Genome-wide mapping of SPDEF and ESR1 binding in MCF-7 cells (ChIP-seq)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE48930 | GEO | 2013/07/17
SECONDARY ACCESSION(S): PRJNA212328
REPOSITORIES: GEO
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Project description:Using a transcriptional network derived from 2000 breast cancer gene expression profiles we identify the master regulators (MRs) of FGFR2 signalling. To validate the identified regulons, we examined whether there was enrichment of TF binding near the transcription start sites (TSS) of genes found in the regulons of a particular MR. For ESR1 and SPDEF, ChIP-seq experiments were performed in MCF-7 cells, while existing data was analysed for FOXA1 (Hurtado et al. Nature Genetics, 43:27–33, 2010) and GATA3 (Theodorou, et al., Genome Res 23: 12-22, 2013). ChIP-seq experiments were performed on three biological replicates per each transcription factor. For each sample, 36bp single-end reads were obtained. Peak regions were identified in all ChIP-seq TF data sets using the peak caller algorithm MACS (Zhang et al., Genome Biology, 9(9):R137, 2008) with default parameters.
2013-07-17 | E-GEOD-48930 | biostudies-arrayexpress
Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways
2013-07-17 | GSE48928 | GEO
Project description:Genome-wide association studies for breast cancer have identified over 80 different risk regions in the genome, with the FGFR2 locus consistently identified as the most strongly associated locus. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Here we use a systems biology approach to elucidate the regulatory networks operating in breast cancer and examine the role of FGFR2 in mediating risk. Using model systems we identify FGFR2-regulated genes and, combining variant set enrichment and eQTL analysis, show that these are preferentially linked to breast cancer risk loci. Our results support the concept that cancer-risk associated genes cluster in pathways The data consists of 18 microarray samples after knocking down PTTG1 and SPDEF in MCF-7 cells. The data have been pre-processed in R using the Beadarray package, and are presented in the form of log2 expression values. The experiment was carried out on Humanv4 BeadChips arrays interrogating 48107 randomly-distributed bead-types, and in this experiment there was a mean of 22 beads per bead-type (Standard Deviation of 5)
2013-07-17 | E-GEOD-48928 | biostudies-arrayexpress
Project description:Genome-wide association studies have identified a locus within the second intron of the FGFR2 gene that is consistently the most strongly associated with estrogen receptor-poisive breast cancer risk. However, we know little about the mechanisms by which the FGFR2 locus mediates risk or the pathways in which multiple risk loci may combine to cause disease. Previously, a systems biology approach was adopted to elucidate the regulatory networks operating in MCF-7 breast cancer cells in order to examine the role of FGFR2 in mediating risk. Here, the same approach has been employed using MCF-7 cells that have been treated with siRNA directed against FGFR2, in order to knock-down FGFR2 expression, to confirm that the differential gene expression that we see when FGF10 signalling is perturbed, on a background of estrogen signalling, is mediated via FGFR2 stimulation.
2016-02-01 | GSE74146 | GEO
Project description: Not available
| S-ECPF-GEOD-48928 | biostudies-other
Project description:Thiele2011 - Genome-scale metabolic network
of Salmonella Typhimurium (STM_v1_0)
This model is described in the article:
A community effort towards a
knowledge-base and mathematical model of the human pathogen
Salmonella Typhimurium LT2.
Thiele I, Hyduke DR, Steeb B, Fankam
G, Allen DK, Bazzani S, Charusanti P, Chen FC, Fleming RM, Hsiung
CA, De Keersmaecker SC, Liao YC, Marchal K, Mo ML, Özdemir
E, Raghunathan A, Reed JL, Shin SI, Sigurbjörnsdóttir
S, Steinmann J, Sudarsan S, Swainston N, Thijs IM, Zengler K,
Palsson BO, Adkins JN, Bumann D.
BMC Syst Biol 2011; 5: 8
Abstract:
BACKGROUND: Metabolic reconstructions (MRs) are common
denominators in systems biology and represent biochemical,
genetic, and genomic (BiGG) knowledge-bases for target
organisms by capturing currently available information in a
consistent, structured manner. Salmonella enterica subspecies I
serovar Typhimurium is a human pathogen, causes various
diseases and its increasing antibiotic resistance poses a
public health problem. RESULTS: Here, we describe a
community-driven effort, in which more than 20 experts in S.
Typhimurium biology and systems biology collaborated to
reconcile and expand the S. Typhimurium BiGG knowledge-base.
The consensus MR was obtained starting from two independently
developed MRs for S. Typhimurium. Key results of this
reconstruction jamboree include i) development and
implementation of a community-based workflow for MR annotation
and reconciliation; ii) incorporation of thermodynamic
information; and iii) use of the consensus MR to identify
potential multi-target drug therapy approaches. CONCLUSION:
Taken together, with the growing number of parallel MRs a
structured, community-driven approach will be necessary to
maximize quality while increasing adoption of MRs in
experimental design and interpretation.
This model is hosted on
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MODEL1507180017.
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2015-07-30 | MODEL1507180017 | BioModels
Project description:Previously, we have shown that HIST1H2ac is overexpressed in MCF-7 breast cancer cell line. It acts as a master regulator of estrogen receptor alpha-dependent gene expression in ER+ breast cancer cells. In the present study, we investigate the genome-wide protein DNA-binding events of HIST1H2ac protein in MCF-7 breast cancer cell line by over-expressing hemagglutinin (HA)-tagged HIST1H2ac and compared with MCF-7 cells over-expressing HA. The protein-bound DNA was recovered by immunoprecipitation using anti-HA antibody. The ChIP DNA and input DNA were sequenced with an Illumina HiSeq 2000 sequencer.
2016-05-19 | E-MTAB-3160 | biostudies-arrayexpress
Project description:Somatic hotspot mutations and structural amplifications and fusions affecting fibroblast growth factor receptor 2 (FGFR2) occur in multiple cancer types. However, clinical responses to FGFR inhibitors (FGFRi) have remained variable, emphasizing a need to better understand which FGFR2 alterations are oncogenic and targetable. Here we applied transposon-based screening and tumor modelling in mice to uncover truncation of exon (E) 18 of Fgfr2 as a potent driver mutation. Human oncogenomic datasets revealed a diverse set of FGFR2 alterations, including rearrangements (REs), E1-E17 partial amplifications, and E18 nonsense and frameshift mutations, each causing transcription of E18-truncated FGFR2 (FGFR2deltaE18). Somatic modelling in mice and human tumor cell lines using a compendium of FGFR2deltaE18 and full-length variants identified FGFR2deltaE18-truncation as potent single-driver alteration in cancer. Here we show the phosphoproteomic landscape of FGFR2 variants in murine epithelial cell (MEC) lines and mouse tumors. Global (STY) phosphoproteomics using IMAC and phosphotyrosine phosphoproteomics using pTyr IP’s are combined with DIA protein expression data to uncover oncogenic signaling of clinically-relevant FGFR2 variants.
2022-06-06 | PXD031711 | Pride
Project description:The Caulobacter cell cycle includes in an asymmetric cell division that is driven by a core regulatory circuit comprised of 4 transcription factors (DnaA, GcrA, CtrA, and SciP) and a DNA methyltransferase (CcrM). Using a modified global 5M-bM-^@M-^Y RACE protocol we mapped 2,726 transcriptional start sites (TSS) in the 4mb Caulobacter genome and identified 586 cell cycle-regulated TSS. The core cell cycle circuit directly controls about 55% of cell cycle-regulated TSS by integrating multiple regulatory inputs within at least 322 promoters, providing a large number of transcription profiles from a small number of regulatory factors. Here, we identified previously unknown features of the core cell cycle circuit, including antisense TSS within dnaA and ctrA, plus newly identified TSS for ctrA and ccrM. Altogether, we identified 615 antisense TSS plus 241 genes that are transcribed from multiple TSS. The multiple TSS in the same promoter region often exhibit different cell cycle activation timing, These novel features of the global transcript profile add significant insight to the system architecture of the Caulobacter cell cycle regulatory circuit. Global 5' RACE was performed to map Transcription Start Sites in the Caulobacter NA1000 genome
2014-10-16 | E-GEOD-57365 | biostudies-arrayexpress
Project description:The Caulobacter cell cycle includes in an asymmetric cell division that is driven by a core regulatory circuit comprised of 4 transcription factors (DnaA, GcrA, CtrA, and SciP) and a DNA methyltransferase (CcrM). Using a modified global 5’ RACE protocol we mapped 2,726 transcriptional start sites (TSS) in the 4mb Caulobacter genome and identified 586 cell cycle-regulated TSS. The core cell cycle circuit directly controls about 55% of cell cycle-regulated TSS by integrating multiple regulatory inputs within at least 322 promoters, providing a large number of transcription profiles from a small number of regulatory factors. Here, we identified previously unknown features of the core cell cycle circuit, including antisense TSS within dnaA and ctrA, plus newly identified TSS for ctrA and ccrM. Altogether, we identified 615 antisense TSS plus 241 genes that are transcribed from multiple TSS. The multiple TSS in the same promoter region often exhibit different cell cycle activation timing, These novel features of the global transcript profile add significant insight to the system architecture of the Caulobacter cell cycle regulatory circuit.
2014-10-16 | GSE57365 | GEO