EGF-Mediated Transcriptional Coregulation of EpICD-LEF1 Alters Nanomechanical Properties of Endometrial Cancer Cells for Promoting Cell Migration
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Tim Huang
PROVIDER: E-GEOD-72948 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Epithelial cell adhesion molecule (EpCAM), a membrane protein known to modulate cell-cell adhesion, is also a signaling molecule internalized into the nucleus for transcriptional regulation. Here we demonstrate that activated EGF/EGFR is a signaling factor to drive the proteolysis of EpCAM. Cleavage of the extracellular fragment EpEX results in topographic fading of cell-surface EpCAM detected by antibody-conjugated cantilevers of atomic force microscope (AFM). As a result, internalization of the cytoplasmic domain EpICD forms a transcription factor complex with LEF1 that regulates gene transcription for enhanced cell-mobility functions. Comprehensive probing of cell surface using AFM tip (without antibody) reveals increased elasticity and non-stickiness of these cells, promoting epithelial to mesenchymal transition. While EpCAM cleavage may contribute to the loss of cell-surface adhesiveness, its internalized EpICD additionally regulates targets for promoting cell migration. Thus, this EGF/EGFR-modulated action on structural EpCAM and regulatory EpICD can enhance invasion potential of transformed cells.
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Project description:The cell surface receptor, Epithelial Cell Adhesion Molecule (EpCAM), is overexpressed in a variety of cancers and has been carefully examined for its functional role in carcinogenesis and its potential as a therapeutic target. However, little is known about the upstream molecular pathways that regulate EpCAM expression. To elucidate the mechanisms by which EpCAM expression is controlled, we designed a combinatorial screening strategy that utilized pooled shRNA gene knockdown with magnetically-activated cell separation for selection of cells with reduced EpCAM protein expression on the cell surface. The pooled RNAi screen was analyzed by comparing EpCAM-enriched and EpCAM-depleted cell populations for shRNA abundance using competitive hybridization to microarrays. Our RNAi screen and further validation efforts revealed that at least four genes (GRM1, SLITRK5, EFNA5, and IL23A) are important for EpCAM expression in the ovarian cancer cell line OVCAR8. Further investigations into the ephrin ligand, EFNA5, suggest that its activation by the Eph receptor, EPHA4, contributes to the regulation of EpCAM expression via a reverse signaling mechanism. Our screening strategy revealed novel mechanisms for the regulation of EpCAM expression and provides insight into the biology of this cancer-associated gene. Importantly, this study also demonstrates the utility of pooled RNAi screening in the identification of genes required for the regulation of cell surface protein expression.
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Project description:Schoeberl2002 - EGF MAPK
Computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways.
The initial model was constructed by Ken Lau from the MATLAB source code.
This model is described in the article:
Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors.
Schoeberl B, Eichler-Jonsson C, Gilles ED, Müller G
Nat. Biotechnol. 2002 Apr; 20(4): 370-375
Abstract:
We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.
This model does not exactly reproduce the results given in the original publication. It has, though, the same reaction graph and gives very similar time courses for the conditions depicted in the article.
Several corrections were applied to the parameters described in the paper's supplementary materials. Some parameter names were replaced by the corresponding identical ones: k(r)26 by k(r)18, k(r)27 by k(r)19, k(r)30 by k(r)20, k(r)38 by k(r)24, k(r)39 by k(r)37, k(r)46 by k(r)44, k51 by k49, k(r)54 by k(r)52 and k62 by k62. In particular the parameter values described in the column "remark" of supplementary table 1 override the values explicitely written in the numerical columns:
name
in suppl. value used
in model value used
remarks
kr16
0.055
0.275
k30
7.9e6
2.1e6
as k20
kr30
0.3
0.4
as kr24
k38
3e7
1e7
as k20
kr38
0.055
0.55
as kr24
k52
1.1e5
5.34e7
k5 was used for v116, v119, v122 and v125 in addition of v107, v110 and v113 as listed in the legend of supplementary figure 2. k5 is calculated using th eformula from the matlab file not given in the supplements.
All rate constants were rescaled to minutes (k[min] = 60*k[sec]) and all second order rate constants additionally to molecules/cell with a cell volume of 1 picolitre (k[molecs/cell] = k[M]/(Vc*Na), with Vc=1e-12 l and Na = 6e23).
The association constant of internalized EGF was rescaled to molecules/endosome using an endosomal volume of 4.3 al (= 4.3*10 -18 litre).
The extracellular EGF concentration was converted to molecules per picolitre with a MW of 6045 Da.
[ng/ml]
[numb/pl]
50
4962
0.5
49.6
0.125
12.4
With the initial conditions given in the paper, the results could not be reproduced at all. Therefore the initial conditions used in the MATLAB file were adopted for SHC (1.01 * 10 5 instead of 1.01 * 10 6 ) and Ras_GDP. (7.2 * 10 4 instead of 1.14 * 10 7 )
This model is hosted on BioModels Database and identified by: BIOMD0000000019.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000019 | BioModels
Project description:SMORE-seq (Simultaneous Mapping Of RNA Ends by sequencing) is developted to simultaneously identify the strongest TSS (Transcription Start Site) and PAS (Polyadenylation Site) SMORE-seq were performed in wild type Saccharomyces cerevisiae
2014-02-04 | E-GEOD-49026 | biostudies-arrayexpress