Supplement. Integrative model of the response of yeast to osmotic shock

Transcription

1 Supplement Integrative model of the response of yeast to osmotic shock Edda Klipp 1,5, Bodil Nordlander 2,51, Roland Krüger 3, Peter Gennemark 4, Stefan Hohmann 2 1 Berlin Center for Genome Based Bioinformatics (BCB), Max-Planck Institute for Molecular Genetics, Dept. Vertebrate Genomics, Ihnestr. 73, Berlin, Germany 2 Department of Cell and Molecular Biology/Microbiology, Göteborg University, Box 462, S Göteborg, Sweden 3 Humboldt University Berlin, Institute for Biology, Invalidenstr. 43, Berlin, Germany 4 Department of Computer Science and Engineering, Chalmers University of Technology, S Göteborg, Sweden Correspondence should be addressed to E. K. (klipp@molgen.mpg.de) and S.H. (hohmann@gmm.gu.se) The supplement lists further details on mathematical modeling and experimental investigations of the response of yeast cells to osmotic stress. 1 These authors contributed equally to this work. 1

2 1. Modeling Details Determination of kinetic parameters The kinetic parameters for these three modules were established in two steps. First, initial kinetic parameters were determined for each module separately such that they were in accordance with available quantitative and qualitative information on the dynamic behavior of this module. This is explained in more detail in subsequent sections. In the second step, following connection of the modules to a complete mathematical description, 24 of the 70 parameters were fine-tuned with respect to the experimental data for the standard experiment. Parameter adjustment was applied to the phosphorelay module, the MAP kinase module and the transciption/translation module as well as to the kinetics of Fps1. The standard experiment is a time course following a single osmotic shock with 0.5 M NaCl in the wild type strain W303A (Supplementary Fig. 1 and Supplementary Table 6). To adjust parameters, simulation of the entire system was performed repeatedly with randomly perturbed parameters according to a normal distribution with the actual value as mean and a varying standard deviation. After each simulation the Euclidean distance between the experimental values and those obtained in the simulation was determined. Experimental values were available for relative levels of Hog1P 2, relative values for the GPD1 (or STL1) mrna, enzyme activity of Gpd1 as well as concentrations of internal and total glycerol. A total of about 6x10 4 simulations were performed after which the distance between simulation and experimental values could not be improved any further. The parameter values given in Tables 1-4 are rounded. Determination of initial kinetic parameters for the isolated phosphorelay module There are no experimental data available for the in vivo temporal behavior of the phosphorelay module or its components. In vitro data for pre-steady state kinetics 3 suggest for the phosphotransfer steps values of = ( M s) 1 k2 TCS 3 μ, k 2 24( ) 1 TCS = μm s and k3 TCS = 50( μm s ) 1. In addition, Ssk1 is dephosphorylated and hence activated within 2

3 less than 1 min since Hog1 phosphorylation can be detected at this time point. The time point for the switch from off to on is determined by the rate constant of the phosphatase reaction TCS TCS v4 = k4 Ssk1P. Since this is a simple decay, the rate constant relates to the characteristic time τ, which is the time point when by TCS TCS 1 k 4 1 τ. Since τ must be smaller than 1 min, k 4 should be s or larger. Ssk1P is reduced to its 1/e-fold, TCS The steady state value of Ssk1( k ) Ssk1 = 1 is given by a quadratic equation with a stable solution (Fig. 2A, main text, curve a) and an unstable solution. The inverse function k1 TCS TCS = k1 ( Ssk1) has a singularity, which determines the horizontal part of curve a, and this in turn determines the steady state of Ssk1 before osmoshock. The intersection with the Ssk1-axis indicates the maximal values of Ssk1 following osmoshock, and the inflection determines the threshold for the activation of Ssk1 by decreasing TCS k 1. We TCS 1 s have chosen as initial kinetic constants k = 1, ( M ) 1 TCS TCS TCS k2 = k 2 = k3 = 40 μ s 3 TCS 4 s, and k = 0.2 allowing for Ssk 1 ( ) M 0 1 = μ k TCS = s ( k TCS = 1s 1 ). M 1 3, and Ssk μ. This means an up 1 to twenty-fold increase of active Ssk1 upon osmoshock, i.e. upon decrease of TCS k 1 from its initial value down to zero. Determination of initial kinetic constants of the MAP kinase cascade module The steady state values of the output signal, i.e. dually phosphorylated MAPK, depend on the input signal Ssk1 and on the ratio of the rate constants of the kinases, k +, and the phosphatases, k -. The characteristic time for the switch from the off state (low value of phosphorylated MAPK) to the on-state (high value of phosphorylated MAPK) is determined by the absolute values of k + and k -. This is illustrated in Supplementary Figure 2 for the isolated MAPK cascade. We have chosen as initial values MAP i ( M s) 1 k = 1 μ k = 0. 01s for i = 1,..., 5 MAP i 3

4 Hog1P2 trans = 0. 2 s Hog1 dephos MAP i k, k = k, Hog1 trans1 Hog1 trans2 k = k = k Hog1P2 trans 10 allowing for a more than 10-fold increase of the concentration of dual phosphorylated Hog1 in the nucleus within less than 5 min, which is in accordance with experimental results (Supplementary Table 6). Initial kinetic constants of the Gene Expression Module Assuming an average mrna production rate at steady state of about ten copies per species per cell and minute (more than thousand transcripts per gene per hour [Alberts, Molecular Biology of the Cell, Fourth Edition]) and a concentration Hog1P 2nuc of about 0. 01μM implies an initial rate constant s. Setting the initial steady state k ts concentrations of mrnacyt and mrna nuc to 0. 01μM implies rate constants of k ex = s and k rd = s. For the mean protein production period we assume 1 min per molecule [Alberts]. This leads, together with the above mentioned assumption for the concentration of mrnacyt, to a minimal rate constant of k tl, min = s. However, the protein production rate should be at least an order of magnitude higher, since translation of one molecule mrna in polysomes gives rise to several translation products per mrna molecule simultaneously. We used as initial value k tl k pd = 0.001s = 0.001s. The initial value for protein degradation was set to the same value,. Kinetic constants of the metabolism module The kinetic constants (except for maximal activities) are taken from the experimental and modeling study of Rizzi et al. 4, 5. The maximal activities are adjusted to allow for the steady state concentrations given in that study 5. 4

5 Sensitivity analysis In order to assess the dependence of model performance on parameter values, a sensitivity analysis was performed. To this end, the parameters were altered between 0.1-fold and 10-fold compared to the values given in Tables 1 to 5 and the sensitivity S of the Euclidean distance D between experimental data and model data was determined. We used the o o relation S ( ΔD D ) ( p p ) =, where p is the value of the altered parameter and the superscript o denotes values for optimized parameters. The sensitivities are represented in Supplementary Figure 3. Alterations of parameter (increase or decrease) lead to higher Euclidian distances between experimental data and simulation results. For most of the parameters the sensitivity has a clear minimum (0) at the value used in the model ( p = p ) and becomes higher if p deviates from p. This confirms that the optimization algorithm revealed parameters that minimize D. For some parameters an altered p value results only in a minor change of S (e.g. or ). These parameters are not determined at sufficiently high precisions by the available experimental data. Altering those within a certain range has only minor influence on the temporal behavior of pathway components. This is also supported by Supplementary Figure 4 showing that alteration in e.g. o TCS k 2 barely results in different maximal values or timing of events. o TCS Hog1 k 2 k trans 1 Supplementary Figure 4 shows the parameter dependence of the maximal value and the time t max, at which the maximal value was attained, for the concentrations of Ssk1, Hog1P 2, mrna 1, and internal glycerol. These values can be considered as measures for strength and timing of the activation. It appears that for the investigated parameter range the activation of the individual compounds depends more strongly on parameters of upstream reactions than on parameters of downstream reactions despite the importance for the entire system of the feedback regulation via turgor pressure: The maximal value and TCS, 0 1 TCS k 4 t max for Ssk1 are controlled mainly by the values of k and, but barely by downstream events. Activation of Hog1P2 depends on the parameters of the phosphorelay system and the MAP kinase cascade, but only to a lesser extent on parameters of the gene expression module. Also the activation of mrna 1 shows a stronger dependence on the parameters of the upstream part of the signaling cascade (including transcription), than on 5

6 downstream events (translation and mrna degradation are presented here). Interestingly, the maximal values obtained within the simulation time for internal glycerol are dependent on all altered parameters. Deviation from the optimized parameters results frequently in lower glycerol concentrations, but barely in higher glycerol concentrations. Normalization of data in graphical representation Simulation results for Ssk1, Hog1P 2, mrna, and protein were normalized with respect to the maximal values attained during a time course following a single shock (Supplementary Fig. 1 and Supplementary Table 6). Experimental data for Hog1P 2 and mrna were normalized with respect to the maximal values following a single shock. 6

7 2. Experimental scenarios used to verify the model To test the mathematical model we have simulated experimental scenarios and compared those with actual experimental data. For this we used experiments previously published as well as experiments performed for the purpose of testing the models and for advancing our understanding of HOG pathway control. a. Osmotic shock treatment with different degrees of osmolarity Supplementary Figure 5 shows simulations of different events following osmotic shock with progressively increasing levels of NaCl. For lower levels of NaCl the response displays lower amplitude with a similar profile as in the standard experiments. The results of simulations for Hog1P 2 are compared with experimental data obtained for Δste11 cells. This strain, in which only the Sln1 branch is active, was chosen because at low stress intensity the two HOG pathway branches seem to respond differently and the model only contains the Sln1 branch. For stronger stimulation, the relative levels of Hog1P 2 and mrna are somewhat increased but in particular the response is prolonged. This is consistent with published reports where gene expression data have been compared at 0.5M NaCl, 0.85M NaCl, 1.4M NaCl, 0.95M sorbitol and 1.5M sorbitol, or Hog1 phosphorylation and gene expression at 0.4M and 1.4M NaCl 6, 7. It has been observed that stronger osmotic shock causes a delay in the response, which is reflected in the simulation 6, 7 As explained in the main text, the maximal response amplitude is reached already at lower NaCl levels in experiments as compared to model simulations. b. Mutants unable to produce or accumulate glycerol Mutants lacking the genes GPD1 and GPD2 are unable to produce glycerol. In such mutants, osmotic shock caused strongly prolonged phosphorylation of Hog1 as well as higher, sustained levels of target mrna. These effects are shown in Figure 4 in the main text and were also well reproduced by simulation. Mutants unable to accumulate glycerol because they express an unregulated Fps1 showed a similar profile. The model correctly simulated this effect (Fig. 4, main text). Supplementary Figure 6 shows data from different events/compounds in osmotic adaptation in wild type cells as well as cells expressing 7

8 an unregulated Fps1. These data illustrate that cells expressing unregulated Fps1 overproduce glycerol and hence prolonged HOG pathway activation is not due to the lack of glycerol but rather to the inability to accumulate glycerol. In addition, these data confirm a prediction of simulations (Figs. 3A and B, main text) showing that downregulation of HOG pathway output (Hog1 phosphorylation and mrna) occurs at intermediate levels of accumulated glycerol. The simulation for this experiment is shown in Supplementary Figure 8. 8

9 c. Cells overproducing glycerol Yeast cells growing under anaerobic conditions or cells that are transformed with a plasmid mediating overexpression of GPD1 produce more glycerol and accumulate it faster. In such strains the period of Hog1 phosphorylation and stimulated mrna levels of target genes was diminished 8. This is reflected in simulations in which the initial glycerol production rate has been increased (Supplementary Fig. 7). d. Artificial overproduction of a protein phosphatase The protein phosphatase Ptp2 is regarded as the most important of several phosphatases that deactivate Hog1 9, 10. Overproduction of PTP2 by transformation of yeast cells with a plasmid that mediates expression from the GAL1 promoter did not alter the osmoshockinduced profile of Hog1 phosphorylation or target gene expression but rather diminished the amplitude of the response. This is reflected in a simulation where the level of phosphatase was increased 4-fold (Supplementary Fig. 8). We then combined in the same cells expression of unregulated Fps1 (causing enhanced and prolonged HOG pathway activity) and overexpression of Ptp2 (causing reduced amplitude of HOG pathway activity) and monitored the osmoshock-induced profile of Hog1 phosphorylation and target gene mrna level (Supplementary Fig. 8). Overexpression of Ptp2 again reduced the amplitude of the response but did not prevent the prolonged HOG pathway activity. Also this effect is reproduced by simulation (Supplementary Fig. 8). e. Reactivation of the HOG pathway by two osmotic shock treatments A crucial test of the model and the feedback control mechanism concerns pathway reactivation. Unlike other MAP kinase systems an osmosensing pathway should remain re-activatable by a second osmotic shock, because in nature repeated osmotic alterations are likely to occur. Indeed, the HOG pathway could be re-stimulated by a second osmotic treatment at times 15, 30 and 60min and this was correctly simulated by the model, as detailed in Figure 5 in the main text. Re-stimulation was also observed after 160min (data not shown). 9

10 Re-stimulation was even apparent for the intracellular glycerol levels, which showed a second peak when the second shock was done after 160min. For shorter time intervals the glycerol level responds too slowly for an apparent second peak (Supplementary Fig. 9). f. Overexpression or overactivation of HOG pathway components The HOG pathway can be activated by genetic manipulations of certain of its components. This has been reported for instance for truncated, hyperactive Ssk2 11, 12 as well as active alleles of Hog1 13. These tests of the model focus on the HOG pathway and its outputs, because osmotic conditions remain unchanged. Increasing the expression of truncated, hyperactive Ssk2 is experimentally achieved for instance by activating the expression of a GAL1-SSK2 construct. This results in increased levels of Hog1 phosphorylation 12, which is correctly simulated using the model (Supplementary Fig. 10). Similarly, enhanced levels of activated alleles of Hog1 by stimulating their expression from a MET25-HOG1 construct causes transiently increased mrna levels of target genes 13. This is not well simulated by the model, which predicts that HOG-dependent gene expression is strongly and continuously enhanced (Supplementary Fig. 10). There are two possible explanations for this discrepancy: (i) The expression of the recombinant gene is only transiently activated; (ii) There are additional feedback mechanisms downstream of Hog1, which have not yet been discovered. We are studying this aspect. 10

11 3. Materials and methods for experiments presented in this study Yeast strains and plasmids The yeast strains used in this study are W303A (MATa leu2-3/112 ura3 trp1 his3-11/15 ade2 can100 GAL SUC2 mal0) 14 and the isogenic mutants stellδ::kanx, gpd1δ::trp1 gpd2δ::ura3 15 and fps1δ::leu2 16, 17. The truncated version of FPS1- Δ1 18 has been cloned into the episomal plasmid YEp The centromeric plasmid prs with PTP2 under control of the GAL1 promoter was kindly provided by Dr. Haruo Saito 21. Growth conditions For all experiments, except for the study with the ste11δ strain, Centraal Bureau Seer (CBS)-based minimal medium 22 supplemented with amino acids (120mg/l) and glucose (2%) or galactose (2%) was used. For growing stellδ, Yeast Peptone (YP) medium supplemented with 2% glucose was employed. All samples were taken from exponentially growing cultures after treatment as indicated in the description of each experiment. For Western and Northern analysis samples were frozen in a dry ice / ethanol bath. After centrifugation, sediments were washed once and subsequently stored at -20 C. Northern blot analysis and probes RNA extraction and electrophoresis were performed as previously described 23. PCR fragments of the STL1 and GPD1 ORFs were prepared from genomic DNA template using primers with the following sequences (listed 5 to 3 ). STL1: TAAGCAGAAC- 11

12 CAGTCACTGG and GTAGATTGTTGCGAAGACCC. GPD1: AACTTCCGGCCACTTGAATG and ATCATGTCCGGCAGGTTCTT. Probes were labeled with 32 P-αdCTP using the Megaprime kit (Amersham) purified on Nick columns (Amersham) and employed with an activity of 1,000,000 CPM/ml. To detect and quantify signals the Molecular Imager FX, the Exposure cassette K (BIORAD) and the Quantity One software v (BIORAD) were used. 18-S RNA was used to normalize transcript levels. Protein extraction and Western blot analysis Cells were resuspended in SDS loading buffer (100mM Tris-HCl ph 6.8, 200mM DTT, 4% SDS, 20% glycerol, 0.2% bromophenol blue, 20mM mercapto ethanol, 10mM NaF, 0.1 mm NaVanadate, Protease inhibitor (Complete EDTA-free Protease Inhibitor cocktail tablets, Roche). Cell suspensions were first boiled at 100 C for 10 min, then centrifuged at 13,000 rpm, 4 C for 10 min to obtain pure protein extracts μg of proteins were loaded on a 10% SDS-PAGE and blotted on a PVDF membrane (Hybond-P, Amersham). Membranes were blocked with 5% milk (Difco) in TBS-T. The antibody recognizing dually phosphorylated Hog1 (phospho-p38 MAPK (Thr180/Tyr182), Cell Signaling) was diluted 1:1,000 in 5% BSA TBS-T and the membrane was incubated over night at 4 C. The antibody recognizing Hog1 independently of phosphorylation status (Hog1 yc-20, Santa Cruz Biotechnology) was used as control. It was diluted 1:200 in 5% milk TBS-T and the membrane was incubated for 1 h at room temperature. Secondary antibodies (Anti-Rabbit antibody, HRP-linked lgg, Cell Signaling, and Donkey anti-goat lgg- HRP, Santa Cruz Biotechnology) were applied in TBS-T in 1:2,000 and 1:1,500 dilu- 12

13 tions, respectively. The Lumi-Light western Blotting Substrate (Roche) as well as the FUJIFILM LAS000 camera were used for visualization. Software ImageGauge 3.46 was used for quantification. Biochemical determinations For determination of total glycerol, samples were boiled at 100 C for 10 min. After centrifugation supernatants were stored in -20 C. For intracellular glycerol, cells from 1 ml of sample were resuspended in 1 ml of water, boiled at 100 C for 10 min and finally supernatants were stored as above. Determination of glycerol concentration was performed with a kit (Boehringer Mannheim, Roche) and a Beckman Biomek 2000 laboratory robot. Protein extractions and enzyme activity assays were performed as previously described 24, except that protease inhibitor (Complete EDTA-free Protease Inhibitor cocktail tablets, Roche), a FastPrep FP 120 (BIO 101 SAVANT) and a Beckman Spectrophotometer (Beckman DU 7400) were used. Protein concentration was determined with the method of Bradford (BIORAD). 13

14 5. References 1. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, (2003). 2. Martinez de Maranon, I., Marechal, P.A. & Gervais, P. Passive response of Saccharomyces cerevisiae to osmotic shifts: cell volume variations depending on the physiological state. Biochem Biophys Res Commun 227, (1996). 3. Janiak-Spens, F., Cook, P.F. & West, A.H. Kinetic analysis of YPD1-dependent phosphotransfer reactions in the yeast osmoregulatory phosphorelay system. Biochemistry 44, (2005). 4. Rizzi, M., Baltes, M., Theobald, U. & Reuss, M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: II. Mathematical model. Biotechnology Bioengineering 55, (1997). 5. Theobald, U., Mailinger, W., Baltes, M., Rizzi, M. & Reuss, M. In vivo analysis of metabolic dynamics in Saccharomyces cerevisiae: I. Experimental observations. Biotechnol Bioeng 55, (1997). 6. Rep, M., Albertyn, J., Thevelein, J.M., Prior, B.A. & Hohmann, S. Different signalling pathways contribute to the control of GPD1 gene expression by osmotic stress in Saccharomyces cerevisiae. Microbiology 145 (Pt 3), (1999). 7. Van Wuytswinkel, O. et al. Response of Saccharomyces cerevisiae to severe osmotic stress: evidence for a novel activation mechanism of the HOG MAP kinase pathway. Mol. Microbiol. 37, (2000). 8. Krantz, M. et al. Anaerobicity prepares Saccharomyces cerevisiae cells for faster adaptation to osmotic shock. Eukaryot Cell 3, (2004). 9. Jacoby, T. et al. Two protein-tyrosine phosphatases inactivate the osmotic stress response pathway in yeast by targeting the mitogen-activated protein kinase, Hog1. J. Biol. Chem. 272, (1997). 10. Warmka, J., Hanneman, J., Lee, J., Amin, D. & Ota, I. Ptc1, a type 2C Ser/Thr phosphatase, inactivates the HOG pathway by dephosphorylating the mitogenactivated protein kinase Hog1. Mol. Cell. Biol. 21, (2001). 11. Maeda, T., Takekawa, M. & Saito, H. Activation of yeast PBS2 MAPKK by MAPKKKs or by binding of an SH3-containing osmosensor. Science 269, (1995). 12. Wurgler-Murphy, S.M., Maeda, T., Witten, E.A. & Saito, H. Regulation of the Saccharomyces cerevisiae Hog1 mitogen activated protein kinase by the Ptp2 and Ptp3 protein tyrosine phosphatases. Mol. Cell. Biol. 17, (1997). 13. Yaakov, G., Bell, M., Hohmann, S. & Engelberg, D. Combination of two activating mutations in one HOG1 gene forms hyperactive enzymes that induce growth arrest. Mol Cell Biol 23, (2003). 14. Thomas, B.J. & Rothstein, R.J. Elevated recombination rates in transcriptionally active DNA. Cell 56, (1989). 15. Ansell, R., Granath, K., Hohmann, S., Thevelein, J.M. & Adler, L. The two isoenzymes for yeast NAD + -dependent glycerol 3-phosphate dehydrogenase encoded by GPD1 and GPD2 have distinct roles in osmoadaptation and redox regulation. EMBO J. 16, (1997). 14

15 16. Luyten, K. et al. Fps1, a yeast member of the MIP family of channel proteins, is a facilitator for glycerol uptake and efflux and is inactive under osmotic stress. EMBO J. 14, (1995). 17. Tamás, M.J. et al. A short regulatory domain restricts glycerol transport through yeast Fps1p. J Biol Chem 278, (2003). 18. Tamás, M.J. et al. Fps1p controls the accumulation and release of the compatible solute glycerol in yeast osmoregulation. Mol. Microbiol. 31, (1999). 19. Gietz, R.D. & Sugino, A. New yeast-e. coli shuttle vectors with in vitro mutagenized yeast genes lacking six base pair restriction sites. Gene 74, (1988). 20. Sikorski, R.S. & Hieter, P. A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae. Genetics 122, (1989). 21. Maeda, T., Wurgler-Murphy, S.M. & Saito, H. A two-component system that regulates an osmosensing MAP kinase cascade in yeast. Nature 369, (1994). 22. Verduyn, C. Physiology of yeasts in relation to biomass yields. Antonie Van Leeuwenhoek 60, (1991). 23. De Winde, J.H., Crauwels, M., Hohmann, S., Thevelein, J.M. & Winderickx, J. Differential requirement of the yeast sugar kinases for sugar sensing in establishing the catabolite-repressed state. Eur. J. Biochem. 241, (1996). 24. Andre, L., Hemming, A. & Adler, L. Osmoregulation in Saccharomyces cerevisiae. Studies on the osmotic induction of glycerol production and glycerol-3- phosphate dehydrogenase (NAD+). FEBS Lett 286, 137 (1991). 15