Báo cáo khoa học: Modelling and simulating interleukin-10 production and regulation by macrophages after stimulation with an immunomodulator of parasitic nematodes potx

Modelling and simulating interleukin-10 production
and regulation by macrophages after stimulation with
an immunomodulator of parasitic nematodes
Ana Sofia Figueiredo
1
, Thomas Ho
¨
fer
2
, Christian Klotz
3
, Christine Sers
4
, Susanne Hartmann
3
,
Richard Lucius
3
and Peter Hammerstein
1
1 Institute for Theoretical Biology, Humboldt University, Berlin, Germany
2 Research Group Modeling of Biological Systems, German Cancer Research Center and BioQuant Center, Heidelberg, Germany
3 Department of Parasitology, Berlin, Germany
4 Institute of Pathology, Universitaetsmedezin Charite
´
, Berlin, Germany
Parasitic nematodes are multicellular organisms occupy-
ing diverse niches within their hosts. The economically
and medically important nematodes reside in the intesti-
nal tract, skin, muscles, blood or connective tissue of
their hosts. In all of these sites, nematodes are

constantly exposed to various host immune responses.
Keywords
autocrine crosstalk; host–parasite
interaction; regulation; signalling cascades;
systems biology
Correspondence
A. S. Figueiredo, Institute for Theoretical
Biology, Humboldt University,
Invalidenstrasse 43, 10115 Berlin, Germany
Fax: +49 30 20938801
Tel: +49 30 20938450
E-mail: s.fi
Note
The mathematical models described here
have been submitted to the Online Cellular
Systems Modelling Database and can be
accessed at />database/figueiredo1/index.html, http://jjj.
biochem.sun.ac.za/database/figueiredo2/
index.html, />database/figueiredo3/index.html and http://
jjj.biochem.sun.ac.za/database/figueiredo4/
index.html free of charge
(Received 5 September 2008, revised 22
February 2009, accepted 21 April 2009)
doi:10.1111/j.1742-4658.2009.07068.x
Parasitic nematodes can downregulate the immune response of their hosts
through the induction of immunoregulatory cytokines such as interleukin-
10 (IL-10). To define the underlying mechanisms, we measured in vitro the
production of IL-10 in macrophages in response to cystatin from Acantho-
cheilonema viteae, an immunomodulatory protein of filarial nematodes, and
developed mathematical models of IL-10 regulation. IL-10 expression

requires stimulation of the mitogen-activated protein kinases extracellular
signal-regulated kinase (ERK) and p38, and we propose that a negative
feedback mechanism, acting at the signalling level, is responsible for tran-
sient IL-10 production that can be followed by a sustained plateau. Specifi-
cally, a model with negative feedback on the ERK pathway via secreted
IL-10 accounts for the experimental data. Accordingly, the model predicts
sustained phospho-p38 dynamics, whereas ERK activation changes from
transient to sustained when the concentration of immunomodulatory
protein of Acanthocheilonema viteae increases. We show that IL-10 can
regulate its own production in an autocrine fashion, and that ERK and
p38 control IL-10 amplitude, duration and steady state. We also show that
p38 affects ERK via secreted IL-10 (autocrine crosstalk). These findings
demonstrate how convergent signalling pathways may differentially control
kinetic properties of the IL-10 signal.
Abbreviations
AIC, Aikaike information criterion; AU, arbitrary units; Av17, cystatin from Acanthocheilonema viteae; ERK, extracellular signal-regulated
kinase; H3, histone 3; IL-10, interleukin-10; IL-10
e
, extracellular interleukin-10; LPS, lipopolysaccharide; MAPK, mitogen-activated protein
kinase; MKP, mitogen-activated protein kinase phosphatase; ODE, ordinary differential equation; rAV17, recombinant cystatin from
Acanthocheilonema viteae; RSS, residual sum of squares; SP1, Sp1 transcription factor; STAT3, signal transducer and activator of
transcription.
3454 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
Nevertheless, many parasitic nematode species live for
years, a fact that has recently been explained by sophisti-
cated immune evasion mechanisms deployed by the
worms. For example, the species Onchocerca volvulus is
the causative agent of the tropical disease river blindness
(which afflicts about 20 million people worldwide), and
can persist in its human host for more than 10 years. A

parasitic nematode of rodents, Acanthocheilonema
viteae, is used as an animal model to study basic ques-
tions of host–parasite interaction, e.g. host immune
responses and parasite immune evasion mechanisms.
Parasitic nematode infections induce a Th2 response,
which has the potential to trigger immune effector
mechanisms that can efficiently kill parasitic worms.
However, the presence of these worms seems to blunt
this effect, and vigorous effector mechanisms do not
develop. One way of interfering with immune effector
mechanisms is to stimulate the production of anti-
inflammatory cytokines such as interleukin-10 (IL-10).
As a consequence, the ability of the host to kill the para-
sites is compromised, and host pathology due to inflam-
matory reactions is minimized. This balance allows
survival of parasites and hosts. Blunted Th2 responses
may represent a benefit for the host, as the ensuing
downregulation of effector mechanisms decreases auto-
immune responses and allergies [1,2].
The search for molecules that modulate host immune
responses has led to the identification of A. viteae cysta-
tin (Av17), a filarial protein constantly secreted by the
nematode [3] that inhibits cysteine proteases with impor-
tant functions in immune processes such as antigen
processing and presentation [4,5]. Furthermore, recom-
binant Av17 (rAv17) has recently been shown to specifi-
cally inhibit allergic and inflammatory responses in mice
[6]. In this scenario, rAv17 induced macrophages to
produce the anti-inflammatory cytokine IL-10 as a key
element of immunomodulation. The fact that signalling

through the extracellular signal-regulated kinase (ERK)
and p38 mitogen-activated protein kinase (p38) induces
IL-10 production in macrophages [7,8] has prompted us
to model the respective signalling pathways.
IL-10 is a cytokine with immunoregulatory proper-
ties. It executes its functions on a wide range of cells,
macrophages being a major source of this cytokine.
One major function of IL-10 is to control and reduce
excessive immune responses during infections and auto-
immunity, mainly by inhibiting the production of pro-
inflammatory cytokines in macrophages and other cell
types. However, several studies show a regulatory role
of IL-10 on T-helper cell responses of different types,
e.g. Th1 responses, which can lead to autoimmune
pathologies, and Th2 responses, which can lead to aller-
gies. IL-10-deficient mice develop spontaneous colitis
under normal conditions and are more prone to immu-
nopathology in general, being able to clear infection by
intracellular pathogens more effectively than wild-type
mice [6,9]. This emphasizes the importance of IL-10 as
an immune regulator.
The promoter region of the il-10 gene in macrophages
contains binding sites for the transcription factors, e.g.
Sp1 transcription factor (SP1) [10–12] and signal trans-
ducer and activator of transcription (STAT3) [7,8], that
regulate gene expression and are controlled by ERK
and p38 [13]. In a sequential mechanism, the ERK
signalling cascade remodels the chromatin of the il-10
promoter region by phosphorylating its histone 3 (H3)
sites, and the p38 signalling pathway activates the tran-

scription factors SP1 and STAT3 [7,8]. These transcrip-
tion factors bind to the phosphorylated H3 sites and
thereby initiate il-10 gene expression [7,8,14]. Macro-
phages express the IL-10 receptor complex on their
surface [15], suggesting feedback regulation by IL-10. A
negative autoregulatory role for IL-10 is suggested for
lipopolysaccharide (LPS)-stimulated or lipoprotein-
stimulated IL-10 production in monocytes and mono-
cyte-derived macrophages [16–19]. On the basis of these
findings, we assume that Av17 activates the ERK and
p38 signalling pathways in macrophages, leading to
IL-10 production, and we hypothesize that IL-10 is reg-
ulated via a negative feedback mechanism of secreted
IL-10 that binds to the macrophages and deactivates
the ERK signalling pathway either by kinase inhibition
or by phosphatase activation. Other ERK-induced
molecules, e.g. mitogen-activated protein kinase
phosphatases (MKPs) can deactivate ERK and thereby
regulate IL-10 induction (Fig. 1).
The ERK and p38 signalling cascades are two exam-
ples of MAPK cascades. They are central and highly
conserved, and are present in many cell types. A myr-
iad of stimuli can activate these kinases, which, in
turn, activate many other transcription factors and
regulators of transcription, controlling the expression
of many genes. Although the mechanisms that control
the different ERK activities are as yet unclear, diverse
activation modes lead to diverging outcomes, despite
the fact that the same cascade is in play [20–22].
In order to better understand the complexity of

MAPK signalling pathways, many mathematical mod-
els of these cascades have been developed. Heinrich
et al. [23] implemented mathematical models for
different topologies of the receptor-stimulated kinase ⁄
phosphatase signalling cascades and analysed key
parameters that characterize the signalling pathways
(signal amplitude, signalling time, and signal duration).
Sasagawa et al. [24] constructed a mathematical model
of ERK signalling based on literature findings and
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3455
predicted ERK dynamics in response to increases in
the growth factors epidermal growth factor and nerve
growth factor. Both studies show that the same
MAPK pathway can undergo a sustained or transient
activation, as experimentally shown by Marshall [20]
and Santos et al. [22].
The aim of this study was to develop mathematical
models of IL-10 regulation on Av17 stimulation in
macrophages in order to understand quantitatively
whether the current qualitative knowledge of the mecha-
nism is compatible with available data. Moreover, the
feedback regulation of IL-10 in macrophages is not well
understood at the moment. Therefore, we implement
models distinguished by different types of regulation of
IL-10 production to test how these different hypotheses
can explain the available experimental data. We select
the model that best represents the data, and analyse its
key features, such as the amplitude and duration of the
signal output. On the based of the results, we provide

insights about the potential regulatory modes of IL-10
production on macrophages after Av17 stimulation.
The mathematical models described here have been
submitted to the Online Cellular Systems Modelling
Database and can be accessed at .
ac.za/database/figueiredo1/index.html, chem.
sun.ac.za/database/figueiredo2/index.html, http://jjj.
biochem.sun.ac.za/database/figueiredo3/index.html and
.a c.za/database/figueir edo4/index.
html free of charge.
Results
The model
On the basis of the experimental and literature
evidence described above, we developed mathematical
models of IL-10 regulation on Av17 stimulation in
macrophages. Model development was based on the
principle of parsimony. In order to keep the number
of parameters as small as possible, we included only
those components and processes that we considered
paramount to describe the systems dynamics and
where data were available (see Fig. 2 for all compo-
nents and reactions included in the models).
We propose two different methods of regulation, via
IL-10 (model 1 and model 2) or via an inhibitor
(model 3), and compare them to a model with no feed-
back (model 0). Model 1 assumes promotion of ERK
dephosphorylation via IL-10 (kinase deactivation).
Model 2 assumes inhibition of ERK phosphorylation
via IL-10 (phosphatase activation). Model 3 assumes
promotion of ERK dephosphorylation via an inhibitor

(kinase deactivation). The components and reactions
of these models are described in Table 1.
Fig. 1. A literature-based model of IL-10 induction and regulation by the helminthic immune modulator Av17. Av17 binds to the macrophage
and activates the p38 signalling pathway (which will activate the transcription factors SP1 and STAT3) and the ERK signalling pathway (which
will phosphorylate the H3 site of the il-10 promoter region). These transcription factors bind to this promoter site, inducing il-10 mRNA
expression [14]. IL-10 protein is subsequently produced and secreted. We assume that extracellular IL-10 binds to the IL-10 receptor of
macrophages and deactivates phospho-ERK, either by kinase inhibition or by phosphatase activation, hence regulating its own production in
a negative feedback loop. IL-10 regulation can also occur through a redundant negative feedback loop: the ERK signalling pathway induces
the expression of MKPs that can deactivate ERK.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3456 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
Fig. 2. Mathematical model of IL-10 production and regulation. The model receives the input stimulation (Av17) as a step function (from 0 to 1),
which activates ERK and p38. Phospho-ERK phosphorylates the H3 sites of the il-10 promoter region. Phospho-p38 activates the set of tran-
scription factors (A) necessary to induce il-10 gene expression, and il-10 mRNA expression (il-10
m
) and translation take place. IL-10 is secreted
by the macrophage (IL-10
e
), and promotes the feedback regulation. We hypothesize that extracellular IL-10 (IL-10
e
) binds to the macrophage
and deactivates phospho-ERK, either by kinase deactivation (model 1) or by phosphatase activation (model 2). IL-10 regulation can also be
achieved by an IL-10-independent inhibitor, X (model 3). These three models have in common the regulation by negative feedback.
Table 1. Description of reactions and its equations for the models of IL-10 production and regulation.
Reaction Description Equation
v
1
, v
2
ERK phosphorylation on Av17 stimulation

and constitutive dephosphorylation
v
1
¼ k
1
Á ERK tðÞÁ2
j
Á stðÞ
v
1model1
¼ k
1
Á ERK tðÞÁ2
j
Á stðÞ= 1 þ k
f
Á IL10
e
tðÞ
h
hi
v
1model3
¼ k
1
Á ERK tðÞÁ2
j
Á stðÞ= 1 þ k
f
Á XtðÞ

h
hi
v
2
¼ k
2
Á ERK
p
tðÞ
v
2model2
¼ k
2
Á ERK
p
tðÞÁk
f
Á IL10
e
tðÞ
v
3
, v
4
P38 phosphorylation on Av17
stimulation and constitutive
dephosphorylation
v
3
¼ k

3
Á 2
j
Á stðÞÁp38 tðÞ
v
4
¼ k
4
Á p38
p
tðÞ
v
5
, v
6
Transcription factor activation
and constitutive deactivation
v
5
¼ k
5
Á A Á p38
p
tðÞ
v
6
¼ k
6
Á A
p

tðÞ
v
7
, v
8
Histone phosphorylation
and dephosphorylation
v
7
¼ k
7
Á ERK
p
tðÞÁH3 tðÞ
v
8
¼ k
8
Á H3
p
tðÞ
v
9
, v
10
Complex formation, constituting
the transcription factor bound to the
phosphorylated H3 site and
constitutive disaggregation
v

9
¼ k
9
Á H3A
p
tðÞ
v
10
¼ k
10
Á H3
p
tðÞÁA
p
tðÞ
v
11
Induction of il-10 mRNA expression v
11
¼ k
11
Á H3A
p
tðÞ
v
12
Degradation of il-10 mRNA v
12
¼ k
12

Á IL10
m
tðÞ
v
13
Transcription and translation to
IL-10 intracellular protein
v
13
¼ k
13
Á IL10
m
tðÞ
v
14
Degradation of IL-10 extracellular protein v
14
¼ k
14
Á IL10
e
tðÞ
v
15
Production of X v
15
¼ k
15
Á ERK

p
tðÞ
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3457
We have implemented these models using ordinary
differential equations (ODEs) (Eqns 1–10), and fitted
them to experimental data on il-10 mRNA and IL-10
protein time series, and il-10 mRNA half-life. ODEs
are an effective way of mathematically describing the
dynamics of a biochemical reaction network through
its components and reactions [25,26]. These equations
allow the in silico representation of qualitative com-
plex systems and the quantification of their parame-
ters, providing insights into their emergent properties.
The models are also available in the SBML format,
which is a widely accepted standard of ODE models in
systems biology [27].
dERK
p
dt
¼ v
2
À v
1
(reversible) ð1Þ
dp38
p
dt
¼ v
4

À v
3
(reversible) ð2Þ
dA
p
dt
¼ v
5
À v
6
þ v
9
À v
10
ð3Þ
dA
dt
¼ v
6
À v
5
ð4Þ
dH3
dt
¼ v
8
À v
7
ð5Þ
dH3

p
dt
¼ v
7
À v
8
þ v
9
À v
10
ð6Þ
dH3A
dt
¼ v
10
À v
9
ð7Þ
dIL10
m
dt
¼ v
11
À v
12
ð8Þ
dIL10
e
dt
¼ v

13
À v
14
ð9Þ
dX
dt
¼ v
15
ð10Þ
Model fitting to the data
The different models were fitted to experimental data
on IL-10 protein and il-10 mRNA time series and il-10
mRNA half-life (for the half-life values, see Experi-
mental procedures). IL-10 protein and il-10 mRNA
time series were obtained by exposing murine macro-
phages to Av17 or NaCl ⁄ P
i
(as control experiment),
respectively. IL-10 protein and mRNA levels were
determined after several time points by ELISA and
quantitative real-time PCR, respectively (Fig. 3). For
experimental details, see Experimental procedures.
The maximum relative il-10 mRNA expression was
measured at 2 h after stimulation. After 4 h, the
mRNA levels reached background levels again. IL-10
protein in the cell supernatant was detectable after
2–3 h, showed a steady increase over time until 8 h,
and declined again after 14–24 h. We observed a
damped oscillation of the IL-10 mRNA between 4
and 8 h after stimulation. To determine whether this

was a biological effect, we repeated the same experi-
ment and obtained results similar to those expected
for the oscillatory effect (Fig. S1). We conclude that
the oscillation of the mRNA in Fig. 3 represents a
technical variation and is not an effect of the biolog-
ical system.
Model fitting was done using copasi [28] (see Exper-
imental procedures for the methods used). The differ-
ent regulation models fit the experimental data for
il-10 mRNA and IL-10 secreted protein (Fig. 4).
Model 0 fits the data with larger error. Figure 4 shows
the fitting of the model of Fig. 2 to IL-10 secreted
protein and il-10 mRNA. These data show that model
0 is not able to fit the decrease of IL-10 production
observed experimentally, keeping it at a sustained
level, whereas the models with regulation (model 1,
model 2, and model 3) can fit the increase and decrease
in IL-10 levels. For a complete listing of the best-
fitting parameters, constraints and initial conditions
for each model, see Doc. S1.
Fig. 3. IL-10 protein and IL-10 mRNA kinetics after stimulation of
macrophages with the helminthic immune modulator Av17. Thiogly-
collate-elicited peritoneal macrophages from BALB ⁄ c mice were
stimulated with 0.25 l
M recombinant Av17 or with NaCl ⁄ P
i
for the
indicated times. The level of IL-10 protein in cell supernatants was
quantified by ELISA. Levels of il-10 mRNA were determined by
real-time PCR, and are presented as expression relative to the

endogenous control GAPDH. All data points represent triplicates.
We show one representative experiment out of two that gave
similar results.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3458 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
Model selection
The fitting results allow to select models. The experi-
mental data (Fig. 3) show that IL-10 production in
macrophages after Av17 stimulation is transient. This
decrease in IL-10 production is evidence for its regula-
tion by negative feedback. Hence, we discard model 0,
which includes no regulation and which presents sus-
tained IL-10 production. Moreover, the disagreement
between fitted and experimental values is very high
(Fig. 4A). We calculated the Aikaike information crite-
rion (AIC) and the residual sum of squares (RSS)
between the estimated values and the experimental
data for the four models, and model 0 yielded the
highest value (compare Table 2).
It is experimentally observed that LPS-induced
IL-10 does not reach zero in the macrophage, after IL-
10 stimulation [18]. Because IL-10 production in model
3 (model of regulation via an inhibitor) approximates
zero, we discard model 3 and focus on the models of
regulation via IL-10 (model 1 and model 2).
Simulation predicts transient phosphorylation of
ERK and sustained phosphorylation of p38 after
Av17 stimulation
On the basis of the estimated parameters, we predict
the kinetics of phospho-ERK and phospho-p38 for

model 1 (Fig. 5A) and model 2 (Fig. 5B). These
Fig. 4. Fitted (lines) and experimental values (dots) for IL-10
secreted protein (maximum value at 8 h) and il-10 mRNA (maxi-
mum value at 2 h). (A) Model 0: no feedback. (B) Model 1: inhibi-
tion of ERK phosphorylation via IL-10. (C) Model 2: activation of
ERK dephosphorylation via IL-10. (D) Model 3: inhibition of ERK
phosphorylation via another molecule; the models fit the data for
the three regulation hypotheses. Model 0 follows the production of
IL-10, but cannot follow the decrease, because there is no regula-
tion, leading to cumulative production of IL-10, which reaches a
steady state.
Table 2. AIC and RSS for the four models. Model 0 yields the
highest value and model 3 the lowest value. AIC presents a relative
value that scores the model, the lowest value being the best score.
A low RSS value indicates that the difference between estimated
and experimental values is low, and a high value indicates the
reverse.
Model AIC RSS
0 )13.1693 0.370
1 )31.4408 0.157
2 )41.6533 0.114
3 )48.3893 0.076
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3459
models show transient phospho-ERK and sustained
phospho-p38 dynamics. These predictions are qualita-
tively in accordance with experimental evidence [7,8].
These authors show that activation of macrophages by
immune complexes leads to IL-10 production through
the activation of ERK (transient) and p38 (sustained).

Both models present weak and transient ERK and
strong and sustained p38 activation.
Changing the Av17 stimulus shows that both
ERK and p38 control the amplitude of IL-10
The density of the parasite population, and hence the
concentration of secreted Av17, can vary. To under-
stand how this affects the system behavior, we changed
the concentration of Av17 (increasing and decreasing
it) and examined how this affects the dynamics of key
elements of each model. We implemented one-step
exponential increases (from 2 to 2
30
) and decreases
(from 2
)10
to 2
0
), and compared their impacts on the
dynamics of phospho-ERK (Fig. 6), phospho-p38 and
IL-10 (Figs 7 and 8) for kinase deactivation (model 1)
and phosphatase activation (model 2).
Phospho-ERK
Changing the stimulus amplitude can switch ERK acti-
vation from transient to sustained. Phospho-ERK of
model 1 reaches maximal activation instantaneously,
and the input changes affect the signal duration as well
as the amplitude (Fig. 6A). The response to these input
variations in model 2 is different: first, they reach
maximal activation with a certain delay, which
decreases as the input increases; and second, they

affect only the phospho-ERK amplitude until ERK
Fig. 5. Phospho-ERK (transient curve) and phospho-p38 (sustained
curve) kinetics for model 1 (A) and model 2 (B). x-axis, time (h);
y-axis, concentration (AU). On the basis of the fitted parameters for
this model, we predict the dynamic behaviour of phospho-ERK
(black) and phospho-p38 (grey). (A) Model 1: ERK activation is weak
and fast; it lasts for 1 h, and is followed by an ERK decrease. At
2 h, the ERK concentration is zero, whereas the phospho-p38 con-
centration is sustained. (B) Model 2: ERK activation is weak and
fast; it has its peak at 1 h, and this is followed by an ERK decrease.
At 2 h, the ERK concentration is zero; the phospho-p38 increase is
slow and sustained, reaching steady state at 40 h.
Fig. 6. Phospho-ERK kinetics for different input amplitudes (2
)10
to
2
30
). (A) Model 1. (B) Model 2. Red line: input amplitude = 1.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3460 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
saturation, and after this level, ERK duration increases
(Fig. 6B).
The mechanism of kinase inhibition (model 1) was
assumed to have a cooperative behavior (Hill coeffi-
cient of 2.5). Therefore, the inhibition has a switch-like
behavior and becomes effective only with a certain
delay, during which the phospho-ERK is maximally
active. When the feedback ‘kicks in’, there is a rapid
deactivation of phospho-ERK following the plateau of
maximal activity (Fig. 6A). In model 2, the inhibition

by the increase in phosphate activity is assumed to be
a linear function of IL-10, and the inhibition therefore
constantly increases, causing direct deactivation after
reaching the maximum (Fig. 6B).
Phospho-p38
P38 activation is sustained for both models after con-
stant increases in the concentration of Av17. The
amplitude of phospho-p38 of both models increases
until saturation is reached [2 arbitrary units (AU)], but
as phospho-p38 activation is faster for model 1 than
for model 2, the former reaches saturation faster than
the later.
Extracellular IL-10 (IL-10
e
)
Figures 7 and 8 show how the different Av17 concen-
trations affect IL-10
e
amplitude, duration, and steady
state. By comparing Fig. 7A with Fig. 7B, we observe
a shift in IL-10
e
behaviour. In terms of signal ampli-
tude, for j =15 (v
1
and v
2
in Table 1), model 1 and
model 2 yield very similar maximal amplitudes. For
both models, a decrease in Av17 concentration shows

that the macrophage produces IL-10 after a certain
threshold of Av17 concentration is attained (compare
Fig. 8). IL-10 production overshoots and goes down
to a steady-state level. As the input concentration
increases, so does the maximum value of IL-10.
In terms of duration, although there is no significant
difference between model 1 and model 2 for IL-10 rise
time (time to reach maximum production), IL-10
downregulation is faster for the former. Moreover, the
Fig. 7. IL-10
e
kinetics for different Av17 concentrations (1 to 2
15
).
(A) Model 1. (B) Model 2. Red line: input amplitude = 1.
Fig. 8. IL-10
e
kinetics for different input amplitudes (2
)10
to 2
0
). (A)
Model 1. (B) Model 2. The results show that there is an effective
Av17 concentration level needed to start the production of IL-10.
Red line: input amplitude = 1.
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3461
difference between the maximum value and the steady-
state value is higher in model 1 than in model 2 (in this
model, the difference disappears for j = 13). These

observations correlate with phospho-ERK dynamics
(Fig. 5), which show a shift from transient to sustained
in both models. Model 1 shows faster activation of
ERK, followed by slower attainment of the sustained
level, which entails the same behaviour for IL-10 pro-
duction with respect to increasing Av17 concentration.
The different feedback mechanisms of model 1
(kinase inhibition) and model 2 (phosphatase activa-
tion) have implications for IL-10 dynamics. Model 2
shows more rapid and robust IL-10 dynamics as more
phosphatase accelerates dephosphorylation, whereas
the dephosphorylation rate of the kinase inhibition
mechanism is constant.
Sensitivity analysis
We performed a sensitivity analysis in order to under-
stand how perturbations in the system affect the out-
put (IL-10 production). Therefore, we perturbed, in a
systematic manner, all the parameters and checked
their influence on phospho-ERK, phospho-p38, il-10
mRNA, and IL-10 protein, in terms of amplitude and
steady state. The sensitivities were calculated using the
formula:
S ¼
DO
O
:
p
Dp
(O is the output and p is the perturbed parameter).
We imposed on these parameters perturbations of

10 and 0.1. The results show different behaviours for
model 1 and model 2. For model 1, the most sensitive
parameter is the Hill coefficient for the feedback inhi-
bition of ERK activation.
For model 2, k
9
and k
12
are the most sensitive
parameters, affecting the IL-10
e
steady state.The
parameter k
9
is associated with the production of
X2(t), which is the complex formed by the phosphory-
lated transcription factors bound to the phosphory-
lated chromatin of the il-10 promoter region. The
parameter k
12
is the parameter associated with il-10
mRNA production, and its value is the half-life of
il-10 mRNA. In terms of amplitude, the system is
insensitive for both models.
Fig. 9. Perturbations of phospho-ERK (model 1). Range of perturba-
tion: factor 10 and factor 0.1. Red line: no perturbation. Green line:
factor of perturbation is 0.1. Blue line: factor of perturbation is 10.
(A) Phospho-ERK: perturbing phospho-ERK affects its duration and
amplitude, but not steady state. (B) il-10 mRNA: perturbations
affect il-10

m
amplitude and the duration but not the steady state.
(C) IL-10 protein: perturbations affect IL-10
e
amplitude, duration and
steady state. (D) Phospho-p38 is not affected at all by phospho-
ERK perturbations.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3462 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
Perturbing phospho-ERK in model 1 affects IL-10
production but has no influence on phospho-p38
Figure 9 shows the variations of parameter k
1
of
model 1. In Fig. 9A, we can see the imposed
perturbations of phospho-ERK and observe that these
changes affect the duration and amplitude of phospho-
ERK. As the perturbation increases, the amplitude of
phospho-ERK increases and the duration decreases.
These same perturbations also affect the amplitude
and the duration of il-10 mRNA (Fig. 9B) and IL-10
protein (Fig. 9C). The steady state of IL-10 protein is
also affected, but not the steady state of il-10 mRNA.
These perturbations have no direct effect on p38, phos-
pho-p38 maintaining its curve over the whole perturba-
tion range (Fig. 9D).
Perturbing phospho-p38 in model 1 affects
phospho-ERK and IL-10 production
We perturbed the phosphorylation rate constant of
p38, k

3
(Fig. 10A) and observed that p38 activity,
although not directly affected by the negative feed-
back, has an indirect impact on the feedback mecha-
nism by influencing the production of IL-10 and,
consequently, ERK activity. This reveals autocrine
feedback between the MAPKs.
In this model, secreted IL-10 binds to the macro-
phage and promotes the dephosphorylation of phos-
pho-ERK, establishing in this way a negative
feedback mechanism. Hence, the production of IL-10
interferes with the ERK signalling pathway, higher
IL-10 production reflecting higher feedback strength
and lower duration of phospho-ERK (Fig. 11B–D).
By comparing both perturbations on parameters k
1
for phospho-ERK and k
3
for phospho-p38, we can
observe that phospho-ERK has a stronger influence
on il-10 mRNA and IL-10 protein amplitude and that
phospho-p38 exerts control over the feedback mecha-
nism strength.
Fig. 10. Perturbations of phospho-p38 (model 1). Range of pertur-
bation: factor 10 and factor 0.1. Red line: no perturbation. Green
line: factor of perturbation is 0.1. Blue line: factor of perturbation is
10. (A) Phospho-p38: perturbing phospho-p38 affects its amplitude.
(B) Phospho-ERK: phospho-ERK is sensitive to phospho-p38 pertur-
bations, owing to the feedback mechanism. Its amplitude maintains
a constant level, its duration increases as the perturbation

decreases, and for a perturbation factor of 0.1, its steady state
increases. (C) il-10 mRNA: amplitude and duration are sensitive to
perturbations of phospho-p38, but not the steady state. (D) IL-10
protein: amplitude, duration and steady state are sensitive to per-
turbations of phospho-p38.
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3463
Perturbing phospho-ERK in model 2 affects IL-10
production but has no influence on phospho-p38
We perturbed ERK activation by factors of 0.1 and 10
(Fig. 11A) and, as in the previous case, we see that
perturbations of ERK do not affect p38 activation
(Fig. 11D). They affect IL-10 production at the protein
and mRNA levels in terms of amplitude and duration,
but only IL-10 protein in terms of steady state, simi-
larly to model 1 (Figs 11B and 12C). ERK activation
is also affected by its perturbations, in terms of ampli-
tude and duration, but not its steady state (Fig. 11A).
Perturbing p38 activation in model 2 affects
ERK activation but has no significant influence
on IL-10
As in model 1, p38 perturbations affect ERK activa-
tion, owing to the feedback mechanism. A high pertur-
bation reflects high feedback strength, as observed by
a lower amplitude and duration of phospho-ERK
(Fig. 12A). In terms of steady state and duration,
IL-10 protein and mRNA are not affected. The curves
for no perturbation and a perturbation factor of 10
are identical. For a perturbation factor of 0.1, the
amplitude is lower (Fig. 12B,C). Finally, in Fig. 12D

we see that p38 activation is faster with increasing
perturbation factor.
Discussion
We modelled the production and regulation of IL-10
via ERK and p38 signalling by macrophages after
Av17 stimulation. Quantification of IL-10 in the super-
natant over a certain time course indicates that there is
a regulatory mechanism that reduces IL-10 concentra-
tion until a constant basal level is reached. This moti-
vated us to design models with three different negative
feedback mechanisms and one without feedback, and
to fit them to the observed time course for il-10
mRNA and IL-10 protein quantities.
IL-10 protein, secreted from the macrophage, binds
to the same cell through the IL-10 receptor and deacti-
Fig. 11. Perturbations of ERK activation (model 2). Range of pertur-
bation: factor 10 and factor 0.1. Red line: no perturbation. Green
line: factor of perturbation is 0.1. Blue line: factor of perturbation is
10. (A) Phospho-ERK: perturbing phospho-ERK affects its ampli-
tude, but not duration or steady state. (B) il-10 mRNA: perturba-
tions affect il-10
m
amplitude and duration, but not steady state. (C)
IL-10 protein: perturbations affect IL-10
e
amplitude, duration, and
steady state. (D) Phospho-p38 is not affected at all by phospho-
ERK perturbations.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3464 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS

vates ERK either by kinase deactivation (model 1) or
by phosphatase activation (model 2). Model 3 assumes
that ERK activates genes with a negative feedback
loop effect on itself. It has been shown in the literature
that dual-specificity phosphatases, a group of MAPK
phosphatases, can deactivate ERK by negative feed-
back [29,30].
The model fitting to time series on IL-10 protein
and il-10 mRNA production allowed us to reject
model 0 and model 3. Model 0 was not able to follow
the decrease in IL-10 production. Model 3 predicted a
decrease in IL-10 protein to zero, which is not
observed experimentally. We thus focused our analysis
on models 1 and 2, which involve IL-10-dependent
regulation. First, we analysed the dynamics of ERK
and p38. Both models suggest transient ERK and
sustained p38 activation.
Bone marrow macrophages exposed to immune
complexes and Leishmania mexicana present transient
ERK and sustained p38 activation [8]. Other authors
have pointed out that transient dynamic behaviour of
ERK could be due to internalization and degradation
of the growth factor receptor [24], a scenario not con-
sidered in our work. Our models assume a constant
input of Av17, which binds to the macrophage at
a constant rate. This follows from the biological
fact that the parasitic nematode A. viteae secretes
Av17 in a constant fashion [3]. In a more systemic
view, the feedback effect could have implications for
the macrophage fate (activation ⁄ deactivation and

differentiation). Av17 binds to the macrophages and
induces IL-10 production [5], and IL-10 deactivates
macrophage function [31]. We hypothesize that this
deactivation is achieved by the regulation of ERK via
IL-10. Other studies have shown that sustained activa-
tion of ERK on macrophage-colony-stimulating-factor
(M-CSF) mediated macrophages leads to differentia-
tion [32]. In mammalian PC12 cells, different ERK
dynamics change the cell fate [20,22,24]. This raises the
question of whether different ERK dynamics change
the fate of Av17-exposed macrophages.
Fig. 12. Perturbations of p38 activation (model 2). Range of per-
turbation: factor 10 and factor 0.1. Red line: no perturbation.
Green line: factor of perturbation is 0.1. Blue line: factor of per-
turbation is 10. (A) Phospho-ERK: perturbing phospho-p38 affects
phospho-ERK amplitude and duration but not steady state.
(B) il-10
m
is insensitive except for a perturbation factor of 0.1,
which shows a decrease in amplitude. (C) IL-10 protein: shows
the same behaviour as il-10 mRNA. (D) Phospho-p38: phospho-
p38 is sensitive to its perturbations in terms of activation (its
activation is faster as the interference increases) and in terms of
amplitude in a linear manner.
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3465
Second, we changed the input concentration and
checked key features of ERK, p38, and IL-10,
namely signal amplitude and signal duration. The
results show that activation of phosphatases (model

2) is a more efficient negative feedback mechanism
for limiting signal duration than inhibition of kinases
(model 1). This is because more phosphatase acceler-
ates dephosphorylation, whereas the dephosphoryla-
tion rate of the kinase inhibition mechanism is
constant.
Increasing input stimulation changes the duration
(from transient to sustained) and the amplitude of
ERK dynamics in both models. p38 dynamics after
increases in Av17 are sustained for both models, but
model 1 presents faster activation than model 2. Horn-
berg et al. [33] have shown that phosphatases tend to
control signal duration and amplitude, which is in
accordance with our results.
Av17 increases change IL-10 dynamics. IL-10
needs a certain minimum level of Av17 to be pro-
duced and, as we increased the concentration of
Av17 in our models, the IL-10 concentration rose.
However, the downregulation of IL-10 production
became less, and sustained production of IL-10 was
achieved. This raises the question of what the effect
of high levels of IL-10 production is on the macro-
phage population in particular and on the immune
system in general.
Third, we performed a sensitivity analysis to deter-
mine how perturbations in the system affect the output
(IL-10 production). The results show that the para-
meter associated with ERK activation (k
1
) influences

the amplitude, duration and steady state of IL-10
e
in
model 1 and model 2. The parameter associated with
p38 activation (k
3
) influences the amplitude, duration
and steady state of IL-10
e
in model 1. These perturba-
tions yield a different result for model 2, which reveals
no change in IL-10
e
behaviour except for the perturba-
tion factor of 0.1, affecting IL-10
e
amplitude. Perturb-
ing ERK activation has no effect on p38 activation,
but perturbing p38 activation affects ERK activation,
revealing a prominent role of p38 in the feedback
mechanism.
Understanding the process behind IL-10 regulation
and macrophage deactivation could open the door to
understanding the role of ERK in macrophage fate.
Av17 and IL-10 deactivate macrophages. This could
be a consequence of the transient time course of
ERK. How IL-10 deactivates ERK is still an open
question. Staples et al. [18] reported that IL-10 acti-
vates the JAK–STAT signalling pathway when
bound to macrophages, and induces IL-10 protein

and il-10 mRNA production on activated macro-
phages, but can also suppress il-10 mRNA produc-
tion in the same cells. Other studies have found that
IL-10 activates JNK1, which in turn activates MKP1
expression. As the lack of MKP1 causes prolonged
ERK activation [34], this would suggest that MKP1
is a feedback mediator.
In this article, we propose a mechanism by which
Av17 triggers macrophages to produce IL-10, but how
this immunomodulatory protein binds to the macro-
phage is still an open question. The malarial parasite
Plasmodium falciparum produces proteins that bind to
the scavenger receptor CD36 and activate the ERK
signalling cascade [35], diverting the monocytes to
IL-10 production. In contrast, Leishmania mexicana-
infected mice present minimal inflammatory responses
and chronic disease. Yang et al. [8] have shown that
L. mexicana binds to FccR and also activates the
ERK signalling cascade, and Buxbaum and Scott
[36] have shown that removal of IL-10 or FccR
leads to resolution of L. mexicana disease. This sug-
gests that engagement of FccR through parasite
products or immune complexes leads to IL-10 pro-
duction. A third path is adopted by eggs of the
blood fluke Schistosoma mansoni that release phos-
phatidylserines. These phospholipids bind to and
activate Toll-like receptor 2, an event leading to the
production of IL-10. All of these receptors (CD36,
FccR, and Toll-like receptor 2) signal through the
ERK signalling cascade, which makes them attractive

for studying the production and regulation of this
cytokine via Av17. Understanding what is the recep-
tor addressed by Av17 could pave the way to under-
standing the dynamics of Av17 and macrophages
and allow refining of the mathematical model that
we currently have.
A mathematical model goes hand in hand with
experimental models. It is a caricature of reality, and
its features depend on the question that the investiga-
tor wants to answer. The aim of this work was to
understand how IL-10 is produced and regulated in
macrophages after Av17 stimulation.
We have shown that: (a) IL-10 is regulated in an
autocrine fashion; (b) phospho-ERK is transient and
phospho-P38 is sustained; (c) kinase deactivation is a
more sensitive mechanism of feedback than phospha-
tase activation; and (d) phospho-p38 affects ERK via
secreted IL-10, revealing autocrine crosstalk between
the two MAPKs. In order to verify the model, we pro-
pose two experiments: (a) test the dose response of
IL-10 protein and phospho-ERK to Av17 increases;
and (b) inhibit the autocrine IL-10 signalling pathway
with anti-IL-10 antibodies and measure il-10 mRNA
and phospho-ERK time series.
Modelling interleukin-10 production and regulation A. S. Figueiredo et al.
3466 FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS
Experimental procedures
Cell isolation and stimulation
Thioglycollate-elicited peritoneal macrophages were isolated
from 8–10-week-old male BALB ⁄ c mice using standard

procedures [37]. Cells were seeded in 24-well plates at a
cell density of 10
6
per well, and cultured in DMEM
supplemented with 10% fetal bovine serum, 1 mml-gluta-
mine, and 100 U ⁄ mL penicillin G and 100 lg ⁄ mL strepto-
mycin. After 18 h, the cells were washed twice with fresh
medium and stimulated with 0.25 lm rAv17 or with
NaCl ⁄ P
i
. The protein was affinity purified and LPS decon-
taminated as described previously [6]. After each time
point, the cell-free supernatants were stored at )20 °C until
further analysis, and cell lysates were stored at )80 °Cin
RNA extraction buffer.
ELISA and real-time PCR
Quantification of IL-10 protein was performed with an
ELISA, according to the protocol of the manufacturer (BD
Biosciences, Heidelberg, Germany). For real-time PCR,
RNA was extracted from cell lysates using the Invisorb
RNA Extraction Kit (Invitec, Berlin, Germany), and
reverse transcribed using the TaqMan Reverse Transcrip-
tion Kit (Applied Biosystems, Darmstadt, Germany).
Real-time PCR was performed using a 7300 Real-Time
PCR System (Applied Biosystems), using TaqMan reagents
for IL-10 (primers and probe: Mm00439616_n1) and glycer-
aldehyde 3-phosphate dehydrogenase (primers and probes:
Mm99999915_g1). PCR conditions were 95 °C for 10 min
followed by 40 cycles of 95 °C for 15 s and 60 °C for
1 min. IL-10 transcript levels of Av17-stimulated cells were

standardized to the endogenous control and expressed as
fold change over transcripts from the control cells [38].
Model fitting
Each model was fitted to experimental data (IL-10 protein,
il-10 mRNA, and IL-10 half-life). The half-life of il-10
mRNA was extracted from the literature (Table 3) [39–41],
and the respective parameter (k
12
) was set to the average of
the available values. Fitting was performed with copasi
[28], using the algorithm for Evolutionary Programming.
Acknowledgements
We thank J. Schaber for stimulating discussions on sig-
nalling cascades and mathematical modelling, S. Legewie
for stimulating discussions on signalling cascades and
regulation motifs, and T. Buhrke for providing us with
access to unpublished data of IL-10 protein and il-10
mRNA time series. This research was funded by SFB
618: Theoretische Biologie.
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Supporting information

The following supplementary material is available:
Fig. S1. Il-10 mRNA and IL-10 protein kinetics (repli-
cate experiment).
Doc. S1. Initial conditions, constraints and parameters
for models 1–3.
This supplementary material can be found in the
online version of this article.
Please note: Wiley-Blackwell is not responsible for
the content or functionality of any supplementary
materials supplied by the authors. Any queries (other
than missing material) should be directed to the corre-
sponding author for the article.
A. S. Figueiredo et al. Modelling interleukin-10 production and regulation
FEBS Journal 276 (2009) 3454–3469 ª 2009 The Authors Journal compilation ª 2009 FEBS 3469