Autocrine loops with positive feedback enable context-dependent
cell signaling
S. Y.
Shvartsman1,
M. P.
Hagan2,
A.
Yacoub2,
P.
Dent2,
H. S.
Wiley3, and
D. A.
Lauffenburger4
1 Department of Chemical Engineering and Lewis Sigler
Institute for Integrative Genomics, Princeton University, Princeton,
New Jersey 08544; 2 Radiation Oncology, Virginia Commonwealth
University/Medical College of Virginia, Richmond, Virginia 23298-0058;
3 Fundamental Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99352; and 4 Division of
Bioengineering and Environmental Health, Department of Chemical
Engineering, and Center for Cancer Research, Massachusetts Institute of
Technology, Cambridge, Massachusetts 02139
 |
ABSTRACT |
We describe a mechanism for
context-dependent cell signaling mediated by autocrine loops with
positive feedback. We demonstrate that the composition of the
extracellular medium can critically influence the intracellular
signaling dynamics induced by extracellular stimuli. Specifically, in
the epidermal growth factor receptor (EGFR) system, amplitude and
duration of mitogen-activated protein kinase (MAPK) activation are
modulated by the positive-feedback loop formed by the EGFR, the
Ras-MAPK signaling pathway, and a ligand-releasing protease. The
signaling response to a transient input is short-lived when most of the
released ligand is lost to the cellular microenvironment by diffusion
and/or interaction with an extracellular ligand-binding component. In
contrast, the response is prolonged or persistent in a cell that is
efficient in recapturing the endogenous ligand. To study functional
capabilities of autocrine loops, we have developed a mathematical model
that accounts for ligand release, transport, binding, and intracellular signaling. We find that context-dependent signaling arises as a result
of dynamic interaction between the parts of an autocrine loop. Using
the model, we can directly interpret experimental observations on
context-dependent responses of autocrine cells to ionizing radiation.
In human carcinoma cells, MAPK signaling patterns induced by a short
pulse of ionizing radiation can be transient or sustained, depending on
cell type and composition of the extracellular medium. On the basis of
our model, we propose that autocrine loops in this, and potentially
other, growth factor and cytokine systems may serve as modules for
context-dependent cell signaling.
mathematical model; epidermal growth factor receptor; ligand
shedding; ionizing radiation; mitogen-activated protein kinase; cross-activation
 |
INTRODUCTION |
WHAT MECHANISMS
ENABLE context-dependent cell signaling? That is, how can
molecular interactions in the cell's microenvironment combine with
those within the cell to modulate response to a particular stimulus? In
part, the specificity of a cell's response is built into the molecular
makeup of signal transduction networks (37). Receptor
binding by soluble ligands, such as growth factors, is translated into
cellular responses by signaling pathways, which rely for their
activation on a number of adaptors and enzymes. In this way, activation
of identical receptors in two cells with different sets of adaptors and
enzymes stimulates different signaling pathways, resulting in two
different responses. The induced responses may differ in the nature of
the stimulated signaling pathways and/or in the dynamic properties of
their activation (48). The extracellular context, e.g.,
composition of the extracellular matrix (ECM), can affect the signaling
response by creating the intracellular background that dynamically
interacts with signaling induced by exogenous signals, such as soluble
growth factors (31, 56). Previously proposed mechanisms of
context-dependent "processing" of external stimuli focus on this
kind of intracellular synergy of multiple inputs (63, 66).
We suggest the existence of a complementary mechanism that enables
cells to process external stimuli both intra- and
extracellularly. This mechanism may be operative in autocrine loops
formed by growth factors and their receptors.
Autocrine loops are established when soluble factors secreted by cells
bind to and stimulate receptors on their own surfaces (64). Commonly, secretion of autocrine ligands is tightly
regulated. In the epidermal growth factor (EGF) receptor (EGFR) system,
ligands such as transforming growth factor-
(TGF-
) are
synthesized in the form of membrane-bound precursors
(49). Thereafter, ligand-releasing proteases, also
known as "sheddases" [e.g., tumor necrosis factor-
(TNF-
)-converting enzyme (TACE)], process the membrane-associated precursors into their active soluble forms (49, 54, 70). The characteristically high levels of cognate receptors expressed by
autocrine cells make them very efficient in recapturing endogenous ligands (15, 19, 44, 53, 62, 72). Along with the
mechanisms for ligand release and capture, autocrine systems are
equipped with numerous mechanisms for cross activation (10, 50,
55, 75). Response to a primary stimulus, such as an exogenous
growth factor, a component of the ECM, or ionizing radiation, can lead to ligand release and recapture and stimulation of intracellular signaling (16, 30, 58). In nonautocrine cells, soluble
growth factors were shown to activate sheddases through the
Ras-mitogen-activated protein kinase (MAPK) pathway (23,
26), one of the central pathways activated by receptor tyrosine
kinases such as EGFR. This suggests that, in an autocrine EGFR system,
a ligand, the receptor, the sheddase, and the intracellular
signaling network can form a positive-feedback loop (52).
The spatially restricted and recurrent nature of autocrine loops makes
their experimental analysis very challenging. As an aid to the
experimental studies of autocrine loops in the EGFR system, we
integrate the biochemical and biophysical knowledge about EGFR
signaling into a mechanistic mathematical model. The model accounts for
the dynamic interaction between ligand release, diffusion in the
extracellular medium, receptor binding, and Ras-MAPK pathway
activation. We find that the positive feedback endows autocrine cells
with nontrivial signal-processing capabilities. We propose a mechanism
for modulating the amplitude and duration of Ras-MAPK activation
induced by external stimuli; these characteristics of MAPK signaling
are critical for a number of cellular responses (48, 56).
Using our model, we demonstrate that this mechanism provides a
substrate for context-dependent cell signaling. Furthermore, our
analysis enables the direct interpretation of the context-dependent radiation responses of autocrine cells. In human carcinoma cells, the
EGFR-TGF-
autocrine loop defines cellular response to radiation (survival or death) by regulating the duration and amplitude of the
Ras-MAPK activation (16).
This report is organized as follows. In COMPUTATIONAL MODEL FOR
AUTOCRINE CELL-SIGNALING LOOP, we describe the modules for ligand
release, binding and transport, and intracellular signaling. Then we
describe how these individual modules are integrated in a model of an
autocrine loop. In RESULTS, we focus exclusively on the
processes defining the operation of autocrine systems. Equations and
details of their analysis can be found in APPENDIX A. After
identifying the key parameters governing the operation of individual
modules, we proceed to analysis of the integrated system; we focus on
the effects of the feedback loop formed by the receptor, the signaling
cascade, and the system for ligand release. This sets up the framework
for the analysis of the experiments reporting context-dependent
radiation responses of autocrine cells in the DISCUSSION.
These experiments support our modeling predictions, demonstrating that
the composition of the extracellular medium, in this particular case
the presence or absence of ligand-binding antibodies, can critically
influence the cell response.
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COMPUTATIONAL MODEL FOR AUTOCRINE CELL-SIGNALING LOOP |
The biochemical events that we aim to model are summarized in Fig.
1A. Initial activation of a
growth factor receptor stimulates downstream signaling pathways (e.g.,
the Ras-MAPK pathway) that, by unknown mechanisms (23,
26), increase the activity of sheddases and lead to release of
soluble growth factor(s). Localized capture of endogenous ligands
increases the number of occupied receptors and initiates a
secondary stimulation of the receptor and downstream signaling
pathways. The positive-feedback
loop
receptor/ signaling/sheddase/ligand/receptor
governs the
duration and amplitude of the signaling transient in response to
external perturbation.

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Fig. 1.
Schematic illustration of ligand-induced autocrine growth
factor release mediated by receptor-activated intracellular signaling.
In the epidermal growth factor (EGF) receptor (EGFR) system, ligands of
the EGFR are shed from the cell surface by surface metalloproteases
activated (among other things) via the extracellular signal-regulated
kinase (ERK)-mitogen-activated protein kinase (MAPK) cascade; the same
cascade is activated when autocrine ligands bind to receptors on the
surface of the releasing cell. TGF- , transforming growth factor- ;
TACE, tumor necrosis factor- -converting enzyme.
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Model of Ligand Binding and Transport
Equilibrium and kinetic parameters for binding and trafficking in
growth factor receptor systems, including the ErbB1-4 system, have
been well documented (18, 43, 60); furthermore,
experiments reporting diffusivities of growth factors in tissues have
recently started to appear (21). Our model of binding and
transport (Fig. 2A) accounts
for the concentration of endogenous ligand (L) and the surface
densities of free and occupied surface receptors (Rs and Cs, respectively). We consider a single
autocrine cell; the cell is modeled as a hemisphere with radius
rcell, placed on an infinite plane (this mimics
an autocrine cell attached to a substrate). Ligand secretion, at rate
Q, is uniformly distributed over the cell surface area
(A). Ligand released in the extracellular medium diffuses
with constant diffusivity (D) and reversibly binds to cell
surface receptors with binding constant Kd.
Newly synthesized receptors arrive at the cell surface with rate
S; they are constitutively internalized with rate constant
kc and are converted to surface complexes with
rate constant kon. Surface complexes dissociate with rate constant koff = konKd and are removed
from the cell surface by endocytosis with rate constant
ke. Endogenous ligand can reversibly bind to the
extracellular "decoys" (molecules that mimic soluble antiligand
antibodies) (12, 25, 44). B denotes the concentration of
decoys; their interaction with ligand is characterized by the forward
and reverse rate constants k
and
k
, respectively. The extracellular concentration of ligand bound to decoys is denoted by
LB.

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Fig. 2.
A: model of binding
and transport: ligand release, extracellular diffusion, binding to
surface receptors, endocytic internalization, and reversible binding to
components of the extracellular medium (see APPENDIX A for
detailed description of model parameters). B: fraction of
endogenous ligand recaptured by an autocrine cell
(Pcap). Pcap is computed
as a function of the total number of cell surface receptors
(RT = S/kc) for several values of
the extracellular ligand diffusivity (D). Values for the
parameters are as follows: kon = 108 M 1 · min 1,
D = 10 6, 10 7,
10 8, and 10 9 cm2/s, and cell
radius (rcell) = 5 × 10 4 cm. C: statistical properties of the
random paths followed by those endogenous ligands that have been
recaptured by the cell. Cumulative distribution function of the maximal
distance (dmax) to which a recaptured secreted
ligand had diffused. Parameters are as follows: RT = 105/cell; all other parameters as in A. D: dynamics of the number of ligand receptor complexes
induced by a step change in the rate of ligand secretion from 0 to
Q
mol · cell 1 · min 1.
Parameters are as follows: RT = 105/cell,
rcell = 5 × 10 4 cm,
kon = 108
M 1 · min 1,
Kd = 1 nM, ke = 0.1 min 1, kc = 0.02 min 1, and extracellular ligand diffusivity
(D) = 10 7 cm2/s. For
transients, Q = 100, 1,000, 3,000, and 5,000 mol · cell 1 · min 1.
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Model of Protease Activation
Ligand-releasing proteases have only recently been added to
the "parts list" of the autocrine loops (7, 19, 54,
70); so far, mathematical modeling of protease activity has not
been attempted. The main facts in the rapidly accumulating information on the molecular nature, mechanism of action, and dynamics of sheddases
can be summarized as follows. Ligand-releasing proteases are not
specific and are capable of processing a wide range of surface
molecules (7); members of the EGFR ligand family are just
one example (70). The rate of ectodomain cleavage depends on the juxtamembrane domain of the precursor molecule (20,
51). Sheddases are under the control of multiple intracellular
signaling pathways; secretion is composed of a slow process mediating
the constitutive release and faster inducible process (23,
26). The inducible activation can occur as fast as 10-15
min after application of the stimulus (26). The activation
of protease is followed by its removal from the surface, most likely
via the endocytic pathway (17). In our model (Fig.
3A), we assume that newly
synthesized protease is inserted in the membrane with the rate
SP and constitutively internalized with the rate
constant k
. The protease is converted to
the "active" form with the rate constant
k
; the active form is internalized via
endocytosis with the rate constant k
. The
model is formulated in terms of variables P and Pa,
denoting the amount of protease in the inactive and active states (see APPENDIX A).

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Fig. 3.
A: main processes in the model of protease dynamics:
synthesis, constitutive degradation, activation, and internalization of
the active form (see APPENDIX A for detailed description of
the model and parameters). B: amount of protease in the
active form computed as a function of activation rate constant
(ka), normalized by the total amount of the
protease in the absence of activation (ka = 0). C and D: dynamics of the total amount of
protease and the fraction of the protease in the active form induced by
the steplike change in the rate of activation. Parameters are as
follows: k = 0.1 min 1,
k = 0.02 min 1, and
ka = 0.1, 0.2, 0.5, and 10 min 1.
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Model of Signaling Cascade
Previously introduced dynamic models of the MAPK cascade account
for the dynamic changes of enzymes at the three levels of the cascade
and the negative feedback exerted by the active form of the last enzyme
on the input to the network (34, 39, 47). For our
purposes, we need the simplest possible model that would be consistent
with the previous descriptions and evolve on the time scales observed
in time-resolved assays of MAPK activity (2). Our model of
the MAPK cascade (Fig. 3A) consists of three enzymes,
E1, E2, and E3 (Fig.
4A). The three stages in the
cascade model the sequential activation of Raf, MAPK kinase (MEK), and MAPK (47). "Kinases" at each level of the cascade can
be in one of the two forms, "base" and "active," which are
interconverted by two distinct enzymes. Active forms of E1
and E2 catalyze forward reactions of the following stages.
In the model, the "phosphatases" catalyzing reactions 2, 4, and 6 are constitutively active. The maximal rate of
reaction 1 depends on the magnitude of the input to the
cascade (I). The phosphorylated form of E3
decreases the input to the cascade reaction; this reflects the fact
that formation of the signaling complex stimulating the input to the
cascade can be negatively regulated by the active form of extracellular signal-regulated kinase (ERK) type 2 (ERK2) MAPK (47).
Other negative feedbacks, such as receptor-mediated endocytosis and covalent modification decreasing receptor tyrosine kinase activity (e.g., via protein kinase C), operate upstream of the MAPK cascade, further serving to provide the network with transient inputs (36, 50, 57).

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Fig. 4.
A: reaction network in the model of the MAPK cascade.
The rate ( i) for each of the 6 reactions of
the cascade is given by the following equation:
i = Vis/(Ki + s), where s is
the concentration of the particular substrate and
Vi and Ki are Michaelis
constants. B: steady input-output behavior computed for
several values for gain of the negative feedback. Ultrasensitive
behavior is turned into a more graded response as strength of the
negative feedback is increased. Parameters are as follows:
K1-6 = 0.2, V1 = 0.5, V2 = 0.15, V3 = 0.15, V4 = 0.15, V5 = 0.25, and V6 = 0.05; for the 3 curves,
Ginh = 0, 1, 2, and 4, respectively.
Ginh = G4 in the
integrated model.
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Coupled System
The integrated model is assembled from the individual blocks on
the basis of the information available about their coupling (Fig.
5). Ligand-mediated receptor
phosphorylation and signaling complex assembly, linking the act of
ligand receptor binding to the MAPK cascade, were recently modeled by a
sequence of reversible reactions (6, 8, 41) for receptor
phosphorylation and signaling complex assembly; detailed kinetic
modeling of these events is beyond the scope of this work. Activation
of ligand-releasing proteases mediated by the MAPK has been
conclusively demonstrated (23, 26, 29). The first kinetic
analysis of the proteolytic release of the EGF-family ligands has been
recently reported; it has been shown that the rate of ligand release
obeys pseudo-first-order kinetics with respect to the amount of surface
protease (20). In the absence of more detailed
information, linear gains (in other words, linear proportionality of
cause and effect) provide the simplest possible descriptions of the
(nonlinear) interconnections between the modules comprising the
autocrine loop. First, the endogenous input to the signaling cascade is
proportional to the number of surface complexes. Second, the rate
constant of protease activation is proportional to the amount of the
active form of the last enzyme in the signaling cascade. Finally, the
rate of ligand secretion is proportional to the fraction of the
protease in the active form. The system of equations describing the
integrated model is presented in APPENDIX A.

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Fig. 5.
Block diagram representation of the mathematical model of
biochemical events shown in Fig. 1. The model incorporates dynamic
interactions between the 3 modules for ligand release, binding and
transport, and signaling.
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RESULTS |
Analysis of the Model
Transport and binding.
The rate of diffusion of endogenous ligand away from the cell surface
is defined as the number of molecules released per unit area in unit of
time; it is termed the "flux" (F). F depends
on the amount of secreted ligand and the ability of the autocrine cell
to remove the endogenous ligand from the extracellular medium by
binding and receptor-mediated endocytosis. The ratio of F to the rate of ligand secretion quantifies the fraction of autocrine ligands escaping from the releasing cell. The remaining fraction of
secreted ligand defines the (steady) probability of ligand capture:
Pcap
1
F/Q. Within our
model, Pcap is related to the binding/transport
parameters by the following expression
|
(1)
|
where
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(2)
|
The first of these dimensionless groups (Au) is the autocrine
number, equal to the ratio of the ligand concentration at the cell
surface in the absence of surface receptors to the dissociation constant of a ligand-receptor pair. The second, Da, is the
Damköhler number, which quantifies the relative importance of
ligand binding and transport (14). The third group,
,
compares the rate at which free receptors are removed by constitutive
internalization with the rate at which they are freed by dissociating
complexes. Finally, for a bound ligand,
is the probability of being
internalized. Detailed analysis of Eq. 1 is reported
elsewhere (62).
Note that when calculating the recaptured fraction of autocrine ligands
using Eq. 1, we discount those that were bound by the cell,
dissociated, and then "lost" to the extracellular medium. However,
even those ligands that had been on the surface only transiently may
contribute to receptor activation. By setting the rate of ligand
dissociation to zero (Eq. 1), we consider only the processes
that occur before the first binding event. In this way, we obtain a
fraction of endogenous ligands that were bound by the cell at least
once
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(3)
|
This limit of Eq. 1 is the classic expression obtained
by Berg and Purcell (5, 61). High capturing efficiency is
promoted by the high number of cognate receptors, fast forward binding, and low extracellular ligand diffusivity. Autocrine cells equipped with
the EGFR autocrine loops often express more surface receptors (105-106) (19, 53) than
nonautocrine-regulated cells (103-104
receptors), which may utilize endocrine mechanisms for their growth and
survival. Furthermore, interaction of secreted growth factors with the
components of the ECM has the potential to make their effective
diffusivity after cleavage and release relatively low
(21). We conclude that, at least in the EGFR system,
autocrine cells have the potential to recover a significant fraction of endogenous ligand. Figure 2B presents the recaptured
fraction of autocrine ligands computed as a function of the total
number of surface receptors (in the model, RT = S/Akc, where RT is the total number of surface receptors) and the magnitude of extracellular ligand diffusivity.
The extent to which autocrine cells interact with their environment may
be estimated by analyzing the statistical properties of the random
trajectories followed by autocrine ligands. Let dmax be the maximal distance to which a particle
released on the surface of a hemisphere had diffused before being bound
by one of the receptors that are uniformly distributed over the
surface. Then, dmax defines a random variable
with the distribution function dependent on the density of surface
receptors (RT), the extracellular ligand diffusivity, and
the forward binding constant. By characterizing the statistical
properties of the maximal distances traveled by recaptured autocrine
ligands, the distribution function for rmax characterizes
the spatial range of autocrine signals. Using the tools for analyzing
the extreme properties of Brownian paths (68), we arrive
at the following expression for the cumulative distribution function of
dmax [we consider only those ligands that have
been recaptured by the releasing cell; hence, the distribution function (Eq. 4) asymptotes to 1]
|
(4)
|
where P{dmax d}
denotes the probability that the random variable
dmax is less than d.
In Fig. 2C, this distribution function is plotted for the
binding parameters characteristic of the EGFR ligand-receptor
interaction, 105 cell surface receptors (within the range
reported for autocrine EGFR systems, i.e.,
104-106), and several values of
extracellular ligand diffusivity. Computations based on Eq. 4 indicate that, before their recapture, autocrine ligands sample
a very small volume; a significant fraction of autocrine ligands is
recaptured after traveling only a few micrometers from the cell
surface. This spatially restricted character of autocrine loops may
account for the initial difficulties in the experimental detection of
autocrine ligands in the extracellular medium [13; see also discussion
of other experiments in a recent review of EGFR transactivation
(28)]. Because the main parameter defining the range of
autocrine loops is a Damköhler number (Eq. 2) that
depends on the combination of ligand diffusivity, receptor density, and
the forward binding constant, all these factors may contribute to
tuning the range of autocrine signals.
The results of simulations presented in Fig. 2D show that
steady levels of receptor occupancy may be attained within 20 min after
the steplike increase in the rate of ligand release. The maximal steady
number of surface ligand-receptor complexes predicted by our model is
kcRT/ke.
However, this number can be exceeded in the transient operation of
autocrine loops; as illustrated by one of the transients in Fig.
2D, the time course of the number of surface complexes need
not be monotonic. Indeed, it has been observed that the high levels of
receptor activation, inferred from the levels of receptor tyrosine
phosphorylation, can be attained very quickly after activation of the
ligand release system (28).
Protease dynamics.
The amount of surface protease in the active form is determined by the
balance between the processes for its synthesis, activation, and
degradation. Within our model, the total amount of activated protease
under steady-state conditions is given by the following expression
|
(5)
|
where µ and
depend on the rate constants for the processes
of protease constitutive degradation, activation, and degradation of
the active form: µ = k
/k
and
= k
/k
. In the absence of activation (
= 0), the total amount of surface
protease is SP/k
.
As the rate of protease activation increases, the amount of the active
form grows and approaches the asymptote defined by the balance of its
synthesis and the endocytosis of the active form:
SP/k
(Fig. 3B).
One can show that whenever the rate constant of constitutive
degradation is smaller than the rate constant for degradation of the
active form (µ < 1), the total amount of the protease will be a
decreasing function of the activation rate constant. Hence, activation
of the ligand-releasing protease will be accompanied by its
downregulation from the cell surface. Doedens and Black (17) recently demonstrated such stimulation-induced
downregulation of the surface metalloprotease for TACE. Because TACE
has been implicated in releasing the EGFR ligands (19,
54), this observation is directly related to the operation of
the EGFR autocrine loops. In these experiments, it was found that the
active form is removed from the surface via an endocytic pathway
(36). As the first approach to modeling this problem, we
take the rate constant of active protease degradation equal to the
endocytic rate constant for the surface ligand-receptor complexes.
The dynamic evolution of the active and inactive forms of the surface
protease after a steplike increase in the activation rate constant is
presented in Fig. 3, B and D. Although the
dynamics of the inactive fraction of the enzyme are always monotonic,
the evolution of the active form of the enzyme can exhibit a strong overshoot (i.e., exceed the value to which it eventually settles) before settling to the steady value predicted by Eq. 5. This
effect may be important for the operation of autocrine loops: under the conditions of the efficient ligand recapture (see Transport and Binding), large-amplitude overshoot in the activity of
ligand-releasing enzyme will be registered by the cell surface
receptors and relayed to the downstream signaling pathways.
Signaling.
Because our model for signaling through the MAPK cascade is closely
related to the previously reported models (6, 34, 39), we
provide only a brief summary of the intracellular signaling module of
the autocrine loop. The input-output behavior of a single stage is
critically dependent on the degree of saturation of the enzymes
catalyzing the forward and reverse
phosphorylation-dephosphorylation reactions (27).
When these enzymes operate close to saturation with respect to
their substrates, even a single stage can exhibit sigmoidal
input-output dependence. Several stages characterized by the relatively
graded input-output behaviors can form a cascade with a very steep
input-output response (34, 40). The steady input-output
behavior of our modeling cascade is shown in Fig. 4B. The
output is negligible below a certain threshold value; past that value,
the output increases to its maximal value within a very narrow range of
inputs (27, 34). In the cell, this network is under the
control of multiple loops that can attenuate or desensitize signaling
pathway activity (47). Incorporating the presence of
negative feedback in the model affects the input-output behavior:
transformation from essentially null to full activation of the last
element occurs, now, over a wider range of inputs (Fig. 4B).
Multiple stages of the signaling cascade separating its input and
output lead to an effective time delay in the dynamic input-output
response (not shown). Negative feedback coupled with kinetic time
lags can give rise to oscillatory behavior, i.e., periodic
oscillations, even for fixed values of external conditions. To distill
the dynamic effects mediated by positive-feedback loops (from ligand
binding to ligand secretion), we have chosen to work in the region of
parameters that does not support oscillatory behavior. Thus, for all
the simulations reported below, the input-output behavior of the
signaling module is stable.
Computational analysis of the integrated model.
We have analyzed the response of our model autocrine loop to pulselike
excitations of the signaling cascade (Fig.
6A). Inputs of short duration
can be delivered to the MAPK cascade by different means, e.g., by
integrins, one of the numerous growth factor receptor systems, or
ionizing radiation (10, 30, 47, 50, 75). In our model,
activation of the signaling cascade stimulates ligand release and
increases receptor occupancy. We have explored the conditions under
which this effect leads to a noticeable modulation of the primary
excitation. Under the "open-loop" conditions (low number of surface
receptors, inhibited receptor or signaling pathway activity), the
primary excitations lead to transient, low-amplitude elevation of the
activity of the last element of the signaling cascade (lower curves in
the transients in Fig. 6, B-D). This primary excitation
of the output simply reflects the pulse response of the intracellular
signaling cascade. As the number of cell surface receptors is
increased, this dynamic response is strongly modulated. Specifically, a
secondary excitation of large amplitude and duration appears in the
response (Fig. 6B). Simultaneously, the time scale of the
dynamic response is dramatically increased from 40 min to several
hours. The emergence of long-lasting secondary excitations is
critically dependent on the ability of an exogenous signal to cross
activate the ligand-releasing protease. See the change in the response
shape as a function of the coupling strength between the output of the
signaling cascade and the rate constant of protease activation (Fig.
6C). Hence, the ability of an autocrine cell to recapture
endogenous ligands released in response to exogenous stimuli provides a
flexible mechanism for tuning the duration of the intracellular
signaling response. Our computational analysis shows that, depending on
parameters of the integrated network, the amplitude of secondary
excitation can be lower and higher than that of the primary excitation
(Fig. 7B). The amplitude of the external perturbation has to be sufficiently large to induce the
long-lasting response with secondary excitations (Fig. 6D). In this way, the quantitative difference of the transient input, e.g.,
its input, may be translated into the qualitative differences of the
output, such as the number of maxima in the induced response.

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Fig. 6.
Autocrine loops can convert transient extracellular inputs to
prolonged excitations of the intracellular signaling pathway.
A: pulselike, 2-min-long, exogenous perturbation can induce
secondary excitations in activity of intracellular signaling that last
for several hours. B-D: outputs with secondary
excitations are promoted by the high number of cell surface receptors
(B) and the high activity of ligand releasing protease
(C). D: amplitude of exogenous stimulus has to be
sufficiently high to induce signaling dynamics with secondary
excitations. Parameters used in simulations B-D are as
follows: rcell = 5 × 10 4 cm, D = 10 6
cm2/s, koff = 0.1 min 1, kon = 108
M 1 · min 1,
ke = 0.1 min 1,
kc = 0.02 min 1,
k = 0.02 min 1,
k = 0.001 min 1,
k = 0.1 min 1,
G2 = 5 × 10 4,
G3 = 1, and G4 = 1. Michaelis constants of reactions at the 3 stages of the signaling
cascade are the same as in Fig. 4. For transients in B,
G1 = 0.3, number of cell surface receptors
in the absence of ligand (RT) = 5 × 104, and amplitude of the pulselike exogenous stimulus
(I0) = 1, 1.5, 2, and 3.5. For transients
in C, G1 = 0.3, I0 = 2.5, and RT = 3 × 104, 4 × 104, 4.5 × 104, and 5 × 104/cell. For transients in
D, I0 = 2.5, RT = 5 × 104, and G1 = 0, 0.1, 0.2, and 0.3.
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Fig. 7.
Memory effect mediated by
positive feedback in autocrine loops: transient exogenous input induces
sustained activation of the intracellular signaling pathway.
A: bistability as a basis of memory effects in autocrine
loops with positive feedback: steady levels of intracellular signaling
activity in a model autocrine loop were computed as a function of
(steady) exogenous stimulus. An autocrine loop can exist in one of the
two stable steady states. The coexisting steady states are
characterized by high and low levels of signaling, even when exogenous
input is set to zero. Solid lines, stable stationary states; dashed
lines, unstable stationary states. B: high levels of ligand
synthesis and/or ligand-releasing protease activity promote
irreversible activation of intracellular signaling. C and
D: memory effects in autocrine loops are abolished in a
dose-dependent way by extracellular addition of antiligand decoys.
Bistability at the zero level of exogenous input is destroyed by
extracellular addition of antiligand decoys (C). Parameters
used in computations are as follows: RT = 3 × 104/cell, width of pulselike excitation = 2 min, and
amplitude (I0) = 3. For transients in
B, gain (G1) between the level of the
active form of surface protease and the rate of ligand secretion = 0.0, 0.4, 0.5, and 0.7. For transients in D,
G1 = 0.7. Antiligand decoys binding the
endogenous ligand with forward and reverse rate constants
(k = 0.1 min 1 and
k = 107
M 1 · min 1) are added at
concentrations (B) of 10 10, 10 9,
10 7, and 10 8 M. Steady-state diagram
(A) is computed at zero concentration of antiligand decoys.
All other parameters as in Fig. 4.
|
|
A combination of the strong nonlinearity of the intracellular signaling
cascade (Fig. 4B) with the positive feedback can result in a
phenomenon known as "bistability" (24).
Bistability is defined as the ability of the system to exist in two
stable configurations at fixed external conditions; selection of a
particular stable configuration depends on the history of the system.
Indeed, in regimens characterized by stronger coupling between the
ligand-releasing protease and ligand secretion rate, larger number of
receptors, or weaker gain of the negative feedback in the signaling
cascade, the system can exist in one of the two stable ("on" and
"off") configurations (Fig. 7A). The off state
is characterized by a low level of signaling, low levels of endogenous
ligand production, and a low number of occupied surface receptors. In
the on state, large quantities of signal circulate through the
autocrine loop: the cell secretes high levels of ligand and captures a
significant fraction of it. The captured ligand further increases the
level of intracellular signaling and, as a result, a high secretion rate. In Fig. 7A, the steady input-output behavior of the
autocrine loop is shown as a function of the strength of the exogenous
input. Below a critical input magnitude the on and off states coexist; above this threshold, the off state can no longer be maintained, and
the system switches to the state characterized by the high levels of
signaling, receptor occupancy, and ligand release. The autocrine system
in a bistable regimen can be flipped between the two steady states by
transient inputs. Figure 7B demonstrates that, after a
primary excitation, the system can become permanently locked in the on
state. Hence, a transient input can induce a permanent transformation
of the signaling state of the cell. The switching ability of the
transient input is conditional on the autocrine cell's ability to
release endogenous ligand (Fig. 7B) and to recapture
endogenous ligand (not shown).
To be recaptured by the releasing cell, the secreted ligand clearly
must return to the cell surface. Return of autocrine ligands can be
prevented by a component of the extracellular medium capable of binding
the secreted ligand and removing it from the autocrine loop. The
components of the extracellular medium, such as exogenously added
antiligand antibodies, may serve as such decoys for autocrine ligands
(25); notice that the components of the ECM, such as heparin, may also act as extracellular "sinks" for autocrine
ligands (38). We illustrate this by computing the
signaling patterns induced in our model autocrine cell when it is
placed in the medium with the various concentrations of extracellular
decoys. Figure 7C illustrates that the on state of an
autocrine loop exists only below a certain threshold concentration of
the extracellular decoys that sequester the endogenous ligand. This
qualitative change in the steady behavior is mirrored by changes in the
dynamic response to a pulselike exogenous stimulus (Fig.
7D). The persistent response is converted to a prolonged
transient that is eventually abolished by increasing the concentration
of extracellular decoys. This demonstrates the context-dependent
signaling capability of autocrine loops: the dynamics of intracellular
signaling may be regulated by the composition of the extracellular
medium. In contrast to the previously proposed modes of cell signaling,
this mode is bidirectional: signals flow into and out of the cell.
Comparison With Experiment
Context-dependent radiation responses of human carcinoma cells.
Pulse responses with large- and intermediate-amplitude secondary
excitations for EGFR and MAPK signaling were recently reported in
studies examining DU145 prostate and A431 squamous carcinoma cells.
Similar to the modeling predictions, a pulselike input to the system (a
pulse of ionizing radiation) was modulated by the autocrine loop,
resulting in complex output (MAPK activity) dynamics. The signaling
dynamics are strongly dependent on the actions of the autocrine ligand
TGF-
. A description of the experimental protocols is given in
APPENDIX B and in Refs. 16, 30, and 58. In the experiments, EGFR-expressing carcinoma cells were
exposed to a clinically relevant low dose of ionizing radiation (2 Gy)
and assayed for EGFR tyrosine phosphorylation and MAPK activity
(16, 30, 58). The response of EGFR tyrosine
phosphorylation and MAPK activity consisted of an initial short
(0-30 min) primary activation followed by secondary activations of
120-240 min in A431 squamous carcinoma cells (Fig.
8A) and 120-1,440 min in
DU145 prostate carcinoma cells (not shown). In both cell types, the secondary excitation of MAPK could be completely abolished by interrupting the TGF-
-EGFR autocrine loop. This was demonstrated experimentally by inhibiting EGFR tyrosine kinase activity (not shown)
and by adding antibodies absorbing the secreted TGF-
(Fig. 8A). When the medium from the irradiated cells was
transferred to nonirradiated cells, 2 h after exposure, a
comparable MAPK activation was induced in the nonirradiated cells that
was dependent on soluble TGF-
in the transferred medium (Fig.
8B). To summarize, experiments in Fig. 8 demonstrate that
the primary exogenous stimulus (radiation, in this particular case)
activates autocrine loops and that this activation depends on the
composition of the extracellular medium. Figure 8A shows
that secondary excitation in MAPK activity disappears when radiation
stimulates the cells placed in the medium containing antiligand
antibodies. A complementary experiment in Fig. 8B shows that
medium from the irradiated cells can stimulate MAPK in the
nonirradiated cells; this effect is abolished when antiligand
antibodies are added to the conditioned medium.

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Fig. 8.
Radiation causes a biphasic activation of the MAPK
pathway in autocrine carcinoma cells; secondary activation of the MAPK
pathway is dependent on cleavage and release of TGF- . A:
cells were irradiated, and MAPK activity was determined as described
previously (16, 30). In the indicated experiments,
anti-TGF- antibody (1 µg/ml media) was added before irradiation.
Cells were lysed, portions (~100 µg) from each plate were used to
immunoprecipitate MAPK, and immune-complex kinase assays were performed
(16, 30). Data are from a representative experiment
(n = 5). B: A431 cells were irradiated,
medium was removed from irradiated cells 120 min after exposure, and
anti-TGF- antibody or control antibody was added to the recovered
medium. Isolated medium was incubated with antibody for 60 min, and
then nonirradiated cells were added. After 5 min, the plates were
aspirated and snap frozen, and MAPK activity was determined after
immunoprecipitation (16, 30). MAPK activity data are shown
as fold increases in 32P incorporation into myelin basic
protein substrate and are normalized to activity at time 0. Values are means ± SE of 3 independent experiments.
|
|
Enhanced ligand secretion and prolonged MAPK activity in the irradiated
cells could be abolished when the A431 squamous carcinoma cells were
treated with PD-98059, an inhibitor of the enzyme MEK1/2, acting
upstream of MAPK in the kinase cascade. In these cells, the basal
release of TGF-
over a 3-h period was ~60 pg/mg total cell
protein. Irradiation (2 Gy) increased TGF-
release by ~80%; this
release was blocked when irradiated cells were incubated with the
MEK1/2 inhibitor PD-98059 (data not shown). Similar data for regulation
of TGF-
release, using ligand stimulation of the EGFR-MAPK system,
have been reported in other cell types (16, 30, 58).
Collectively, these experiments support an MAPK-mediated positive-feedback loop between ligand binding and ligand release.
Interestingly, the dynamics of MAPK signaling observed in these
irradiated autocrine cells indicate the presence of the multiple steady
states (16, 30, 58). As in our computational study, transient input seems to permanently lock MAPK in the active on state
in DU145 cells, whereas in A431 cells the duration of secondary MAPK
activation was only 2-3 h (16, 30, 45). The greater secondary activation of MAPK in DU145 cells may be explained because these cells have a genetic rearrangement of the TGF-
gene, resulting in its triplication (11), and thus express more autocrine
ligand than A431 cells.
 |
DISCUSSION |
We have developed a mechanistic model of autocrine loops in the
EGFR system (Fig. 9). The model accounts
for the dynamic interaction of ligand release, extracellular
transport, receptor binding, and signaling through the Ras-MAPK
pathway. Our computational analysis demonstrates that autocrine loops
with positive feedback allow cells to modulate the amplitude and the
duration of the signaling response to external stimuli. Specifically, a
pulselike input lasting only several minutes induces a signaling
transient that lasts for several hours. Furthermore, transient inputs
can permanently leave the autocrine loop in the on state, characterized by the high signaling level. Both of these features are demonstrated in
the study of the EGFR autocrine loops in human carcinoma cells (see
also Refs. 16, 30, and 58). In these
cells, a 1-min pulse of
-radiation results in TGF-
release and
leads to complex MAPK dynamics on the time scale of hours. Sustained
activation of the ERK1/2 MAPK in the EGFR-TGF-
autocrine loop has
also been detected in cancerous pancreatic cells and suggested to be a
cause of their serum-free growth (52). We have focused on
the positive feedback that operates exclusively at the level of
signaling. Under the additional control of feedbacks acting at the
level of gene expression, the nonlinear effects reported here are
likely to become even more pronounced.

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Fig. 9.
Concept of context-dependent cell signaling mediated by
positive feedback in autocrine loops. Amplitude and duration of
activation of an intracellular signaling cascade by exogenous stimulus
depend on the primary stimulus, its ability to activate the
ligand-release system, and the ability of the cell to recapture
endogenous ligands. Autocrine loop in the "on" state (A)
converts transient external perturbation (B) to a
long-lasting and dynamic transient of the intracellular signaling
(C). Autocrine loop is interrupted when the cell has a low
number of surface receptors (D) or when the endogenous
ligands are captured by components of the cellular microenvironment
(E). In this case, the exogenous stimulus is incapable of
inducing long-lasting or sustained activation of the intracellular
signaling circuitry (F).
|
|
We have demonstrated that dynamics of autocrine loops with positive
feedback are governed jointly by the intracellular and extracellular
processes. In particular, interactions of secreted ligands with
components of the extracellular environment are translated into the
dynamics of the intracellular signaling. On the basis of our
computational and experimental studies, we propose that the EGFR
autocrine loop with positive feedback is a module for context-dependent
signal processing. Two cells equipped with the same intracellular
signaling capabilities but different in their ability to release and
recapture autocrine ligands may respond differently to the same
external input. In this work, the extracellular context, characterized
by the presence or absence of antiligand antibodies, is reflected in
the MAPK dynamics and defines cell response to radiation: the presence
or absence of long-lived MAPK excitations mediated by the EGFR-TGF-
autocrine loop defines whether the irradiated cell survives or dies.
Further supporting our model of context-dependent signaling is a recent
experiment in which the EGFR autocrine loop regulates the ability of a
G protein agonist (thrombin) to induce cell migration
(38). There, stimulation of the EGFR by the
heparin-binding EGF, released in response to exogenous thrombin, was
necessary for thrombin-induced migration of rat and baboon smooth
muscle cells; interruption of autocrine signaling by soluble heparin or
antiligand antibodies abrogated thrombin-induced cell motility.
On the basis of our computational studies and the experiments reporting
the effect of exogenously added soluble antiligand antibodies or other
factors interacting with autocrine ligands, we suggest that the
composition of the ECM can affect signaling dynamics via a similar
bidirectional mechanism. Mathematical analysis of this hypothesis would
involve changing the freely diffusing ligand decoys in our model to
immobile extracellular components interacting with the secreted
ligands. Merely as suggestive examples, we outline experiments that
could potentially support or refute this extended hypothesis. A
technique for patterning of the ECM components could be used to create
a surface gradient in the concentration of heparin; this patterned
substrate could then be used for migration studies of autocrine cells
expressing heparin-binding EGF. Our extended hypothesis would predict
that the static gradient of heparin would lead to chemotaxis of
autocrine cells toward the higher concentration of heparin; the effect
should be abolished by the addition of antibodies blocking the surface
receptors. If successful, this experiment would demonstrate that the
composition of the ECM can affect cell physiology (in this case, cell
migration) by perturbing the operation of autocrine loops. Presumably,
similar studies could be performed using other ECM components such as collagen or fibronectin. Another experiment, aimed at demonstrating this effect at the level of cell signaling, would follow the MAPK dynamics induced by exogenously added growth factors in cells plated on
substrates with different levels of heparin.
A critical question arising in the design of anticancer therapies
targeting autocrine loops is how to optimally select particular components within a signaling network to be targeted for
pharmacological inhibition. Another question is how to choose among
multiple antireceptor and antiligand antibodies, tyrosine kinase
inhibitors, inhibitors of membrane-associated proteases that release
autocrine ligands, and inhibitors of the downstream signaling
components. Rational design of therapies that target systems as complex
as autocrine loops may benefit from the further development and
validation of our model. In particular, a combination of further
modeling and parameter estimation techniques is required to convert our model to a practical tool for the analysis and eventually the design of
radiation responses of autocrine cells. Within the last 2 yr, several
groups have reported time-resolved measurements of reactive oxygen
species (ROS) (46) generation and intracellular signaling
activation in response to ionizing radiation (for review see Refs.
42 and 59). These experiments have identified several mechanisms by which radiation can impact the autocrine loop. By generating ROS, ionizing radiation can potentially inhibit receptor tyrosine phosphatases and cause ligand-independent receptor activation; at the same time, ROS can activate ligand-releasing proteases (74) and the components of the intracellular signaling
pathways, such as Raf-1 (see Ref. 59 and references
therein). A mechanistic description of these effects will be a natural
extension of the model presented here. In the present version of the
model, the signaling activity of the ligand-receptor complex ceases
with its internalization, and the rate of ligand recycling is set to zero. The model can be made more quantitative by accounting for the
effects of receptor and ligand recycling and signaling from early
endosomes (3, 9, 22, 32, 65, 67, 71, 73) and for the
effects of signaling through other pathways stimulated by the EGFR
(33, 57, 69). Given the fact that autocrine signaling
through the EGFR loops has been identified as one of the major
cytoprotective mechanisms determining the success of cancer
radiotherapy (1, 35, 58), we believe that this modeling effort is necessary.
 |
APPENDIX A |
Model Equations
Binding and transport.
We consider the axially symmetrical spatial distribution of ligand
around the cell. The governing equations and boundary conditions are
|
(A1)
|
|
(A2)
|
|
(A3)
|
|
(A4)
|
|
(A5)
|
|
(A6)
|
In the present version of the model, the diffusivity of the
ligand (D) does not depend on its association with an
antiligand antibody. The system of equations is rendered dimensionless
by the following transformations
|
(A7)
|
The cell radius scales the coordinate; the time (t)
is scaled by the inverse dissociation rate constant; extracellular
ligand concentrations are scaled by the equilibrium binding constant; the surface densities of free and occupied receptors are scaled by the
density of receptors in the absence of ligand (RT
s/kc). The rescaled problem takes the
following form (see RESULTS for definitions of
dimensionless groups Au, Da,
, and
)
|
(A8)
|
|
(A9)
|
|
(A10)
|
|
(A11)
|
|
(A12)
|
|
(A13)
|
The time scale of extracellular diffusion is defined by
= r
koff/D.
Dimensionless binding constants, k+ and
k
, reflect the strength with which the
secreted ligand is bound by the extracellular component:
k+ = k
Br
/D and k
= k
r
/D. For high values of the secreted growth factor diffusivity (
1),
concentrations of soluble species evolve on the time scale that is much
shorter than that of surface receptors and ligand-receptor complexes.
In this regimen, the binding/transport model can be simplified by using
a steady-state approximation for the concentration of endogenous
ligand; we can solve for the value of this concentration at the surface
of our autocrine cell
|
(A14)
|
where W depends on the composition of the
extracellular medium and the rate constants of the forward and reverse
binding processes: W = [1 + (k+ + k
)1/2]/[1 + k
/(k+ + k
)1/2]. The
pseudo-steady-state approximation (Eq. A14) for the
concentration of secreted ligand leads to the following model for the
dynamics of free and bound cell surface receptors
|
(A15)
|
|
(A16)
|
More subtle details of receptor binding and trafficking, such as
dimerization, stoichiometric saturation of internalization, and
endosomal sorting to recycling, could be easily incorporated into the model.
Protease dynamics.
The model is formulated in terms of P and Pa
|
(A17)
|
|
(A18)
|
After rescaling according to
|
(A19)
|
the system is converted to its dimensionless form
|
(A20)
|
|
(A21)
|
Signaling cascade.
Let e1P, e2P, and e3P denote the
dimensionless (scaled by the total amount of the enzyme) concentrations
of the active ("phosphorylated") form of the enzymes. The model for
dynamics of e1P, e2P, and e3P has
the following form
|
(A22)
|
|
(A23)
|
|
(A24)
|
In this form, the equilibrium Michaelis constants
(Km,i) are rescaled by the total
amount of enzyme at a given stage of the cascade;
Vmax is maximal reaction velocity.
Coupled system.
The input to the signaling cascade is given by I = I0 + G2RT
, where
I0 denotes (possibly time-dependent) input to
the signaling network, independent of endogenous ligand, and
G2 quantifies the efficiency with which occupied
receptor stimulates the input to the signaling cascade. The (scaled)
rate of protease activation is given by
=
0 + G3e3P, where
is the rate of activation mediated by "MAPK-independent"
processes. Finally, the rate of ligand secretion is Q ~ Au = G1Pa. The integrated
model of an autocrine cell then becomes (the units of time are now the
same in all 3 modules of our autocrine loop)
|
(A25)
|
|
(A26)
|
|
(A27)
|
|
(A28)
|
|
(A29)
|
|
(A30)
|
|
(A31)
|
 |
APPENDIX B |
Materials and Experimental Methods
Reagents.
Anti-p42MAPK (sc-154AC) was obtained from Santa Cruz
Biotechnology (Santa Cruz Biotechnologies, CA), the neutralizing
monoclonal antibody to TGF-
(Ab-3) and control antibody to TFIID
(Ab-2) from Calbiochem (San Diego, CA), and [
-32P]ATP
from NEN. The enzyme-linked immunosorbent assay to measure TGF-
release was purchased from Oncogene Research Products (San Diego, CA),
and studies were performed according to the manufacturer's instructions. Other materials were as described previously (16, 30).
Culture of A431 cells.
A431-TR25-EGFR antisense cells were plated at a density of 3.2 × 104 cells/cm2 and cultured for 4 days in RPMI
1640 medium supplemented with 5% (vol/vol) fetal calf serum at 37°C
in 95% (vol/vol) air-5% (vol/vol) CO2, as described
previously (16). Cells were cultured in reduced-serum
(0.5% vol/vol) medium for 2 h before irradiation. Cells were at
~70% confluency at the time of irradiation.
Treatment of cells with ionizing radiation and cell
homogenization.
Cells were cultured as described above and serum starved for 12 h
before irradiation (16). In the experiments, anti-TGF-
antibody (1 µg/ml medium) was added 60 min before irradiation. For
media transfer assays, anti-TGF-
antibody (2 µg) was added to
recovered medium 120 min after irradiation, and the medium was
incubated with antibody for 60 min before further use. A control antibody to the transcriptional regulator TFIID corresponding to the
same monoclonal antibody subtype (IgG2) as the anti-TGF-
antibody
was used as a control. Cells were irradiated using a 60Co
source at 1.8 Gy/min, to a total of 2 Gy. Time 0 is the time at which exposure to radiation ceased. After radiation treatment, cells
were incubated for specified times, and then the medium was aspirated
and snap frozen at
70°C on dry ice. Cells were homogenized as
described previously (16, 30).
Immunoprecipitation and assay of MAPK activity.
Immunoprecipitates were incubated with [
-32P]ATP and
myelin basic protein, as described elsewhere (16, 30).
32P incorporation into myelin basic protein was quantified
by liquid scintillation spectroscopy, as described previously
(16, 30).
Data analysis.
The effects of various treatments were compared using one-way analysis
of variance and a two-tailed t-test. Differences with P < 0.05 were considered statistically significant.
Values are means ± SE of multiple individual points from multiple
separate experiments.
 |
ACKNOWLEDGEMENTS |
This work was partially funded by Defense Advanced Research
Projects Agency Grant MDA972-00-1-0030 to D. A. Lauffenburger and
National Institute of General Medical Sciences Postdoctoral Fellowship
F32 GM-20847 to S. Y. Shvartsman. P. Dent is funded by National
Institutes of Health Grants R01 DK-52825 and R01 CA-88906 and US
Department of Defense Grant BC98-0148. A. Yacoub is funded by the
Department of Radiation Oncology, Virginia Commonwealth University, and
is a postdoctoral fellow in the laboratory of Dr. M. P. Hagan.
 |
FOOTNOTES |
Address for reprint requests and other correspondence: D. A. Lauffenburger, Dept. of Chemical Engineering, MIT, Cambridge, MA
02139 (E-mail: lauffen{at}mit.edu).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
10.1152/ajpcell.00260.2001
Received 11 June 2001; accepted in final form 15 October 2001.
 |
REFERENCES |
1.
Akimoto, T,
Hunter N,
Buchmiller L,
Mason K,
Ang K,
and
Milas L.
Inverse relationship between the epidermal receptor expression and radiocurability of murine carcinomas.
Clin Cancer Res
5:
2884-2890,
1999[Abstract/Free Full Text].
2.
Asthagiri, A,
Nelson C,
Horwitz A,
and
Lauffenburger D.
Quantitative relationship among integrin-ligand binding, adhesion, and signaling via focal adhesion kinase and extracellular signal-regulated kinase 2.
J Biol Chem
274:
119-127,
1999.
3.
Bao, J,
Alroy I,
Waterman H,
Schejter E,
Brodie C,
Gruenberg J,
and
Yarden Y.
Threonine phosphorylation diverts internalized epidermal growth factor receptors from a degradative pathway to the recycling endosome.
J Biol Chem
275:
26178-26186,
2000[Abstract/Free Full Text].
5.
Berg, HC,
and
Purcell EM.
Physics of chemoreception.
Biophys J
20:
193-219,
1977[Abstract].
6.
Bhalla, U,
and
Iyengar R.
Emergent properties of networks of biological signaling pathways.
Science
283:
339-340,
1999[Free Full Text].
7.
Blobel, C.
Remarkable roles of proteolysis on and beyond the cell surface.
Curr Opin Cell Biol
12:
606-612,
2000[ISI][Medline].
8.
Brigthman, F,
and
Fell D.
Differential feedback regulation of the MAPK cascade underlies the quantitative differences in EGF and NGF signaling in PC12 cells.
FEBS Lett
482:
169-174,
2000[ISI][Medline].
9.
Burke, P,
Schooler K,
and
Wiley H.
Regulation of epidermal growth factor receptor signaling by endocytosis and intracellular trafficking.
Mol Cell Biol
12:
1897-1910,
2001.
10.
Carpenter, G.
Employment of the epidermal growth factor receptor in growth factor-independent signaling pathways.
J Cell Biol
146:
697-702,
1999[ISI][Medline].
11.
Ching, K,
Ramsey E,
Pettigrew N,
D'Cunha R,
Jason M,
and
Dodd J.
Expression of mRNA for epidermal growth factor, transforming growth factor-
, and their receptors in human prostate tissue and cell lines.
Mol Cell Biochem
126:
151-158,
1993[ISI][Medline].
12.
Ciardiello, F.
Epidermal growth factor receptor tyrosine kinase inhibitors as anticancer agents.
Drugs
60:
25-32,
2000[ISI][Medline].
13.
Daub, H,
Weiss F,
Wallasch C,
and
Ullrich A.
Role of transactivation of the EGF receptor in signalling by G-protein-coupled receptors.
Nature
379:
557-560,
1996[ISI][Medline].
14.
Deen, WM.
Analysis of Transport Phenomena. New York: Oxford University Press, 1998.
15.
Dempsey, P,
and
Coffey R.
Basolateral targeting and efficient consumption of transforming growth factor-
when expressed in Madin-Darby kidney cells.
J Biol Chem
269:
16878-16889,
1994[Abstract/Free Full Text].
16.
Dent, P,
Reardon D,
Park J,
Bowers G,
Logsdon C,
Valerie K,
and
Schmidt-Ullrich R.
Radiation-induced release of transforming growth factor-
activates the epidermal growth factor receptor and mitogen-activated protein kinase pathway in carcinoma cells, leading to increased proliferation and protection from radiation-induced cell death.
Mol Biol Cell
10:
2493-2506,
1999[Abstract/Free Full Text].
17.
Doedens, JR,
and
Black RA.
Stimulation-induced down-regulation of tumor necrosis factor-
-converting enzyme.
J Biol Chem
275:
14598-14607,
2000[Abstract/Free Full Text].
18.
Domagala, T,
Konstantopoulos N,
Smyth F,
Jorissen R,
Fabri L,
Geleick D,
Lax I,
Schlessinger J,
Sawyer W,
Howlett G,
Burgess A,
and
Nice E.
Stoichiometry, kinetic and binding analysis of the interaction between epidermal factor (EGF) and the extracellular domain of the EGF receptor.
Growth Factors
18:
11-29,
2000[ISI][Medline].
19.
Dong, JY,
Opresko LK,
Dempsey PJ,
Lauffenburger DA,
Coffey RJ,
and
Wiley HS.
Metalloprotease-mediated ligand release regulates autocrine signaling through the epidermal growth factor receptor.
Proc Natl Acad Sci USA
96:
6235-6240,
1999[Abstract/Free Full Text].
20.
Dong, JY,
and
Wiley HS.
Trafficking and proteolytic release of epidermal growth factor receptor ligands are modulated by their membrane-anchoring domains.
J Biol Chem
275:
557-564,
1999[Abstract/Free Full Text].
21.
Dowd, CJ,
Cooney CL,
and
Nugent MA.
Heparan sulfate mediates bFGF transport through basement membrane by diffusion with rapid reversible binding.
J Biol Chem
274:
5236-5244,
1999[Abstract/Free Full Text].
22.
Ettenberg, SA,
Magnifico A,
Cuello M,
Nau MM,
Rubinstein YR,
Yarden Y,
Weissman AM,
and
Lipkowitz S.
Cbl-b-dependent coordinated degradation of the epidermal growth factor receptor signaling complex.
J Biol Chem
276:
27677-27684,
2001[Abstract/Free Full Text].
23.
Fan, HZ,
and
Derynck R.
Ectodomain shedding of TGF-
and other transmembrane proteins is induced by receptor tyrosine kinase activation and MAP kinase signaling cascades.
EMBO J
18:
6962-6972,
1999[Abstract/Free Full Text].
24.
Ferrell, JJ,
and
Machleder E.
The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes.
Science
280:
895-898,
1998[Abstract/Free Full Text].
25.
Forsten, KE,
and
Lauffenburger DA.
Interrupting autocrine ligand-receptor binding: comparison between receptor blockers and ligand decoys.
Biophys J
63:
857-861,
1992[Abstract].
26.
Gechtman, Z,
Alonso JL,
Raab G,
Ingber DE,
and
Klagsbrun M.
The shedding of membrane-anchored heparin-binding epidermal-like growth factor is regulated by the Raf/mitogen-activated protein kinase cascade and by cell adhesion and spreading.
J Biol Chem
274:
28828-28835,
1999[Abstract/Free Full Text].
27.
Goldbeter, A,
and
Koshland DJ.
An amplified sensitivity arising from covalent modification in biological systems.
Proc Natl Acad Sci USA
78:
6840-6844,
1981[Abstract].
28.
Gschwind, A,
Zwick E,
Prenzel N,
Leserer M,
and
Ullrich A.
Cell communication networks: epidermal growth factor receptor transactivation as the paradigm for interreceptor signal transmission.
Oncogene
20:
1594-1600,
2001[ISI][Medline].
29.
Guan, R,
Zhang Y,
Jiang J,
Baumann C,
Black R,
Baumann G,
and
Frank S.
Phorbol ester- and growth factor-induced growth hormone (GH) proteolysis and GH-binding protein shedding: relationship to GH receptor downregulation.
Endocrinology
142:
1137-1147,
2001[Abstract/Free Full Text].
30.
Hagan, M,
Wang L,
Hanley JR,
Park JS,
and
Dent P.
Ionizing radiation-induced mitogen-activated protein (MAP) kinase activation in DU145 prostate carcinoma cells: MAP kinase inhibition enhances radiation-induced cell killing and G2/M-phase arrest.
Radiat Res
153:
371-383,
2000[ISI][Medline].
31.
Halfon, M,
Carmena A,
Gisselbrecht S,
Sackerson C,
Jimenez F,
Baylies M,
and
Michelson A.
Ras pathway specificity is determined by the integration of multiple signal-activated and tissue-restricted transcription factors.
Cell
103:
63-74,
2000[ISI][Medline].
32.
Haugh, J,
Huang A,
Wiley H,
Wells A,
and
Lauffenburger D.
Internalized epidermal growth factor receptors participate in the activation of p21ras in fibroblasts.
J Biol Chem
274:
34350-34360,
1999[Abstract/Free Full Text].
33.
Haugh, J,
Wells A,
and
Lauffenburger D.
Mathematical modeling of epidermal growth factor receptor signaling through the phospholipase C pathway: mechanistic insights and predictions for molecular interventions.
Biotechnol Bioeng
70:
225-238,
2000[ISI][Medline].
34.
Huang, C,
and
Ferrell JJ.
Ultrasensitivity in the mitogen-activated protein kinase cascade.
Proc Natl Acad Sci USA
93:
10078-10083,
1996[Abstract/Free Full Text].
35.
Huang, S,
and
Harari P.
Modulation of radiation response after epidermal growth factor blockade in squamous cell carcinomas: inhibition of damage repair, cell cycle kinetics, and tumor angiogenesis.
Clin Cancer Res
6:
2166-2174,
2000[Abstract/Free Full Text].
36.
Hunter, T.
The Croonian Lecture 1997. The phosphorylation of proteins on tyrosine: its role in cell growth and disease.
Philos Trans R Soc Lond B Biol Sci
353:
583-605,
1998[ISI][Medline].
37.
Hunter, T.
Signaling
2000 and beyond.
Cell
100:
113-127,
2000[ISI][Medline].
38.
Kalmes, A,
Vesti B,
Daum G,
Abraham J,
and
Clowes A.
Heparin blockade of thrombin-induced smooth muscle cell migration involves inhibition of epidermal growth factor (EGF) receptor transactivation by heparin-binding EGF-like growth factor.
Circ Res
87:
92-98,
2000[Abstract/Free Full Text].
39.
Kholodenko, B.
Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades.
Eur J Biochem
267:
1583-1588,
2000[Abstract/Free Full Text].
40.
Kholodenko, B,
Hoek J,
Westerhoff H,
and
Brown G.
Quantification of information transfer via cellular signal transduction pathways.
FEBS Lett
419:
430-434,
1997.
41.
Kholodenko, BN,
Demin OV,
Moehren G,
and
Hoek JB.
Quantification of short-term signaling by the epidermal growth factor receptor.
J Biol Chem
274:
30169-30181,
1999[Abstract/Free Full Text].
42.
Knebel, A,
Bohmer F,
and
Herrlich P.
Radiation-induced signal transduction.
Methods Enzymol
319:
255-272,
2000[ISI][Medline].
43.
Lauffenburger, D,
and
Linderman J.
Receptors: Models for Binding, Trafficking, and Signalling. New York: Oxford University Press, 1993.
44.
Lauffenburger, DA,
Oehrtman GT,
Walker L,
and
Wiley HS.
Real-time quantitative measurement of autocrine ligand binding indicates that autocrine loops are spatially localized.
Proc Natl Acad Sci USA
95:
15368-15373,
1998[Abstract/Free Full Text].
45.
Lavoie, J,
Rivard N,
L'Allemain G,
and
Pouyssegur JA.
A temporal and biochemical link between growth factor-activated MAP kinases, cyclin D1 induction and cell cycle entry.
Prog Cell Cycle Res
2:
49-58,
1996[Medline].
46.
Leach, J,
Van Tuyle G,
Lin P,
Schmidt-Ullrich R,
and
Mikkelsen R.
Ionizing radiation-induced, mitochondria-dependent generation of reactive oxygen/nitrogen.
Cancer Res
61:
3894-3901,
2001[Abstract/Free Full Text].
47.
Lewis, T,
Shapiro P,
and
Ahn N.
Signal transduction through MAP kinase cascades.
Adv Cancer Res
74:
49-139,
1998[ISI][Medline].
48.
Marshall, CJ.
Specificity of receptor tyrosine kinase signaling: transient versus sustained extracellular signal-regulated kinase activation.
Cell
80:
179-185,
1995[ISI][Medline].
49.
Massague, J,
and
Pandiella A.
Membrane-anchored growth factors.
Annu Rev Biochem
62:
515-541,
1993[ISI][Medline].
50.
Moghal, M,
and
Sternberg PW.
Multiple positive and negative regulators of signaling by the EGF receptor.
Curr Opin Cell Biol
11:
190-196,
1999[ISI][Medline].
51.
Montero, J,
Yuste L,
Diaz-Rodriguez E,
Espasis-Ogando A,
and
Pandiella A.
Differential shedding of transmembrane neuregulin isoforms by tumor necrosis factor-
-converting enzyme.
Mol Cell Neurosci
16:
631-648,
2000[ISI][Medline].
52.
Murphy, L,
Cluck M,
Lovas S,
Otvos F,
Murphy R,
Schally A,
Permert J,
Larsson J,
Knezetic J,
and
Adrian T.
Pancreatic cancer cells require and EGF receptor-mediated autocrine pathway for proliferation under serum-free conditions.
Br J Cancer
84:
926-935,
2001[ISI][Medline].
53.
Oehrtman, GT,
Wiley HS,
and
Lauffenburger DA.
Escape of autocrine ligands into extracellular medium: experimental test of theoretical model predictions.
Biotechnol Bioeng
57:
571-582,
1998[ISI][Medline].
54.
Peschon, J,
Slack J,
Reddy P,
Stocking K,
Sunnarborg S,
Lee D,
Russell W,
Castner B,
Johnson R,
Fitzner J,
Boyce R,
Nelson N,
Kozlosky C,
Wolfson M,
Rauch C,
Cerretti D,
Paxton R,
March C,
and
Black R.
An essential role for ectodomain shedding in mammalian development.
Science
282:
1281-1284,
1998[Abstract/Free Full Text].
55.
Pierce, K,
Luttrell L,
and
Lefkowitz R.
New mechanisms in heptahelical receptor signaling to mitogen-activated protein kinase cascades.
Oncogene
20:
1532-1539,
2001[ISI][Medline].
56.
Roovers, K,
and
Assoian R.
Integrating the MAP kinase signal into the G1 phase cell cycle machinery.
Bioessays
22:
818-826,
2000[ISI][Medline].
57.
Schlessinger, J.
Cell signaling by receptor tyrosine kinases.
Cell
103:
211-225,
2000[ISI][Medline].
58.
Schmidt-Ullrich, R,
Contessa J,
Dent P,
Mikkelsen R,
Valerie K,
Reardon D,
Bowers G,
and
Lin P.
Molecular mechanisms of radiation-induced accelerated repopulation.
Radiat Oncol Investig
7:
321-330,
1999[ISI][Medline].
59.
Schmidt-Ullrich, RK,
Dent P,
Grant S,
Mikkelsen RB,
and
Valerie K.
Signal transduction and cellular radiation responses.
Radiat Res
153:
245-257,
2000[ISI][Medline].
60.
Sherrill, J,
and
Kyte J.
Activation of epidermal growth factor receptor by epidermal growth factor.
Biochemistry
35:
5705-5718,
1996[ISI][Medline].
61.
Shoup, D,
and
Szabo A.
Role of diffusion in ligand binding to macromolecules and cell-bound receptors.
Biophys J
40:
33-39,
1982[Abstract].
62.
Shvartsman, SY,
Wiley HS,
Deen WM,
and
Lauffenburger DA.
Spatial range of autocrine loops: modeling and computational analysis.
Biophys J
81:
1854-1867,
2001[Abstract/Free Full Text].
63.
Simon, M.
Receptor tyrosine kinases: specific outcomes from general signals.
Cell
103:
13-15,
2000[ISI][Medline].
64.
Sporn, M,
and
Roberts A.
Autocrine secretion
10 years later.
Ann Intern Med
117:
408-414,
1992[ISI][Medline].
65.
Swindle, C,
Tran K,
Johnson T,
Banerjee P,
Mayes A,
Griffith L,
and
Wells A.
Epidermal growth factor (EGF)-like repeats of human tenascin-C as ligands for EGF receptor.
J Cell Biol
154:
459-468,
2001[Abstract/Free Full Text].
66.
Tan, P,
and
Kim S.
Signaling specificity: the RTK/RAS/MAP kinase pathway in metazoans.
Trends Genet
15:
145-149,
1999[ISI][Medline].
67.
Waterman, H,
and
Yarden Y.
Molecular mechanisms underlying endocytosis and sorting of ErbB receptor tyrosine kinases.
FEBS Lett
275:
142-152,
2001.
68.
Weiss, GH.
Aspects and Applications of the Random Walk. Amsterdam: North-Holland, 1994.
69.
Wells, A.
EGF receptor.
Int J Biochem Cell Biol
31:
637-643,
1999[ISI][Medline].
70.
Werb, Z,
and
Yan YB.
Cell biology
a cellular striptease act.
Science
282:
1279-1280,
1998[Free Full Text].
71.
Wiley, H,
and
Burke P.
Regulation of receptor tyrosine kinase signaling by endocytic trafficking.
Traffic
2:
13-18,
2001.
72.
Wiley, HS,
Woolf MF,
Opresko LK,
Burke PM,
Will B,
Morgan JR,
and
Lauffenburger DA.
Removal of the membrane-anchoring domain of epidermal growth factor leads to intracrine signaling and disruption of mammary epithelial cell organization.
J Cell Biol
143:
1317-1328,
1998[Abstract/Free Full Text].
73.
Yarden, Y,
and
Sliwkowski M.
Untangling the ErbB signalling network.
Nat Rev Mol Cell Biol
2:
127-137,
2001[ISI][Medline].
74.
Zhang, Z,
Oliver P,
Lancaster JR, Jr,
Schwarzenberger PO,
Joshi MS,
Cork J,
and
Kolls JK.
Reactive oxygen species mediate tumor necrosis factor-
-converting, enzyme-dependent ectodomain shedding induced by phorbol myristate acetate.
FASEB J
15:
303-305,
2001[Abstract/Free Full Text].
75.
Zwick, E,
Hackel P,
Prenzel N,
and
Ullrich A.
The EGF receptor as central transducer of heterologous signalling systems.
Trends Pharmacol Sci
20:
408-412,
1999[ISI][Medline].
Am J Physiol Cell Physiol 282(3):C545-C559
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