B6
Switching towards Termination – Modulation of the Dynamic
Behaviour of the SMAD Signaling Cascade
Projectleaders:
Experimental:

PD Dr. U. Klingmüller, DKFZ, Heidelberg,
+49 6221 42 4481, u.klingmueller@dkfz-heidelberg
Modeling:

Prof. J. Timmer, Center for Data Analysis and Modeling, University of
Freiburg, +49 761 203 5829, jeti@fdm.uni-freiburg.de
Summary:
Based on time-resolved quantitative data for the activation of the SMAD signaling
cascade in primary hepatocytes (Klingmüller et al., IEE Proc Systems Biology) we
established a data-based mathematical model indicating the importance of negative
feed-back loops for determining the dynamic behavior of the signaling cascade. To
extand this model we will examine the activation of the transforming growth factor
(TGF)beta -activated kinase (TAK)1 and the Nemo-like kinase (NLK). To understand
mechanisms promoting the switch towards termination of hepatocyte regeneration
we will monitor cross-talk with other signaling cascades that could influence signal
ingtegration by the TGFbeta stimulated signaling. Since recent evidence suggests
that serine phosphorylation of STAT3 contributes to TGFbeta-mediated
developmental processes, potentially including termination of hepatocyte
regeneration, we will compare the induction of target genes in response to TGFbeta
stimulation in normal versus hepatocytes obtained from S727A STAT3 knock-in mice
(collaboration with B1). Furthermore, we will examine the induction of the
phosphoinositide 5 phosphatase SHIP-1 that is a target gene of SMAD2/3. Since
SHIP-1 is an important regulator of PI3 kinase signaling that is activated by the Met
receptor during the proliferative phase, we will focus in collaboration with B5 on the
suppressive effects of SMAD signaling on c-met receptor signaling.The stoichimoetry
of pathway components and cell state specific alterations will be monitored by
quantitative proteomics. Target gene induction will be examined initially by
microarray analysis and later on by qRT-PCR. To address spatial effects,
fluoresecently labeled SMAD and TAK1 will be expressed at endogenous levels by
applying the Tet-inducible system developed in B5. The dynamics of nuclearcytoplasmic cycling of SMAD will be quantitatively monitored by live cell imaging and
the kinetics of the TAK1 and STAT3 interaction will be analyzed by fluorescence
resonance energy transfer. The aquired data will be used to establish a spatiotemporal model. The comprehensive model of SMAD signaling will be employed to
identify mechanisms promoting the coordinated induction of the regeneration of
hepatocytes.
Work Plan:
Year 1:
(a)
We will monitor the time course of TAK-1 activation in response to TGFbeta
stimulation and compare it to the acctivation of SMAD2 and 3. Furthermore the
impact of TGFbeta stimulation on the extent and duration of STAT 3 serine
phosphorylation will be analyzed by quantitative immunoblotting.
(b)
To monitor the transition between the priming and the proliferative phase
assays to quantify the induction of DNA synthesis in primary hepatocytes will be
established at a single cell level by Bromodeoxyuridine (BrdU) incorporation and
in the cell population by measuring the incorporation of 3H-Thymidine.
(c)
To determine the stochiometry of the pathway components and determine
absolute numbers we we will purify in addition to SMAD2 and 3, TGFbeta type I
and II receptor, TAK-1 and determine phosphorylated and unphosphorylated
peptides suitable for quantitative analysis (based on the experience in the sugar
grant and collaboration with A3).
Year 2
(a)
Time-resolved quantitative data for activation of TAK-1 will be used to extend
our model for the TGFbeta stimulated activation of the SMAD signaling cascade.
Furthermore TGFbeta induced target genes will be quantified by Realtime PCR
and used to extend the mathematical model.
(b)
Quantitative measurements by massspectrometry for selected time points and
comparison to quantitative immunoblotting.
(c)
Establishment of GFP-tagged SMAD2 and 3 and of fluorescently labeled TAK1
suitable for FRET analysis with STAT3.
Year 3
(a)
Incorporation of the autocrine loop and other modes of cross-talk into our
mathematical model. Predicting events required for the transition from priming
phase towards proliferation and experimental validation.
(b)
Experimental validation of the importance of serine phosphorylatin of STAT3
on TGFbeta mediated responses. The time course of TGFbeta stimulated
signaling pathways and target gene activation in primary hepaotcytes from S727A
STAT3 knock-in mice.
(c)
Time-resolved quantitative proteomics and quality control by quantitative
immunoblotting.
(d)
Spatio-temporal modeling of nuclear-cytoplasmic cycling of SMAD2 and 3 and
of the cross-talk with the JAK1-STAT3 signaling cascade.
Milestones:
Extending our SMAD signaling cascade model by incorporating the dynamics of
TAK-1 and NLK-1 activation
Establishing reliable assays to monitor the induction of DNA synthesis in primary
hepatocytes
Identifiying the cross-modulatory effects on STAT3 signaling
Determining the dynamics of SHIP-1 induction
Identification of the suppressive effects on c-met receptor signaling
Establishing a spatio-temporal model of SMAD2 and 3 nucelar-cytoplasmic cycling
and cross-talk with the JAK1-SAT3 signaling cascade.
Budget Klingmüller
Personal:
One graduate student (BATIIa/2) with knowledge in molecular biology
and cell biology as well as an interest in live cell imaging
Consumables:
Preparation, cultivation and stimulation of hepatocytes (purchasing
BL6 mice, collagenase, collagene treated plates, FCS, Williams
medium, TGFbeta and TNFalpha)
73.780
10.000
Quantitative immunoblotting (primary antibodies for TGFbeta type I
and II, SMAD2, 3, 4, 7, SnoN, TAK1, NKL-1, STAT3, phospho-specirif
antibodies and secondary antibodies, nitrocellulose/PVDF membrane,
BSA, recombinant proteins as calibroators and antibodies against
actin, HSC70, PDI, calnexin, clathrine as normalizer)
Mass spectrometry (recombinant proteins for the identification of
suitable peptides and isotope labeled or mutated as standards, isotope
labeled peptides (AQUA technology from Sigma)
Generation of fluorescent labeld SMAD2 and 3 and TAK1 and live cell
imaging (restriction enzymes, chambers for live cell imaging), Target
gene activation (Affymetrix microarrays, primers and polymerase for
qRT-PCR)
20.000
10.000
20.000
60.000
Budget Timmer
Personal:
One graduate student (BATII/a/2) for mathematical modeling
85.650
Budget Travel:
4 trips to Freiburg per year per person for the experimental group, 4
trips to Heidelberg for the modeling group, participation in 1 European
conference in the first two years and 1 international conference in the
third year.
10.000
Gemeinkosten
Is doch jetzt geklärt fürs DKFZ, wie viel darf da angesetzt werden
?????
References:
Heinrich, A. C., R. Pelanda, U. Klingmüller. A Mouse Model for Visualization and
Targeted Mutations in the Erythroid Lineage. Blood. (2004) 104(3):659-66.
Klingmüller, U., A. Bauer, S. Bohl, P. J. Nickel, K. Breitkopf, S. Dooley, S. Zellmer, C.
Kern, I. Merfort, T. Sparna, J. Donauer, G. Walz, M. Geyerr, C. Kreutz, M.
Hermes, F. Götschel, A. Hecht, D. Walter, L. Egger, K. Neubert, C. Borner, M.
Brulport, W. Schormann, C. Sauer, F. Baumann, R. Preiss, S. MacNelly, P.
Godoy, E. Wiercinska, L. Ciuclan, P. Illes, K. Zeilinger, M. Heinrich, U. M. Zanger,
M. Reuss, A. Bader, R. Gebhardt, T. Maiwald, J. Timmer, F. von Weizsäcker, J.
G. Hengstler Primary mouse hepatocytes for systems biology approaches: a
standardized in vitro system for modeling of signal transduction pathways. IEE
Proc Systems Biology in press.
Schilling, M., T. Maiwald, S. Bohl, M. Kollmann, C. Kreutz, J. Timmer, U. Klingmüller.
Computational Processing and Error Reduction Strategies for Standardized
Quantitative Data in Biological Networks. FEBS Journal, 272, 6400-6411.
Schilling, M., T. Maiwald, S. Bohl, M. Kollmann, C. Kreutz, J. Timmer, U. Klingmüller.
Quantitative Data Generation for Systems Biology – The Impact of Randomisation,
Calibrators and Normalisers. In press in IEE Proc Systems Biology.
Swameye, I., T. G. Müller, J. Timmer, O. Sandra, U. Klingmüller. Identification of
nucleocytoplasmic cycling as a remote sensor in cellular signaling by data-based
dynamic modeling. PNAS (2003) 100:1028-33.