrichment in annotated functions as described by gene ontology [22]. We applied this algorithm to serum response fac- tor (SRF) and cAMP response element binding protein (CREB), two central transcription factors downstream N. Blu ¨ thgen et al. Transcriptional feedback in ERK signaling FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS 1025 of ERK [23]. Our algorithm identified the terms ‘protein amino acid dephosphorylation’ and ‘dephos- phorylation’ as the only terms that are significantly enriched within the group of putative SRF ⁄ CREB target genes (P FDR < 0.05, where FDR is false discov- ery rate). Therefore, we speculated that phosphatases might feed back into MAPK signaling. Good candi- dates for such feedback mechanisms are the classical DUSPs, a family of phosphatases that specifically dephosphorylate MAPKs [24]. Therefore, we collected evidence that DUSPs are regulated by these two tran- scription factors. A recent chromatin-immunoprecipia- tion on chip (ChIP-on-chip) experiment demonstrated that the promoters of DUSP1, DUSP3, DUSP4, DUSP6 and DUSP11 are directly bound by CREB [25], suggesting a direct involvement of CREB in their transcriptional regulation. To further confirm SRF- dependent DUSP regulation experimentally, we tested the effect of SRF silencing on DUSP4 and DUSP6 expression. After transient transfection of HRAS-trans- formed immortalized human embryonal kidney cells [26] with two independent small intefering RNAs (siR- NAs) specifically targeting the SRF gene, we analyzed SRF, DUSP4 and DUSP6 mRNAs by real-time PCR. Transfection of the cells with SRF-specific siRNAs sup- pressed SRF expression itself, but also that of the two phosphatase genes, after 96 h (Fig. 1). These results largely confirm our prediction of a strong impact of SRF on DUSP regulation, and suggest that ERK might regulate its own activity by inducing phos- phatases, at least under certain biological conditions. Model selection suggests that DUSP6 is induced and modulates ERK activity Having identified putative direct links between MAP- Ks, the transcription factors SRF and CREB and the regulation of DUSPs, we aimed at investigating whether the regulation of these phosphatases consti- tutes feedback loops that modulate MAPK activation in vivo. Experimental evidence indicated that many receptor-mediated stimuli cause rapid adaptation and desensitization of the receptors and of signaling mole- cules by post-translational modifications and receptor internalization [27]. Thus, it is impossible to distinguish feedbacks due to the transcriptional regulation of phosphatases from feedbacks due to receptor deactiva- tion when the cells are stimulated at the level of the receptors. Consequently, we decided to stimulate the canonical MAPK cascade using RAS constructs encoding mutationally activated RAS proteins that signal constitutively without requiring receptor activa- tion. In immortalized rat fibroblasts, expression of oncogenic RAS (H-RAS V12 ) elicits prolonged activa- tion of ERK and cellular transformation [17]. To investigate the dynamic implications of a putative DUSP-mediated feedback, we used an inducible onco- genic H-RAS V12 gene construct controlled by an isopro- pyl-thio-b-d-galactoside (IPTG)-sensitive promoter [28]. After addition of IPTG to the medium, the cells express oncogenic RAS [29]. We monitored RAS expression and ERK phosphorylation by western blot, and the transcriptional levels of several DUSPsby interrogating custom microarrays [30] and by northern blots in a time-resolved manner (Fig. 2A,B). The RAS protein is strongly induced and accumulates through- out the measurement period, whereas ERK is initially strongly activated, then declines, and is subsequently maintained at an intermediate level of activation. Among several DUSPs, the relatively unspecific DUSP1 and the ERK-specific DUSP6 show rapid induction. Therefore, both are likely candidates for negatively regulating ERK activity and causing the biphasic response of ERK activation. To further DUSP4 mRNA 0 0.4 0.8 1.2 0 0.4 0.8 1.2 0 0.04 0.08 0.12 DUSP6 mRNA SRF mRNA SRF 1 SRF 2 SCR 1 SCR 2 SCR 3 MOCK 1 siRNA ExpressionExpressionExpression Fig. 1. Expression of DUSP4 and DUSP6 depends on SRF. Real- time PCR analysis of SRF, DUSP4 and DUSP6 mRNA expression 96 h following transfection with siRNAs suppressing SRF. Two independent SRF siRNAs (SRF_1 and SRF_2) were used, and three scrambled siRNAs (SRC1 ⁄ 2 ⁄ 3) were used as controls. Reactions for SRF, DUSP4 and DUSP6 were performed, and the cycle thresh- old (CT) values are depicted. All reactions were normalized to relative levels of tubulin as an internal standard. Transcriptional feedback in ERK signaling N. Blu ¨ thgen et al. 1026 FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS explore this hypothesis, we quantified time-resolved lev- els of RAS and phosphorylated ERK proteins by wes- tern blot, and of DUSP6 and DUSP1 mRNA by northern blot, and fitted different mathematical models to the data. The induction kinetics of RAS in the experimental system varied from time-series experiment to time-series experiment. It is therefore important that we used both mRNA and protein samples from the same experimental time-series experiments for model construction and fitting, and did not use the microarray data, which came from a different experimental run. We applied a model selection process based on the likelihood ratio test [31]. Briefly, for each of the models investigated, the best fit of the model to the data was obtained by a maximum likelihood method. The good- ness of fit was quantified by calculating the v 2 -value, i.e. the sum of the squared differences between data and model fit divided by the variance of the data. A more complex model can fit better, because it describes the system better, or because it fits the experimental error (also called overfitting). In order to discriminate between these two scenarios, we calculated P-values that quantified the probability that a model fits the data better just because the alternative model fits the noise better. These P-values were estimated using a Monte Carlo method (for details, see Appendix S2 and [32]). Using this approach, we can determine whether adding additional molecular processes to the model or assuming different mechanisms in the model improves the description of data just because the model has more degrees of freedom, which would lead to model rejec- tion. If the new molecular steps are essential to the model, the model will be accepted. We first constructed two mathematical models: one model describing ERK activation with DUSP6-medi- ated ERK dephosphorylation and another model with- out (Fig. 3A,B). We found that DUSP6-mediated ERK dephosphorylation is indeed required for the model to properly describe the data, as otherwise the biphasic response in ERK phosphorylation cannot be ERK P P ERK RAS DUSP6 DUSP6 0 1 2 RAS 0 1 2 ERKpp 05 10 15 20 Time (h) 0 1 2 DUSP6 IPTG 0 1 2 RAS 0 1 2 ERKpp 0 5 10 15 20 Time (h) 0 1 2 DUSP6 western blot western blot northern blot 0 AB CD 2468 Time (h) Expression (AU) DUSP6 DUSP1 DUSP5 DUSP9 Array Northern blot Fig. 2. Model construction from time-series data. (A) After induction of oncogenic RAS, several DUSPs are transcriptionally regulated, as detected by microarrays (gray) and northern blots (black). DUSP6, a very specific phosphatase for ERK, is rapidly upregulated after induction. (B) Quantified western blot and northern blot time series show that RAS expression increases monotonically over the first 24 h after induc- tion. ERK phosphorylation is first increased, and then briefly decreased, followed by a plateau. Actin measurements are used for normaliza- tion of RAS signals, and phospho-ERK levels are normalized by total ERK intensities. DUSP6 mRNA levels rapidly rise after RAS is induced. (C) Schematic representation of the selected mathematical model. RAS is induced by IPTG and degraded. ERK is phosphorylated as a conse- quence of RAS activation, and phospho-ERK in turn induces DUSP6 mRNA expression. DUSP6 is translated into DUSP6 protein, which dephosphorylates ERK. In the final model, ERK (de)phosphorylation is assumed to be in quasi-steady state, with nonlinear dependence on RAS and DUSP6. (D) Time-series of the best fit of the final model together with the quantified time-series data from western blots (for ERKpp and RAS), and northern blot (for DUSP6 mRNA). N. Blu ¨ thgen et al. Transcriptional feedback in ERK signaling FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS 1027 ERK P P ERK RAS dusp6 ERK P P ERK RAS dusp6 DUSP6 Model A without feedback Model B with DUSP6 Model D reduced model Model E with ultrasensitivity ERK P P RAS dusp6 DUSP6 Ultrasensitive ERK P P RAS dusp6 DUSP6 Linear Model reduction explains data similarly well (P > 0.6) Model with DUSP6 feedback explains data better (P < 0.01) Model fits better with ultrasensitivit y (P < 0.05) ERK P P ERK RAS dusp1 DUSP1 Model C with DUSP1 DUSP6 feedback explains data better than with DUSP1 (P < 0.01) 0 1 2 Ras 0 1 2 ERK-p 05 Time (h) 0 1 dusp1 0 1 2 Ras 0 1 2 ERK-p 05 Time (h) 0 1 2 dusp6 0 1 2 Ras 0 1 2 ERK-p 05 Time (h) 0 1 2 dusp6 0 1 2 Ras 0 1 2 ERK-p 05 Time (h) 0 1 2 dusp1 0 1 2 Ras 0 1 2 ERK-p 05 Time (h) 0 1 2 dusp6 Fig. 3. Model selection procedure. The structure and the best fit to the first data points of the five models are shown, as well as the P-val- ues from the likelihood ratio test. (A) First, a model without feedback was constructed and fitted to time-series data of RAS protein expres- sion, ERK phosphorylation, and dusp6 mRNA expression. This model could not reproduce the biphasic response. (B) A model that includes dephosphorylation of ERK explains the data significantly better. (C) Fitting the same model to time-series of dusp1 mRNA results in a signifi- cantly worse fit. (D) Model B was reduced by quasi-steady-state approximation of ERK activity. This reduced model fits the data similarly well. (E) Erk activation and deactivation was assumed to be ultrasensitive, and this model fits the data significantly better than model D. Transcriptional feedback in ERK signaling N. Blu ¨ thgen et al. 1028 FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS reproduced. We also investigated whether DUSP1 can similarly account for the observed dynamics in ERK phosphorylation by fitting the model with feedback to the time course of ERK, RAS and DUSP1 mRNA (Fig. 3C). This model fitted significantly less well, which suggests that DUSP6 is the important regulator in the first hours of ERK signaling. Therefore, we chose the model structure shown in Fig. 3B, and investigated whether we can reliably determine the parameters in the mathematical model from the experimental data. We used a Monte Carlo approach to define confidence intervals for and corre- lation coefficients between the parameters. The param- eters describing ERK activation and deactivation showed large confidence intervals, and were highly cor- related, which indicates that they are not identifiable (for details see Appendix S1). This is not too surpris- ing, as the typical time scale for activation and deacti- vation of ERK is much smaller than the intervals between the time points of measurements of ERK phosphorylation. Thus, the detailed activation and deactivation rates cannot be inferred separately from our data. Moreover, the parameters describing the impact of ERK phosphorylation on DUSP6 expression and vice versa were especially highly correlated. There- fore, we reduced model complexity by applying a quasi-steady-state approximation for phosphorylation and dephosphorylation of ERK. Model selection shows that the resulting reduced model fits the data similarly well as the more detailed model (Fig. 3D and Appendix S2). The model also allows us to investigate whether ERK activation is responding to upstream events in a linear or nonlinear manner. Mechanistic modeling has suggested that ERK and MEK respond in an ultrasen- sitive fashion [6,33,34], but so far this has only been confirmed for signaling processes in Xenopus oocytes. We modified the model such that ERK activation is nonlinear with an exponent of 2. This modified model fitted the data significantly better and allowed us to describe the biphasic response of ERK more precisely (Fig. 3E). The structure and time-series of the best fit of this final model is shown in Fig. 2C,D. In conclusion, model selection of the time-series data resulted in two testable predictions. First, the two parameters describing DUSP6 mRNA and protein decay in the model have a direct biophysical meaning. Both DUSP6 mRNA and protein are estimated to be rapidly decaying, which can be compared to the bio- chemical data. Second, the model selection predicts that the activation of ERK is nonlinear. As described in the following, we collected quantitative experimental measurements to test these model predictions. Model prediction 1 – DUSP6 is unstable at the mRNA and protein levels Most of the model parameters are given in relative units; thus, they cannot be compared to biochemical measurements. However, two parameters in the model have a direct biophysical meaning: the decay rates of DUSP6 protein and mRNA are estimated to be relatively fast, at 3.5 and 0.9 h )1 , respectively. These values correspond to half-lives of 11 and 46 min for 0 20 0 40 0 40 0 30 0 20 0 20 0 30 0 250 500 750 1000 ERKpp (IF) 0 10 n = 53 n = 159 n = 168 n = 156 n = 170 n = 159 n = 267 n = 55 0 0.1 0.2 0.5 1 2 5 10 PDGF PDGF (ng·mL –1 ) 200 400 600 800 Mean flourescence Hill coefficient 3.8 ± 0.7 024681 0 0 20 40 60 80 100 A B C 0246810 mRNA half-life (h) Fraction of mRNAs (%) Fig. 4. Validation of model predictions. (A) Cumulative distribution of mRNA half-lives. DUSP6 has a very low half-life (median 0.55 h, marked with an arrow), which is significantly smaller than average mRNA half-lives. (B, C) Distribution of ERK phosphorylation is single cells after platelet-derived growth factor (PDGF) stimulation shows that ERK responds with a unimodal distribution (B), in an ultrasensi- tive fashion at the population level with a Hill coefficient of about 4 (C). N. Blu ¨ thgen et al. Transcriptional feedback in ERK signaling FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS 1029 DUSP6 protein and mRNA, respectively. Recently, two studies of mRNA decay rates, measured at the level of the genome, showed a median half-life of DUSP6 mRNA of 33 min. This is one of the shortest half-lives in the entire dataset (Fig. 4A) [35,36]. In addition, the half-life of the DUSP6 protein has been reported to be < 1 h [37]. Thus, the model prediction of very short half-lives for DUSP6 is congruent with the data available. Model prediction 2 – ERK activity is ultrasensitive The next prediction of the model selection procedure is that ERK activation is ultrasensitive. Possible mecha- nisms underlying ultrasensitivity have been discussed earlier. The most likely mechanism is distributed, sequential phosphorylation ⁄ dephosphorylation of ERK, which typically gives rise to a Hill coefficient of 2 [33]. It was not possible to reliably measure small quantitative changes in MEK activation in our experi- mental system. Therefore, we tested this prediction by stimulating fibroblasts with different concentrations of the platelet-derived growth factor (PDGF), and mea- sured ERK phosphorylation in the nucleus 20 min poststimulation by immunofluorescence. Single-cell measurements were employed, as it has been proposed earlier that PDGF-stimulated fibroblasts react in a bistable manner, with individual cells responding in an all-or-none fashion [8,38]. Such cellular behavior is expected to give rise to a bimodal histogram of ERK activity for intermediate stimuli. The distribution of ERK activity is shown in Fig. 4B. In contrast to bista- ble responses, the stimulation experiments showed a monomodal distribution of ERK activity, which grad- ually shifts to higher activity levels as the stimulus increases. This suggests that ERK activity is not bista- ble in fibroblasts stimulated with PDGF. The average activity shows a Hill-type response with a coefficient of approximately 4 (Fig. 4C). Thus, ERK activation is ultrasensitive when it responds to PDGF stimulation. As Hill coefficients are determined in cell populations, the strong sensitivity of the response observed at the population level could be even more pronounced at the level of individual cells. A similar estimate for the Hill coefficient can be derived from previously pub- lished data obtained by flow cytometry [39] (for details, see Appendix S3). Such ultrasensitivity may arise at any point during the transduction from recep- tor to ERK. Ultrasensitivity is partly due to the func- tion of the receptor, which has been determined to respond with a Hill coefficient of 1.7 in fibroblasts [40]. Hill coefficients of signaling cascades are maxi- mally the product of the Hill coefficients of the individual elements of the signaling pathway [41,42]. Therefore, the remaining coefficient of at least 2 can be attributed to MAPK signaling. It remains to be shown, however, whether the resulting ultrasensitivity results from the double phosphorylation of ERK, or from a combination with processes further upstream, such as MEK phosphorylation. Discussion MAPK signaling is central to proliferation control in many cells, and quantitative aspects of ERK activa- tion, such as signal amplitude and duration, determine the cell fate. However, little is known of how MAPK signaling is regulated quantitatively by transcriptional feedback loops, although the time scale of decision- making is often well beyond that of early transcrip- tional feedbacks. To improve our knowledge of the transcriptional responses involved in MAPK signaling, we have employed a systems biological approach to identify a feedback loop that shapes the activation of ERK within the first hours of cellular transformation. We present evidence that DUSP6 is transcriptionally upregulated by oncogenic RAS signaling through the potential cooperation of SRF with CREB, and thus causes a biphasic response of ERK. Current sequence- based methods fail to provide a genome-wide predic- tion of target genes, due to the high number and length of the mammalian promoters and the short binding motifs of transcription factors. However, the combination of ‘conventional’ promoter analysis with gene ontology-term-based functional annotation [22] revealed phosphatase genes as primary targets of SRF and of CREB. SRF is a key determinant of muscle dif- ferentiation, and plays a major role in the regulation of proliferation through the activation and repression of a variety of target genes [43,44]. The transcriptional activity of SRF is stimulated through ERK-dependent phosphorylation. Specificity is achieved by interaction of SRF with cofactors in a signal-specific or tissue- specific manner. These cofactors bind either together with SRF at the serum response element or in close proximity to Ets binding sites [45]. Also, the CREB transcription factor has been implicated in the regula- tion of proliferation, mainly in leukemias through the induction of proto-oncogenes and cell cycle regulatory genes [46]. A direct impact of CREB-mediated gene activation on signaling feedback control during trans- formation or tumor development has not been reported previously. Thus, our approach identified a hitherto unknown combinatorial role of both tran- scription factors, which is likely to determine both the onset and quantity of mitogenic signaling in several Transcriptional feedback in ERK signaling N. Blu ¨ thgen et al. 1030 FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS different cellular contexts. In a recent publication [44], DUSP6 was predicted to harbor an SRF-binding site; however, this was not confirmed experimentally. Therefore, it remains to be tested whether CREB and SRF both interact with the phosphatase genes, or whether there is an indirect contribution of SRF, which does not seem likely, because of the rapid induc- tion. SRF might also play a role as a mediator for MAPK-dependent, ETS1-controlled induction of DUSP6, as suggested very recently [47]. The fact that we found phosphatases to be overrepresented in the joint list of SRF and CREB targets suggests that regulation of protein phosphorylation is a common function of the two transcription factors. Another important aspect of our model is that it predicted the very short half-lives of DUSP6 mRNA and protein, which have been reported to be £ 1h [37]. The time span required for a protein to reach a steady-state expression level is determined by its half- life [48]. Therefore, short-lived molecules such as DUSP6 can respond quickly to any alteration in signaling, and thus can influence ERK activity within 1–2 h. The functional relevance of DUSP-mediated feedback is supported by a recent study using meta- bolic control analyses on epidermal growth factor receptor models [49]. This study predicted a central role for the dephosphorylation of ERK. The distribu- tion of control strength within the epidermal growth factor receptor-induced network of MAPK signaling showed that relatively few, distinct steps in the signal- ing cascade appeared to have a significant control function for the signaling amplitude, duration and integrated output of transient ERK phosphorylation. The dephosphorylation of ERK by DUSP6 and also the overall protein concentrations of both ERK and DUSP6 had a significant influence on signaling control [49]. Such differential control functions might have important implications for the efficacy of targeted pathway inhibition, as blocking of different pathway components might cause different and eventually unex- pected biological responses. One important aspect is that systems controlled by negative feedbacks may be very robust with respect to manipulations of different components within the feedback loop [50]. MAPK phosphatase genes, such as DUSP6 and DUSP4, are at least partially understood in terms of their transcrip- tional regulation downstream of ERK [44,47,51]. In addition, there are other feedback regulators within the RAS–RAF–MEK–ERK pathway that might con- tribute to signaling modulation. We tested whether feedback regulation via DUSP1, an unspecific MAPK phosphatase, contributes to early signal attenuation, but found that it does not play a major role, possibly because the cells are not serum-starved. DUSP9, which is induced at later a time point, may mediate signal attenuation at time points after 10 h. Other classes of signaling proteins might also mediate transcriptional feedback. Most recently, Ding and Lengyel [52] described a novel regulator of RAS, p204, which is induced by Egr1, a transcription factor directly down- stream of ERK. Moreover, Sprouty, an inhibitor act- ing at the receptor level, seems to be transcriptionally regulated upon pathway activation [48]. Therefore, fur- ther quantification and more detailed modeling includ- ing time-resolved analysis of phosphatase expression will be required to determine whether therapeutic approaches targeting DUPSs or signaling components further downstream of MAPK could be beneficial. Several lines of evidence suggest that the feedback mediated by DUSP6 ‘steps in’ whenever noncancerous cells are exposed to prolonged stimulation. This allows switching-off of the pathway [53]. Several studies have demonstrated the role of DUSP6 as a central feedback regulator dampening ERK levels in developmental programs [14,54]. Our study shows that a strong onco- genic signal can overcome this negative feedback and achieve constitutive ERK activation. However, it also shows that the feedback keeps ERK activity at a mod- erate level. One could speculate that the robustness gained from this feedback in normal cells is ‘hijacked’ or co-opted by cancer cells to circumvent apoptosis caused by ERK overactivation. The role of DUSP6 in controlling the robustness of tumor cell proliferation and progression seems to be dependent on tumor type. Pancreatic cancer cells progress towards a more aggressive and invasive phenotype following loss of DUSP6 expression [55]. In breast cancer cells, activa- tion of the DUSP6 feedback correlated with chemo- therapy resistance following tamoxifen treatment [56]. Moreover, DUSP6 is part of a predictive gene signa- ture for non-small cell lung cancer based on five infor- mative genes [57]. These examples show that it is crucial to understand MAKP-dependent control mech- anisms in more quantitative terms, and suggest that molecules involved in feedback regulation can play ambiguous roles as oncogenes and tumor suppressors, depending on quantitative differences. From our experimental data, we could derive a role for one of the regulated DUSPs. As other DUSPs are regulated as well, MAPK signaling is most likely regu- lated by a complex network of negative feedback regu- lators. Moreover, the stability of several DUSP proteins is regulated by post-translational modification [37]. Our study based on model selection and time course data could not fully resolve the complexity of this regulatory network downstream of MEK. In order N. Blu ¨ thgen et al. Transcriptional feedback in ERK signaling FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS 1031 to disentangle this network, a much more complex study needs to be conducted, including pathway inter- ference, and incooperating biophysical data such as binding constants and protein concentrations, which crucially influence the dynamics of the pathway [33]. Only then we will be able to understand why such a complex network of negative feedback players controls MAPK signaling. Moreover, the biological variability in our experi- mental system, which caused different induction kinet- ics of RAS, was a limitation, as all data used to calibrate the model had to come from one experimen- tal time course. In future studies, other means of receptor-independent stimulation need to be exploited. Our study also warrants the conclusion that mathe- matical modeling of signaling pathways needs to incor- porate the response of the transcriptome, if it is aimed at modeling the pathways for physiologically relevant time intervals. Previous detailed mathematical models have emphasized the importance of post-translational feedbacks, but have generally neglected transcriptional feedback loops. A recent analysis has shown that tran- scriptional feedback regulation by short-lived inhibi- tory molecules controls all major signaling pathways in humans [48]. Therefore, we expect that similar semi- quantitative studies on the feedback regulation of other disease-related pathways are required to fully appreciate the complexity of pathway control. Such studies could guide searches for new and more patient- tailored therapeutic interventions and provide solutions that either bypass the feedback loops or even modulate the loops and achieve high therapeutic potential. Experimental procedures Cell culture conditions, transfection and imunofluorescence Immortal rat 208F fibroblasts, the HRAS G12V -transformed derivatives FE-8 [58] and NIH3T3 cells were cultured in DMEM supplemented with 10% fetal bovine serum, 2% penicillin ⁄ streptomycin, and 2 mml-glutamine. HRAS G12V - transformed human embryonal kidney cells were described by Hahn et al. [26], and were cultivated in MEM, alpha modification, supplemented with 10% inactivated fetal bovine serum, 2 mm ultraglutamin, 1% penicillin ⁄ strepto- mycin, 0.1 mgÆmL )1 hygromycin, 0.5 lgÆmL )1 puromycin, and 0.4 mgÆmL )1 G418. Transient siRNA transfections against SRF were per- formed for 96 h after double transfection with two different oligonucleotides: SRF-1 (UGAGUGCCACUGGCUUUG Att sense, UCAAAGCCAGUGGCACUCAtt antisense), constructed with the Silencer siRNA Construction Kit (#1620; Ambion, Applied Biosystems, Carlsbad, CA, USA) and SRF-2 predesigned by Ambion (ID 142734). In both cases, a final concentration of 50 nm was used. Immortal rat 208F fibroblast-derived inducible RAS (IR) cells (clone IR-4) harbor an IPTG-inducible HRAS onco- gene, and have been described previously [29]. Expression of HRAS was induced by the addition of 20 mm IPTG. NIH3T3 cells grown on coverslips were serum-starved for 48 h and then treated with increasing concentrations of PDGF. PhosphoERK immunofluorescence was determined with the phospho-p44 ⁄ 42 MAPK antibody (Thr202 ⁄ Tyr204) (New England Biolabs, Ipswich, MA, USA) after 15 min of fixation in 3% paraformalde- hyde ⁄ NaCl ⁄ P i and 1 min of permeabilization in 0.2% Triton X-100 ⁄ NaCl ⁄ P i . Cells were then treated with the pMAPK antibody for 2 h and with an Alexa546-labelled antibody against rabbit for 1 h. Pictures were taken with a standard fluorescence microscope and quantified as described in Appendix S3. Western blot analyses Cells were solubilized in lysis buffer [20 mm Tris ⁄ HCl pH 8.0, 100 mm NaCl, 1% sodium deoxycholate, 1% NP-40, 0.1% SDS, complete protease inhibitor mix (Roche, Mannheim, Germany)], and 20 lg of the whole cell extracts were separated by SDS ⁄ PAGE. After semidry blotting (TransBlot SD; BioRad, Laboratories, Munich, Germany) to polyvinylidenefluoride membranes (Hybond P; Amer- sham, Little Chalfont, UK), the membranes were blocked for 1 h in NaCl ⁄ Tris-T (10 mm Tris, pH 8.0, 150 mm NaCl, 0.05% Tween-20) with 5% nonfat dry milk, and incubated with primary antibodies against RAS (Transduction Lab- oratories, BD Biosciences, San Jose, CA, USA) and phospho-p44 ⁄ 42 MAPK (Thr202 ⁄ Tyr204) (New England Biolabs). Membranes were washed and incubated with per- oxidase-conjugated secondary antibodies. Signals were detected by chemiluminescence reaction (ECL; Amersham Pharmacia, Little Chalfont, UK) according to the manufac- turer’s instructions. Microarray experiments Predesigned 70-mer oligonucleotides produced by Illumina Inc. (San Diego, CA, USA) were spotted at 20 lm in 3· SSC buffer, containing 0.01% SDS, onto poly(l-lysine)- treated glass slides. Spotting was performed with the Micro- Grid microarrayer (Genomic Solutions, Ann Arbor, MI, USA). Every oligonucleotide was spotted six times. In addi- tion, 20 different housekeeping genes and positive and negative controls provided by the Alien SpotReport cDNA Array Validation System were included (Stratagene, La Jolla, CA, USA). Labeling and microarray hybridization Transcriptional feedback in ERK signaling N. Blu ¨ thgen et al. 1032 FEBS Journal 276 (2009) 1024–1035 ª 2009 The Authors Journal compilation ª 2009 FEBS was performed manually according to the Genisphere 3DNA Array 50 kit protocol (Genisphere, Hatfield, PA, USA). For every hybridization a Cy3 ⁄ Cy5 dye swap experi- ment was performed. Microarrays were scanned with two wavelengths for Cy3 (570 nm) and Cy5 (660 nm) by using a laser fluorescent scanner (Agilent G2565BA Scanner; Agilent Technologies, Palo Alto, CA, USA) with three different photomultiplier gains. Data analysis was performed using imagene version 3.0 (BioDiscovery, Los Angeles, CA, USA). Raw data obtained with the highest photomultiplier gain were routinely used for quantification. Spots with saturated sig- nal intensity were reanalyzed using a lower photomultiplier gain. The fluorescence intensity of each spot in both the Cy3 and Cy5 images was quantified, and fluorescence levels of the local background were subtracted. Normalization of Cy3 and Cy5 images was performed by adjusting the total signal intensities of two images. A Lowess curve was fitted to the log intensity versus log ratio plot. Twenty per cent of the data were used to calculate the Lowess fit at each point. This curve was used to adjust the control value for each measurement. If the control channel was lower than 10, then 10 was used instead. Northern blot analysis Ten micrograms of RNA were denatured for 5 min at 95 °C and run on a 0.8% agarose ⁄ 0.6 m formaldehyde gel for 4 h. The RNA was transferred to a nylon membrane (Nytran N; Schleicher & Schuell, Dassel, Germany) and crosslinked by UV light. Membranes were prehybridized for 1 h at 66 °C in hybridization buffer (ExpressHyb; Clontech, Takara Biogroup, Mountain View, CA, USA) with 100 lgÆmL )1 yeast tRNA. Twenty-five nanograms of the cDNA probe were radiolabeled with [ 32 P]dCTP[aP] (ICN) by random priming, and between 0.5 and 1 · 10 6 c.p.m. of the labeled probe was added per milliter of hybridization buffer and hybridized overnight at 66 °C. Membranes were washed to a stringency of 2· SSC ⁄ 0.1% SDS at 42 °C, exposed to X-ray films, and stored at – 80 °C until detection. To verify equal loading and integrity of RNA, all gels were stained with ethidium bromide. mRNA levels were normalized with glyceraldehyde 3-phosphate dehydrogenase or 18S rRNA. Real-time PCR analysis RNA was prepared as described above 96 h after the sec- ond siRNA transfection. 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