E-MTAB-6120
Ontology highlight
ABSTRACT:
PROVIDER: E-MTAB-6120 | ExpressionAtlas | 2020-07-20
REPOSITORIES: ExpressionAtlas
Similar Datasets
Project description:Uncoupling effects of ERalfa on LKB1/AMPK interaction upon adiponectin exposure in breast cancer
| PRJEB36821 | ENA
Project description:Adipose tissue is a metabolic and endocrine organ that secretes numerous bioactive molecules called adipocytokines. Among these, adiponectin has been argued to have a crucial role in obesity-associated breast cancer. The key molecule of adiponectin signaling is AMP-activated protein kinase (AMPK), mainly activated by Liver Kinase B1 (LKB1). Here, we demonstrated how the ERalfa/LKB1 interaction may negatively interfere with the capability of LKB1 to phosphorylate AMPK and then inhibit its downstream signaling TSC2/mTOR/p70S6k. In MCF-7 cells upon adiponectin AMPK signaling was not working, keeping its downstream protein Acetyl-CoA Carboxylase (ACC) still active. In contrast, in MDA-MB-231 cells the phosphorylation of AMPK and ACC was enhanced with consequent inhibition of both lipogenesis and cell growth. Thus, upon adiponectin, ERalfa signaling switched the energy balance of breast cancer cells towards a lipogenic phenotype. In other words, adiponectin in all the concentrations tested played an inhibitory role on ERalpha-negative breast cancer cell growth and progression either in vitro or in vivo. In contrast, low adiponectin levels, similar to those circulating in obese patients, worked on ERalfa-positive cells as a growth factor, stimulating their growth and progression. The latter effect seems to be blunted in vivo only in the presence of high adiponectin concentration. Based on the present results, it can be concluded that if we prospectively address adiponectin as a pharmacological tool, a separate therapeutic treatment should be carefully assessed in ERalfa-positive and negative breast-cancer patients.
2020-02-27 | E-MTAB-6120 | biostudies-arrayexpress
Project description:Reprogramming of cellular metabolism plays a central role in fuelling malignant transformation, and AMPK as well as the PGC-1α/ERRα axis are key regulators of this process. Intersection of gene expression and binding event datasets in breast cancer cells shows that activation of AMPK significantly increases the expression of PGC-1α/ERRα and promotes the binding of ERRα to its cognate sites. Unexpectedly, the data also reveal that ERRα, in concert with PGC-1α, negatively regulates the expression of several one-carbon metabolism genes resulting in substantial perturbations in purine biosynthesis. This PGC-1α/ERRα-mediated repression of one-carbon metabolism promotes the sensitivity of breast cancer cells and tumors to the anti-folate drug methotrexate. These data implicate the PGC-1α/ERRα axis as a core regulatory node of folate cycle metabolism and further suggest that activators of AMPK could be used to modulate this pathway in cancer. We used microarrays to detail the global program of gene expression following AMPK activation by AICAR in BT474 breast cancer cells.
2016-02-02 | GSE75727 | GEO
Project description:Mufudza2012 - Estrogen effect on the dynamics
of breast cancer
This deterministic model shows the
dynamics of breast cancer with immune response. The effects of
estrogen are incorporated to study its effects as a risk factor for
the disease.
This model is described in the article:
Assessing the effects of
estrogen on the dynamics of breast cancer.
Mufudza C, Sorofa W, Chiyaka
ET.
Comput Math Methods Med 2012; 2012:
473572
Abstract:
Worldwide, breast cancer has become the second most common
cancer in women. The disease has currently been named the most
deadly cancer in women but little is known on what causes the
disease. We present the effects of estrogen as a risk factor on
the dynamics of breast cancer. We develop a deterministic
mathematical model showing general dynamics of breast cancer
with immune response. This is a four-population model that
includes tumor cells, host cells, immune cells, and estrogen.
The effects of estrogen are then incorporated in the model. The
results show that the presence of extra estrogen increases the
risk of developing breast cancer.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000642.
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.
2017-07-26 | BIOMD0000000642 | BioModels
Project description:Reprogramming of cellular metabolism plays a central role in fuelling malignant transformation, and AMPK as well as the PGC-1α/ERRα axis are key regulators of this process. Intersection of gene expression and binding event datasets in breast cancer cells shows that activation of AMPK significantly increases the expression of PGC-1α/ERRα and promotes the binding of ERRα to its cognate sites. Unexpectedly, the data also reveal that ERRα, in concert with PGC-1α, negatively regulates the expression of several one-carbon metabolism genes resulting in substantial perturbations in purine biosynthesis. This PGC-1α/ERRα-mediated repression of one-carbon metabolism promotes the sensitivity of breast cancer cells and tumors to the anti-folate drug methotrexate. These data implicate the PGC-1α/ERRα axis as a core regulatory node of folate cycle metabolism and further suggest that activators of AMPK could be used to modulate this pathway in cancer. We used microarrays to detail the global programme of gene expression following AMPK activation by AICAR in BT474 breast cancer cells.
2016-02-02 | GSE75876 | GEO
Project description:Cells were cotreated with dihydrotestosterone, progesterone or medroxyprogesterone acetate and estrdiol to assess the combinatorial effects of hormone exposure in breast cancer cells
2015-10-18 | GSE61368 | GEO
Project description:Weâve undertaken a genome-wide approach to identify and test genes in fibroblasts that are both induced upon interaction with basal breast cancer cells in culture and upregulated in stromal cells from primary human breast cancers. Several of the upregulated genes encode secreted growth factors or cytokines. Using RNAi and a co-injection tumorigenicity assay, we determined that the majority of secreted factors selected for functional validation played significant, yet functionally diverse, roles in promoting tumorigenicity. Rather than a single major mediator, these results indicate multiple points of intervention to prevent fibroblasts from supporting basal breast cancer. Additionally, we show that breast cancer subtypes differ markedly in the expression of these and other stromally secreted proteins using data from microdissected stromal samples. Induction of genes in four different fibroblast strains (HFFF2, HFF1, CCD1112Sk and Wi38) upon coculture with Cal51 and MDAMB231 human basal breast cancer cell lines. Monocultures of each group are used as the experimental control with each group having 3-4 independent biological replicates.
2013-11-21 | E-GEOD-41678 | biostudies-arrayexpress
Project description:Analysis of genes regulated by RU486 (an progesterone antagonist) in human breast cancer T47D cells and human uterine leiomyoma smooth muscle cells. The hypothesis is that RU486 inhibits tumor growth by inactivating the transcription of multiple genes which trigger critical signaling pathways to induce tumorigenesis in both breast caner and uterine leomyoma. Tissue-specific and common patterns of gene regulation may determine the therapeutic effects of antiprogestins in uterine leiomyoma and breast cancer. We applied ChIP-seq to identify PR-interaction sites in T47D breast cancer cells and primary uterine leiomyoma cells treated with RU486.
2012-09-11 | E-GEOD-40724 | biostudies-arrayexpress
Project description:Morrison1989 - Folate Cycle
The model describes the folate cycle
kinetics in breast cancer cells.
This model is described in the article:
Folate cycle kinetics in
human breast cancer cells.
Morrison PF, Allegra CJ.
J. Biol. Chem. 1989 Jun; 264(18):
10552-10566
Abstract:
A mathematical description of polyglutamated folate kinetics
for human breast carcinoma cells (MCF-7) has been formulated
based upon experimental folate, methotrexate (MTX), purine, and
pyrimidine pool sizes as well as reaction rate parameters
obtained from intact MCF-7 cells and their enzyme isolates. The
schema accounts for the interconversion of highly
polyglutamated tetrahydrofolate, 5-methyl-FH4, 5-10-CH2FH4,
dihydrofolate (FH2), 10-formyl-FH4 (FFH4), and 10-formyl-FH2
(FFH2), as well as formation and transport of the MTX
polyglutamates. Inhibition mechanisms have been chosen to
reproduce all observed non-, un-, and pure competition
inhibition patterns. Steady state folate concentrations and
thymidylate and purine synthesis rates in drug-free intact
cells were used to determine normal folate Vmax values. The
resulting average-cell folate model, examined for its ability
to predict folate pool behavior following exposure to 1 microM
MTX over 21 h, agreed well with the experiment, including a
relative preservation of the FFH4 and CH2FH4 pools. The results
depend strongly on thymidylate synthase (TS) reaction
mechanism, especially the assumption that MTX di- and
triglutamates inhibit TS synthesis as greatly in the intact
cell as they do with purified enzyme. The effects of cell cycle
dependence of TS and dihydrofolate reductase activities were
also examined by introducing G- to S-phase activity ratios of
these enzymes into the model. For activity ratios down to at
least 5%, cell population averaged folate pools were only
slightly affected, while CH2FH4 pools in S-phase cells were
reduced to as little as 10% of control values. Significantly,
these folate pool dynamics were indicated to arise from both
direct inhibition by MTX polyglutamates as well as inhibition
by elevated levels of polyglutamated FH2 and FFH2.
Note: two flow BCs were converted into two downstream
concentration BCs, thus removing the GAR and dUMP state
variables. This dropped the number of ODEs from 21 to 19.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000018.
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.
2005-06-29 | BIOMD0000000018 | BioModels