www.sciencesignaling.org/cgi/content/full/6/305/ra106/dc1 Supplementary Materials for Controlling Long-Term Signaling: Receptor Dynamics Determine Attenuation and Refractory Behavior of the TGF-β Pathway Pedro Vizán, Daniel S. J. Miller, Ilaria Gori, Debipriya Das, Bernhard Schmierer,* Caroline S. Hill* *Corresponding author. E-mail: caroline.hill@cancer.org.uk (C.S.H.); bernhard.schmierer@ki.se (B.S.) Published 10 December 2013, Sci. Signal. 6, ra106 (2013) DOI: 10.1126/scisignal.2004416 The PDF file includes: Texts S1 to S3 Fig. S1. The refractory period is not unique to HaCaT cells. Fig. S2. Kinetics of Smad2 phosphorylation. Fig. S3. Blocking and depletion of ligand. Fig. S4. Specificity of the TβRII antibody in Western blotting. Fig. S5. Recovery from the refractory state. Fig. S6. Biotinylation of surface receptors in HaCaT cells at different time points. Fig. S7. Data fitting with previous data sets. Fig. S8. Comparison between simulations and experimental data in Figs. 1 and 2. Fig. S9. Comparison between simulations and experimental data in Fig. 3. Fig. S10. Restimulation of naïve cancer cell lines with medium from the D3 line. Table S1. Model parameters. Table S2. List of model species. Table S3. Ordinary differential equations and initial conditions. References (36, 37)
Supplementary Text S1. Model conception: the receptor module To model the findings of signal attenuation and the existence of a refractory period, the Smad model (4) was expanded to include a mathematical description of receptor biology that involved receptor maturation, receptor trafficking to the plasma membrane, ligand binding, receptor internalization, and receptor turnover. As described in the main text, binding of TGF-β is sufficient for signal attenuation, which does not require actively signaling receptors, but does require a functional proteasome. Because we excluded degradation of PSmad2 as the cause for attenuation, we concluded that degradation of a subpool of receptors that is enhanced by TGF-β binding attenuates the signal, which is consistent with previous experimental evidence (22, 23) and previous models (24, 36). The model has to account for signal attenuation and has to capture the refractory state. Although receptor turnover (synthesis and degradation) could, in principle, account for the observed refractory period, the measured half-lives of ALK5 and TβRII (~4 h and ~2 h, respectively; Fig. 3C) indicate that receptor turnover alone is too fast to account for the slow recovery from the refractory period (12 24 hours, Fig. 2C). These slow recovery kinetics point to at least one additional slow step distinct from receptor synthesis, such as posttranslational modification or trafficking of nascent receptors to the plasma membrane. As shown in Fig. 3D and fig. S6A, it is TβRII, rather than ALK5, that is rapidly depleted from the cell surface and thus responsible for loss of responsiveness. We used this finding to simplify the model, which contains only a single receptor species with the measured turnover rate for TβRII. TGF-β binds to competent surface receptors. We 1
know this step is fast, because a short time of TGF-β treatment is sufficient to induce a maximum response at 1 h (Fig. 2A). In contrast, the Smad2 phosphorylation time course data (fig. S7) exhibit a delay between TGF-β addition and the appearance of phosphorylated R-Smads. These findings suggest that receptor activation in response to ligand occurs in two steps: a fast TGF-β-binding step, which rapidly occupies the pool of available surface-exposed receptors, and a slower receptor activation step, which further converts TGF-β-bound receptors into fully active, signaling receptors. In the model, receptors mature from a precursor form () to a fully processed form ( ). has an intracellular form ( ) and a surface-exposed form ( ), which are in equilibrium. This equilibrium assumption holds if trafficking to and from the plasma membrane are fast compared with receptor maturation, which is likely, given the slow maturation rate. In the absence of TGF-β, the system is fully described by and. There is good evidence that the fraction of total cellular receptors that are competent and exposed on the cell surface is very small, and we set this fraction 0.05, that is 5% of total receptors are assumed to be at the surface in the absence of signal (6). We define the maturation efficiency α as the ratio of the maturation rate constant and the receptor degradation rate constant, and scale the system such that, in the absence of a signal, the total amount of receptors 1. We can now describe the system in the absence of signal by two ordinary differential equations (ODEs) and the three parameters,, and : 1 1 1 1 The fractions of in the cytoplasm and the cell surface, respectively, are given by 2
1 1 1 Where is the equilibrium constant of receptor trafficking to and from the membrane. The steady state of this system defines the initial conditions prior to the addition of TGF-β: 1 1 1 S2. Model parameterization The model is expressed in dimensionless form with the total number of receptors, as well as total Smad2 and total Smad4, set to unity. In the model, 2 ng/ml TGF-β corresponds to a concentration of 4 (that is, there are 4 molecules of TGF-β for every molecule of receptor). This value was estimated by simulating the dose dependency of the response (fig. S2) and corresponds to a total number of roughly 15,000 receptor molecules per cell. TGF-β is depleted from the medium through active signaling and also by a constitutive, cell-dependent but signaling-independent TGF-β clearance (k cc ). Parameter values were either taken from previous work (4) or estimated from the data sets presented here. Three parameters, the receptor activation rate k act, the Smad2 phosphorylation rate k p, and the dissociation constant of the receptor inhibitor SB- 431542, K SBI, were optimized by curve fitting to the data shown in Fig. 2D of (4) (fig. S7). The fact that these data are derived from GFP-Smad2 overexpressing HaCaT cells 3
rather than the parental cell line used in this study does not cause significant differences and does not affect the fitting results. In summary, the comprehensive model presented here is not only consistent with the data sets shown in this manuscript, but also reproduces the data sets shown in (4). S3. Modeling autocrine signaling To simulate TGF-β-dependent autocrine TGF-β production, Equation 5 (table S3) was replaced by 1 24 Where is a small background synthesis rate ( 0.001), and is the rate of TGF-β-dependent synthesis, both expressed relative to k d. The model is available in the Biomodels database (www.ebi.ac.uk/biomodels) under the ID MODEL1203120000. 4
Fig. S1. The refractory period is not unique to HaCaT cells. The mouse fibroblast cell line NIH-3T3, the transformed mouse mammary cell line EpRas, and the human breast cancer cell line MDA-MB-231 were treated for 1 h (b) or 8 h (c) with TGF-β (Tβ), after which cells were re-induced with fresh TGF-β for 1 h (e). Medium was also taken from cells after 8 h of ligand exposure and used to induce naïve cells for 1 h (d). Lysates were assayed by Western blot using antibodies recognizing PSmad2 and Smad2, with Tubulin or MCM6 as a loading control. In the experimental scheme shown, green arrows indicate addition of TGF-β; curved arrow, induction of naïve cells with conditioned media. Quantifications are the normalized average and ranges of two independent experiments. In all cases the amount of PSmad2 after a 1-h TGF-β stimulation was set to 1. 5
Fig. S2. Kinetics of Smad2 phosphorylation. Top panels: HaCaT cells were treated with 2 ng/ml or 0.1 ng/ml TGF-β for 60 min, harvesting samples every 10 minutes. PSmad2 was assayed by Western blot. Tubulin was used as a loading control. Representative panels of at least three independent experiments are shown. Bottom panel: Simulation of the amount of PSmad2 in cells treated with 2 ng/ml versus 0.1 ng/ml TGF-β. 6
Fig. S3. Blocking and depletion of ligand. (A) The ligand-neutralizing TGF-β antibody can be washed out. HaCaT cells were treated with ligand-neutralizing TGFβ antibody (block) or control antibody for 20 min. Cells were incubated with or without TGF-β for 1 h following washing, as indicated. PSmad2 was assayed by Western blot, using Tubulin as a loading control. (B) Time course of TGF-β depletion by the cells. HaCaT cells were treated with either 0.5 ng/ml or 2 ng/ml TGF-β for the times indicated. At each time point samples of media were taken and assayed in triplicate for TGF-β by ELISA. The values were normalized to the amount of TGF-β in the medium at the zero time point. Quantifications are the averages and standard deviations of triplicates. (C) Cell-dependent depletion of TGF-β. TGF-β was added to tissue culture plates with (+) or without (-) plated HaCaT cells and kept under normal culture conditions for 10 or 30 h (pre-incubation). The conditioned media was then used to induce naïve cells for 1 h. Induction capacity, as measured by ability to induce PSmad2, was assayed by Western blot as shown. Tubulin was used as a loading control. In A and C, representative blots of at least three independent experiments are shown. 7
Fig. S4. Specificity of the TβRII antibody in Western blotting. Whole-cell extracts were prepared from HaCaT cells or from T47D cells, which are negative for TβRII (37), and were assayed by Western blotting using antibodies recognizing TβRII and MCM7 as a loading control. Extracts were treated ± PNGase F to remove N-linked sugars. Representative blots of three independent experiments are shown. 8
Fig. S5. Recovery from the refractory state. HaCaT cells were either unstimulated (a) or stimulated with TGF-β for 1 h, 24 h, 30 h, 36 h or 48 h (b, c, e, g, i). At the latter four time points, cells were also restimulated with 2 ng/ml TGF-β for 1 h (d, f, h, j). Whole-cell extracts were assayed for PSmad2 and MCM7 by Western blotting. Quantifications are the normalized (relative to loading control) average and standard deviations of three replicates. The amount of PSmad2 after a 1-h TGF-β stimulation was set to 1. 9
Fig. S6. Biotinylation of surface receptors in HaCaT cells at different time points. (A) The experiment design was exactly as in Fig. 3D, except that cells were stimulated at time points between 0 and 8 h, or between 0 and 60 min. Quantifications are the normalized average and standard deviations of three independent experiments. (B) The experiment design was exactly as in Fig. 3D, except that cells were stimulated at time points between 0 and 48 h. Quantifications are the normalized average and standard deviations of three independent experiments. (C) Biotinylation of surface receptors in MDA-MB-231 cells. The experiment design was exactly as in Fig. 3D. Quantifications are the normalized average and ranges of two independent experiments. For each experiment, the amount of surface receptor in unstimulated cells was set to 1. 10
Fig. S7. Data fitting with previous data sets. The parameter set for the extended model described here fits the set of data used in (4). Nuclear accumulation of Smad2 upon TGF-β stimulation and its clearance from the nucleus upon SB-431542 addition is shown, as are the amounts of PSmad2 during a 45-min stimulation with TGF-β. 11
Fig. S8. Comparison between simulations and experimental data in Figs. 1 and 2. Lines represent time course simulations, points and bars represent the experimentally determined values +/- their standard deviations or ranges. For a detailed description refer to the corresponding figures (indicated at the top of each graph) in the main text. 12
Fig. S9. Comparison between simulations and experimental data in Fig. 3. (A) Time course simulation in cells treated with TGF-β ± cycloheximide (CHX) ± MG-132 or MG-132 followed by SB-431542. Left panel, replotting of the simulation shown in Fig. 5E. Right panel, experimental data from Figs 3A and B. (B) Time course of receptor degradation in cells treated with CHX ± TGF-β. Left panel, replotting of parts of the simulation shown in Fig. 5F. Right panel, experimental data for TβRII from Fig. 3C. 13
Fig. S10. Restimulation of naïve cancer cell lines with medium from the D3 line. Medium from D3 cells, the cancer cell line with the most autocrine TGF-β production, was added to naïve cancer cell lines and PSmad2 was detected by Western Blot after 1 h. Quantifications are the normalized (relative to the cell line, CN5) average and standard deviations of three independent experiments. 14
Table S1. Model parameters. Parameters of the receptor module Value Source receptor degradation 0.32 h -1 estimate from Fig. 3C competent surface receptors in the absence of a signal 0.05 Ref (6) receptor synthesis rate, defined such that in the absence of TGF-β, 1 0.304 h -1 1 maturation efficiency 0.08 estimate from Fig. 2C ligand-induced to constitutive degradation ratio 4 estimate from Fig. 1A equilibrium constant for receptor trafficking, defined such that in the absence of TGF-β, 0.71 1 1 TGF-β binding relative to 100 estimate from Fig. 2A receptor activation relative to 24.54 optimized by data fitting dissociation constant of SB-431542 0.197 optimized by data fitting Molecules of TGF-β per receptor if TGF-β = 1 ng/ml 2 estimate from fig. S2 constitutive TGF-β clearance relative to 0.35 Parameters of the Smad module Value Source estimate from Fig. 2D and fig. S4B Smad2 phosphorylation rate constant 21.37 h -1 optimized by data fitting Smad2 dephosphorylation rate constant 23.6 h -1 Ref (4) nuclear import of Smad2 and Smad4 9.36 h -1 Ref (4) nuclear export of Smad2 20 h -1 Ref (4) nuclear export of Smad4 9.36 h -1 Ref (4) on-rate of Smad complex association 350 h -1 Ref (4) off-rate of Smad complex dissociation 60 h -1 Ref (4) % % of Smad2 phosphorylated at t = 0.75 h 0.31 Ref (4) Complex import factor 5.7 Ref (4) cytonucleoplasmic volume ratio 2.27 Ref (4) 15
Table S2. List of model species. See table S3 for the equations. Ligand and Receptors Compartment Definition Nascent receptors Cytoplasm Eq. 1 Competent receptors Cytoplasm Eq. 2 1 Competent, intracellular receptors Cytoplasm 1 Competent surface receptors Cytoplasm 1 TGF-β bound receptors, not yet active Cytoplasm Eq. 3 Actively signaling receptors Cytoplasm Eq. 4 TGF-β Cytoplasm Eq. 5 Unphosphorylated Smad2 Compartment Definition 2 Cytoplasmic unphosphorylated Smad2 Cytoplasm Eq. 6 2 Nuclear unphosphorylated Smad2 Nucleus 2 12 2 2 Smad4; sum of free and complexed Compartment Definition 4 Total cytoplasmic Smad4 Cytoplasm Eq. 7 4 Total nuclear Smad4 Nucleus 4 1 4 4 psmad2; sum of free and complexed Compartment Definition 2 Total cellular psmad2 Cell Eq. 8 2 Total cytoplasmic psmad2 Cytoplasm Eq. 9 2 Total nuclear psmad2 Nucleus 2 1 2 2 Smad complexes Compartment Definition 24 Total cellular heteromeric complexes Cell Eq. 10 24 Cytoplasmic heteromeric complexes Cytoplasm Eq. 11 24 Nuclear heteromeric complexes Nucleus 24 1 24 24 22 Total cellular homomeric complexes Cell Eq. 12 22 Cytoplasmic homomeric complexes Cytoplasm Eq. 13 22 Nuclear homomeric complexes Nucleus 22 1 22 22 Free, monomeric forms of psmad2 and Smad4 Compartment Definition 2 Cytoplasmic monomeric psmad2 Cytoplasm 2 2 2 22 24 2 Nuclear monomeric psmad2 Nucleus 2 2 2 22 24 4 4 Cytoplasmic monomeric Smad4 Cytoplasm 4 4 24 Nuclear monomeric Smad4 Nucleus 4 4 24 16
Table S3. Ordinary differential equations and initial conditions. Receptor Module 1 1 1 Eq. 1 1 Eq. 2 Initial condition 1 1 1 1 Eq. 3 0 1 Eq. 4 0 1 Eq. 5./ Smad Module 2 2 2 Eq. 6 Initial condition 1 4 2 4 4 24 Eq. 7 1 1 1 2 2 Eq. 8 0 2 24 2 2 2 24 2 22 Eq. 9 0 1 1 4 2 4 2 24 24 Eq. 10 0 24 22 4 2 24 Eq. 11 0 1 1 2 2 22 22 Eq. 12 0 22 2 22 Eq. 13 0 17