信号传递网络 networks of biological signaling pathways 信号传递网络 康海岐 高方远...

Networks of BiologicalSignaling Pathways

信号传递网络信号传递网络

康海岐 高方远 马欣荣

一、生物体内的信号传递一、生物体内的信号传递 1. The sense of signal transduction ： intercelluar information exchange,regulation of metobolis

m, on body level 2. Type of signals ： neuroregulation: neurotransmitter( 乙酰胆碱 , 胺类 氨基酸 , 调节肽类等 ),neuroregulator chemical signals ： cAMP, Ca2+ , hormone ， 3. Mechanisms: 3.1 pr. ←→pr., 3.2 E reaction(±p ) 3.3 E activity 3.4 pr. degradation 3.5 intracelluar messager 3.6 seconder messager

E cell

一、生物体内的信号传递一、生物体内的信号传递 4. Signaling pathways ： 4.1 Ca2+

4.2 cAMP 4.3 tyrosine kinase: EGFR,insulinR 4.4 other pr. kinase cascade:PKC,PKA,PKG 4.5 intracelluar protease cascade Signal transmission occur: i. Pr.—pr. Interaction ii. Enzymatic reaction: ±p iii. Pr. Degradation iiii. Production of intracellular messager

一、生物体内的信号传递一、生物体内的信号传递 5. cytoplasm membrane receptor ： 5.1 neurotransmitter-dependention channel ( 依赖神经递质的离子通道 ) ： nAChR( 烟碱型乙酰胆碱受体 )

GABA(γγ - 氨基丁酸 )

GlyR( 甘氨酸受体 )

5.2 receptor connecting to signal transduction protein (G,N protein →second messenger →activate E.): mAChR( 毒蕈碱型乙酰胆碱受体 )

adrenergic α-,β-receptor ( 肾上腺素能 α-,β- 受体 )

5.3 growth factor receptor(tyrosine kinase activity) ： PDGFR( 血小板衍生的生长因子受体 ) ， EGFR( 表皮生长因子受体 ) ， insulin R( 胰岛素受体 )

Peptide Signaling in Plants

PNAS, Nov. 6, 2001, vol.98 no. 23

• In plants, only a few peptide have been identified

that act as signaling molecules.

• In contrast, signaling peptides are major players

in all aspects of the life cycle in animals and yeast.

• suggests that signaling mechanisms across the

eukaryotic kingdom are fundamentally different.

1. 目前有关植物中信号肽的研究主要基于以下 5 种：番茄 systemin PSK ENOD40 CLV3 SCR 18 aa 10-13 aa 72-75 aa 53-55 aa

2. 最近分离到另外 3 种活性信号肽：RALF: rapid alkalinization factor, 5 kd; Tobacco systemin: Tob sys I, Tob sys II

1 ） tomato systemin: 由食草动物损伤后引起的系统 损伤反应 ( a systemic wounding response)• 在悬浮培养细胞中可以激活促细胞分裂蛋白激酶 [mitogen-activated protein(MAP) kinase]• 并诱导培养基地碱化 (alkalinization)• 诱导蛋白酶抑制蛋白编码基因的表达 (induce expression of proteinase-inhibitor protein-encoding genes)

3. 功能：

2 ） tobacco systemin Tob I and Tob II: 激活 MAP kinase ，但不诱导蛋白酶抑制蛋白编码 基因的表达3 ） RALF (rapid alkalinizaton factor):• 激活 MAP kinase ，但不诱导蛋白酶抑制蛋白编码 基因的表达 ;• 快速引起 medium 碱化

From the followings support the idea that peptide and nonpeptide hormone-activated signaling cascades are linked in plants as they are in animals:• 植物生长素类似 5 －羟色胺，乙烯类似一氧化碳， 油菜素类固醇是类固醇，茉莉酮酸与前列腺素相关； • Systemin-induced wound response is regulated through the octadecanoid pathway, involving jasmonic acid;

4. 信号调控网络

• PSK-induced cell proliferation requires the hormones auxin or cytokinin;• Some of the developmental distortions in roots induced on addition of RALF are reminiscent of impaired nonpeptide hormone-controlled processes.

因此，揭开两种信号 cascades 之间关系，将是非常有趣的事。

一、生物体内的信号传递一、生物体内的信号传递 6.2 IP3 system

Hermone/neurotransmitter

G protein

PLCPIP2

IP3+DAG

CaM

mAChR,EGFR,insulinR,adrenergicαR , 组胺 R,5- 羟色胺 R, 多肽激素 R

[Ca2+]

PKC 等蛋白激酶，磷酸酯酶，核苷酸环化酶，离子通道蛋白，肌肉收缩蛋白等依赖Ca2+ /CaM 的蛋白。

Ca2+ /CaM

PKC*

使各种受体，膜蛋白，收缩蛋白，细胞骨架蛋白，核蛋白和酶类的丝氨酸或苏氨酸残基磷酸化，从而影响细胞代谢、生长和分化。

AA GC [cGMP]

多种酶及依赖 cGMP 的蛋白激酶。

激活多种酶和依赖 cGMP 的蛋白激酶而发挥生理作用。

激活蛋白激酶活性，自身与tyrosine残基磷酸化 ,促进 cell 生长和分化。

二、海马趾二、海马趾 CA1CA1 神经元区室化模型神经元区室化模型中的中的 1515 个信号途径个信号途径

A:EGF,SOSB:GEF,RasC:cAMP,AC1,AC2D:GE: AA, PLA2 F: PLC, PLC G: DAG, IP3 H: MAPK Cascade I: CaMKII J: PKA K: PKC L: Ca, IP3 M: CaM N: CaN O: PP1

Reaction A:A:EGFEGF,SOS,SOS

Reaction B:GEF,Ras

Reaction C:C:cAMP,AC1,AC2cAMP,AC1,AC2

Reaction D:G

Reaction E: AA, PLA2

Reactions F,G: PLCPLC, PLC, PLC, DAG, IP3

Reaction H: MAPK Cascade

The various phosphorylation states of CaMKII have different enzyme kinetics, and each of these were explicitly modeled. For simplicity the autophosphorylation steps are represented by a single enzyme arrow in this figure, with CaMKII_a as the combined activity of the various phosphorylation states. The individual kinetic terms used in the model are indicated by the multiple rate references on the arrows.

Reaction I: CaMKII

Reaction J: PKA

Reaction K: Reaction K: PKCPKC

Reaction L: Ca, IP3

Reaction M: CaM

Reaction N: CaN

Reaction O: PP1

三三、、 establishing the individual pathwestablishing the individual pathwaysays

1. steps 1. steps1. Set up model activation of single component.

2.generate the model for an individual signaling pathway.

3. Obtain a good empirical model which fit the experimental data.

4.examine experimentally defined combination of 2 or

3 such individual signaling pathways.

5.test these combined models.

2. Materials and methord2. Materials and methord

(1). Hippocampal CA1 neuron(in GENSIS),

(2).NMDAR[on dendritic spine( 树突棘 ) on the model]

(3).Synaptic input(3 tetanic bursts at 100HZ,1s each) →LTP

→Ca2+ waveforms

3. Computation 3. Computation formulationformulation

Genesis formulation:         S + E <--k2---k1--> SE ---k3---> P + E Vmax = max velocity = k3. Substrate is saturating, so all of E is in SE form. So Vmax.[Etot] = [SE].k3 == [Etot].k3

Km = (k3 + k2)/k1 k2 = k3 * 4 Kd=Kb/Kf

If [A]*[Bhalf]*Kf=[Chalf=Bhalf]*Kb

then [A]=Kb/Kf=Kd

Ka=Kf/Kb=1/Kd

4.4.verificationverification (i). Model simple kinetic schemes that could be calculated analytically, compare simulated results with analytical r

esults. (ii). Use the law of mass conservation and microscopic reversibility principles( 微观可逆

性原理 ) →test accuracy in complex reaction schemes. (iii). Run the same model at different time steps, compare the resulting simulated values.

5. Protein Kinase C modeling examp5. Protein Kinase C modeling examplele

Simulation parameters: Simulation parameters: PKCPKC

Reaction K: PKC  References

Figure 

Reac # kf kb 

K  1 1 50

K  2 2E-10  0.1

K  3 1.2705 3.5026

K  4 0.000000002 0.1

K  5 1 0.1

K  6 2 0.2

K  7 0.000001 0.5

K  8 1.3333E-08 8.6348

K  9 0.000000001 0.1

K  10 0.00000003 2

ReferencesReferences

Concs K: PKC References

Figure  Name  Conc

K  PKC_inactive  1

1.    Review: Y. Nishizuka, Nature 334, 661 (1988)

2.    J. D. Schaechter and L. I. Benowitz, J. Neurosci.13, 4361 (1993)

3.    T. Shinomura, Y. Asaoka, M. Oka, K. Yoshida, Y. Nishizuka, Proc. Natl. Acad. Sci. U.S.A. 88, 5149 (1991)

U. Kikkawa, Y. Takai, R. Minakuchi, S. Inohara, Y. Nishizuka, J. Biol. Chem. 257, 13341 (1982).

A. Block diagram of activation for A. Block diagram of activation for PKC pathway by Ca2+, AA and DAG. PKC pathway by Ca2+, AA and DAG.

built up simulations iteratively: First: matched AA activation of

PKC at zero Ca. Then: matched activation of PKC

with Ca at zero AA, Third: matched the curves in B w

ith 1 uM Ca and varying AA. Four: test the match for C, with v

arying Ca and 50 uM AA. Last:incorporated DAG interacti

ons into the model.

B: Activation of PKC by AA, with B: Activation of PKC by AA, with (triangles) or without (squares) 1 mM (triangles) or without (squares) 1 mM

Ca2+. Ca2+.

Open symbols and dashed lines represent simulations, solid symbols and solid lines are experimental data. Shows:Ca2+ is necessary for the activation of PKC.

•experimental concentration-effect curves from two main sources: •J. D. Schaechter and L. I. Benowitz, J. Neurosci. 13, 4361 (1993);

•T. Shinomura, Y. Asaoka, M. Oka, K. Yoshida, Y. Nishizuka, Proc. Natl. Acad. Sci. U.S.A. 88, 5149 (1991)

C: Activation of PKC by Ca2+, with C: Activation of PKC by Ca2+, with (triangles) or without (squares) 50 (triangles) or without (squares) 50

mM AA. mM AA.

The curve in the presence of 50 mM AA (triangles) was predicted from the parameters obtained by matching the curves in B and the curve without AA (squares) in C, without further adjustment.

D: Activation of PKC by DAG, with D: Activation of PKC by DAG, with (triangles) or without (squares) 50 (triangles) or without (squares) 50

mM AA. mM AA.

Both curves in D were obtained in the presence of 1 mM Ca2+. Due to different methods for estimating DAG concentrations the levels of DAG used in the model are scaled 15-fold up with respect to the experimental conditions from Shinomura et al.

四、四、 develope the network model in stagesdevelope the network model in stages First : model individual pathways Then: examin experimentally defined combinations of t

wo or three such individual pathways and test these combined models against published data.

Third: repeat this process using larger assemblies of pathways until the entire network model of interacting pathways was

formed. Pathways were linked by two kinds of interactions: (i) Second messengers such as AA and DAG, produced b

y one pathway were used as inputs to other pathways. (ii) Enzymes whose activation was regulated by one path

way were coupled to substrates belonging to other pathways.

11、、 oneone Signaling pathways exampleSSignaling pathways exampleS(1).(1).EGF’s stimulation of MAPK1,2EGF’s stimulation of MAPK1,2

Fig. 2. EGF receptor signaling pathways.

(A). Block diagram of signaling pathways. Rectangles represent en

zymes, and circles represent messenger

molecules. This model used modules shown in Fig. 1, reaction A(EGF), B(Ca2+/CaM), E(AA,PLA2), H(PKC),F(PLCγ,DAG,IP3), H(MAPK ascade), K(PKC), I(CaMKII), L(Ca,IP3).

Fig.2B Fig.2B the time course of the time course of activation of MAPK by EGFactivation of MAPK by EGF

(B) Predicted (open triangle) and experimental (filled triangles) time course of response of MAPK to a steady EGF stimulus of 100 nM.

the y axis represents fractional activation.

The fall in the MAPK activity after the initial stimulation is due to a combination of EGF receptor internalization and MAPK phosphorylation

and inactivation of SoS.

11、、 oneone Signaling pathways exampleSSignaling pathways exampleS (2).(2). Activation of Activation of PLCγPLCγ by Ca2+by Ca2+ in the presence (triin the presence (tri

angles) or absence (squares) of EGF.angles) or absence (squares) of EGF.

(C) Concentration-effect curves.

Dashed lines are model data, and solid lines are experimental data. The y axis represents activation.

Three stimulus conditions: 10 min at 5 nM EGF (short bar, ci

rcles), 100 min at 2 nM EGF (long bar,s

quares), 100 min at 5 nM EGF (long bar, t

riangles). Only the third condition succee

ds in causing activation of the feedback loop. Why?

22 、、 Two connected pathwaysTwo connected pathways(1). Activation of the fractional feedback loop by (1). Activation of the fractional feedback loop by

EGF receptor : EGF receptor : (D)(D) Activation of feedback loop Activation of feedback loop by EGF.by EGF.

B (basal), T (threshold), and A(active).

Point A represents high activity for

both PKC and MAPK, whereas point B represents low activity. Both of these points represent distinct steady-state levels. Such a system with two distinct steady states is a bistable system. The bifurcation point T is important because it defines threshold stimulation.

2.(1) Activation of the fractional feedback loop2.(1) Activation of the fractional feedback loop by EGF receptor : by EGF receptor : (E) Bistability plot for feedback loop (E) Bistability plot for feedback loop

Bistability is present over a range comparable to th

e experimental uncertainty, indicating that the

phenomenon is robust. (Horizontal stripes: experimental uncert

ainty in concentration; diagonal stripes, simulated bistability range for c

oncentrations.) MAPK has a particularly large uncertaint

y in concentration range because of large differences in tissue distributions.

2.(1) Activation of the fractional feedback loop by 2.(1) Activation of the fractional feedback loop by EGF receptor :EGF receptor : (F) estimated experimetal uncertainty in E (F) estimated experimetal uncertainty in E parameters parameters

initially activating: a suprathreshold stimulus, and then one of three inhibitory inputs was applied: 10 min at 8 nM (short bar, circles), 20 min at 4 nM (long bar, squares), and 20 min at 8 nM (long bar, triangles.).

Only the third condition is able to inactivate the feedback loop.

The rebound in the first two cases is due to two factors: the persistence of AA due to a relatively slow time course of removal and the time course of

dephosphorylation of activated kinases in the MAPK cascade.

2.(1)Activation of the fractional feedback loop 2.(1)Activation of the fractional feedback loop by EGF receptor: by EGF receptor: (G) Inactivation of feedback loop (G) Inactivation of feedback loop

by MKP-1. by MKP-1.

2.(1) Activation of the fractional feedback loop 2.(1) Activation of the fractional feedback loop by EGF receptor: by EGF receptor: (H) Thresholds for inactivation of (H) Thresholds for inactivation of

feedback loop. feedback loop.

MKP was applied for varying times

and amounts. At high MKP levels, inactivation occurs more quickly, but there is a minimum threshold of nearly 10 min. Conversely, when MKP is applied for very long times, at least 2 nM MKP is required to inactivate the feedback loop.

Some conclusions for EGFR Some conclusions for EGFR signaling pathwayssignaling pathways

(1).100 nM EGF can activate MAPK. (2).Ca2+ activate PLCγ,which has more high activity under

0.1uM EGF. (3).100 min at 5 nM EGF activated the feedback loop. (4).Activation of MAPK and PKC by EGF has a threshold(poi

nt T). (5).The phenomenon is robust as comparing with Sim and

Expt on Km and Conc. (6).MPK-1(20 min,8nM) can inactivate the feedback loop. (7).High MKP level ,necessary for nearly 10 min. Long time application of MKP requires at least 2nM MKP.

About bistable systemAbout bistable system (1). Such a bistable system has the potential to store information.

Signaling events [the initial stimulation (amplitude and duration)] that push the levels of either activated PKC or activated MAPK past the intersection point T will cause the system to flip from one state to another. This analysis can be generalized to any combination of pathways in a feedback loop.

(2). The emergent properties of this feedback system define not only the amplitude and duration of the extracellular signal required to activate the system but also the magnitude and duration of processes such as phosphatase action required to deactivate the system.

(3). These properties make a feedback system, once activated, capable of delivering a constant output in a manner unaffected by small fluctuations caused by activating or deactivating events.

This capability to deliver a stimulus-triggered constant output signal even after the stimulus is withdrawn may have numerous biological consequences.

2.(2) 2.(2) CaMKII ((Ca2+/calmodulin-dependent protein kinase II ) functions in LTP of synaptic responses in the hippocampus.

The cAMP pathway gates CaMKII signaling through the regulation

of protein phosphatases. NMDAR and Ca influx are modele

d in a compartmental model of a CA1 neuron with a series of three tetanic

stimuli at 100 Hz, lasting 1 s each, separated by 10 min. This model used modules shown in Fig. 1, C,

I, J, M, N, and O(B to E). Open squares: full model; Filled triangles:cAMP(fixed at res

ting concentrations → prevent PKA activity ↑).

2.(2) (B) Activation of CaMKII.

The initial increase in intracellular Ca2+ caused an activation of CaMKII, AC1,and CaN through CaM binding and of PKA through increase in cAMP produced through activation of AC1-AC8.

cAMP ↑→ PKA activation→ PP1↓ → CaMKII ↑

The presence of a cAMP-operated gate leads to a large increase in the amplitude of the CaMKII response and prolongation of its activity. Nevertheless, it does not lead to a persistent activation of CaMKII.

2.(2) (C) Activation of PKA.

AC1-AC8 binding to Ca/CaM

↓

producing cAMP.

↓

PKA activity rises sharply

Otherwise,its activity: don’t rise

2.(2) (D) Activity of PP1.

[ Ca/CaM ↑ + cAMP(fixed)] → CaN activation ↑ →smalltransients

cAMP fixed → PKA activation↓

cAMP unfixed → PKAactivation↑ → PP1 activity↓

Active PP1 →dephosphorylate CaMKII(Thr286) →CaMKII ↓.

2.(2) (E) CaN (PP2B) activation byCa/CaM elevation.

The full model–cAMP fixed curves overlap almost erfectly.

↓ CaN uninfluenced by cAMP

四、四、 3. A model for interaction between 4 3. A model for interaction between 4 signaling pathways: form a networksignaling pathways: form a network( PKC 、 MAPK pathways + CaMKII 、 cAMP pathways )

Glu(+postsynaptic depolarization) →Ca2+ influx through NMDAR→ [Ca2+]↑ →postsynaptic PK(CaMKII,PKC,PKA,MAPK) ↑

四、四、 3. 3. Combined model with feedback loop, synaptic input, and CaMKII activity and Re

gulation.

cPLA2(held activity) →less AA→ FBOFF

MKP(timer of FB in early LTP of synapse)→FBOFF

cPLA2(activity↑) → AA↑→ FBON

四、四、 3.3. Activity profile of major en Activity profile of major enzymes in pathway zymes in pathway

Fig B to G

▲ ： full model(FBON)

□ ： feedback

blocked(FBOFF) (AA fixed at resting concentrations)FBON : present feedbackFBOFF : absence feedback

四、四、 3.3. (B) Activity profile of PKC (B) Activity profile of PKC

FBOFF : →PKC↓FBON →larger successive

spikes (initial spike+FBON )

→DAG+AA→PKC↑↑

四、四、 3. 3. (C)(C)Activity profile of MAPKActivity profile of MAPK

FBON →MAPK turn on FBOFF →MAPK turn off (initial spike+FBON ) →DAG+AA →PKC↑↑→MAPK↑(steady)

四、四、 3. 3. (D) Activity profile of PK(D) Activity profile of PKA. A.

Ca2+ inflow→AC1,8↑→PKA↑ Ca2+ → identical PKA ↑

FBON : PKC→ AC2↑→ cAMP↑→PKA ↑↑

sustained PKC→sustained PKA activit

y

Several emergent properties of networkSeveral emergent properties of network

(1).Extended signal duration.(2).Activation of feedback loop.(3).Definition of threshold stimulation for biological effects.(4).Multiple signal outputs.

四、四、 3. 3. (E) Activity profile of Ca(E) Activity profile of CaMKII.MKII.

Ca2+ inflow→CaMKII↑Ca2+ → identical CaMKII ↑FBON : PKC→ AC2↑→ cAMP↑→

PKA baseline ↑(twofold) PKA↑→PP1↓→CaMKII↑{ [dephosphorylate CaMKII(Thr286)] →Ca

MKII autophosphorylation↓}

四、四、 3. 3. (F) Activity profile of PP1. (F) Activity profile of PP1.

Ca2+→PP1↓ (overlap:FBON,FBOF

F)

FBON→PKA↑(sustained)→PP1↓→ PP1 (sustained)

CaMKII↑

四、四、 (G) Activity profile of CaN (PP2B).(G) Activity profile of CaN (PP2B).

FBOFF or FBON ：

CaN is naffected

→its’ effect on PP1 limited to the duration of the initial signals.

On NetworkOn Network

(1).Network→sustained PK activity(after initial stimulus)

correspond to early LTP (2). MKP induction→ Other transcriptional events be initiated → gene products→reach the active synapse with MKP (3).FBloop may gate incorporation of these products into the cytoskeleton. act as bridge between extremly short stimuli and longer

term synaptic change and also between local synaptic events and cell wide production of synaptic proteins.

On the modelOn the model

(1).Such a model facilitates “thought experiments” on involved signaling pathways to predict hierarchies. (2). The model also provides a framework for understanding biological consequences of multiple modes of stimulating a single component. (3).Such models provide insights into the possible roles of isoform diversity. [CaM→AC1,PKC→AC2(connection,sustain CaMKII activation)]

On the modelOn the model (4).Limitations: The biochemical parameters are not unaltered

with the cell. Given these uncertainties, models such as these should not be considered as definitive descriptions of networks within the cell, but rather as one approach that allows us to understand the capabilities of complex systems and devise experiments to test these capabilities.

(5). Conclusion: simple biochemical reactions can, with

appropriate coupling, be used to store information. Thus, reactions within signaling pathways may constitute one locus for the biochemical basis for learning and memory.