Targeting Transcriptional Addiction by a Novel Highly Selective CDK9 Kinase Inhibitor for AML.
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE123287 | GEO | 2019/02/28
REPOSITORIES: GEO
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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: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];
2011-02-14 | BIOMD0000000314 | BioModels
Project description:The expression level of microRNAs in FLT3-ITD+ AML is unknown. Using empty vector (EV) lentiviral CRISPR-Cas9 infected FLT3-ITD+ AML cell lines (MV4-11 cells), we performed next generation RNA sequencing on small RNAs to determine microRNA expression level in these cells. We found a variety of evolutionarily conserved and non-conserved microRNAs expressed in our cells of interest. Small RNAseq on EV lentiviral CRISPR-Cas9 infected MV4-11 cell lines was performed on triplicate cultures.
2016-07-01 | E-GEOD-71543 | biostudies-arrayexpress
Project description:The expression profile in miR-155-/- FLT3-ITD+ AML is unknown. Using empty vector (EV) or two distinct miR-155 (S3 or S10) lentiviral CRISPR-Cas9 infected FLT3-ITD+ AML cell lines (MV4-11 cells), we performed next generation RNA sequencing to determine the expression profile in these cells dependent on miR-155. We found a number of pathways dysregulated, including STAT5 activation. RNAseq was performed on EV or miR-155 lentiviral CRISPR-Cas9 infected MV4-11 cell lines in triplicate cultures.
2016-07-01 | E-GEOD-71541 | biostudies-arrayexpress
Project description:We report the pro-leukemic activities of PRMT5 in AML partly via modulating the transcription of key genes. The goal of this experiment is to confirm the changes observed in expression of genes targeted by PRMT5 activities. RNA Deep sequencing was employed to validate and reproduce the changes measured by quantitative reverse transcription polymerase chain reaction (qRTâPCR) techniques. Total RNA samples from MV4-11 cell line (FLT3-ITD AML) following PRMT5 knockdown using specific short hairpin RNA (shRNA) was used to compare gene expression pattern between PRMT5/Knockdown and control (control: MV4-11 cells transduced with scramble control).
2016-05-19 | E-GEOD-75215 | biostudies-arrayexpress
Project description:Enhanced homing and uncontrolled expansion are the characteristics of AML cells. We identified a transcription factor FEV (fifth Ewing variant) as the regulator. To assess the regulation mechanisms, FEV expression was knocked down in AML cell line MV4-11 cells using shRNA. MV4-11 cells transduced with non-silencing control (NC), FEV shRNA (shFEV) were cultured for 3 days and sorted by FACS. The cells were collected and total RNA was isolated by the Trizol kit (Takara Bio, Otsu, Japan) according to manufacturer’s protocol. High-throughput RNA sequencing (RNA-seq) was performed by Illumina HiSeq 2500 (Illumina, San Diego, CA) at CapitalBio Corporation (Beijing, China). Differentially expressed genes were analysed between the two groups.
2021-02-12 | GSE166621 | GEO
Project description:Poor outcome of AML patients is closely related with therapy failure or relapse after initial response. The underlying mechanisms remain largely unknown. In this study we discovered the histone methyltransferase EZH2 as a novel marker for response to chemotherapy and patients` survival. Protein levels of EZH2 and subsequently H3K27 trimethylation (H3K27me3) are reduced in chemoresistant AML cells. To analyze which genomic regions were affected by the loss of EZH2 and reduced H3K27me3 mark we performed Chromatin Immunoprecipitation followed by high-throughput DNA sequencing (ChIP-Seq). ChIP-Seq for the H3K27me3 repressive mark revealed genome-wide differences between sensitive (MV4-11) and resistant (MV4-11R) cells. Overall, 4699 genomic regions showed a more than 1.5 fold reduction of H3K27me3 around TSS ±5kb in MV4-11R compared to MV4-11cells. Promoter regions of HOX genes, classical targets of PRC2, revealed drastically reduced H3K27me3 enrichment in the MV4-11R cells. The H3K27me3 level at the transcriptional start site (TSS) of the ABCC1 promoter was decreased in resistant compared to sensitive cells (Fig. 6D) with increased levels of H3K4me3 and H3K27ac. As expected, we found H3K27me3 enrichments to be inversely correlated with gene expression whereas histone modifications H3K4me3 and H3K27ac, known as markers for active genes, positively correlated with gene expression in our analysis. Differentially regulated genes were characterized to be involved in protein modification/phosphorylation pathways and signaling and apoptosis.
2016-11-22 | GSE61785 | GEO
Project description:As a direct consequence of the high diversity of the aggressive blood cancer acute myeloid leukemia (AML), proteomic samples from patients are strongly heterogeneous, rendering their accurate relative quantification challenging. In the present study, we investigated the benefits of using a super-SILAC mix of AML derived cell lines as internal standard for quantitative shotgun studies. The Molm-13, NB4, MV4-11, THP-1, and OCI-AML3 cell lines were selected for their complementarity with regard to clinical, cytogenetic and molecular risk factors used for prognostication of AML patients. The resulting internal standard presents a high coverage of the AML proteome compared to single cell lines allied with high technical reproducibility, thus enabling its use for AML patient comparison. This was confirmed by comparing the protein regulation between the five cell lines and applying the internal standard to patient material.
2014-07-15 | PXD000441 | Pride
Project description:Our gene set analysis of MV4-11-R versus MV4-11 indicated decreased depolarization of mitochondria and mitochondrial membrane, mitochondrial dysfunction and anti-apoptosis as other top ranked molecular or cellular functions of differential gene sets. expression of most genes encoding glycolytic enzymes was up-regulated in MV4-11-R cells we revealed a metabolic alteration in sorafenib-resistant cell lines with mitochondrial respiration deficiency, leading to substantial decrease of mitochondria-derived ATP generation and a significant increase in glycolytic activity to maintain sufficient ATP production. Our study revealed a metabolic signature of sorafenib resistance and indicated that increase of glycolytic activity including upregulation of major glycolytic enzymes may be viewed as a marker for early detection of sorafenib resistance in AML patients with FLT3/ITD mutation and glycolytic inhibitors warrant further investigation as alternative therapeutic agents for sorafenib-resistant cells Sorafenib resistant cells MV411-R VS. parental MV4-11 cells. Biological replicates: 3 control replicates, 3 treated replicates.
2016-08-01 | E-GEOD-74666 | biostudies-arrayexpress
Project description:Bromodomain and extra-terminal (BET) protein inhibitors have been reported as treatment options for acute myeloid leukemia (AML) in preclinical models and are currently being evaluated in clinical trials. This work presents a novel potent and selective BET inhibitor (BI 894999), which has recently entered clinical trials (NCT02516553). In preclinical studies, this compound is highly active in AML cell lines, primary patient samples, and xenografts. HEXIM1 is described as an excellent pharmacodynamic biomarker for target engagement in tumors as well as in blood. Mechanistic studies show that BI 894999 targets super-enhancer-regulated oncogenes and other lineage-specific factors, which are involved in the maintenance of the disease state. BI 894999 is active as monotherapy in AML xenografts, and in addition leads to strongly enhanced antitumor effects in combination with CDK9 inhibitors. This treatment combination results in a marked decrease of global p-Ser2 RNA polymerase II levels and leads to rapid induction of apoptosis in vitro and in vivo. Together, these data provide a strong rationale for the clinical evaluation of BI 894999 in AML.
2018-03-02 | GSE101821 | GEO