EP1392850A2 - Procede, systeme et leur utilisation pour l'identification de cibles par cyclage nucleocytoplasmique - Google Patents

Procede, systeme et leur utilisation pour l'identification de cibles par cyclage nucleocytoplasmique

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Publication number
EP1392850A2
EP1392850A2 EP02750862A EP02750862A EP1392850A2 EP 1392850 A2 EP1392850 A2 EP 1392850A2 EP 02750862 A EP02750862 A EP 02750862A EP 02750862 A EP02750862 A EP 02750862A EP 1392850 A2 EP1392850 A2 EP 1392850A2
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EP
European Patent Office
Prior art keywords
stat
epor
stat5
model
dynamical
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Application number
EP02750862A
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German (de)
English (en)
Inventor
Ursula KLINGMÜLLER
Ira Swameye
Jens Timmer
Thorsten G. MÜLLER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Max Planck Gesellschaft zur Foerderung der Wissenschaften eV
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Max Planck Gesellschaft zur Foerderung der Wissenschaften eV
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Priority to EP02750862A priority Critical patent/EP1392850A2/fr
Publication of EP1392850A2 publication Critical patent/EP1392850A2/fr
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P43/00—Drugs for specific purposes, not provided for in groups A61P1/00-A61P41/00

Definitions

  • the present invention relates to a method, a system and the use of the method and/or the system for identifying targets, for efficient medical intervention and/or for determining the effect of therapeutic agents and/or for remotely sensing cellular signaling by nucleocytoplasmic cycling.
  • Signaling pathways form complex intracellular networks that control proliferation, differentiation and survival. To understand these networks at the system level the dynamic interactions of individual components have to be examined which is facilitated by mathematical models (1 , 2). Previous attempts to model signaling pathways have been primarily based on qualitative data reflecting various interactions between the components and on simulations with ad hoc fixed parameters (3-6). However, to quantitatively predict the behavior of signaling pathways data driven models are required (7). To introduce meaningful simplifications and to establish a mechanism based model, detailed qualitative knowledge of a signaling pathway is necessary. A signaling pathway that has been studied in great detail is the JAK-STAT pathway (8-10).
  • This pathway is involved in signaling through multiple cell surface receptors including hematopoietic cytokine receptors such as the erythropoietin receptor (EpoR) (11, 12). Signal transmission is initiated by ligand induced activation of a receptor bound Janus kinase (JAK) facilitating rapid tyrosine phosphorylation of the receptor cytoplasmic domain.
  • JAK-STAT pathway mediating rapid signal transduction from the cell surface receptor to the nucleus is represented by STAT-proteins that are tyrosine phosphorylated upon recruitment to the activated receptor and migrate as dimers to the nucleus where they stimulate the transcription of target genes (8-10).
  • the molecular composition of signaling pathways has been studied in detail, but the dynamics of information processing is not understood. The dynamics of molecular signaling are, however, required for determining suitable targets, the activity of which must be modified for efficient medical treatment.
  • compositions comprise drugs which act as modifiers of the activities of signalling molecules, such as ligands of receptor molecules.
  • signalling molecules such as ligands of receptor molecules.
  • most conventional therapies are unspecific and could be replaced by more efficient and specific therapies.
  • a critical issue for the efficacy and specificity of a therapy is to determine key targets which should be modified by a drug. If the key targets are known, a more effective drug can be identified by suitable screening assays or be designed based on known lead compounds. Suitable means for determining targets which are therapeutically valuable as described above have not been described yet but are nevertheless highly appreciated.
  • the method of the present invention forms the basis for the development of new or improved drug therapies wherein the efficacy and specificity of the treatment is improved while the undesireable side effects are reduced or avoided.
  • the method of the present invention can be integrated into a production process for said new or improved drugs.
  • said production process comprises at least the further step of producing, identifying and/or formulating a drug which efficiently modifies the activity of a target identified by the method of the invention in a therapeutically useful form.
  • the present invention relates to a method to determine a mathematical model of the core module of the JAK-STAT signaling pathway based on experimental data.
  • Fig.1 Time course (points with error bars) and mathematical modeling (solid lines) of the STAT5 nucleocytoplasmic cycle.
  • IP immunoprecipitation
  • PTyr anti-phosphotyrosine
  • the Lumilmager files are displayed.
  • B Linear interpolation of EpoR tyrosine phosphorylation as input function for the four dimensional differential equation. The time course of EpoR tyrosine phosphorylation in response to Epo stimulation was quantified with LumiAnalyst software and is displayed in arbitrary units.
  • C) and (D) show for the cytoplasmic tyrosine phosphorylated STAT5 and the total STAT5 pool in the cytoplasm the measured data in arbitrary units and the corresponding fit obtained with the linear model (C) and the model including nucleocytoplasmic cycling (D) (28).
  • Fig. 2 Predicting the behavior of the STAT5 nucleocytoplasmic cycle based on the dynamical parameters determined in the previous experiments.
  • A Time course of EpoR tyrosine phosphorylation was used as input function to model
  • B STAT5 tyrosine phosphorylation in the cytoplasm
  • C the total amount of cytoplasmic STAT5 in an independent experiment. Points with error bars indicate the experimental data whereas solid lines represent the mathematical modeling. The indicated error was determined based on duplicated measurements.
  • Fig. 3 In silico investigations.
  • A Time courses of unobserved individual STAT5 populations. Depicted is the predicted quantitative behavior of unphosphorylated STAT5 (blue line), tyrosine phosphorylated STAT5 monomers (black line) and dimers (green line) in the cytoplasm and of cycling activated STAT5 molecules in the nucleus (red line).
  • B Predicted the effect of parameter variations on target gene activation. As an indirect indicator for target gene activation the amount of nuclear activated STAT5 involved in cycling was determined by calculating the area under the red curve in (A). The effect of relative changes of the dynamical parameters k (black line), k 2 (blue line), 1 (green line), k ⁇ (yellow line) and ⁇ (red line) on the integrated area is shown.
  • Fig. 4 Effect of impaired nuclear export on the amount of activated STAT5 in the cytoplasm and the transcriptional yield.
  • A Time courses of cytoplasmic STAT5 and EpoR phosphorylation as Lumilmager files (upper panels) and corresponding quantification of STAT5 phosphorylation.
  • Cytoplasmic extracts were subjected to immunoprecipitation with anti-STAT5 antiserum followed by immunoblotting analysis with an anti- phosphotyrosine antibody.
  • Quantification shown in the lower panel was performed using the LumiAnalyst software. The quantification of a representative experiment is shown in Boehringer Light Units (BLU).
  • BLU Boehringer Light Units
  • C Effect of nuclear export inhibition on activation of a STAT5 reporter gene.
  • the STAT5 reporter construct pSac-CIS and as a control pSac-CIS-STAT lacking the STAT5 binding sites were introduced by electroporation into starved BaF3-EpoR cells.
  • the cells were either left untreated or were pretreated with LMB and then unstimulated or were stimulated with 5 U/ml Epo. After 120min cell lysates were prepared and used for the determination of ⁇ ⁇ -galactosidase activity.
  • the results are displayed in relative light units (R.L.U.) and represent the mean of triplicate measurements ⁇ SD normalized to protein content.
  • Fig. 5 Time course (points with error bars) and mathematical modeling (solid lines) of the STAT5 signaling pathway (a) fit with model 2, the feed-forward model with back-reaction (b) fit with model 4, the preferred model discussed above with additional back-reaction (c) fit with model 5, the preferred model discussed above with additional delay-distribution.
  • the experimental data and the corresponding fits are shown for cytoplasmic tyrosine phosphorylated STAT5 (left panel) and total cytoplasmic STAT5 (right panel).
  • Tyrosine phosphorylation of the receptor reflects the extent of JAK2 activation and the effect of negative regulatory molecules including SOCS family members (13) and the tyrosine phosphatase SHP-1 (14).
  • PIAS protein inhibitor of activated STAT
  • Potential cytoplasmic STAT-dephosphorylation is not considered, since the nucleus has been identified as the major compartment for STAT-dephosphorylation (15).
  • the scaling factor k ⁇ can not be disentangled from the rate constant k ⁇ , and k 2 is coupled to , (0) (for detailed discussion of the identifiable parameter combinations see (16)).
  • the identifiable parameter combinations were simultaneously estimated based on results from three independent experiments while the nuisance parameters k 5 ,k 6 and k ⁇ were determined separately for each experiment.
  • the time courses of the fitted model are displayed in Fig. 1 D. They describe all features of the experimental data including the plateau of phosphorylated STAT5 in the cytoplasm between 10 and 30 min.
  • tyrosine phosphorylated STAT5 monomers are formed, but rapidly converted into STAT5 dimers, providing an explanation for the technical difficulty of detecting monomeric tyrosine phosphorylated STAT5 experimentally.
  • the amount of tyrosine phosphorylated STAT5 dimers reaches an initial maximum after approximately 7min and then declines due to nuclear translocation.
  • Dephosphorylated STAT5 monomers are subsequently exported from the nucleus and rapidly re-phosphorylated at the activated receptor, thereby resulting in a second maximum of tyrosine phosphorylated STAT5 dimers in the cytoplasm after approximately 17 min. Therefore by mathematical modeling it can be revealed that the experimentally observed plateau of tyrosine phosphorylated STAT5 in the cytoplasm between 10 to 30min is the result of repetitive reactivation facilitated by nucleocytoplasmic cycling of STAT5.
  • Fig. 3A shows that unphosphorylated STAT5 in the cytoplasm is limited and approaches zero approximately 9min after Epo stimulation.
  • Data driven mathematical modeling identifies nucleocytoplasmic cycling as an essential behavior of the JAK-STAT core module and should be applicable to other STAT-family members since STAT-1 , a STAT5 related molecule, is dephosphorylated in the nucleus and then exported to the cytoplasm (15, 20, 21).
  • reporter gene assays (23) were performed (Fig. 4C) comparing the Epo induced induction of ⁇ -galactosidase by reporter gene vectors harboring the CIS promoter limited to the STAT5 binding sites (pSac-CIS) or harboring point mutations that inactivate the respective sites (pSac- CIS-STAT " ) (24).
  • the paper is organised as follows: In Section 2 we describe the biochemical background and formulate the mathematical properties based on a priori knowledge. Furthermore we describe the experimental setup focusing on questions related to partial observability of the system and on measurement noise. In Section 3 we reformulate the mathematical problem and resolve the question of cycling as a testing problem. Moreover we describe how to deal with s ecial nonstandard conditions and present the results from this investigation. In Section 4 we present two candidates of global parametric models which comply with the results from the previous section and test for compatibility with the data.
  • signal transmission is initiated within the cell leading to a cascade of biochemical reactions. In many cases this leads to migration of s ecific components into the nucleus where target genes are stimulated (Klingmuller et al., 1996).
  • a signaling pathway that has been studied in great detail is the JAK-STAT pathway (Darnell, 1997; Pellegrini and Dusanter-Fourt, 1997) which can be activated by several receptors.
  • Epo erythropoietin
  • EpoR Epo receptor
  • the unphosphorylated STAT5 component (Signal Transducer and Activator of Transcription) is phosphorylated.
  • phosphorylated STAT5 molecules form dimers which are then able to enter the nucleus and to stimulate activation of target genes, see Fig.l (Has el et al., 1996).
  • this signalling pathway can be described as a dynamical system consisting of one activation function, the time course of the activated EpoR, and four dynamical variables: unphosphorylated STAT5, phosphorylated STAT5, dimeriased STAT5 and STAT5 in the nucleus.
  • E(t) is the time course of the activated EpoR at time t s serving as input function to the system.
  • x ⁇ (t) t x?z (t) and -Cg(f-) are the amounts of unphosphorylated, phosphorylated and dimeri- ⁇ ed STATS in the cytoplasm, xAt-) is the amount of STATS in the nucleus.
  • the distribution of the test statistic L critically depends on the amount of information that can be drawn from the system. Before we deal with the actual setting of the recorded experimental data, we will investigate other realistic experimental settings and their influence on the distribution of the test statistic.
  • Model selection between models A and B may be also resolved by approximating the probability distribution of estimated parameter p, ⁇ with a bootstrap procedure (Efron, 1982; Hinkley, 1984; Efron and Tibshirani, 1993). Drawing 10 4 different bootstrap samples from the original data set and computing parameter estimates for every bootstrap sample, we obtain an approximation of the probability distribution for p as displayed in Fig.5, solid line. It is important to note that using this distribution to test if p ⁇ % is significantly greater than zero does produce false positive results. Our simulation reveals that the estimate of 4 is biased, see Fig.5 dashed fine. This stems from the fact that it is not possible to obtain a maximum likelihood estimator due to the complex dependency structure of the random variables.
  • V2 -r ⁇ V ⁇ D - ⁇ $
  • yz rg (t ⁇ + v% + vg)
  • ⁇ 4 +r V 3 [-r 4 v 4 ] ⁇ [-rsvsit - ⁇ )]z-
  • Figs. 6 to 11 shall provide a better but not limiting understanding of the invention and/or preferred embodiments thereof:
  • Fig. 10 Approximated probabiity density for the estimated dynamical parameter p .
  • Left curve under the nul hypothesis, right curve: with a bootstrap procedure with 10 4 bootstrap samples.
  • the wildtype EpoR was cloned into the retroviral expression vector pMOWS (27) and introduced into BaF3 cells by retroviral transduction.
  • Cell lines stably expressing the EpoR (BaF3-EpoR) were selected in the presence of puromycin (18).
  • Starved BaF3-EpoR cells were stimulated with 5 U/ml Epo (Janssen-Cilag) for the time indicated. For each time point 10 7 cells were taken from the pool of cells and lysed by the addition of 2 x NP-40 lysis buffer to terminate the reaction.
  • Cytoplasmic extracts were subjected to immunoprecipitation with anti-EpoR and anti-STAT5 antiserum (both Santa Cruz Biotechnology), were resolved on 15% SDS-PAGE and transferred to nitrocellulose membranes.
  • the detection was performed by immunoblotting with the anti-phosphotyrosine (PTyr) antibody 4G10 (Upstate Biotechnology) or reprobed with anti-STAT5 antiserum followed by enhanced chemiluminescence and detection using a Lumilmager (Roche Diagnostics).
  • PTyr anti-phosphotyrosine
  • 4G10 Upstate Biotechnology
  • a Lumilmager Roche Diagnostics
  • the immunoblots were performed under standardized conditions, were incubated with enhanced chemiluminescence substrate (Amersham) for 1min and were exposed for 10min on a Lumilmager (Roche Diagnostics).
  • the LumiAnalyst software was used applying the single band analysis package with automated lane and band identification, flat background correction and slant correction.
  • the reporter plasmid pSac-CIS was generated by amplifying the CIS promoter from genomic DNA and inserting the Sacl/Xhol subfragment, comprising the two STAT5 binding sites and the authentic transcriptional start site, into pGL2 basic (Promega) and transferring the promoter cassette via Kpnl and Xhol into pCMVD (Clontech) using the EcoRI and Xhol restriction sites in the multiple cloning site.
  • pGac-CIS-STAT The reporter plasmid pSac-CIS was generated by amplifying the CIS promoter from genomic DNA and inserting the Sacl/Xhol subfragment, comprising the two STAT5 binding sites and the authentic transcriptional start site, into pGL2 basic (Promega) and transferring the promoter cassette via Kpnl and Xhol into pCMVD (Clontech) using the EcoRI and Xhol restriction sites in the multiple cloning site.
  • pCMVD Clontech

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Abstract

L'invention concerne un procédé, un système et leur utilisation, en particulier, pour l'identification de cibles pour une intervention médicale efficace et/ou pour prévoir les effets d'agents thérapeutiques et/ou pour modeler dynamiquement des réseaux de signalisation cellulaire complexe et/ou pour la détection à distance d'une signalisation cellulaire. Le procédé est caractérisé en ce qu'il comprend les étapes suivantes : analyse d'au moins un processus de signalisation cellulaire, séparation du processus de signalisation cellulaire en deux étapes fonctionnelles individuelles, transfert des étapes individuelles dans des équations différentielles couplées respectives comprenant des paramètres dynamiques quantitatifs par spécification de l'équation différentielle générale ψ = f (ψ,p) en vue de décrire la dynamique des étapes individuelles de la transduction des signaux, telles que modifications covalentes, formation complexe, clivage, dégradation, libération et échange.
EP02750862A 2001-04-27 2002-04-25 Procede, systeme et leur utilisation pour l'identification de cibles par cyclage nucleocytoplasmique Withdrawn EP1392850A2 (fr)

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EP01110071 2001-04-27
EP01110071 2001-04-27
PCT/EP2002/004602 WO2002088383A2 (fr) 2001-04-27 2002-04-25 Procede, systeme et leur utilisation pour l'identification de cibles, pour une intervention medicale efficace et/ou pour determiner les effets d'agents therapeutiques et/ou pour la detection a distance d'une signalisation cellulaire par cyclage nucleocytoplasmique
EP02750862A EP1392850A2 (fr) 2001-04-27 2002-04-25 Procede, systeme et leur utilisation pour l'identification de cibles par cyclage nucleocytoplasmique

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SG11201606292WA (en) * 2014-03-27 2016-08-30 Procter & Gamble Methods for evaluating effects of a treatment on biological processes and pathways
WO2023231203A1 (fr) * 2022-05-31 2023-12-07 医渡云（北京）技术有限公司 Procédé et appareil de prédiction d'efficacité de médicament basés sur un modèle de cellule numérique, support et dispositif
CN116343912B (zh) * 2023-05-26 2023-07-28 山东科技大学 生物信号通路中磷酸化系统的磷酸化活性预测方法及系统

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US5930154A (en) * 1995-01-17 1999-07-27 Intertech Ventures, Ltd. Computer-based system and methods for information storage, modeling and simulation of complex systems organized in discrete compartments in time and space
US5914891A (en) * 1995-01-20 1999-06-22 Board Of Trustees, The Leland Stanford Junior University System and method for simulating operation of biochemical systems
US6132969A (en) * 1998-06-19 2000-10-17 Rosetta Inpharmatics, Inc. Methods for testing biological network models
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