IL13_0-12h_RAIA_et_al
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Andreas Kowarsch
PROVIDER: E-GEOD-23591 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.
Raia Valentina V Schilling Marcel M Böhm Martin M Hahn Bettina B Kowarsch Andreas A Raue Andreas A Sticht Carsten C Bohl Sebastian S Saile Maria M Möller Peter P Gretz Norbert N Timmer Jens J Theis Fabian F Lehmann Wolf-Dieter WD Lichter Peter P Klingmüller Ursula U
Cancer research 20101202 3
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 ...[more]
Similar Datasets
Project description:Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of Janus-activated kinase (JAK) / signal transducer and activator of transcription (STAT) signaling pathway. Due to complex non-linear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. To untangle these features, we used dynamic pathway modeling that employs model development and calibration based on extensive quantitative data generation. Quantitative data were collected on JAK/STAT pathway signaling components in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We showed that the amounts of STAT5 and STAT6 are higher whereas the amount of SHP1 is lower in the two lymphoma cell lines compared to B cells from healthy donors. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In our experimental setting interleukin-13 (IL13) stimulation levels remained constant over time. In MedB-1 cells surface IL13 receptor alpha 2 had a strong IL13-sequestering/decoy function. In both lymphoma cell lines we observed IL13-induced activation of interleukin-4 receptor alpha, JAK2 and STAT5, but not of STAT6, which was highly phosphorylated even without stimulus. Furthermore, the known STAT-inducible negative regulators CISH and SOCS3 were up-regulated within 2 hours in MedB-1 but not in L1236 cells. Global transcription profiling revealed 11 early and 16 sustained common genes up-regulated by IL13 in both lymphoma cell lines. Based on this detailed information we established two individual mathematical models, MedB-1 and L1236 model, which were able to describe the respective experimental data. Sensitivity analysis of the model identified six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1 cells. By inhibition of STAT5 phosphorylation we successfully validated one of the predicted targets demonstrating the potential of the approach in guiding target identification for highly deregulated signaling networks in cancer cells. We established mathematical models of the JAK/STAT pathway in two lymphoma cell types (PMBL and cHL), able to reproduce experimental data and to predict possible therapeutic targets.
2010-12-09 | GSE23591 | GEO
Project description:This is the model of IL13 induced signalling in L1236 cells 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 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 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_L1236()
m = pwGetEmptyModel();
%% Meta information
m.ID = 'Raia2010_IL13_L1236';
m.name = 'Raia2010_IL13_L1236';
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' , 1.8, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'Rec_i' , 118.598421286338, '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' , 24, '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' , 9.4, 'fix' , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', [] , [] , [] , [] , 'protein.generic');
m = pwAddX(m, 'STAT5' , 209, '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, '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 * 3.776', {'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' }, 'E' , [] , [] , {'JAK2_phosphorylation' });
m = pwAddR(m, {'JAK2' }, {'pJAK2' }, {'p_IL13_Rec' }, 'E' , [] , [] , {'JAK2_phosphorylation' });
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, { }, {'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' , 0.00174086832237195, 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_phosphorylation' , 9.07540737285078 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_intern' , 0.324132341358502 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pRec_degradation' , 0.417538218767296 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_intern' , 0.259685756311325 , 'global', 1e-009, 1000);
m = pwAddK(m, 'Rec_recycle' , 0.00392430355501153, 'global', 1e-009, 1000);
m = pwAddK(m, 'JAK2_phosphorylation' , 0.300019047540849 , 'global', 1e-009, 1000);
m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000);
m = pwAddK(m, 'STAT5_phosphorylation' , 0.00426766529531612, 'global', 1e-009, 1000);
m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000);
m = pwAddK(m, 'CD274mRNA_production' , 0.0115927572109515 , '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' , '0.1 * y + 0.1 * max(y)');
m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', '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' , '0.10 * y + 0.15 * max(y)');
m = pwAddY(m, 'pJAK2' , 'pJAK2_obs' , 'scale_pJAK2' , '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, 'pSTAT5' , 'pSTAT5_obs' , 'scale_pSTAT5' , '0.1 * y + 0.1 * max(y)');
%% S: Scaling parameters
% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)
m = pwAddS(m, 'scale_pJAK2' , 0.469836894150194, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pIL4Ra' , 1.80002942264669, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_RecSurf' , 1, 'fix', 0.0001, 10000);
m = pwAddS(m, 'scale_IL13-cell', 174.726805005048, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global', 0.001, 10000);
m = pwAddS(m, 'scale_pSTAT5' , 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];
2024-09-02 | BIOMD0000000314 | BioModels
Project description:Here we investigated the effects of JAK/STAT pharmacological inhibition on cHL cell models using ruxolitinib, a JAK 1/2 inhibitor. We use five classical Hodgkin lymphoma cell lines: L428, L1236, L540, KMH2, L591
2021-10-15 | GSE185818 | GEO
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.
2024-09-02 | BIOMD0000000313 | BioModels
Project description:Primary mediastinal large B-cell lymphoma (PMBL) represents a clinically and pathologically distinct subtype of large B-cell lymphomas. Furthermore, molecular studies, including global gene expression profiling, have provided evidence that PMBL is more closely related to classical Hodgkin lymphoma (cHL). Although targeted sequencing studies have revealed a number of mutations involved in PMBL pathogenesis, a comprehensive description of disease-associated genetic alterations and perturbed pathways is still lacking. Here, we performed whole-exome sequencing of 95 PMBL tumors to inform on oncogenic driver genes and recurrent copy number alterations. The integration of somatic gene mutations with gene expression signatures provides further insights into genotype-phenotype interrelation in PMBL. We identified highly recurrent oncogenic mutations in the JAK-STAT and NF-kB pathways, and provide additional evidence of the importance of immune evasion in PMBL (CIITA, CD58, B2M, CD274, PDCD1LG2). Our analyses highlight the IRF-pathway as a putative novel hallmark with frequent alterations in multiple pathway members (IRF2BP2, IRF4, IRF8). In addition, our integrative analysis illustrates the importance of JAK1, RELB and EP300 mutations driving oncogenic signaling. The identified driver genes were significantly more frequently mutated in PMBL as compared to diffuse large B-cell lymphoma, whereas only a limited number of genes were significantly different between PMBL and cHL, emphasizing the close relationship between these entities. Our study, performed on a large cohort of PMBL, highlights the importance of distinctive genetic alterations for disease taxonomy with relevance for diagnostic work-up and therapeutic decision-making.
2019-09-18 | GSE134511 | GEO
Project description:The pathogenesis of classical Hodgkin lymphoma (cHL), the most common lymphoma in the young, is still enigmatic, largely because its Hodgkin and Reed-Sternberg (HRS) tumor cells are rare in the involved lymph node and therefore difficult to analyze. Here, by overcoming this technical challenge and performing for the first time a genome-wide transcriptional analysis of microdissected HRS cells in comparison to other B-cell lymphomas, cHL lines and normal B-cell subsets, we show that they differ extensively from the usually studied cHL cell lines, that the lost B-cell identity of cHLs is not linked to the acquisition of a plasma cell-like gene expression program, and that Epstein-Barr virus infection of HRS cells has a minor transcriptional influence on the established cHL clone. Moreover, although cHL appears a distinct lymphoma entity overall, HRS cells of its histological subtypes diverged in their similarity to other related lymphomas. Unexpectedly, we identified two molecular subgroups of cHL associated to differential strengths of the transcription factor activity of the NOTCH1, MYC and IRF4 proto-oncogenes. Finally, HRS cells display deregulated expression of several genes potentially highly relevant to lymphoma pathogenesis, including silencing of the apoptosis-inducer BIK and of INPP5D, an inhibitor of the PI3K-driven oncogenic pathway. The present study complements the GSE12453 and GSE14879 records by adding the following 10 samples: 5 primary tumor samples and 5 cell line samples. The 5 primary tumor samples represent 1000-2000 neoplastic cells microdissected from frozen biopsies of 5 cases of primary mediastinal B-cell lymphoma (PMBL). The 5 cell line samples represent 500-1000 living neoplastic cells isolated by fluorescence-activated cell sorting from growing cultures of the classical Hodgkin lymphoma (cHL) cell lines L1236, L428, KMH2 and HDLM2 and the lymphocyte-predominant Hodgkin lymphoma (lpHL) cell line DEV.
2012-09-17 | E-GEOD-40160 | biostudies-arrayexpress
Project description:The genetics of classical Hodgkin lymphoma (cHL) is poorly understood. The finding of a JAK2-involving t(4;9)(q21;p24) in one case of cHL prompted us to characterize this translocation on a molecular level and to determine the prevalence of JAK2 rearrangements in cHL. We showed that the t(4;9)(q21;p24) leads to a novel SEC31A-JAK2 fusion. Screening of 131 cHL cases identified one additional case with SEC31A-JAK2 and two additional cases with rearrangements involving JAK2. We demonstrated that SEC31A-JAK2 is oncogenic in vitro and acts as a constitutively activated tyrosine kinase that is sensitive to JAK inhibitors. In vivo, SEC31A-JAK2 was found to induce a T-lymphoblastic lymphoma or myeloid hyperplasia in a murine bone marrow transplant model. Altogether, we identified SEC31A-JAK2 as a first aberration characteristic for cHL and provide evidence that JAK2 rearrangements occur in a minority of cHL cases. Given the proven oncogenic potential of this novel fusion, our studies provide new insights into the pathogenesis of cHL and indicate that in at least some cases, constitutive activation of the JAK-STAT pathway is caused by JAK2 rearrangements. The finding that SEC31A-JAK2 responds to JAK inhibitors indicates that patients with cHL and JAK2 rearrangements may benefit from targeted therapies. Genomic profiling of primary and secondary transplanted mice expressing SEC31A-JAK2 with myeloid hyperplasia and T-lymphoblastic lymphoma Individual sample against a normal control
2010-07-08 | E-GEOD-22404 | biostudies-arrayexpress
Project description:The genetics of classical Hodgkin lymphoma (cHL) is poorly understood. The finding of a JAK2-involving t(4;9)(q21;p24) in one case of cHL prompted us to characterize this translocation on a molecular level and to determine the prevalence of JAK2 rearrangements in cHL. We showed that the t(4;9)(q21;p24) leads to a novel SEC31A-JAK2 fusion. Screening of 131 cHL cases identified one additional case with SEC31A-JAK2 and two additional cases with rearrangements involving JAK2. We demonstrated that SEC31A-JAK2 is oncogenic in vitro and acts as a constitutively activated tyrosine kinase that is sensitive to JAK inhibitors. In vivo, SEC31A-JAK2 was found to induce a T-lymphoblastic lymphoma or myeloid hyperplasia in a murine bone marrow transplant model. Altogether, we identified SEC31A-JAK2 as a first aberration characteristic for cHL and provide evidence that JAK2 rearrangements occur in a minority of cHL cases. Given the proven oncogenic potential of this novel fusion, our studies provide new insights into the pathogenesis of cHL and indicate that in at least some cases, constitutive activation of the JAK-STAT pathway is caused by JAK2 rearrangements. The finding that SEC31A-JAK2 responds to JAK inhibitors indicates that patients with cHL and JAK2 rearrangements may benefit from targeted therapies.
2010-06-19 | GSE22404 | GEO
Project description:Cell lines of mediastinal lymphomas, one each of primary mediastinal (thymic) large B cell lymphoma (PMBL), classic Hodgkin lymphoma (CHL), B-cell lymphoma, unclassifiable, with features intermediate between diffuse large B-cell lymphoma and CHL, and T-cell lymphoma were treated with rituximab (RmAb), brentuximab vedotin (BV), ibrutinib (IBR), prednisone (PRED) or untreated (UT) for 48 hours. Flow cytometry for PD-L1 protein was performed, and differential expression (DE) analysis of mRNA and miRNA performed by RNAseq. B-cell lines exhibited 2- fold increase in PD-L1 protein (p<.05) with RmAb compared to UT. Others showed no effect of RmAb, however, the T-cell line showed 2-fold increase on treatment with BV (P<.05). DE performed by limma showed no genomic level changes. ANOVA analysis of PMBL treated with RmAb, however, showed physiologic level DE involving >3000 genes involving multiple pathways associated with cell death and survival, protein degradation, mRNA translation, mRNA degradation, immunologic functions, and PD-1/PD-L1 immunotherapy. In the B cell lines, DE of CD274 (PD-L1) was decreased or not significant. MiRNA DE showed upregulation of 7 miRNAs, including miR-155-5p.
2022-06-14 | GSE206130 | GEO