Bachmann2011_JAK2-STAT5_FeedbackControl
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PRJNA135265
SUBMITTER: 
Marcel Schilling
PROVIDER: BIOMD0000000347 | BioModels | 2011-08-09
REPOSITORIES: BioModels
Publications
Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.
Bachmann Julie J Raue Andreas A Schilling Marcel M Böhm Martin E ME Kreutz Clemens C Kaschek Daniel D Busch Hauke H Gretz Norbert N Lehmann Wolf D WD Timmer Jens J Klingmüller Ursula U
Molecular systems biology 20110719
Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitate ...[more]
Similar Datasets
Project description:Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. We combined mathematical modeling and extensive time-resolved data sets in primary erythroid progenitor cells and dissected the roles of the two transcriptional feedback regulators of the SOCS family, CIS and SOCS3 in JAK2/STAT5 signaling. Our model revealed that both feedbacks are most effective at different ligand concentration ranges. To identify the relevant transcriptional feedback regulators that are involved in attenuation of Epo-induced JAK2-STAT5 signaling in addition to CIS, we performed a time-resolved genome-wide expression profiling of murine erythroid progenitor cells at the colony forming unit erythroid (CFU-E) stage up to 24 and 7 hours after EPO stimulation and in control, repectively. The analysis identified SOCS3 as the only further de novo regulated gene upon Epo-induced JAK2-STAT5 signaling. From subsequent mathematical modeling, we calculated the STAT5 response in previously unobserved Epo concentrations, which provided a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. In conclusion, our combined modeling approach revealed novel insights into the orchestrated action of feedback control to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations. Freshly sorted CFU-E cells were starved for 1 h in Panserin 401 supplemented with BSA and 50 µM β-Mercaptoethanol. Subsequently, cells were stimulated with 0.5 U/ml Epo or left untreated and RNA was extracted at different time points (0,1,2,3,4,5,6,7,8,14,19,24 hrs after Epo stimulation and at 0,1,2,3,4,5,6,7 hrs without Epo treatment) using the RNeasy Mini Plus Kit (Qiagen, Hilden, Germany).
2011-06-30 | E-GEOD-26151 | biostudies-arrayexpress
Project description:Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. We combined mathematical modeling and extensive time-resolved data sets in primary erythroid progenitor cells and dissected the roles of the two transcriptional feedback regulators of the SOCS family, CIS and SOCS3 in JAK2/STAT5 signaling. Our model revealed that both feedbacks are most effective at different ligand concentration ranges. To identify the relevant transcriptional feedback regulators that are involved in attenuation of Epo-induced JAK2-STAT5 signaling in addition to CIS, we performed a time-resolved genome-wide expression profiling of murine erythroid progenitor cells at the colony forming unit erythroid (CFU-E) stage up to 24 and 7 hours after EPO stimulation and in control, repectively. The analysis identified SOCS3 as the only further de novo regulated gene upon Epo-induced JAK2-STAT5 signaling. From subsequent mathematical modeling, we calculated the STAT5 response in previously unobserved Epo concentrations, which provided a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. In conclusion, our combined modeling approach revealed novel insights into the orchestrated action of feedback control to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.
2011-06-30 | GSE26151 | GEO
Project description:This is a dynamic pathway model examining the roles of of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK/STAT5 signaling, within the context of primary erythroid progenitor cells.
2019-11-14 | BIOMD0000000861 | BioModels
Project description:Splitting Function enables Dual Feedback Regulation to Control JAK2/STAT5 Signaling for a Wide Ligand Range
| PRJNA135265 | ENA
Project description:Targeted therapy of cancer typically focuses on the development of agents that will inactivate a transforming oncogene. In this study, we tested the concept that besides the oncogene itself, factors that enable permissiveness of a normal cell to oncogenic signaling represent a novel class of therapeutic targets. This hypothesis was based on our finding that acute activation of oncogenes in normal pre-B cells typically results in immediate cell death, unless pre-B cells were capable of adapting quickly enough to a high level of signaling output in Erk and Stat5 pathways. Surviving pre-B cell clones achieved permissiveness to oncogenic signaling by strong upregulation of negative feedback regulators of Erk (DUSP6, SPRY2, ETV5) and Stat5 (CISH, SOCS2, SOCS3) signaling. Genetic deletion of the sprouty family Ras inhibitor Spry2, the Erk phosphatase Dusp6 or Etv5, a transcriptional activator of SPRY2/DUSP6, decreased robustness of negative feedback control and compromised oncogenic transformation. Likewise, ablation of the suppressors of cytokine signaling (SOCS) family molecules Cish, Socs2 and Socs3 reversed permissiveness of pre-B cells to oncogenic signaling. Searching for factors that limit permissiveness of normal pre-B cells to oncogene signaling, we identified the lymphoid transcription factor IKZF1, which functions as a critical tumor suppressor in pre-B ALL. IKZF1 binds directly to and transcriptionally represses DUSP6, SPRY2, ETV5, CISH, SOCS2, and SOCS3 promoters, hence lowering the threshold of maximum allowable oncogene signaling strength. Interstingly, a small molecule inhibitor of DUSP6 acutely subverted negative feedback regulation and selectively induced cell death in pre-B ALL cells. In addition, small molecule inhibition of DUSP6 was sufficient to overcome conventional mechanisms of drug-resistance in pre-B ALL and had strong selective activity on drug-resistant patient-derived pre-B ALL cells that were injected into NOD/SCID transplant recipient mice. These findings identify permissive negative feedback control of oncogenic signaling as a previously unrecognized vulnerability of pre-B ALL cells and a new class of potential therapeutic targets.
2017-01-12 | GSE58739 | GEO
Project description:Growth hormone signaling in hepatocytes is fundamentally important. Disruptions in this pathway have led to fatty liver and other metabolic abnormalities. Growth hormone signals through the JAK2/STAT5 pathway. Mice with hepatocyte specific deletion of STAT5 were previously shown to develop fatty liver. Our aim in this study was to determine the effect of deleting JAK2 in hepatocytes on liver gene expression. To do so, we generated animals with hepatocyte specific deletion of JAK2. Hepatocyte-specific JAK2-deficient mice (JAK2L) were generated by mating floxed JAK2 mice (in a mixed (C57Bl/6:129Sv) background) to mice carrying an Alb promoter-regulated Cre transgene on a 100% C57Bl/6 background purchased from the Jackson Labs. Livers were harvested from 8 week old animals for RNA extraction and hybridization.
2011-04-30 | E-GEOD-26188 | biostudies-arrayexpress
Project description:This is the model of IL13 induced signalling in MedB-1 cell described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, Böhm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, Möller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingmüller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID:21127196; DOI:10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.
All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 () and the conversion factor 2.265 molecules IL13/cell = 1 ng/ml to be around 60 fl.
SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:
% PottersWheel model definition file
function m = Raia2010_IL13_MedB1()
m = pwGetEmptyModel();
%% Meta information
m.ID = 'Raia2010_IL13_MedB1';
m.name = 'Raia2010_IL13_MedB1';
m.description = '';
m.authors = {'Raia et al'};
m.dates = {'2010'};
m.type = 'PW-2-0-47';
%% X: Dynamic variables
% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)
m = pwAddX(m, 'Rec' , 1.3, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'Rec_i' , 113.193916718733, 'global', 0.001, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'p_IL13_Rec' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'p_IL13_Rec_i', 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'JAK2' , 2.8, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'pJAK2' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'SHP1' , 91, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'STAT5' , 165, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'pSTAT5' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'SOCS3mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'DecoyR' , 0.34, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'IL13_DecoyR' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'SOCS3' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'CD274mRNA' , 0, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
%% R: Reactions
% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)
m = pwAddR(m, {'Rec' }, {'IL13_Rec' }, {'IL13stimulation' }, 'C' , [] , 'k1 * m1 * r1 * 2.265' , {'Kon_IL13Rec' });
m = pwAddR(m, {'Rec' }, {'Rec_i' }, { }, 'MA', [] , [] , {'Rec_intern' });
m = pwAddR(m, {'Rec_i' }, {'Rec' }, { }, 'MA', [] , [] , {'Rec_recycle' });
m = pwAddR(m, {'IL13_Rec' }, {'p_IL13_Rec' }, {'pJAK2' }, 'E' , [] , [] , {'Rec_phosphorylation' });
m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'IL13_Rec','SOCS3' }, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});
m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'p_IL13_Rec','SOCS3'}, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});
m = pwAddR(m, {'p_IL13_Rec' }, {'p_IL13_Rec_i'}, { }, 'MA', [] , [] , {'pRec_intern' });
m = pwAddR(m, {'p_IL13_Rec_i'}, { }, { }, 'MA', [] , [] , {'pRec_degradation' });
m = pwAddR(m, {'pJAK2' }, {'JAK2' }, {'SHP1' }, 'E' , [] , [] , {'pJAK2_dephosphorylation' });
m = pwAddR(m, {'STAT5' }, {'pSTAT5' }, {'pJAK2' }, 'E' , [] , [] , {'STAT5_phosphorylation' });
m = pwAddR(m, {'pSTAT5' }, {'STAT5' }, {'SHP1' }, 'E' , [] , [] , {'pSTAT5_dephosphorylation' });
m = pwAddR(m, {'DecoyR' }, {'IL13_DecoyR' }, {'IL13stimulation' }, 'C' , [] , 'k1 * m1 * r1 * 2.265' , {'DecoyR_binding' });
m = pwAddR(m, { }, {'SOCS3mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'SOCS3mRNA_production' });
m = pwAddR(m, { }, {'SOCS3' }, {'SOCS3mRNA' }, 'C' , [] , 'm1*k1/(k2+m1)' , {'SOCS3_translation','SOCS3_accumulation' });
m = pwAddR(m, {'SOCS3' }, { }, { }, 'MA', [] , [] , {'SOCS3_degradation' });
m = pwAddR(m, { }, {'CD274mRNA' }, {'pSTAT5' }, 'C' , [] , 'm1*k1' , {'CD274mRNA_production' });
%% C: Compartments
% m = pwAddC(m, ID, size, outside, spatialDimensions, name, unit, constant)
m = pwAddC(m, 'cell', 1);
%% K: Dynamical parameters
% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)
m = pwAddK(m, 'Kon_IL13Rec' , 0.00341992477561527 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_phosphorylation' , 999.630699390459 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_intern' , 0.152540135862128 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_degradation' , 0.17292753960894 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_intern' , 0.103345784175639 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_recycle' , 0.00135598001330518 , 'global', 1e-009, 1000);
m = pwAddK(m, 'JAK2_phosphorylation' , 0.157057142470047 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.000621906059346898 , 'global', 1e-009, 1000);
m = pwAddK(m, 'STAT5_phosphorylation' , 0.0382596267705733 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.000343391620492938 , 'global', 1e-009, 1000);
m = pwAddK(m, 'SOCS3mRNA_production' , 0.00215826062955433 , 'global', 1e-009, 1000);
m = pwAddK(m, 'DecoyR_binding' , 0.000124391087466499 , 'global', 1e-009, 1000);
m = pwAddK(m, 'JAK2_p_inhibition' , 0.0168267797836881 , 'global', 1e-009, 1000);
m = pwAddK(m, 'SOCS3_translation' , 11.9086462945188 , 'global', 1e-009, 1000);
m = pwAddK(m, 'SOCS3_accumulation' , 3.70803336415341 , 'global', 1 , 1000);
m = pwAddK(m, 'SOCS3_degradation' , 0.0429185935645562 , 'global', 1e-009, 1000);
m = pwAddK(m, 'CD274mRNA_production' , 8.21752278733562e-005, 'global', 1e-009, 1000);
%% U: Driving input
% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)
m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0] , [0 1] , [], [], [], [], {}, [], 'protein.generic');
%% Default sampling time points
m.t = 0:1:120;
%% Y: Observables
% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)
m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec' , 'RecSurf_obs' , 'scale_RecSurf' , '0.10 * y + 0.1 * max(y)');
m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i + IL13_DecoyR', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');
m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i' , 'pIL4Ra_obs' , 'scale_pIL4Ra' , '0.1 * y + 0.15 * max(y)');
m = pwAddY(m, 'pJAK2' , 'pJAK2_obs' , 'scale_pJAK2' , '0.15 * y + 0.1 * max(y)');
m = pwAddY(m, 'SOCS3mRNA' , 'SOCS3mRNA_obs', 'scale_SOCS3mRNA', '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'CD274mRNA' , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'SOCS3' , 'SOCS3_obs' , 'scale_SOCS3' , '0.1 * y + 0.15 * max(y)');
m = pwAddY(m, 'pSTAT5' , 'pSTAT5_obs' , 'scale_pSTAT5' , '0.15 * y + 0.1 * max(y)');
%% S: Scaling parameters
% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)
m = pwAddS(m, 'scale_pJAK2' , 1.39039557075997, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pIL4Ra' , 1.88700484471494, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_RecSurf' , 1, 'fix', 0.001, 10000);
m = pwAddS(m, 'scale_IL13-cell', 5.56750251420935, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_SOCS3mRNA', 17.6699101927908, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_CD274mRNA', 2.48547378765387, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pSTAT5' , 1, 'fix', 0.001, 10000);
m = pwAddS(m, 'scale_SOCS3' , 1, 'fix', 0.001, 10000);
%% Designer properties (do not modify)
m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov��re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2011-02-14 | BIOMD0000000313 | BioModels
Project description:Growth hormone signaling in hepatocytes is fundamentally important. Disruptions in this pathway have led to fatty liver and other metabolic abnormalities. Growth hormone signals through the JAK2/STAT5 pathway. Mice with hepatocyte specific deletion of STAT5 were previously shown to develop fatty liver. Our aim in this study was to determine the effect of deleting JAK2 in hepatocytes on liver gene expression. To do so, we generated animals with hepatocyte specific deletion of JAK2.
2011-05-01 | GSE26188 | GEO
Project description:While PAX5 is an important tumor suppressor in B-ALL, it is also involved in oncogenic translocations coding for PAX5 fusion proteins. PAX5-JAK2 encodes a protein consisting of the PAX5 DNA-binding region fused to the constitutively active JAK2 kinase domain. Here, we studied the oncogenic function of PAX5-JAK2 in a mouse model expressing it from the endogenous Pax5 locus. The Pax5Jak2/+ mice rapidly developed an aggressive B-ALL in the absence of another cooperating mutation. The DNA-binding function and kinase activity of Pax5-Jak2, as well as IL-7 signaling, all contributed to leukemia development. Interestingly, all Pax5Jak2/+ tumors lost the wild-type Pax5 allele, allowing efficient DNA binding of Pax5-Jak2. While we could not find evidence for a nuclear role of Pax5-Jak2 as an epigenetic regulator, active phosphorylated Stat5 was present at a high level in Pax5Jak2/+ B-ALL tumors, consistent with increased expression of Stat5 target genes. Together, these data identified Pax5-Jak2 as an important nuclear driver of leukemia formation by maintaining phosphorylated Stat5 levels in the nucleus.
2022-02-04 | GSE174775 | GEO
Project description:Identification of direct transcriptional targets of EPO-JAK2-STAT5 signalling in erythroid cells
2017-09-06 | GSE94301 | GEO