HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3

Model Identifier
BIOMD0000000907
Short description
All cells and organisms exhibit stress-coping mechanisms toensure survival. Cytoplasmic protein-RNA assemblies termedstress granules are increasingly recognized to promote cellularsurvival under stress. Thus, they might represent tumor vul-nerabilities that are currently poorly explored. The translation-inhibitory eIF2αkinases are established as main drivers ofstress granule assembly. Using a systems approach, we identifythe translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regu-lator mammalian target of rapamycin complex 1 (mTORC1) topromote stress granule assembly. When highly active, PI3K is themain driver of stress granules; however, the impact of p38becomes apparent as PI3K activity declines. PI3K and p38 thusact in a hierarchical manner to drive mTORC1 activity and stressgranule assembly. Of note, this signaling hierarchy is also presentin human breast cancer tissue. Importantly, only the recognition ofthe PI3K-p38 hierarchy under stress enabled the discovery of p38’srole in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as theyhierarchically promote stress granule formation
Format
SBML
(L2V4)
Related Publication
-
The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner.
- Heberle AM, Razquin Navas P, Langelaar-Makkinje M, Kasack K, Sadik A, Faessler E, Hahn U, Marx-Stoelting P, Opitz CA, Sers C, Ines Heiland, Schäuble S, Thedieck K
- Life science alliance , 4/ 2019 , Volume 2 , Issue 2 , PubMed ID: 30923191
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- All cells and organisms exhibit stress-coping mechanisms to ensure survival. Cytoplasmic protein-RNA assemblies termed stress granules are increasingly recognized to promote cellular survival under stress. Thus, they might represent tumor vulnerabilities that are currently poorly explored. The translation-inhibitory eIF2α kinases are established as main drivers of stress granule assembly. Using a systems approach, we identify the translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regulator mammalian target of rapamycin complex 1 (mTORC1) to promote stress granule assembly. When highly active, PI3K is the main driver of stress granules; however, the impact of p38 becomes apparent as PI3K activity declines. PI3K and p38 thus act in a hierarchical manner to drive mTORC1 activity and stress granule assembly. Of note, this signaling hierarchy is also present in human breast cancer tissue. Importantly, only the recognition of the PI3K-p38 hierarchy under stress enabled the discovery of p38's role in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as they hierarchically promote stress granule formation.
Contributors
Submitter of the first revision: Mohammad Umer Sharif Shohan
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Mohammad Umer Sharif Shohan
Submitter of this revision: Mohammad Umer Sharif Shohan
Modellers: Mohammad Umer Sharif Shohan
Name | Description | Size | Actions |
---|---|---|---|
Model files |
|||
Navas2019_model3.xml | SBML L2V4 HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3 | 131.63 KB | Preview | Download |
Additional files |
|||
Navas2019_model3.cps | COPASI version 4.24 (Build 197) HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3 | 307.97 KB | Preview | Download |
Navas2019_model3.sedml | SEDML L1V2 HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3 | 19.50 KB | Preview | Download |
- Model originally submitted by : Mohammad Umer Sharif Shohan
- Submitted: Jan 3, 2020 12:57:26 PM
- Last Modified: Jan 3, 2020 12:57:26 PM
Revisions
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species | Initial Concentration/Amount |
---|---|
X5 0 | 9.99999995282768 mol |
X1 0 | 1.0 mol |
X2 2 | 2.22472108942362E-4 mol |
X2 0 | 9.99999999999923 mol |
PRAS40 pS183 obs | 0.54269846683442 mol |
Akt pT308 obs | 0.241624144113638 mol |
X5 1 | 0.0 mol |
X9 2 | 0.0119987114529515 mol |
X1 1 | 0.0 mol |
X9 0 | 2.67747019439286 mol |
Reactions
Reactions | Rate | Parameters |
---|---|---|
=> X5_0; X4_1, X5_0, X5_1 | default*(X5_1*b_X5_1-X4_1*X5_0*a_X5_0)/default | b_X5_1 = 0.077833118821602; a_X5_0 = 9.99999969718096 |
=> X1_0; X1_0, X1_1 | default*(X1_1-X1_0*Y1)/default | Y1 = 0.0 |
=> X2_2; X11_1, X11_3, X2_0, X2_1, X2_2 | default*((X2_1*(X11_1+X11_3)-X2_2*b_X2_2)+X2_0*a1_X2_0*(X11_1+X11_3))/default | b_X2_2 = 0.106214679132925; a1_X2_0 = 0.0014976539751451 |
=> X2_0; X11_1, X11_3, X1_1, X2_0, X2_2 | default*((X2_2*b_X2_2-X1_1*X2_0)-X2_0*a1_X2_0*(X11_1+X11_3))/default | b_X2_2 = 0.106214679132925; a1_X2_0 = 0.0014976539751451 |
PRAS40_pS183_obs = ModelValue_113*X10_1+ModelValue_113*X10_3 | [] | ModelValue_113 = 3.98428884870299 |
Akt_pT308_obs = ModelValue_109*X8_1+ModelValue_109*X8_3 | [] | ModelValue_109 = 1.54625898449999 |
=> X5_1; X4_1, X5_0, X5_1 | default*(X4_1*X5_0*a_X5_0-X5_1*b_X5_1)/default | b_X5_1 = 0.077833118821602; a_X5_0 = 9.99999969718096 |
=> X9_2; X8_1, X8_3, X9_0, X9_2 | default*(X9_0*a2_X9_0*(X8_1+X8_3)-X9_2*b_X9_2)/default | a2_X9_0 = 0.0216220006084923; b_X9_2 = 0.0369559223359753 |
=> X1_1; X1_0, X1_1 | default*(X1_0*Y1-X1_1)/default | Y1 = 0.0 |
=> X9_0; X8_1, X8_3, X9_0, X9_2 | default*(X9_2*b_X9_2-X9_0*a2_X9_0*(X8_1+X8_3))/default | a2_X9_0 = 0.0216220006084923; b_X9_2 = 0.0369559223359753 |
Curator's comment:
(added: 03 Jan 2020, 12:57:16, updated: 03 Jan 2020, 12:57:16)
(added: 03 Jan 2020, 12:57:16, updated: 03 Jan 2020, 12:57:16)
The model has been encoded in COPASI 4.24 (Build 197) and the figure 3 d has been generated using COPASI