Dataset Information

MCF7 Cells: Control siRNA -E2 vs. Control siRNA +E2, Control siRNA -E2 vs. PES1 siRNA -E2,Control siRNA -E2 vs. PES1 siRNA +E2

ABSTRACT: The initiation of breast cancer is associated with increased expression of tumor-promoting estrogen receptor α (ERα) protein and decreased expression of tumor-suppressive ERβ protein. However, the mechanism underlying this process is unknown. Here we show that Pescadillo/PES1, an estrogen-inducible protein that is over-expressed in breast cancer, can regulate the balance between ERα and ERβ. PES1 enhances transcriptional activity of ERα and reduces that of ERβ, and modulates many estrogen-responsive genes. Consistent with this regulation of ERα and ERβ transcriptional activity, PES1 increases the stability of the ERα protein and decreases that of ERβ through the ubiquitin-proteasome pathway, mediated by the carboxyl terminus of Hsc70-interacting protein (CHIP). Moreover, PES1 can transform normal human mammary epithelial cells and is required for estrogen-induced breast tumor growth in nude mice. Further analysis of clinical samples showed that expression of PES1 correlates positively with ERα expression and negatively with ERβ expression, and predicts good clinical outcome in breast cancer. Our data demonstrate that PES1 contributes to breast tumor growth through regulating the balance between ERα and ERβ and may be a better target for the development of drugs that selectively regulate ERα and ERβ activities.

ORGANISM(S): Homo sapiens

PROVIDER: GSE38621 | GEO | 2012/06/10

SECONDARY ACCESSION(S): PRJNA168250

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:The closely related transcription factors (TFs), estrogen receptors ERα and ERβ, regulate divergent gene expression programs and proliferative outcomes in breast cancer. Utilizing MCF-7 breast cancer cells with ERα, ERβ, or both receptors as a model system to define the basis of differing response specification by related TFs, we show that these TFs and their key coregulators, SRC3 and RIP140, generate overlapping as well as unique chromatin-binding and transcription-regulating modules. Cistrome and transcriptome analyses and use of clustering algorithms delineated 11 clusters representing different chromatin-bound receptor and coregulator assemblies that could be functionally associated through enrichment analysis with distinct patterns of gene regulation and preferential coregulator usage, RIP140 with ERβ and SRC3 with ERα. The receptors modified each other’s transcriptional effect, and ERβ countered the proliferative drive of ERα through several novel mechanisms associated with specific binding site clusters. Our findings delineate distinct TF-coregulator assemblies that function as control nodes specifying precise patterns of gene regulation, proliferation, and metabolism, as exemplified by two of the most important nuclear hormone receptors in human breast cancer. MCF-7 cells expressing endogenous ERalpha were infected with adenovirus carrying either estrogen receptor beta (AdERb) or no insert (Ad) at multiplicity of infection (moi) of 50. ERβ only cells were generated from these cells by knockdown of ERα in parental cells using the following siERα sequences from Dharmacon: forward, 5’-UCAUCGCAUUCCUUGCAAAdTdT-3’, and reverse, 5’-UUUGCAAGGAAUGCGAUGAdTdT-3’. siRNA experiments were performed as previously described, and resulted in knocknown of ERα mRNA and protein by greater than 95% (GSE4006, PMID 16809442). Briefly, cells were transfected with 20 nM siCtrl [GSE4006] or siERα for 48 h after infection. Then cells were treated with 0.1% EtOH (Veh) or 10 nM E2 (Sigma-Aldrich) for 24h. All experiments were conducted with two replicates. key words; Adenovirus infection,siRNA knock-down, ligand treatment

Dataset Information

MCF7 Cells: Control siRNA -E2 vs. Control siRNA +E2, Control siRNA -E2 vs. PES1 siRNA -E2,Control siRNA -E2 vs. PES1 siRNA +E2

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets