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. Author manuscript; available in PMC: 2014 Dec 11.
Published in final edited form as: FEBS Lett. 2013 Nov 6;587(24):3912–3920. doi: 10.1016/j.febslet.2013.10.041

Live-cell single-molecule imaging reveals clathrin and caveolin-1 dependent docking of SMAD4 at the cell membrane

Yong Yang 1,*, Joy Wolfram 1,2, Jianliang Shen 1, Yuliang Zhao 2,3, Xiaohong Fang 4, Haifa Shen 1,5, Mauro Ferrari 1,6,*
PMCID: PMC3903459  NIHMSID: NIHMS545793  PMID: 24211445

Abstract

Transforming growth factor β (TGF-β) signaling is important for many biological processes. Although the sequential events of this cascade are known, the dynamics remain speculative. Here, live-cell single-molecule total internal reflection fluorescence microscopy was used to monitor the dynamics of SMAD4, a TGF-β downstream effector, in MDA-MB-231 breast cancer cells. Contrary to previous belief, SMAD4 was detectable at the cytoplasmic membrane, displaying two subpopulations with different membrane docking behaviors. These subpopulations were regulated by clathrin and caveolin-1, and had opposing roles in the nuclear shuttling of SMAD4 and the subsequent transcriptional regulation of genes associated with cell migration. The notion that membrane-docking behaviors of downstream molecules could predict the cellular response to growth factors may revolutionize the way we view cell signaling.

1. Introduction

Transforming growth factor-β (TGF-β) signaling is implicated in the pathogenesis of many diseases, including cancer. TGF-β can signal through SMAD transcription factors, which can inhibit cell proliferation, while simultaneously increasing the migratory and invasive behavior of cells [1]. TGF-β signaling is initiated by ligand binding to the TGF-β type II receptor (TGFBRII), which triggers the recruitment of TGF-β receptor type I (TGFBRI), forming a heteromeric signaling complex at the cell membrane. Formation of this complex leads to phosphorylation of TGFBRI and subsequent phosphorylation of the intracellular receptor-activated SMAD proteins, including SMAD2 and SMAD3. Phosphorylated R-SMAD proteins bind to SMAD4 and enter the nucleus, where they act as transcription factors [2-6]. Although the sequential events of TGF-β-induced SMAD4 signaling have been relatively well established, the transient process of SMAD4 dynamics remains largely speculative, due to methodological limitations. Previous knowledge regarding cellular location and transport of SMAD4 has mainly been obtained through in vitro biochemical assays such as co-immunoprecipitation using cell lysates and immunofluorescence with fixed or live cells [7,8]. These and other methodologies suggest that SMAD4 shuttles between the cytoplasm and the nucleus. Hence, binding of SMAD4 to the receptor-activated complex is generally considered as an event that takes place in the cytoplasm.

Here, we have designed a unique approach to investigate the dynamics of SMAD4 in live MDA-MB-231 breast cancer cells using single-molecule total internal reflection fluorescence microscopy (TIRFM). TIRFM is an emerging technique that has been successfully applied to measure the dynamic behavior of signaling proteins at the cell membrane, e.g. growth factor receptors, small G proteins and ion channel receptors [9-12]. A recent study also revealed the dynamic behavior of the GRB2 intracellular effector protein of the EGFR pathway using purified cell membrane fractions [13]. In the present work, we employed a live-cell single-molecule TIRFM technique to measure the dynamics of the intracellular SMAD4 protein in order to gain an improved understanding of the TGF-β pathway.

2. Materials and methods

2.1. Materials

GFP-SMAD4 plasmid was generated through amplification of the human SMAD4 coding sequence with PCR, followed by subcloning into the pEGFP-C1 plasmid (Clontech), as previously described [7,8]. Clathrin heavy chain, caveolin-1 and GFP plasmids were obtained from Axxel Biosystem LLC. TGF-β1 was purchased from R&D, SB431542 was from Sigma and SMAD4, clathrin heavy chain and caveolin-1 siRNA were from Thermo Scientific. The following antibodies were acquired from cell signaling: SMAD2, p-SMAD2, SMAD4, clathrin heavy chain, caveolin-1, matrix metalloproteinase-2 (MMP2), matrix metalloproteinase-9 (MMP9), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), Rho GDP-dissociation inhibitor (RhoGDI) and poly ADP ribose polymerase (PARP). Alexa 488-conjugated anti-rabbit and IgG Alexa, 555-conjugated anti-mouse IgG were purchased from Invitrogen. Biotin coupled GFP antibody was obtained from Clontech.

2.2. Cell culture and transfection

Human MDA-MB-231 breast cancer cells were maintained in DMEM containing 10% fetal bovine serum (FBS) at 37°C with 5% CO2. The transfection of the cells was performed with lipofectamine 2000 (Invitrogen), according to the manufacturer’s instructions.

2.3. Single-molecule fluorescence imaging

Single-molecule fluorescence imaging was performed with the objective-type total internal reflection fluorescence microscopy using an inverted microscope (IX 81, Olympus), a total internal reflective fluorescence illuminator, a 100X/1.45NA Plan Apochromat TIR objective (Olympus) and a 14-bit back-illuminated EMCCD (Andor iXon DU-897 BV). The microscope was equipped with a CO2 incubation system (INU-ZIL-F1) and live cell imaging was performed at 37°C in 5% CO2. GFP was excited at 488-nm by an argon laser (Melles Griot) with the power of 2 mW, as measured after the laser passed through the objective in the epi-fluorescence mode. The collected fluorescent signals were passed through a HQ 525/50 filter (Chroma Technology), before being directed to the EMCCD. The gain of the EMCCD was set at 300. Only the central quarter of the chip (256×256 pixels) was used for imaging analysis to ensure homogeneous illumination. Movies of 200 frames were acquired for each sample at a frame rate of 5 Hz using MetaMorph software (Molecular Device). The illumination time for the movies was 40 seconds.

The imaging of single GFP molecules on a coverslip was performed by dissolving GFP in a salt buffer (600 mM NaCl, 150 mM PBS buffer, pH 7.4) to prevent dimer formation, followed by immobilization of the protein on a coverslip by using a biotin coupled GFP antibody, as previously reported [12]. We recognize that the photobleaching of GFP on a glass surface may differ slightly from the photobleaching of GFP on a biological membrane. However, this technique for determining bleaching parameters is generally accepted in the literature.

2.4. Analysis of single-molecule fluorescence intensity

Analysis of single-molecule fluorescence intensity in movies acquired from living cells was achieved by subtracting the background fluorescence from the first two frames following the appearance of a fluorescent spot, using the rolling ball method in the Image J software (NIH). In the experiments two forms of SMAD4 molecules could be detected. One was temporarily docked to the surface, while the other was continuously diffusing. Since the continuously moving molecules could only be observed for less than three frames, these were excluded from analysis. An intensity of four times that of an area lacking fluorescent spots was used as a threshold to identify spots, which were then filtered with Matlab. For the analysis of fluorescence intensity, regions of interest were selected according to a previously described procedure [12].

2.5. Analysis of single-molecule on-time

Single-molecule GFP-SMAD4 trajectories were extracted with MetaMorph tracking software (Molecular Device). Two types of trajectories were observed, those that appear and disappear during a movie and those that are cut off due to the beginning or ending of a movie, i.e. objects that are present in the first or last frame of the movie. For the former, the on-time is equal to the length of time between the appearance and disappearance of the fluorescent spot. For the latter, the precise on-time is unknown and these spots were excluded from analysis. The on-times were fitted to a series of models that differed in the number and types of sub-distributions. The goal was to identify the minimal number of kinetically distinct subpopulations that could account for the overall on-time distribution. Model selection was achieved using two strategies, as previously described [14]. The first strategy involved the minimization of the Bayesian Information Criterion (BIC), were the fitting error was approximated to follow a normal distribution. The application of the BIC requires a priori knowledge of the distribution of experimental errors, which is unknown for on-time data. Hence, the nonparametric test of the distribution of exponential decay was also used.

2.6. TIRFM imaging for protein co-localization

For the cell membrane protein co-localization study, TGF-β1 stimulated cells were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100. Nonspecific sites were blocked with 1% BSA in PBS containing 0.1% Tween-20 (PBST). Primary and secondary antibodies were diluted in 1% BSA in PBST. Fluorescence imaging of the cell membrane was performed with the objective-type TIRFM as mentioned above. A 488 nm laser and 561 nm argon ion laser were used to excite Dylight 488 and Alexa 555, respectively. The signals were detected with a dual-view optics Dual-View™ (Optical Insight) mounted in front of the EMCCD. The Dual-View™ filter cube consisted of a fixed dichroic filter (565dcxr, Chroma technology) and two emission filters (D525/50 and D605/50, Chroma Technology). The fluorescence from the individual molecules was passed through the Dual-View™ filter box. The images with different colors were obtained from two parts of the EMCCD. Namely, two different pictures were captured and later merged together. The position of the spots did not change, since the cells were fixed. 6-8 cells were used for the SMAD4 and clathrin/caveolin-1 co-localization studies. The co-localization coefficient was calculated by dividing the number of co-localized SMAD4 spots with the total number of SMAD4 spots in a cell.

2.7. Confocal imaging

MDA-MB-231 cells were grown in 35 mm dishes for 24 h, and transfected with control siRNA (siControl), clathrin heavy chain siRNA (siCLTC), caveolin-1 siRNA (siCav-1), SMAD4 siRNA, control plasmid (plControl), clathrin heavy chain plasmid (plCLTC) or caveolin-1 plasmid (plCav-1). The transfected cells were starved for 4 h and then treated with TGF-β1 for 1 h. Potassium depletion was performed as previously described. The treated cells were fixed with 4% paraformaldehyde and permeabilized with 0.1% Triton X-100. Nonspecific sites were blocked with 1% BSA in PBST. The fixed cells were incubated with Smad2 and SMAD4 monoclonal rabbit primary antibodies overnight (1:200 dilution), followed by washing and incubation with Alexa 488-conjugated anti-mouse IgG secondary antibody for 1 h at room temperature (1:500 dilution). All the antibodies were diluted in PBST solution containing 1% BSA. Fluorescence imaging was performed by confocal microscopy (Olympus IX81).

2.8. Western blot assay

MDA-MB-231 cells were grown in 6-well plates for 24 h, followed by transfection with control siRNA (siControl), clathrin heavy chain siRNA (siCLTC), caveolin-1 siRNA (siCav-1), SMAD4 siRNA, control plasmid (plControl), clathrin heavy chain plasmid (plCLTC) or caveolin-1 plasmid (plCav-1). The transfected cells were starved for 4h and then treated with TGF-β1 for 1h. The treated cells were washed and incubated with lysis buffer containing protease and phosphatase inhibitors. Protein lysates were mixed with SDS loading buffer and heated at 95°C for 5 min. Samples were ran in Any KD gels (Bio-Rad) and transferred to PVDF membranes. Membranes were blocked for 1 h in 5% nonfat milk in TBST (Tris Buffered Saline with 0.1% Tween-20), incubated with desired primary antibody overnight, washed and incubated with HRP-conjugated secondary antibody for 1 h. Membranes were washed and bands were detected by enhanced chemiluminescence (Amersham Life Sciences).

2.9. Real-time RT-PCR

RNA extraction was performed using the ALLPrep RNA Kit (QIAGEN), according to the manufacturer’s instructions, and samples were quantified by absorption spectroscopy. Real-time RT-PCR was conducted as previously described[15].

2.10. Migration and invasion assay

Migration and invasion assays were performed to investigate TGF-β1-induced cell migration. MDA-MB-231 cells were grown in 6-well plates for 24 h, and transfected with control siRNA (siControl), clathrin heavy chain siRNA (siCLTC), caveolin-1 siRNA (siCav-1), SMAD4 siRNA, control plasmid (plControl), clathrin heavy chain plasmid (plCLTC) or caveolin-1 plasmid (plCav-1). The transfected cells were starved for 4h. For the migration assay the transfected cells were incubated with 10 ng/ml TGF-β1 in the presence of in 1% FBS media for 8 h. Cells that migrated through the pores of the filter were stained and counted in triplicates using QCMTM 24-Well Cell Migration Assay (Millipore), according to the manufacturer’s instructions. For the invasion assay the transfected cells were incubated with 10 ng/ml TGF-β1 in the presence of in 3% FBS media for 24 h. The assay was performed in triplicates using QCMTM 24-Well Cell Invasion Assay (Millipore) according to the manufacturer’s instructions.

2.11. Statistical analysis

Data is presented as the mean±SEM. The statistical significance between two samples was assessed by the paired Student’s t-test.

3. Results

3.1. TGF-β1 induced membrane docking of SMAD4

Contrary to previous belief, we found that SMAD4 was detectable at the cytoplasmic membrane in the presence of TGF-β1. Transfecting cells with GFP-SMAD4 plasmid enabled visualization of SMAD4 (Fig. 1 A-B, Supplementary Video 1). Conversely, cells transfected with a control GFP plasmid did not exhibit any fluorescent spots (Fig. S1C), suggesting that SMAD4 is responsible for the presence of the recombinant protein at the membrane. The fluorescence intensity distribution of the spots (Fig. 1C) was similar to that of single GFP proteins mounted on a slide (Fig. S2A), indicating that each spot represents a single recombinant protein. To ensure that the membrane translocation of SMAD4 was dependent on activation of the TGF-β pathway, cells were imaged after treatment with SB431542, an inhibitor of TGFBRI phosphorylation (Fig. S1A-B). In the presence of the inhibitor, SMAD4 could not be detected at the cell membrane.

Fig. 1.

Fig. 1

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Single-molecule imaging of GFP-SMAD4 docking to the cell membrane. (A, B) TIRFM visualization of GFP-SMAD4 bound to the plasma membrane without (A) and with (B) TGF-β1 stimulation in living MDA-MB-231 cells. (Scale bar: 4 μm). (C) Distribution of the fluorescence intensities of diffraction-limited GFP-SMAD4 spots in stimulated cells. The solid curve shows the fitting of the Gaussian function and the arrowhead indicates the peak positions of the fitting curve. (D) Changes in fluorescence intensity at a single binding site plotted against time. (E) Identification of two subpopulations of GFP-SMAD4. The grey histogram shows the distribution of all GFP-SMAD4 on-times detected by TIRFM. The on-times for each event were determined from the fluorescence intensity. The data was best-fit to represent two kinetically distinct subpopulations with shorter-on-time (red line) and longer-on-time (green line).

The discovery that SMAD4 associates with the plasma membrane led us to explore the dynamic behavior of this protein at the cell surface. In order to establish an unbiased and complete inventory of all visible GFP-SMAD4 trajectories, previously reported particle tracking software and statistical methods were employed [14] (see Methods). The membrane docking time and intensity trace of each GFP-SMAD4 molecule was determined by measuring the fluorescence on-time, which refers to the time when the fluorescent signal first appears at the membrane to the time when it disappears (Fig. 1D, Fig S3). By analyzing the on-time behaviors we identified two dynamically distinct SMAD4 subpopulations. The short-lived subpopulation had an on-time of ~1.8 s, while the long-lived one had an on-time of ~5.4 s (Fig. 1E). Notably, the on-time values were much shorter than the photobleaching time for a single GFP molecule under identical excitation conditions (12.0 s) (Fig. S2B), suggesting that the association and dissociation of SMAD4 caused the transient appearance of a fluorescent signal at the cell membrane, rather than the photobleaching of GFP. It is possible that on-times longer than 3.5 s (Fig. S2B) could be slightly affected by GFP photobleaching, however this contribution is minimal.

3.2. Clathrin and caveolin-1 regulate SMAD4 membrane docking

Next, we investigated the underlying mechanism for the presence of two distinct sub-populations of this protein at the membrane. We speculated that the on-time patterns stemmed from the association of SMAD4 with two different membrane domains. As clathrin and caveolin-1 membrane pits have been implicated in TGF-β signaling [16-18], we hypothesized that they may be responsible for mediating SMAD4 dynamics. Using immunofluorescence imaging of fixed cells, we studied the potential colocalization between SMAD4 and clathrin or caveolin-1. Indeed, there were regions on the cell membrane were the proteins co-localized (Fig. 2A). The partition coefficient for co-localization of SMAD4 with clathrin or caveolin-1 was approximately 29% and 44%, respectively. Next, we used siRNA and plasmid vectors to downregulate and overexpress clathrin or caveolin-1. The results indicate that caveolin-1 and clathrin induce shorter and longer SMAD4 docking times, respectively (Fig. 2B-E).

Fig. 2.

Fig. 2

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Distinct dynamic patterns of SMAD4 membrane docking mediated by clathrin and caveolin-1. (A) Dual-view TIRFM imaging of SMAD4 and clathrin or caveolin-1 in the presence of TGF-β1. (B-E) Identification of two dynamic SMAD4 membrane docking patterns regulated by expression of clathrin heavy chain (CLTC) or caveolin-1 (Cav-1). (B), CLTC knock-down, (C), CLTC overexpression, (D), Cav-1 knock-down, (E), Cav-1 overexpression. Grey histograms show the distribution of all GFP-SMAD4 on-times detected by TIRFM. These data were best fit to form two kinetically distinct subpopulations with shorter on-time (red line) and longer on-time (green line).

3.3. Clathrin and caveolin-1 regulate nuclear translocation of SMAD4

It has previously been shown that clathrin promotes TGF-β signaling transduction, while caveolin-1 mediates degradation of the receptor complex [19]. However, the specific intracellular transport of SMAD4 in response to clathrin or caveolin-1 expression has not been determined. Confocal microscopy and western blot results revealed that clathrin overexpression increased SMAD4 translocation to the nucleus, while caveolin-1 overexpression did the opposite (Fig. 3A). These observations demonstrate the opposing roles of the membrane domains in TGF-β signaling. Interestingly, while SMAD4 shuttling was affected by the expression of clathrin and caveolin-1, it was independent of endocytosis. Inhibition of endocytosis by incubating cells with potassium-free media did not affect the dynamics of SMAD4 membrane docking or hinder the translocation of SMAD4 to the nucleus (Fig. S4A-D). These results suggest that clathrin and caveolin-1 mediate SMAD4 intracellular transport by an endocytosis-independent mechanism.

Fig. 3.

Fig. 3

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Nuclear translocation of SMAD2 and SMAD4 in the presence of SB431542 (SB), plasmids (pl) and small interfering RNA (si) for clathrin heavy chain (CLTC) and caveolin-1 (Cav-1). (A) Immunofluorescence imaging of TGF-β1 induced nuclear import of SMAD2 and SMAD4 (B) Nuclear translocation of SMAD2 and SMAD4 using cytoplasmic (C) and nuclear (N) fractions, verified with antibodies against RhoGDI and PARP.

Since phosphorylated SMAD2 is able to bind to SMAD4, we also investigated the intracellular trafficking of SMAD2 in response to varying levels of clathrin or caveolin-1. There also exists controversy regarding whether the phosphorylation and nuclear translocation of SMAD2 is dependent on endocytic pathways [18,20]. Here, we demonstrate that the phosphorylation and translocation of SMAD2 are independent of clathrin/caveolin-1 expression as well as endocytosis (Fig. 3B, Fig. S4B-C, Fig. S5). Moreover, the suppression of SMAD4 using siRNA did not affect the shuttling of SMAD2 (Fig. S6), indicating that the intracellular dynamics of SMAD2 were independent of SMAD4.

3.4. SMAD4 dynamics determine transcriptional regulation

As can be expected from the nuclear translocation studies, we demonstrate that clathrin expression increases SMAD4 induced gene transcription. Western blot and PCR experiments indicate that clathrin overexpression and caveolin-1 downregulation stimulate increased mRNA and protein levels of a variety of genes that have been shown to be transcriptionally regulated by SMAD4, including plasminogen activator inhibitor-1 (PAI-1), laminin gamma 2 (LAMC2), matrix metalloproteinase-2 (MMP2) and matrix metalloproteinase-9 (MMP9) [21-23] (Fig. S7, Fig. 4A-B). These observations suggest that longer SMAD4 on-times correlate with nuclear translocation and gene activation, while shorter on-times are associated with the interruption of signal transduction. To further insure that the above-mentioned transcriptional response was mediated by SMAD4, anti-SMAD4 siRNA was employed, resulting in the suppression of transcription, despite the overexpression of clathrin (Fig. S8A-C).

Fig. 4.

Fig. 4

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The effect of clathrin heavy chain (CLTC) and caveolin-1 (Cav-1) expression on TGF-β1 induced MMP2 and MMP9 mRNA and protein expressions. (A) The mRNA expressions of MMP2 and MMP9 were measured by RT-PCR. (B) The protein expressions of MMP2 and MMP9 were measured by Western blot. Results are expressed as the mean fold change (±SEM) compared with the control siRNA (siControl) or control plasmid (plControl) and normalized to GAPDH. * P<0.05, ** P<0.01.

3.5. SMAD4 dynamics regulate cell migration in vitro

As indicated by the results above, SMAD4 dynamics regulate the production of various mRNAs and proteins. Hence, we asked whether the expression of SMAD4 and clathrin and caveolin-1 result in phenotypical changes in cell behavior that have been associated with TGF-β signaling. By using cell migration and invasion assays we illustrate that the expression of clathrin increases migratory behavior in vitro, while caveolin-1 has the opposing effect (Fig. 4C-D). Anti-SMAD4 siRNA was employed to verify that SMAD4 was responsible for inducing this response (Fig. S8D-E). Hence, longer SMAD4 on-times induced by clathrin causes the cells to display a migratory phenotype.

4. Discussion

In this study we performed live-cell single-molecule imaging using TIRFM to investigate the dynamics of SMAD4, a downstream effector of the TGF-β pathway. To the best of our knowledge, this is the first report showing that the dynamics of an intracellular protein can be monitored on the single-molecule level in living cells. By employing this novel technique we were able to gain new insight into the TGF-β pathway, which would not have been possible with traditional imaging modalities.

In conclusion, we found that SMAD4 can dock to the cytoplasmic membrane in the presence of TGF-β, contrary to previous belief that SMAD4 only shuttles between the cytoplasm and the nucleus. In addition, we demonstrate that clathrin and caveolin-1 are responsible for the enrichment and suppression of subpopulations of SMAD4 with distinct dynamic behavior at the cell membrane. The expression of clathrin resulted in longer SMAD4 on-times, while caveolin-1 enhanced the SMAD4 subpopulation with shorter membrane docking times. The results also indicate that clathrin and caveolin-1 have opposing roles in the nuclear translocation of SMAD4, and the subsequent transcriptional gene activation and induction of migratory cell behavior. Furthermore, we show that the shuttling of SMAD4 may in certain circumstances be independent of SMAD2, suggesting that other proteins could be involved in the nuclear translocation and/or membrane docking of SMAD4.

The presence of SMAD4 at the membrane may not be an isolated event. The observation that a downstream effector, thought to solely exist in the cytoplasm and nucleus, associates with the membrane with distinct dynamic patterns may pertain to several signaling pathways. The results warrant further investigation into the dynamic behavior and cellular location of downstream effector proteins in various signaling pathways. The notion that the membrane docking behaviors of downstream molecules could predict the cellular response to growth factors could revolutionize the way we view and study cell signaling.

Supplementary Material

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Highlights.

  • SMAD4 docks to the cell membrane in the presence of TGF-β1.

  • SMAD4 displays two subpopulations with different membrane docking behaviors, which are regulated by clathrin and caveolin-1. .

  • The subpopulations have opposing roles in the nuclear shuttling of SMAD4 and the subsequent transcriptional regulation.

Acknowledgments

We are very grateful for the guidance of Dr. Joan Massagué and Dr. Laurel Raftery during the preparation of this manuscript. This research was supported by funds from the Methodist Hospital Research Institute. Partial funds were from the following sources: the Ernest Cockrell Jr. Distinguished Endowed Chair (M.F.), the US Department of Defense (W81XWH-09-1-0212) (M.F.), the National Institute of Health (U54CA143837, U54CA151668) (M.F.), Nylands nation Finland (J.W.), Department of Defense grant W81XWH-12-1-0414 (M.F.) and the State of Texas CPRIT grant RP121071 (M.F. and H.S.).

Footnotes

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Additional information

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