Dataset Information

Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites.

ABSTRACT: BACKGROUND: Transcription factor binding sites (TFBS) impart specificity to cellular transcriptional responses and have largely been defined by consensus motifs derived from a handful of validated sites. The low specificity of the computational predictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requirements for binding. We posited that the inadequacy is due to limited input of empirically verified sites, and demonstrated a multiplatform approach to constructing a robust model. RESULTS: Using the TFBS for the estrogen receptor (ER)alpha (estrogen response element [ERE]) as a model system, we extracted EREs from multiple molecular and genomic platforms whose binding to ERalpha has been experimentally confirmed or rejected. In silico analyses revealed significant sequence information flanking the standard binding consensus, discriminating ERE-like sequences that bind ERalpha from those that are nonbinders. We extended the ERE consensus by three bases, bearing a terminal G at the third position 3' and an initiator C at the third position 5', which were further validated using surface plasmon resonance spectroscopy. Our functional human ERE prediction algorithm (h-ERE) outperformed existing predictive algorithms and produced fewer than 5% false negatives upon experimental validation. CONCLUSION: Building upon a larger experimentally validated ERE set, the h-ERE algorithm is able to demarcate better the universe of ERE-like sequences that are potential ER binders. Only 14% of the predicted optimal binding sites were utilized under the experimental conditions employed, pointing to other selective criteria not related to EREs. Other factors, in addition to primary nucleotide sequence, will ultimately determine binding site selection.

SUBMITTER: Vega VB

PROVIDER: S-EPMC1794554 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites.

Vega Vinsensius B VB Lin Chin-Yo CY Lai Koon Siew KS Kong Say Li SL Xie Min M Su Xiaodi X Teh Huey Fang HF Thomsen Jane S JS Yeo Ai Li AL Sung Wing Kin WK Bourque Guillaume G Liu Edison T ET

Genome biology 20060101 9

<h4>Background</h4>Transcription factor binding sites (TFBS) impart specificity to cellular transcriptional responses and have largely been defined by consensus motifs derived from a handful of validated sites. The low specificity of the computational predictions of TFBSs has been attributed to ubiquity of the motifs and the relaxed sequence requirements for binding. We posited that the inadequacy is due to limited input of empirically verified sites, and demonstrated a multiplatform approach to ...[more]

PMID: 16961928

Similar Datasets

Project description:Using a chromatin immunoprecipitation-paired end diTag cloning and sequencing strategy, we mapped estrogen receptor alpha (ERalpha) binding sites in MCF-7 breast cancer cells. We identified 1,234 high confidence binding clusters of which 94% are projected to be bona fide ERalpha binding regions. Only 5% of the mapped estrogen receptor binding sites are located within 5 kb upstream of the transcriptional start sites of adjacent genes, regions containing the proximal promoters, whereas vast majority of the sites are mapped to intronic or distal locations (>5 kb from 5' and 3' ends of adjacent transcript), suggesting transcriptional regulatory mechanisms over significant physical distances. Of all the identified sites, 71% harbored putative full estrogen response elements (EREs), 25% bore ERE half sites, and only 4% had no recognizable ERE sequences. Genes in the vicinity of ERalpha binding sites were enriched for regulation by estradiol in MCF-7 cells, and their expression profiles in patient samples segregate ERalpha-positive from ERalpha-negative breast tumors. The expression dynamics of the genes adjacent to ERalpha binding sites suggest a direct induction of gene expression through binding to ERE-like sequences, whereas transcriptional repression by ERalpha appears to be through indirect mechanisms. Our analysis also indicates a number of candidate transcription factor binding sites adjacent to occupied EREs at frequencies much greater than by chance, including the previously reported FOXA1 sites, and demonstrate the potential involvement of one such putative adjacent factor, Sp1, in the global regulation of ERalpha target genes. Unexpectedly, we found that only 22%-24% of the bona fide human ERalpha binding sites were overlapping conserved regions in whole genome vertebrate alignments, which suggest limited conservation of functional binding sites. Taken together, this genome-scale analysis suggests complex but definable rules governing ERalpha binding and gene regulation.

Project description:The transformation of prostate cancer from an androgen-dependent state to an androgen-independent state is a lethal progression. Alterations in transcriptional programs are the basis of prostate cancer deterioration. The androgen receptor (AR), a member of the nuclear hormone receptor superfamily, mediates prostate cancer progression by functioning primarily through the ligand-activated transcription of target genes. Therefore, a detailed map of AR-regulated genes and AR genomic binding sites is required for hormone-naive and castration-resistant prostate cancer. Through the use of chromatin immunoprecipitation in combination with direct sequencing, 4,143 AR binding sites were defined in the LNCaP androgen-sensitive prostate cancer cell line. Using the same method, 2,380 AR binding regions were identified in the LNCaP-AI long-term androgen-deprived cell line. Approximately 8.5% (354/4,143) of the binding regions were mapped to within 2 kb of the transcription start site (TSS) in the LNCaP cells, while ?12.6% (299/2,380) were mapped to within 2 kb of the TSS in the LNCaP-AI cells. In total, the study mapped 2,796 genes in LNCaP cells and 1,854 genes in LNCaP-AI cells. The cell lines shared 789 mutual genes. In addition, gene ontology (GO) analysis of the genes revealed that there was a notable overlap between the GO terms in the LNCaP cells and LNCaP-AI cells. However, GO terms within the biological process domain that were only observed in the LNCaP-AI cells included the reproduction process, death, immune system process, multi-organism process, pigmentation and viral reproduction. The major genes in the different GO terms were TNFAIP8, RTN4, APP and SYNE1. Through analyzing the AR binding sites in the two cell types, the present study aimed to map potential AR-regulated genes, identify their associated transcription factors and provide a new perspective on the biological processes in the development of prostate cancer. The results provided a valuable data set that furthered the understanding of the genome-wide analysis of AR binding sites in prostate cancer cells, which may be exploited for the development of novel prostate cancer therapeutic strategies.

Dataset Information

Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites.

Publications

Multiplatform genome-wide identification and modeling of functional human estrogen receptor binding sites.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets