1. Calcium Homeostasis and Signaling in Yeast Cells and Cardiac Myocytes Jiangiun Cui, J.A. Kaandorp, P.M.A. Sloot Section Computational Science University of Amsterdam

2. Overview General Introduction Calcium Networks in Yeast and Cardiac Myocytes Yeast Ca 2+ Dynamics & A Mathematical Model ---Control Block Diagram ---Mathematical Model ---Simulation Results Ca 2+ -Calcineurin Network Controlling Heart Growth Advantages of Calcium Signaling Research in Yeast Conclusions and Future works Acknowledgements & References

3. Ca 2+, the Most Ubiquitous & Versatile Intracellular 2 nd Messenger Ca 2+ Homeostasis : -Dynamical balance -Basal level cytosolic Ca 2+ : 50-200nM -Extracellular Ca 2+ : ~1mM Intracellular Ca 2+ Signaling : -Various extracellular stimuli Ca 2+ transients, sparks, oscillations, puffs, etc. downstream components cell response (proliferation, muscle contraction, neurotransmitter release, programmed cell death, etc.) [1,7]

4. Calcium Homeostasis Systems Left: budding yeast cell Right: mammalian cardiac myocytes CaM: calmodulin; CaN: calcineurin; LTCC: L-Type Ca 2+ channel; ATP: ATPase; NCX: Na + /Ca 2+ exchanger; SR: sarcoplasmic reticulum; SERCA: SR Ca 2+ -ATPase; ATP: ATPase; RyR: ryanodine receptor; mRyR: mitochondrial ryanodine receptor; UP: mitochondrial uniporter; PLB: phospholamban; RaM: rapid-mode uptake pathway Cui and Kaandorp, Cell Calcium, 39: 337-348 (2006); Bers, Nature 415:198-205 (2002) [2,3,5,8]

5. Comparison of Two Systems Both systems have a set of calcium homeostasis and signaling toolkits composed of channels, pumps, exchangers and other relevant components (sensors like calmodulin, effectors such as calcineurin, etc.) Calcium homeostasis process in cardiac myocytes is inseparable from the calcium signaling process related to excitation-contraction coupling. Sarcolemmal membrane potential plays critical role regulating sarcolemmal Leak, LTCC and sarcolemmal NCX, thus calcium homeostasis/signaling in mammalian cardiac myocytes is tightly coupled with other ion homeostasis processes such as Na + and K + homeostasis Phosphorylation of several key proteins (PLB, LTCC and RyR) by kinases such as protein kinase A (PKA) and calmodulin- dependent protein kinase II (CaMKII) play vital role in calcium homeostasis process in cardiac myocytes because of the short time duration of the heart beat (typically <1s) [2,3,5,9]

6. Control Block Diagram (cch1 yvc1 mutant) Cui et al., Cell Calcium, in press, (2008) (doi:10.1016/j.ceca.2008.07.005) [Ca ex ]: extracellular Ca 2+ concentration; Vol: the volume of the cytosol x(t): cytosolic Ca 2+ concentration; Ca 2+ -bound calmodulin is assumed to inhibit the activity of both transporters M and X [5]

7. Mathematical Model (composed of 3 equations) The main equation: Cui et al., Cell Calcium, in press, (2008) (doi:10.1016/j.ceca.2008.07.005) x(t): the change rate of cytosolic free Ca 2+ concentration J M, J X, J Pmc1, J Vcx1 and J Pmr1 : the calcium ion flux through Transporter M, Transporter X, Pmc1, Vcx1 and Pmr1 respectively Vol(t) : the change rate of cytosolic volume f: the calcium buffer effect constant Uptake kinetics of Pmc1,Vcx1 and Pmr1: Michaelis-Menten equation Uptake behavior of Transporters M and X: Michaelis-Menten kinetics with competitive inhibition by extracellular Mg 2+ concentration [5]

8. Ca 2+ Transient Curves under Hypertonic Shock The necessity of having two Mg 2+ -sensitive influx pathways (Transporters M and X) rather than having only one Mg 2+ -sensitive influx pathway (i.e., Transporter X) was demonstrated by both theoretical analysis and optimal fitting to the experimental data (left graph) using hybrid optimization algorithm. The validity of the model was further confirmed by its ability of reproducing the experimentally determined effects of Vcx1 on cytosolic free Ca 2+ dynamics and simulating the effects of extracellular Mg 2+ removal Stimuli: extracellular Ca 2+ shock (800mM) + Mg 2+ challenge [5]

9. Complex Calcium-Calcineurin Signaling Network CaM: calmodulin; MCIP: modulatory calcineurin-interacting protein NFAT: nuclear factor of activated T-cells; NFAT P : phosphorylated NFAT Stress: hypertrophic stimuli; PO: pressure Overload; CaN * : activated CaN [4,10] Cui and Kaandorp, LNCS 5103: 110–119 (2008)

10. Transient Curves for CaN * Over-expression Modeling idea: complex network 17 reactions + 1 process 28 equations [4]

11. Dual Roles of MCIP in Cardiac Hypertrophy Left: reported (HW/BW: heart weight/body weight; TG: transgenic) Right: simulated CaN* overexpression causes the dissociation of Complex2 by promoting MCIP PP MCIP P MCIP which associates with CaN* to inhibit its activity. Moreover, the feedback loop of MCIP expression controlled by NFAT contributes significantly to the inhibition. In the case of PO, activated BMP1 promotes MCIPMCIP P MCIP PP which associates 14-3-3 to relieve its inhibition on hypertrophic response. [4]

12. Why Study Yeast Calcium Signaling? Small cell size (1-7μm), excluding the effect of diffusion Unicellular, easy growth, relatively small genome size (6300 genes, about 1/5 of human genome) A small set of relevant calcium transporters Numerous mature technologies available (gene knockout, mass spectrometry, aequorin probing, etc.) Most of the factors known in the yeast calcium homeostasis/signaling network are retained and operate similarly in mammalian cells including cardiac myocytes (Dolinski and Botstein 2007). For example, NFAT translocation in mammalian cardiac myocytes is strikingly similar as Crz1 translocation in yeast, MCIP signaling in cardiac myocytes is similar as Rcn signaling in yeast [3,6,8]

13. Conclusions The extreme complexity of calcium homeostasis/signaling processes in cardiac myocytes arise from their built-in coupling with other ion homeostasis processes, the quite important spatial and stochastic effects, the great number of involved factors and the extremely sophisticated regulations (e.g., RyRs are regulated by numerous proteins) Due to the universal conservation of yeast calcium homeostasis/signaling system across the eukaryotic kingdom, its understanding can be a shortcut to help understand the corresponding systems in mammalian cardiac myocytes and treat human diseases such as pathological cardiac hypertrophy and heart failure [2,6,7,9,11]

14. Mass-spectrometry-based proteomics can be a very powerful tool for searching the missing components, detecting and determining the protein interactions and quantifying the concentrations of proteins Molecular genetic assays (e.g., yeast two-hybrid assay and gene knock-out technology) are important complementary methods to help elucidating the networks and provide powerful check for the validity of models Effective collaborations among scientists who are proficient in genetics, proteomics and computational science via high-throughput experimental and computational methods through iterative systems biology procedure (modelexperimentmodel) are necessary Future Works

15. Acknowledgements & References -Thank the Dutch Science Foundation (NWO) and the European Commission (EC) for funding my research. -Thank Prof. Peter Sloot and Dr. Jaap Kaandorp for sustaining supports. -Thank Prof. Kyle Cunningham for valuable data & stimulating discussions. -Thank all the collaborators: Y. Fomekong Nanfack, Olufisayo O. Ositelu, Veronica Beaudry, Alicia Knight and Dr. Catherine M. Lloyd. -Thank Dr. Catherine M. Lloyd for translating two relevant models (Cui and Kaandorp 2006, 2008a) into CellML codes (see http://www.cellml.org/models/cui_kaandorp_2006_version03 http://www.cellml.org/models/cui_kaandorp_2006_version03 and http://www.cellml.org/models/cui_kaandorp_2008_version02, respectively) and include them into the CellML Model Repository.http://www.cellml.org/models/cui_kaandorp_2008_version02 Main References: -1. Berridge et al., Nat. Rev. Mol. Cell Biol. 4: 517-529 (2003). -2. Bers Nature 415: 198-205 (2002). -3. Cui and Kaandorp, Cell Calcium 39: 337-348 (2006). -4. Cui and Kaandorp, Lecture Notes in Computer Science 5103: 110–119 (2008). -5. Cui et al., Cell Calcium (in press, 2008). (doi:10.1016/j.ceca.2008.07.005) -6. Dolinski and Botstein, Annu. Rev. Genet. 41: 465-507 (2007). -7. Heineke and Molkentin, Nat. Rev. Mol. Cell Biol. 7: 589-600 (2006). -8. Hilioti et al., Genes & Dev. 18: 35 – 47 (2004). -9. Shannon et al., Biophys. J. 87: 3351-3371 (2004). -10. Shin et al., FEBS Letters 580: 5965-5973 (2006). -11. Sobie et al., Biophys. J. 83: 59–78 (2002).