cell communication and signaling biomed central · 2017. 8. 24. · cell communication and...

BioMed CentralCell Communication and Signaling

ss
Open AcceResearchExpression of excess receptors and negative feedback control of signal pathways are required for rapid activation and prompt cessation of signal transductionHiroshi Kobayashi*1, Ryuzo Azuma2,3 and Takuo Yasunaga2,3
Address: 1Department of Biochemistry, Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba 260-8675, Japan, 2Department of Bioscience and Bioinformatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-0067, Japan and 3Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (JST/CREST), Sanbancho Bldg, 5, Sanbancho, Chiyoda-ku, Tokyo 102-0075, Japan

Email: Hiroshi Kobayashi* - [email protected]; Ryuzo Azuma - [email protected]; Takuo Yasunaga - [email protected]

* Corresponding author

AbstractBackground: Cellular signal transduction is initiated by the binding of extracellular ligands tomembrane receptors. Receptors are often expressed in excess, and cells are activated when a smallnumber of receptors bind ligands. Intracellular signal proteins are activated at a high level soon afterligand binding, and the activation level decreases in a negative feedback manner without ligandclearance. Why are excess receptors required? What is the physiological significance of thenegative feedback regulation?

Results: To answer these questions, we developed a Monte Carlo simulation program tokinetically analyze signal pathways using the model in which ligands are bound to receptors and thenmembrane complexes with other membrane proteins are formed. Our simulation results showedthat excess receptors are not required for cell activation when the dissociation constant (Kd) ofthe ligand-receptor complex is 10-10 M or less. However, such low Kd values cause delayed signalshutdown after ligand clearance from the extracellular space. In contrast, when the Kd was 10-8 Mand the ligand level was less than 1 μM, excess receptors were required for prompt signalpropagation and rapid signal cessation after ligand clearance. An initial increase in active cytosolicsignal proteins to a high level is required for rapid activation of cellular signal pathways, and a lowlevel of active signal proteins is essential for the rapid shutdown of signal pathways after ligandclearance.

Conclusion: The present kinetic analysis revealed that excess receptors and negative feedbackregulation promote activation and cessation of signal transduction with a low amount ofextracellular ligand.

BackgroundCellular signal transduction is mediated by a complex sys-tem involving many proteins. Experimental studies haveunraveled several transduction pathways and the roles of

many proteins, but many questions remain unanswered.It has been shown that successful cellular activation onlyrequires ligand binding by a small fraction of the availablereceptors. For example, HeLa cells have been shown to

Published: 3 March 2009

Cell Communication and Signaling 2009, 7:3 doi:10.1186/1478-811X-7-3

Received: 29 December 2008Accepted: 3 March 2009

This article is available from: http://www.biosignaling.com/content/7/1/3

© 2009 Kobayashi et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

of 11(page number not for citation purposes)

http://www.biosignaling.com/content/7/1/3

http://creativecommons.org/licenses/by/2.0

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Abstract&list_uids=19254388

http://www.biomedcentral.com/

http://www.biomedcentral.com/info/about/charter/

Cell Communication and Signaling 2009, 7:3 http://www.biosignaling.com/content/7/1/3

express approximately 50,000 EGF receptors per cell, butbinding of only 300 EGF molecules to the cell surface issufficient to activate 50% of cells [1]. Why do cells expressan excess number of receptors?

It is generally accepted that the binding of extracellular lig-ands to membrane receptors initiates the phosphoryla-tion of signal proteins in a stepwise manner. Many studieshave suggested that the majority of signal proteins arephosphorylated immediately after the binding of a ligandto its receptor, after which the level of signal proteindecreases [2-7]. To explain this change, it has beenhypothesized that the inactivation of signal proteins isregulated in a negative feedback manner by the activeform of the signal protein of a late reaction step, therebydecreasing the levels of active proteins [8-10]. However, itremains unclear why this kind of negative feedback regu-lation is required.

Kinetic analysis is a useful way to analyze these questions.Simple reactions that are mediated by a single enzymehave been analyzed using classical enzyme kinetics, butthis method is hard to apply to the kinetic analysis of sig-nal pathways because of their complexity. Recently, anelegant way to facilitate quantification has been devel-oped with the aid of computers. Two types of computersimulation techniques are now available for theoreticalstudies of biological phenomena: numerical integrationof differential equations and Monte Carlo simulation. Theformer method can be used to evaluate average behaviorinvolving a large number of molecules and stochastic var-iation. In contrast, the latter can simulate both populationbehavior and single molecule dynamics. Monte Carlosimulation can also address time-dependent fluctuationsinvolving noise as well as cell-to-cell population heteroge-neity.

Receptor-ligand complex formation has been simulatedusing Monte Carlo techniques [11-13], but these previousanalyses have not answered the above questions. Ourgroup developed Monte Carlo simulation programs usinga conventional personal computer with Windows XP or2000 operating systems to examine the kinetic signifi-cance of the clustering of membrane receptors and theirassociate proteins and found that the pre-clustering ofsuch proteins promotes cellular signaling [14]. In thepresent study, our previous technique was applied to clar-ify why cells have an excess amount of receptors and whynegative feedback regulation is required. We used the fol-lowing model in the present simulation. Extracellular lig-ands are bound to receptors and then membranecomplexes with other membrane proteins are formed. Inthis model, pre-clustering of membrane proteins isrequired for efficient cell activation as shown previously

[14]. The complex activates the first cytosolic signal pro-tein and other signal proteins are activated step by step.

MethodsIn the present study, we assumed a simplified model inwhich the cell surface is represented as a 2-dimensionalplane between 3-dimensional extracellular and cytosolicspaces (Figure 1-I). The cell surface and the extracellularspace were divided into subspaces. Real-type pseudo uni-form random numbers (N) with the range 0 ≤ N < 1 weregenerated as reported previously [15]. Each molecule wasassumed to undergo random motion with a diffusion rate(υ) that has a pseudo-normal probability distributionfrom 0 to 100. υ was generated as described in Table 1,and the resulting distribution is shown in Figure 1-II. Eachmolecule has υ and its direction of movement (positive ornegative direction on each axis), and molecules move intotheir neighboring subspace when τ < υ, where τ is a

Cell model for simulation, molecular diffusion rates, and receptor movementFigure 1Cell model for simulation, molecular diffusion rates, and receptor movement. (A) The cell model used for simulation. See text for details. (B) Distribution of diffusion rates (υ). For details, see text. (C,D) Movement of R was plotted for 1 × 104 steps at intervals of 10 steps. The diffu-sion rates of R used were υ(C) and 0.1υ(D). The numbers of subspaces were 600 × 600 × 1 (C) and 300 × 300 × 1 (D).



pseudo uniform random number (0 ≤ τ < 100) obtainedas described in Table 1. υ was defined as the diffusion rateof the extracellular ligand and cytosolic proteins, and 0.1υwas used for the rate of membrane proteins and theircomplexes with the ligand. When υ = 0, the moleculesremained in the same subspace. The diffusion rates anddirections were updated for 1% of all molecules at eachstep, and this sample population was selected randomly.The trajectories of the receptors are shown in Figure 1-IIIand 1-IV. We assumed periodic boundary conditions; i.e.,a molecule moved to the opposite side when it reachedthe boundary of its simulation box, except that when itreached the cell surface or its opposite boundary, it wasreflected in the mirror direction.

Nine clustering areas were assumed for membrane com-ponents, as illustrated in Figure 1-I, and each area con-sisted of 3 × 3 subspaces with energy barriers at eachboundary. The energy required to escape from the cluster-ing area was defined as 23.7 kilojoules·mole-1. Thismeans that the probability of escaping from the clusteringarea beyond its boundary was 0.01% at a temperature of310 K. To avoid bias, the candidate for each trial wasselected randomly. The molecules that were not selectedwere reflected in the mirror-direction at the boundaries ofthe clustering areas. Under these conditions, 99.2% ofmembrane components were clustered in the 9 clusteringareas. When the escaping possibility was set to 0.1%, 92%of membrane components were clustered.

All molecules were initially distributed into randomlyselected subspaces of their own compartments with equalprobability, and 99.2% of the membrane proteins clus-tered within 1.2 × 106 steps under standard conditions.

The LR and LRA complexes were assumed to form in themembranes by the following reactions:

L + R ↔ LR, LR + A ↔ LRA

where L, R, and A are an extracellular ligand, a membranereceptor, and a membrane protein such as an adaptor or alinker, respectively. LR and LRA are binary and ternarycomplexes, respectively. Two molecules of different spe-

cies may bind to each other when they occupy the samesubspace. The binding probability and the dissociationprobability of the complexes were defined as describedpreviously [14].

LR was rapidly converted to LRA, since R and A clusteredbefore L was added. Therefore, the rate constant of LRAformation (k) was calculated as follows:

d[LRA]/dt = k[L][R]

A cytosolic signal pathway was postulated as follows. Thefirst cytosolic signal protein (B) is activated by the follow-ing reaction:

LRA + B → LRA·B → LRA·B* → LRA + B*,

where B* is the activated form of B. B* is inactivated by

IB* + B* → IB*·B* → IB*·B → IB* + B,

where IB* is the active form of enzyme IB, which inacti-vates B*.

The second signal protein (C) is activated by the followingreaction:

B* + C → B*·C → B*·C* → B* + C*,

where C* is the activated C, and C* is inactivated by

IC* + C* → IC*·C* → IC*·C → IC* + C,

where IC* is the activated form of the enzyme that inacti-vates C*. We considered a signal pathway consisting of 5signal proteins, B to F, all of which were subjected to thesame reaction as described above. The probability of eachreaction was defined based on its activation energy asdescribed previously [14].

The source code of our simulation program was imple-mented with Visual Studio C++.net (Microsoft Co.), andthe program was run on a personal computer with Win-dows XP or 2000 (Microsoft Co.).

Table 1: A list of integral random numbers

Used for Range Equations

Diffusion rate (υ) 0–99Absolute value of (S/6-200), where (1)

Selection of moving step (τ) 0–99 N × 100(2)

A pseudo uniform random number (N) from 0 to (1 - 1 × 10-15) was generated as described in the Methods.(1)S is the integer of the obtained value, and S values of 100 or less were used.(2)The integer of the obtained value was used.

S Nii

= ×=∑( )200

1

12



ResultsValidation of our simulation methodsIn the previous simulation [14], the volume of a singlesubspace was defined as 1.728 (1.203) nm3, but this vol-ume is smaller than the size of a large number of proteincomplexes. Therefore, in this study we defined that a sin-gle subspace was a cubic box with a volume of 166.1(5.4973) nm3, as described previously [16]. In this model,one molecule per subspace corresponds to a concentra-tion of 10 mM. Each calculation step was assumed to take0.02 milliseconds.

To validate our simulation procedure, the dissociationconstant (Kd) of the following reaction in the cytosolicspace was evaluated.

A + B ↔ AB

In the equilibrium state, the following equation holds:

P1 × NA × NB/NS = P2 × NAB,

where NA, NB, and NAB are the numbers of molecules A, B,and AB, respectively. P1 and P2 are the binding and disso-ciation probabilities and are defined as exp(-ΔE1/RT) andexp(-ΔE2/RT), respectively. Where E, R, and T are the acti-vation energy, gas constant, and absolute temperature,respectively. NS is the number of subspaces in thecytosolic space. Since one molecule per subspace corre-sponds to a concentration of 10 mM as described above,the molarities of A (MA), B (MB), and AB (MAB) are givenby NA/(100 × NS), NB/(100 × NS), and NAB/(100 × NS),respectively. By defining the dissociation constant (Kd) as(MA × MB)/MAB, we get Kd = P2/(P1 × 100).

The numbers of A and B were both set to 600, and thecytosolic space contained 300 × 300 × 100 subspaces. P1was set to 0.670. As shown in Table 2, the simulated Kdvalues are in agreement with those estimated by P2/(P1 ×100).

To save calculation time, the definition of υ was changedfrom the previous definition [14]. Under the present defi-nition, random movements were simulated (Figure 1-IIIand 1-IV). The diffusion coefficient (D) was calculated asfollows:

D = [average value of {x(0)-x(t)}2+{y(0)-y(t)}2]/4t

for the receptor, and

D = [average value of {x(0)-x(t)}2+{y(0)-y(t)}2+ {z(0)-z(t)}2]/6t

for cytosolic proteins, where x(0), y(0), z(0), x(t), y(t),and z(t) are their positions in the x, y, and z directions at0 and t seconds. The mean value for 1000 molecules wascalculated. The cytosolic space and the cell surface con-tained 1200 × 1200 × 1200 and 1200 × 1200 × 1 sub-spaces, respectively. The average values were obtainedfrom 4 sets of calculations, which took between 0.03 and0.06 seconds. The diffusion coefficients of the cytosolicprotein and the receptor were 10.8 ± 0.3 and 0.163 ±0.008 (μm)2·second-1, respectively. These values reflectexperimental data for membrane and cytosolic proteinspreviously reported in prokaryotes [17] and eukaryotes[18,19]. These data suggested that our simulation proce-dure is adequate to achieve our purpose. We assumed thatthe diffusion rate of the ligand is close to that of cytosolicmolecules because the extracellular space and cytosolboth contain a large number of molecules including pro-teins.

Ligand-receptor membrane complex formationIt is generally accepted that receptors and associated mem-brane proteins such as adaptors and linkers are clusteredin the micro-domains of the cell surface and that this clus-tering plays a role in intracellular signaling. Lipid raftsconsisting of glycosphingolipids, cholesterol, and mem-brane proteins have been observed on the cell surface[20]. Other interactions of membrane proteins have beenreported to involve the F-actin skeleton [21]. Previous

Table 2: Kd at equilibrium

Dissociation constant (Kd)P2

Theoretical [P2/(P1 × 100)] Simulated(1) Ratio(2)

3.35 × 10-4 5.00 × 10-6 4.94 × 10-6 ± 0.33 × 10-6 0.996.71 × 10-5 1.00 × 10-6 1.05 × 10-6 ± 0.06 × 10-6 1.053.36 × 10-5 5.02 × 10-7 5.12 × 10-7 ± 0.36 × 10-7 1.02

6.72 × 10-6 1.00 × 10-7 1.05 × 10-7 ± 0.06 × 10-7 1.05

Mean 1.03

(1)mean value and standard deviation of (MA·MB)/MAB calculated from 5 simulations.(2)simulated value/theoretical value.



simulations by our group suggested that the clustering ofmembrane proteins such as receptors and adapters beforeligand binding to receptors stimulated cell activation [14],in agreement with previous experimental data showingthat receptors are associated with other membrane pro-teins in non-stimulated cells [22-25]. Therefore, we postu-lated that the membrane proteins associated with signalpathways cluster before ligand binding.

In the first simulation (model 1), the extracellular spaceand the cell surface consisted of 300 × 300 × 100 and 300× 300 × 1 subspaces, respectively. The numbers of L, R,and A at zero time were set as described in Figure 2. Thesetting of L to 180 corresponds to 200 nM. The setting ofR and A to 86 corresponds to approximately 10,000 mol-ecules per cell, since the surface area of a spherical cellwith a diameter of 10 μm is 314 μm2. More than 99% ofR and A were clustered within 1.2 × 106 steps, and henceL was added immediately before the 1.2 × 106th step. Theconcentration of extracellular ligands may not decreasewith their binding to membrane receptors in situ becauseof the continuous supply of ligands from producing cells.To maintain a constant concentration of L, one moleculeof L was set at the opposite side when L was bound to Ron the cell surface. When LR dissociated, L was deletedfrom the opposite boundary until extracellular Ldecreased to its original level. After the 1.5 × 107th step (t= 3 × 102 seconds), L was allowed to pass through the bor-ders of the simulation box, except for the cell surface andwas deleted from the borders.

When dissociation constants of LR (Kd1) and LRA (Kd2)were equally set to 1 × 10-7 M or 1 × 10-6 M, respectively, Lwas bound to 80% of the receptors (8,000 receptors percell, Figure 2A and 2B). Even when the ligand concentra-tion decreased to 20 or 5 nM, L was bound to 8,000 and4,000 receptors, respectively, although the rate of LRA for-mation was slow (Figure 2I to 2L). In contrast to complexformation, dissociation of the complex was very slow withthese dissociation constants. When both Kd1 and Kd2 were10-5 M, L was bound to approximately 6,000 receptors inthe presence of 200 nM L, and fast dissociation was shown(Figure 2C). Five thousand and 2,000 complexes wereformed when the concentration of L was 20 and 5 nM,respectively (Figure 2M and 2N). When both Kd1 and Kd2were 10-4 M, the complex level was 1,000 and 4,000 percell in the presence of 200 and 2000 nM L, respectively(Figure 2D and 2E). However, experimental studiesshowed that a ligand concentration of less than 100 nMactivated cells. When the cells contained a 5 fold excess ofR and A (50,000 per cell), L was bound to approximately4,000 receptors even when the concentration of L was 20nM, and very rapid dissociation was demonstrated (Figure2P).

These results suggest that the excess amount of receptorsand its associate membrane proteins are not required forcell activation when the Kd is low or the concentration ofL is high. However, it was shown that the prompt activa-tion of the intracellular signal in the presence of a lowamount of ligands and its rapid cessation after ligandclearance require an excess amount of receptors and mem-brane proteins. Cell activation with a low amount of lig-ands is favorable in situ.

When Kd1 and Kd2 were set to10-6 M and 10-4 M, respec-tively (Figure 2F), the simulation results were similar tothat of Figure 2C. The product of the dissociation constant(Kd1 × Kd2) was 10-10 M in both cases. The same result wasagain obtained when Kd1 and Kd2 were set to10-4 M and10-6 M, respectively (Figure 2G).

The rate constants of LRA formation shown in Figure 2C(Kd1 × Kd2 = 10-10 M), M (Kd1 × Kd2 = 10-10 M), and H (Kd1× Kd2 = 10-8 M) were calculated to be 2.7 × 105, 3.2 × 105,and 1.4 × 105 M-1·s-1, respectively. These values are higherthan those reported by Andrews, et al. [26], similar torates observed in PMA (4 beta-Phorbol 12-myristate 13-acetate) treated cells [27], and lower than values reportedin other studies [28,29].

Negative feedback regulation of cytosolic signal pathwaysIn the simulation of cytosolic signal transduction, the sig-nal pathway was assumed to be as described in the Meth-ods section. When Kd1 and Kd2 were set at 10-5 M and 10-

4 M, respectively, ligands were bound to 40% of receptorsin the presence of 200 nM L, and rapid formation and dis-sociation of LRA were demonstrated (Figure 2H). There-fore, we used these conditions for the signal transductionsimulation. The concentrations of proteins and kineticparameters were set as described in Additional file 1 and2. All signal proteins (B to F) were activated rapidly afterthe ligand addition under conditions in which all signalproteins were activated at a high level (Figure 3-I, model2A). However, inactivation of F was shown to be very slowunder these conditions, even if the dissociation of the LRAcomplex was rapid (Figure 3-I, model 2A). As shown inFigure 3-I, when the levels of active B to D were low (Fig-ure 3-I, model 2B), the activation rate of F (1.62 × 10-9 ±0.08 M s-1, n = 3) was slower than the rate in model 2A(1.89 × 10-9 ± 0.03 M s-1, n = 3), but the inactivation of Fwas fast in model 2B.

Next, we postulated the following negative feedback regu-latory mechanism (Figure 3-II) in which the amount ofenzyme IB*, which inactivates B*, increased with theincrease in E* as follows:

E* + I → E*·I → E*·IB* → E* + IB*,




Simulation of LRA formationFigure 2Simulation of LRA formation. The number of subspaces for L and the number of molecules were set as shown in the upper Table. The dissociation constants of LR (Kd1) and LRA (Kd2) are indicated in the Figure. The probabilities of LR and LRA formations were set to 0.67. L was added immediately before the 1.2 × 106th step (down arrow, 24 seconds). After the 1.5 × 107th step (upper arrow, 300 seconds), L was allowed to pass through the borders of the simulation box for L, except for the cell surface, and L was deleted in the outside area. Three simulation results are represented.



Negative feedback regulation of the signal pathwaysFigure 3Negative feedback regulation of the signal pathways. I. The protein amounts and reaction probabilities were set as described in Additional file 1 and 2, respectively. L was added immediately before the 1.2 × 106th step (down arrow, 24 sec-onds) and removed after the 1.2 × 107th step (upper allow, 240 seconds), as described in the legend of Figure 2. Three simula-tion results are represented. II. Models for negative feedback regulation used in this study.


where I is the precursor of IB*. Under this negative feed-back regulation, the rapid activation of F (3.57 × 10-9 ±0.13 M s-1, n = 3) and prompt shutdown of active F weredemonstrated (Figure 3-I, model 3). These simulationresults led us to conclude that high levels of active inter-mediate signal proteins promote the activation of the finalstep of the signal pathway, and low levels of the activeproteins are required for prompt signal shutdown.

The mechanism in which the first signal protein is regulated in a negative feedback manner is more effectiveVarious models of negative feedback regulation wereexamined (Figure 3-II). When the level of B* was regu-lated by E*, the rapid activation of F and inactivation ofF* were simulated (Figure 3-I, model 3), as comparedwith regulatory systems in which the level of C* (Figure 4,model 4) or D* (Figure 4, model 5) was regulated by E*,namely IC* and ID* increased with increases in E*. IC* andID* were produced from I, as described above. Rapid shut-down was obtained when the level of B* was regulated byD* (Figure 4, model 6) or C* (Figure 4, model 8), as com-pared with model 7 (the level of C* was regulated by D*).These results suggest that negative feedback regulation ofthe B* level is the most effective for the prompt activationand cessation of signal pathways, but the regulation of B*by different molecules (C* to E*) had a similar effect onsignal transduction.

It was shown that the membrane receptor complex wasinactivated by the cytosolic signal protein in a negativefeedback manner [30]. The final simulation was carriedout under conditions in which LRA was inactivated by anincrease in the level of E* (model 9) as follows:

where LRA# is unable to activate B. P11, P12, P13, P14,and P15 were set to 0.0200, 2.00 × 10-6, 1.99 × 10-4, 3.35× 10-7, and 0.670, respectively. The results showed that theshutdown speed in model 9 was close to the speed inmodel 3.

DiscussionThe present simulation provided a kinetic explanation forwhy cells have a higher amount of receptors than isrequired to initiate signal transduction. When the dissoci-ation constant is low, excess receptors are not required forcell activation, but signal shutdown is delayed after clear-ance of the ligand from the extracellular space. Even whenthe dissociation constant (Kd1 × Kd2) was 10-8 M, theexcess receptor was not required in the presence of 2 μMextracellular ligand, but such a high concentration of lig-and may not represent physiological conditions. There-

fore, an excess amount of receptors is useful for the rapidactivation and inactivation of intracellular signal trans-duction when ligand concentrations are at a physiologicallevel.

Our simulation results can be applicable to other models.For example, when ligand-receptor complexes withoutother membrane proteins initiate cellular signaling, thedissociation constant of LR more than 10-9 M was essen-tial for rapid signal cessation and an excess amount ofreceptors was required for activation at a ligand levelbelow 1 μM (data not shown). If receptors are crosslinkedby multivalent ligands, the following reactions are used

L + R ↔ LR and LR + R ↔ LRR instead of L + R ↔ LR and LR + A ↔ LRA.

Therefore, similar results could be obtained because bothhave kinetic similarity. The signal transduction that isdeactivated by the binding of other membrane compo-nents to ligand-receptor complexes shows similar kineticsto the model 9 using the component instead of E* with-out P15.

Previous examinations of signal transfer pathways withthe aid of computer simulations theoretically supportedthe negative feedback control of signal transfer observedexperimentally [8-10], but its physiological meaningremained unclear. The present results clearly demonstratethat negative feedback regulation is required to promotethe termination of a signal transfer system. The presentsimulation also suggests that negative feedback regulationof the first cytosolic signal protein is the most effectivepathway. Experimental results have shown that the earlysteps of signal pathways are regulated in a negative feed-back manner in many cases [31-36]. Other experimentalstudies revealed that the activation levels of signal pro-teins at the steady-state stage are very low [2-7], leading usto debate the physiological significance of such a low levelof activation. The present simulation results have shownthat a low activation level of signal proteins at the steady-state stage is of physiological importance for cellular sign-aling.

It has been proposed that negative feedback regulationrepresses fluctuations in signal transduction [10]. How-ever, our present data revealed that the activation of signalproteins was not significantly stabilized by negative feed-back regulation under our conditions (Figure 3 and 4).

Most computer simulations of biological phenomenahave been performed with supercomputers using Unixbased operating systems. This may be the reason whyMonte Carlo techniques are still largely unavailable tomost researchers. In contrast, our simulations were carried

E LRA E LRA E LRA E LP

P

P

P

P∗ ∗ ∗ ∗+ ⎯ →⎯⎯← ⎯⎯⎯ ⎯ →⎯⎯← ⎯⎯⎯ ⎯ →⎯⎯ +11

12

13

14

15i i # RRA #,




Simulations of various types of negative feedback regulationFigure 4Simulations of various types of negative feedback regulation. The simulations were carried out as described in the leg-end of Figure 3. For detailed models, see the text. Three simulation results are represented.


out on a conventional personal computer using the Win-dows XP or 2000 operating systems. The simulated valuesof diffusion coefficients and kinetic parameters were con-sistent with experimental data from literatures, demon-strating that our Monte Carlo simulation procedure isuseful for kinetic analysis of cellular signal transduction.Furthermore, our simulation program can be used byother investigators for kinetic analysis of other biologicalphenomena with minor modifications, and neither spe-cial computing hardware nor special training is required.

ConclusionThe present kinetic analysis revealed that excess receptorsand negative feedback regulation promote activation andcessation of signal transduction when ligand concentra-tions are at a low physiological level.

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsHK carried out the simulation and wrote the manuscript.RA and TY participated in the design of simulation pro-gram and preparation of the manuscript.

Additional material

References1. Uyemura T, Takagi H, Yanagida T, Sako Y: Single-molecule analy-

sis of epidermal growth factor signaling that leads to ultra-sensitive calcium response. Biophys J 2005, 88:3720-3730.

2. Muroya K, Hattori S, Nakamura S: Nerve growth factor inducesrapid accumulation of the GTP-bound form of p21ras in ratpheochromocytoma PC12 cells. Oncogene 1992, 7:277-281.

3. Traverse S, Seedorf K, Paterson H, Marshall CJ, Cohen P, Ullrich A:EGF triggers neuronal differentiation of PC12 cells thatoverexpress the EGF receptor. Curr Biol 1994, 4:694-701.

4. Alessi DR, Gomez N, Moorhead G, Lewis T, Keyse SM, Cohen P:Inactivation of p42 MAP kinase by protein phosphatase 2Aand a protein tyrosine phosphatase, but not CL100, in vari-ous cell lines. Curr Biol 1995, 5:283-295.

5. Nguyen TT, Scimeca JC, Filloux C, Peraldi P, Carpentier JL, VanObberghen E: Co-regulation of the mitogen-activated proteinkinase, extracellular signal-regulated kinase 1, and the 90-kDa ribosomal S6 kinase in PC12 cells. Distinct effects of theneurotrophic factor, nerve growth factor, and the mitogenic

factor, epidermal growth factor. J Biol Chem 1993,268:9803-9810.

6. Brysha M, Zhang JG, Bertolino P, Corbin JE, Alexander WS, NicolaNA, Hilton DJ, Starr R: Suppressor of cytokine signaling-1attenuates the duration of interferon gamma signal trans-duction in vitro and in vivo. J Biol Chem 2001, 276:22086-22089.

7. Cherniack AD, Klarlund JK, Conway BR, Czech MP: Disassembly ofSon-of-sevenless proteins from Grb2 during p21ras desensi-tization by insulin. J Biol Chem 1995, 270:1485-1488.

8. Brightman FA, Fell DA: Differential feedback regulation of theMAPK cascade underlies the quantitative differences in EGFand NGF signalling in PC12 cells. FEBS Lett 2000, 482:169-174.

9. Yamada S, Shiono S, Joo A, Yoshimura A: Control mechanism ofJAK/STAT signal transduction pathway. FEBS Lett 2003,534:190-196.

10. Kholodenko BN: Cell-signalling dynamics in time and space.Nat Rev Mol Cell Biol 2006, 7:165-176.

11. Woolf PJ, Linderman JJ: Self organization of membrane proteinsvia dimerization. Biophys Chem 2003, 104:217-227.

12. Nudelman G, London Y: Cell surface dynamics: the balancebetween diffusion, aggregation and endocytosis. IEE Proc-SystBiol 2006, 153:34-42.

13. Brinkerhoff CJ, Woold PJ, Linderman JJ: Monte Carlo simulationsof receptor dynamics: insights into cell signaling. J Mol Hiosol2004, 35:667-677.

14. Kobayashi H, Azuma R, Konagaya A: Clustering of membraneproteins in the pre-stimulation stage is required for signaltransduction: a computer analysis. Signal transduction 2007,7:329-339.

15. Matsumoto M, Nishimura T: Mersenne twister: A 623-dimen-sionally equidistributed uniform pseudo-random numbergenerator. ACM Transactions on Modeling and Computer Simulation1998, 8:3-30.

16. Azuma R, Kitagawa T, Kobayashi H, Konagaya A: Particle simula-tion approach for subcellular dynamics and interactions ofbiological molecules. BMC Bioinformatics 2006, 7(Suppl 4):S20.

17. Mullineaux CW, Nenninger A, Ray N, Robinson C: Diffusion ofgreen fluorescent protein in three cell environments inEscherichia coli. J Bacteriol 2006, 188:3442-3448.

18. Ishihara A, Hou Y, Jacobson K: The Thy-1 antigen exhibits rapidlateral diffusion in the plasma membrane of rodent lymphoidcells and fibroblasts. Proc Natl Acad Sci USA 1987, 84:1290-1293.

19. Zhang F, Crise B, Su B, Hou Y, Rose JK, Bothwell A, Jacobson K: Lat-eral diffusion of membrane-spanning and glycosylphosphati-dylinositol-linked proteins: toward establishing rulesgoverning the lateral mobility of membrane proteins. J CellBiol 1991, 115:75-84.

20. Horejsi V: The roles of membrane micro-domains (rafts) in Tcell activation. Immunol Rev 2003, 191:148-164.

21. Luna EJ, Hitt AL: Cytoskeleton-plasma membrane interac-tions. Science 1992, 258:955-964.

22. Varma R, Mayor S: GPI-anchored proteins are organized insubmicron domains at the cell surface. Nature 1998,394:798-801.

23. Zacharias DA, Violin JD, Newton AC, Tsien RY: Partitioning oflipid-modified monomeric GFPs into membrane microdo-mains of live cells. Science 2002, 296:913-916.

24. Sharma P, Varma R, Sarasij RC, Ira , Gousset K, Krishnamoorthy G,Rao M, Mayor S: Nanoscale organization of multiple GPI-anchored proteins in living cell membranes. Cell 2004,116:577-589.

25. Leksa V, Godar S, Schiller HB, Fuertbauer E, Muhammad A, SlezakovaK, Horejsi V, Steinlein P, Weidle UH, Binder BR, Stockinger H: TGF-beta-induced apoptosis in endothelial cells mediated byM6P/IGFII-R and mini-plasminogen. J Cell Sci 2005,118:4577-4586.

26. Andrews AL, Holloway JW, Puddicombe SM, Holgate ST, Davies DE:Kinetic analysis of the interleukin-13 receptor complex. J BiolChem 2002, 277:46073-46078.

27. Felder S, LaVin J, Ullrich A, Schlessinger J: Kinetics of binding,endocytosis, and recycling of EGF receptor mutants. J Cell Biol1992, 117:203-212.

28. Wilkinson JC, Stein RA, Guyer CA, Beechem JM, Staros JV: Real-time kinetics of ligand/cell surface receptor interactions inliving cells: binding of epidermal growth factor to the epider-mal growth factor receptor. Biochemistry 2001, 40:10230-10242.

Additional file 1Protein amounts. Amounts of proteins used for simulation.Click here for file[http://www.biomedcentral.com/content/supplementary/1478-811X-7-3-S1.doc]

Additional file 2Reaction probabilities. Reaction probabilities of each steps used for sim-ulation.Click here for file[http://www.biomedcentral.com/content/supplementary/1478-811X-7-3-S2.doc]


http://www.biomedcentral.com/content/supplementary/1478-811X-7-3-S1.doc

http://www.biomedcentral.com/content/supplementary/1478-811X-7-3-S2.doc






























































Publish with BioMed Central and every scientist can read your work free of charge

"BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime."

Sir Paul Nurse, Cancer Research UK

Your research papers will be:

available free of charge to the entire biomedical community

peer reviewed and published immediately upon acceptance

cited in PubMed and archived on PubMed Central

yours — you keep the copyright

Submit your manuscript here:http://www.biomedcentral.com/info/publishing_adv.asp

BioMedcentral

29. Teramura Y, Ichinose J, Takagi H, Nishida K, Yanagida T, Sako Y: Sin-gle-molecule analysis of epidermal growth factor binding onthe surface of living cells. EMBO J 2006, 25:4215-4222.

30. Reth M, Brummer T: Feedback regulation of lymphocyte signal-ling. Nat Rev Immunol 2004, 4:269-277.

31. Jove R: Preface: STAT signaling. Oncogene 2000, 19:2466-2467.32. Kerr IM, Costa-Pereira AP, Lillemeier BF, Strobl B: Of JAKs,

STATs, blind watchmakers, jeeps and trains. FEBS Lett 2003,546:1-5.

33. Heinrich PC, Behrmann I, Müller-Newen G, Schaper F, Graeve L:Interleukin-6-type cytokine signalling through the gp130/Jak/STAT pathway. Biochem J 1998, 334:297-314.

34. Langlois WJ, Sasaoka T, Saltiel AR, Olefsky JM: Negative FeedbackRegulation and Desensitization of Insulin and EpidermalGrowth Factor stimulated p21ras Activation. J Biol Chem 1995,270:25320-25323.

35. Brummer T, Naegele H, Reth M, Misawa Y: Identification of novelERK-mediated feedback phosphorylation sites at the C-ter-minus of B-Raf. Oncogene 2003, 22:8823-8834.

36. Dhillon AS, von Kriegsheim A, Grindlay J, Kolch W: Phosphataseand feedback regulation of Raf-1 signaling. Cell Cycle 2007,6:3-7.






















http://www.biomedcentral.com/info/publishing_adv.asp


cell communication and signaling biomed central · 2017. 8. 24. · cell communication and...

Documents