EGF stimulates the Mg2+ re-absorption in the DCT by way of its receptor on the basolateral membrane and through activation of TRPM6 in the apical membrane in vitro ITI-007[12,13]. Nonetheless, the final results from the present research jointly with the benefits of our prior examine of CsA-induced nephrotoxicity advise that an extra EGF-induced system is concerned in TRPM6 regulation. Equally studies present simultaneous TRPM6 and EGF mRNA downregulation, which might show that EGF also influences the TRPM6 mRNA synthesis [9]. Also the locating of an upregulated TRPM6 mRNA expression in rats handled with EGF in our preceding research more supports this speculation [nine]. To even more elucidate the relationship among EGF and TRPM6 in vivo, the result of cisplatin on the EGFR pathway was analyzed. Dependent on the activation mechanism of TRPM6 via EGF, as recognized in vitro, Pi3, Akt and Rac1 expression profiles ended up evaluated. Activation of these EGFR pathway associates qualified prospects to TRPM6 activation and redistribution [thirteen]. However, the existing examine could not set up a simple association in between the cisplatin-induced EGF downregulation and the down-stream activation of the EGFR pathway members. This could be associated to the reality that the investigated intermediates are activated in a lot of other pathways concerned in kidney damage this sort of as the insulindependent Pi3/Akt activation, the platelet-derived development factorinduced Pi3/Akt pathway and the TGF-b1-elevated collagen1 expression via the Pi3-PDK1-AKT pathway [twenty,21,22]. All these pathways are possibly upregulated in the cisplatin-induced nephrotoxicity rat product. Additionally, in the current research, the whole cortex protein and mRNA amounts had been analyzed, which may well be a way too crude extract to detect particular DCT associated changes in the Pi3/Akt pathway. However, there is proof for the EGFR pathway-mediated activation of TRPM6 in vivo. Dimke et al. examined the effect of an EGFR inhibitor on renal Mg2+ managing. Erlotinib-injected mice failed to minimize the FE Mg2+ in response to a reduced serum Mg2+ focus. TRPM6 mRNA was downregulated but not the TRPM6 protein stage, indicating that the hypomagnesemia is because of to the inactivation of TRPM6 at protein degree [23]. In addition, numerous clinical reports described hypomagnesemia due to renal Mg2+ wasting after remedy with EGFR-inhibiting antibodies [24,twenty five,26]. The mRNA expression stages of the EGFR remained unchanged. EGFR protein expression remained unchanged after 4 months but elevated soon after 9 months. This up-regulation is possibly a reaction in purchase to repair cisplatin-induced tubular hurt [27]. TRPM7 is really homologue to TRPM6 (<50% homology) and also responsible for the cellular Mg2+ homeostasis [28,29]. TRPM6 specifically interacts with TRPM7 to form a functional ion channel complex at the cell surface of human embryonic kidney 293 cells [30,31]. In our study, the cisplatin-treated groups showed no TRPM7 downregulation. At this point, it is unclear whether TRPM6 and TRPM7 are both expressed in the DCT of rat kidneys, which is the case for mice and humans [32,33]. Immunocytochemical stainings for both channels were unsuccesful due to the unavailability of appropriate primary anti-rat antibodies. Since NCC is only expressed in de DCT, renal NCC mRNA expression and FE Na+ were measured to evaluate DCT damage [34]. These parameters did not differ between the control group and the cisplatin-treated group after 4 weeks, suggesting that the TRPM6 downregulation is not due to damaged DCTs. This is in contrast to a recent report which describes the downregulation of 3 DCT markers (TRPM6, NCC and Parvalbumine) after cisplatintreatment in mice and concluded that cisplatin affects the entire DCT, leading to renal Mg2+, K+, Na+ and Ca2+ wasting [35]. The main difference with our rat model is that we induced low grade functional nephrotoxicity, leading to a selective down regulation of TRPM6, while Van Angelen et al. induced in mice tubular necrosis using a higher dose of cisplatin and less time between the consecutive cisplatin administrations, leading to acute DCT necrosis with downregulation of all DCT markers. Our data indicate a time-related negative effect of cisplatin on the NCC, with a decreased NCC expression level at 9 weeks. However, the FE Na+ was stable which reflects the maintenance of the sodium re-absorption by compensatory regulatory mechanisms. Moreover, at 9 weeks, the TRPM6 mRNA expression and the FE Mg2+ partially recovered compared to the cisplatin-treated group at 4 weeks. In our rat model, the cisplatin-induced Mg2+ loss is DCTspecific. The mRNA expression of the tight junction proteins claudin-16 (also known as paracellin-1) and claudin-19, which are the key players of the paracellular Mg2+ transport of the TAL, did not differ between the cisplatin-treated rats and the control rats [5,6]. Claudin-19 protein expression increased at 4 and at 9 weeks, which suggests the activation of a compensatory mechanism for the TRPM6 downregulation. In conclusion, this study shows that cisplatin treatment results in EGF and TRPM6 downregulation in the rat kidney, causing renal Mg2+ loss. Our results are in line with the hypothesis that EGF influences the renal expression or activation of TRPM6 and plays a significant role in Mg2+ loss in medication-induced nephropathy.MAP kinases (MAPKs) and CREB and the immediate early gene products (IEGs) have been shown to comprise a core processor of cellular information with limited numbers of molecular species [1]. Many studies have been attempted to examine signaling specificity [4]. However, how a wide range of growth factors encode information into specific temporal patterns and combinations of signaling molecules such as MAPKs, including ERK, JNK, p38, and CREB, that are further decoded by expression of IEGs including c-FOS, EGR1, c-JUN, FOSB, and JUNB to exert biological functions, remains to be elucidated (Figure 1A) [7]. For example, nerve growth factor (NGF) has been shown to encode information for cell differentiation by sustained ERK phosphorylation, whereas epidermal growth factor (EGF) has been shown to encode information for cell proliferation into transient ERK phosphorylation in PC12 cells [92]. In contrast, pituitary adenylate cyclase activating peptide (PACAP) has been shown to encode information for cell differentiation by ERK and CREB phosphorylation, the latter of which is mainly regulated by a cAMP-dependent pathway [13]. Anisomysin, a translation inhibitor, has been shown to encode information for cell death by JNK and p38 phosphorylation [14,15]. Such specific temporal patterns and combinations of MAPK and CREB phosphorylation are further decoded by a limited numbers of IEGs to exert biological functions (Figure 1A). However, how such limited numbers of IEGs can selectively decode upstream signals remains unknown. Because the detailed biochemical network from MAPKs and CREB to the IEGs remains unknown, it is difficult to develop a computational model of biochemical networks based on the literature. Therefore, we employed a system identification method [16] that enabled us to build a data-driven model of the decoding system of MAPKs and CREB by IEG expression. The aim of system identification in this study is a quantitative, computational description of the input output relationship from time courses of phosphorylated MAPKs (pMAPKs), phosphorylated CREB (pCREB), and IEG expression in response to various doses of different growth factors in order to determine how upstream signals are selectively decoded by downstream IEG expression. Kinetic modeling based on biochemical reactions from the literature is often used for systems biological analysis of signaling pathway [179]. However, kinetic modeling explicitly uses biochemical reactions of known signaling pathways and requires the detailed knowledge of signaling pathway, which means that it is applicable only to the field with sufficient knowledge of signaling Figure 1. Decoding of MAPK and CREB phosphorylation by IEG expression. (A) A variety of growth factors such as EGF, NGF, PACAP, and anisomycin encode their information by specific temporal patterns of MAPK (ERK, p38, and JNK) and CREB phosphorylation, which are selectively decoded by expression of IEGs such as c-FOS, EGR1, c-JUN, JUNB, and FOSB to exert biological functions. (B) The temporal patterns of phosphorylation of MAPKs and CREB, and the expression of IEGs in response to NGF (5 ng/ml, red), PACAP (100 nM, blue), EGF (5 ng/ml, green), and anisomycin (50 ng/ml, black) were measured by QIC at 3-min intervals for 180 min. These data, together with responses to other doses of the growth factors (Figure S3), were used for parameter estimation of the nonlinear ARX model in Figure 2. Intensities of the signaling activity and the IEGs between experiments were normalized by internal control of each 96 well plate. doi:10.1371/journal.pone.0057037.g001 pathway. At the same time, this means that unknown pathway(s) is not modeled, and therefore, the model can not be able to capture the IO relationship for which the unknown pathway(s) is responsible. In contrast, data-driven modeling can identify system directly from experimental data without detailed knowledge of signaling pathway [179]. Therefore, the data-driven modeling can represent the IO relationship involving the unknown pathway(s). In particular, given that amplitude and temporal patterns of signaling activities are essential properties of cellular signaling, the dose response and time course of signaling activities characterize a cellular system. Therefore, we divided the characteristics of a cellular system into dose response and time course, and used data-driven model based on the time course data with doses of growth factors, and selected the nonlinear ARX model, which consist of amplitude conversion by Hill function and a linear temporal filter, as the data-driven modeling approach in this study. Regarding signaling pathways as transmission channel, the nonlinear ARX model directly gives an essential and inherent property of signal processing of the system without detailed knowledge of signaling pathways. To build the data-driven model, a quantitative high-throughput measurement system for protein phosphorylation and protein expression are required. We have recently developed a fully automated assay technique, termed quantitative image cytometry (QIC) [20], which integrates a quantitative immunostaining technique and a high-precision image-processing algorithm for cell identification. QIC allows gathering huge amounts of quantitative data on protein phosphorylation and expression without personal skill variation. In this study, we used QIC to measure the time course of MAP kinases and CREB phosphor-Figure 2. System identification by the nonlinear ARX model. (A) The modeling scheme of the nonlinear ARX model. Upstream dependency was determined by lag order number, m. For example, if m = 0, upstream signal is not transmitted downstream, otherwise signal is transmitted downstream. The signals of the selected upstream molecules were transformed successively by Hill function and linear ARX model, that characterise a system with switch-like (solid line) or graded (dotted line) dose response, and with temporal filters such as a low-pass filter (dotted line) and that with an inverse notch (solid line), respectively (see Materials and methods). (B) Temporal signal transformation in the nonlinear ARX model. For example, signal transformation in the nonlinear ARX model of c-FOS was shown. pERK and pCREB were selected upstream molecules, but pp38 and pJNK were not (m = 0). The signals of pERK and pCREB were transformed by the Hill equations. 20354191Then, the transformed signals by the Hill equations were temporally transformed by the linear ARX model. The sum of the transformed signals by the linear ARX model was c-FOS, the final output of the nonlinear ARX model of c-FOS. doi:10.1371/journal.pone.0057037.g002 ylation and expression the IEGs, and built the data-driven model to identify signal processing of the system. We found that specific temporal patterns and combinations of MAPKs and CREB phosphorylation can be decoded by selective IEG expression via distinct temporal filters and switch-like responses.Mouse anti-phospho-ERK1/2 (Thr 202/Tyr 204) monoclonal antibody (mAb) (9106), rabbit anti-phospho-CREB (Ser 133) mAb (9198), rabbit anti-phopho-JNK (Thr183/Tyr185) mAb (4668), rabbit anti-EGR1 mAb (4154), rabbit anti-c-JUN mAbFigure 3. The nonlinear ARX model of the IEGs. (A) The simulation result of the nonlinear ARX model (solid lines) together with the experimental results in Figure 1B (dots). The colour codes are the same as in Figure 1B. The experimental data in Figure 1B and Figure S2 were used for parameter estimation of the nonlinear ARX model. (B) The identified systems by the nonlinear ARX model. The upstream dependency (selected inputs), Hill functions, and frequency response curve of the nonlinear ARX model were shown. The selected inputs, pERK (solid line), pCREB (dotted line), pJNK (dashed line), and c-FOS (dashed and dotted line) were numbered. doi:10.1371/journal.pone.0057037.g003(9165), rabbit anti-c-FOS mAb (2250), rabbit anti-JUNB mAb (3753) and rabbit anti-FOSB mAb (2251) were purchased from Cell Signaling Technology (Beverly, MA). Rabbit antiphospho p38 mAb (v1211) was purchased from Promega (Madison, WI).PC12 cells (kindly provided by Masato Nakafuku, Cincinnati Children’s Hospital Medical Center, Ohio) [21] were cultured at 37uC under 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 5% horse serum (Invitrogen, Carlsbad, CA), and stimulated by recombinant mouse b-NGF (R&D Systems, Minneapolis, MN), EGF (Roche, Mannheim, Germany), PACAP (Sigma, Zwijndrecht, The Netherlands), or anisomycin (EMD Biosciences, Inc., San Diego, CA) as previously described [21]. We used a low dose of anisomycin (50 nM) to activate p38 and JNK without inhibiting translation. For inhibitor experiment, we stimulated by NGF in the presence of 10 nM PD (PD0325901, a MEK inhibitor, Sigma Zwijndrecht, The Netherlands), 5 mM H89 (PKA inhibitor, Sigma Zwijndrecht, The Netherlands). The inhibitors were added 30 min before growth factor stimulation. For the QIC assays, cells were seeded at a density of 104 cells per well in 96-well poly-L-lysine 4 March 2013 | Volume 8 | Issue 3 | e57037 coated glass-bottomed plates (Thermo Fisher Scientific, Pittsburgh, PA), and then starved in DMEM containing 25 mM HEPES and 0.1% bovine serum albumin for approximately 18 h before stimulation.
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