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
Overview
• General Introduction
• Calcium Networks in Yeast and Cardiac Myocytes
• Yeast Ca2+ Dynamics & A Mathematical Model
---Control Block Diagram
---Mathematical Model
---Simulation Results
• Ca2+-Calcineurin Network Controlling Heart Growth
• Advantages of Calcium Signaling Research in Yeast
• Conclusions and Future works
• Acknowledgements & References
Ca2+, the Most Ubiquitous & Versatile
Intracellular 2nd Messenger [1,7]
• Ca2+ Homeostasis :
-Dynamical balance
-Basal level cytosolic Ca2+ : 50-200nM
-Extracellular Ca2+ : ~1mM
• Intracellular Ca2+ Signaling :
-Various extracellular stimuli → Ca2+ transients,
sparks, oscillations, puffs, etc. → downstream
components → cell response (proliferation,
muscle contraction, neurotransmitter
release, programmed cell death, etc.)
Calcium Homeostasis Systems [2,3,5,8]
Cui and Kaandorp, Cell Calcium, 39: 337-348 (2006); Bers, Nature 415:198-205 (2002)
Left: budding yeast cell
CaM: calmodulin;
CaN: calcineurin;
NCX: Na+/Ca2+ exchanger;
ATP: ATPase;
Right: mammalian cardiac myocytes
LTCC: L-Type Ca2+ channel;
SR: sarcoplasmic reticulum;
RyR: ryanodine receptor;
UP: mitochondrial uniporter;
ATP: ATPase;
SERCA: SR Ca2+-ATPase;
mRyR: mitochondrial ryanodine receptor;
PLB: phospholamban;
RaM: rapid-mode uptake pathway
Comparison of Two Systems
[2,3,5,9]
• 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 calmodulindependent 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)
Control Block Diagram (cch1 yvc1 mutant) [5]
Cui et al., Cell Calcium, in press, (2008) (doi:10.1016/j.ceca.2008.07.005)
[Caex]: extracellular Ca2+ concentration; Vol: the volume of the cytosol
x(t): cytosolic Ca2+ concentration;
Ca2+-bound calmodulin is
assumed to inhibit the activity of both transporters M and X
Mathematical Model (composed of 3 equations)
[5]
Cui et al., Cell Calcium, in press, (2008) (doi:10.1016/j.ceca.2008.07.005)
• The main equation:
x' (t )  f  (( J M  J X  J Pmc1  J Vcx1  J Pmr1 )  x(t )Vol ' (t )) / Vol (t )
x’(t): the change rate of cytosolic free Ca2+ concentration
JM, JX, JPmc1, JVcx1 and JPmr1 : 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 Mg2+ concentration
Ca2+ Transient Curves under Hypertonic Shock
[5]
Stimuli: extracellular Ca2+ shock (800mM) + Mg2+ challenge
The necessity of having two Mg2+-sensitive influx pathways (Transporters
M and X) rather than having only one Mg2+-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 Ca2+ dynamics and simulating the effects of extracellular Mg2+ removal
Complex Calcium-Calcineurin Signaling Network [4,10]
Cui and Kaandorp, LNCS 5103: 110–119 (2008)
CaM: calmodulin;
MCIP: modulatory calcineurin-interacting protein
NFAT: nuclear factor of activated T-cells; NFATP: phosphorylated NFAT
Stress: hypertrophic stimuli; PO: pressure Overload; CaN*: activated CaN
Transient Curves for CaN* Over-expression [4]
Modeling idea: complex network → 17 reactions + 1 process→ 28 equations
Dual Roles of MCIP in Cardiac Hypertrophy
[4]
Left: reported (HW/BW: heart weight/body weight; TG: transgenic) Right: simulated
CaN* overexpression causes the dissociation of Complex2 by promoting
MCIPPP→MCIPP→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 MCIP→MCIPP→MCIPPP
which associates 14-3-3 to relieve its inhibition on hypertrophic response.
Why Study Yeast Calcium Signaling? [3,6,8]
• 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
Conclusions
[2,6,7,9,11]
• 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
Future Works
• 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 (model→experiment→model) are necessary
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
•
and http://www.cellml.org/models/cui_kaandorp_2008_version02, respectively) and
include them into the CellML Model Repository.
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).