Integration of external signaling pathways with the core transcriptional network in embryonic stem cells.

Transcription factors (TFs) and their specific interactions with targets are crucial for specifying gene-expression programs. To gain insights into the transcriptional regulatory networks in embryonic stem (ES) cells, we use chromatin immunoprecipitation coupled with ultra-high-throughput DNA sequencing (ChIP-seq) to map the locations of 13 sequence-specific TFs (Nanog, Oct4, STAT3, Smad1, Sox2, Zfx, c-Myc, n-Myc, Klf4, Esrrb, Tcfcp2l1, E2f1, and CTCF) and 2 transcription regulators (p300 and Suz12). These factors are known to play different roles in ES-cell biology as components of the LIF and BMP signaling pathways, self-renewal regulators, and key reprogramming factors. Our study provides insights into the integration of the signaling pathways into the ES-cell-specific transcription circuitries. Intriguingly, we find specific genomic regions extensively targeted by different TFs. Collectively, the comprehensive mapping of TF-binding sites identifies important features of the transcriptional regulatory networks that define ES-cell identity.

An oestrogen-receptor-alpha-bound human chromatin interactome.

Genomes are organized into high-level three-dimensional structures, and DNA elements separated by long genomic distances can in principle interact functionally. Many transcription factors bind to regulatory DNA elements distant from gene promoters. Although distal binding sites have been shown to regulate transcription by long-range chromatin interactions at a few loci, chromatin interactions and their impact on transcription regulation have not been investigated in a genome-wide manner. Here we describe the development of a new strategy, chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) for the de novo detection of global chromatin interactions, with which we have comprehensively mapped the chromatin interaction network bound by oestrogen receptor alpha (ER-alpha) in the human genome. We found that most high-confidence remote ER-alpha-binding sites are anchored at gene promoters through long-range chromatin interactions, suggesting that ER-alpha functions by extensive chromatin looping to bring genes together for coordinated transcriptional regulation. We propose that chromatin interactions constitute a primary mechanism for regulating transcription in mammalian genomes.

The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells.

Oct4 and Nanog are transcription factors required to maintain the pluripotency and self-renewal of embryonic stem (ES) cells. Using the chromatin immunoprecipitation paired-end ditags method, we mapped the binding sites of these factors in the mouse ES cell genome. We identified 1,083 and 3,006 high-confidence binding sites for Oct4 and Nanog, respectively. Comparative location analyses indicated that Oct4 and Nanog overlap substantially in their targets, and they are bound to genes in different configurations. Using de novo motif discovery algorithms, we defined the cis-acting elements mediating their respective binding to genomic sites. By integrating RNA interference-mediated depletion of Oct4 and Nanog with microarray expression profiling, we demonstrated that these factors can activate or suppress transcription. We further showed that common core downstream targets are important to keep ES cells from differentiating. The emerging picture is one in which Oct4 and Nanog control a cascade of pathways that are intricately connected to govern pluripotency, self-renewal, genome surveillance and cell fate determination.

Zebrafish whole-adult-organism chemogenomics for large-scale predictive and discovery chemical biology.

The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly), is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated) aromatic hydrocarbons [P(H)AHs] and estrogenic compounds (ECs), we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology.

Whole-genome cartography of estrogen receptor alpha binding sites.

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.

Conservation of gene expression signatures between zebrafish and human liver tumors and tumor progression.

The zebrafish (Danio rerio) has been long advocated as a model for cancer research, but little is known about the real molecular similarities between zebrafish and human tumors. Comparative analysis of microarray data from zebrafish liver tumors with those from four human tumor types revealed molecular conservation at various levels between fish and human tumors. This approach provides a useful strategy for identifying an expression signature that is strongly associated with a disease phenotype.

Gene expression preferentially regulated by tamoxifen in breast cancer cells and correlations with clinical outcome.

The beneficial effect of the selective estrogen receptor (ER) modulator tamoxifen in the treatment and prevention of breast cancer is assumed to be through its ability to antagonize the stimulatory actions of estrogen, although tamoxifen can also have some estrogen-like agonist effects. Here, we report that, in addition to these mixed agonist/antagonist actions, tamoxifen can also selectively regulate a unique set of >60 genes, which are minimally regulated by estradiol (E2) or raloxifene in ERalpha-positive MCF-7 human breast cancer cells. This gene regulation by tamoxifen is mediated by ERalpha and reversed by E2 or ICI 182,780. Introduction of ERbeta into MCF-7 cells reverses tamoxifen action on approximately 75% of these genes. To examine whether these genes might serve as markers of tamoxifen sensitivity and/or the development of resistance, their expression level was examined in breast cancers of women who had received adjuvant therapy with tamoxifen. High expression of two of the tamoxifen-stimulated genes, YWHAZ/14-3-3z and LOC441453, was found to correlate significantly with disease recurrence following tamoxifen treatment in women with ER-positive cancers and hence seem to be markers of a poor prognosis. Our data indicate a new dimension in tamoxifen action, involving gene expression regulation that is tamoxifen preferential, and identify genes that might serve as markers of tumor responsiveness or resistance to tamoxifen therapy. This may have a potential effect on the choice of tamoxifen versus aromatase inhibitors as adjuvant endocrine therapy.

Inhibitory effects of estrogen receptor beta on specific hormone-responsive gene expression and association with disease outcome in primary breast cancer.

INTRODUCTION: The impact of interactions between the two estrogen receptor (ER) subtypes, ERalpha and ERbeta, on gene expression in breast cancer biology is not clear. The goal of this study was to examine transcriptomic alterations in cancer cells co-expressing both receptors and the association of gene expression signatures with disease outcome. METHODS: Transcriptional effects of ERbeta overexpression were determined in a stably transfected cell line derived from ERalpha-positive T-47D cells. Microarray analysis was carried out to identify differential gene expression in the cell line, and expression of key genes was validated by quantitative polymerase chain reaction. Microarray and clinical data from patient samples were then assessed to determine the in vivo relevance of the expression profiles observed in the cell line. RESULTS: A subset of 14 DNA replication and cell cycle-related genes was found to be specifically downregulated by ERbeta. Expression profiles of four genes, CDC2, CDC6, CKS2, and DNA2L, were significantly inversely correlated with ERbeta transcript levels in patient samples, consistent with in vitro observations. Kaplan-Meier analysis revealed better disease outcome for the patient group with an expression signature linked to higher ERbeta expression as compared to the lower ERbeta-expressing group for both disease-free survival (p = 0.00165) and disease-specific survival (p = 0.0268). These findings were further validated in an independent cohort. CONCLUSION: Our findings revealed a transcriptionally regulated mechanism for the previously described growth inhibitory effects of ERbeta in ERalpha-positive breast tumor cells and provide evidence for a functional and beneficial impact of ERbeta in primary breast tumors.

BEARR: Batch Extraction and Analysis of cis-Regulatory Regions.

Transcription factors play important roles in regulating biological and disease processes. Microarray technology has enabled researchers to simultaneously monitor changes in the expression of thousands of transcripts. By identifying specific transcription factor binding sites in the cis-regulatory regions of differentially expressed genes, it is then possible to identify direct targets of transcription factors, model transcriptional regulatory networks and mine the dataset for relevant targets for experimental and clinical manipulation. We have developed web-based software to assist biologists in efficiently carrying out the analysis of microarray data from studies of specific transcription factors. Batch Extraction and Analysis of cis-Regulatory Regions, or BEARR, accepts gene identifier lists from microarray data analysis tools and facilitates identification, extraction and analysis of regulatory regions from the large amount of data that is typically generated in these types of studies. The software is publicly available at http://giscompute.gis.a-star.edu.sg/~vega/BEARR1.0/.

Genome-wide expression profiling; a panel of mouse tissues discloses novel biological functions of liver X receptors in adrenals.

The liver X receptors alpha and beta (LXRalpha and LXRbeta ) are members of the nuclear receptor superfamily of proteins which are highly expressed in metabolically active tissues. They regulate gene expression of critical genes involved in cholesterol catabolism and transport, lipid and triglyceride biosynthesis and carbohydrate metabolism in response to distinct oxysterols and intermediates in the cholesterol metabolic pathway. The biological roles of the LXRs in tissues other than liver, intestine and adipose tissue are poorly elucidated. In this study we used global gene-expression profiling analysis to detect differences in expression patterns in several tissues from mice fed an LXR agonist or vehicle. Our results show that LXR plays an important role in the kidney, lung, adrenals, brain, testis and heart where several putative LXR target genes were found. The effects of the LXRs were further analysed in adrenals where treatment with an LXR agonist induced expression of adrenocorticotrophic hormone receptor, suppressed expression of uncoupling protein (UCP)-1 and UCP-3 as well as several glycolytic enzymes and led to increased serum corticosterone levels. These results indicate novel biological roles of the LXR including regulation of energy metabolism, glycolysis and steroidogenesis in the adrenals via alteration of expression profiles of putative target genes.

A human in vitro model system for investigating genome-wide host responses to SARS coronavirus infection.

BACKGROUND: The molecular basis of severe acute respiratory syndrome (SARS) coronavirus (CoV) induced pathology is still largely unclear. Many SARS patients suffer respiratory distress brought on by interstitial infiltration and frequently show peripheral blood lymphopenia and occasional leucopenia. One possible cause of this could be interstitial inflammation, following a localized host response. In this study, we therefore examine the immune response of SARS-CoV in human peripheral blood mononuclear cells (PBMCs) over the first 24 hours. METHODS: PBMCs from normal healthy donors were inoculated in vitro with SARS-CoV and the viral replication kinetics was studied by real-time quantitative assays. SARS-CoV specific gene expression changes were examined by high-density oligonucleotide array analysis. RESULTS: We observed that SARS-CoV was capable of infecting and replicating in PBMCs and the kinetics of viral replication was variable among the donors. SARS-CoV antibody binding assays indicated that SARS specific antibodies inhibited SARS-CoV viral replication. Array data showed monocyte-macrophage cell activation, coagulation pathway upregulation and cytokine production together with lung trafficking chemokines such as IL8 and IL17, possibly activated through the TLR9 signaling pathway; that mimicked clinical features of the disease. CONCLUSIONS: The identification of human blood mononuclear cells as a direct target of SARS-CoV in the model system described here provides a new insight into disease pathology and a tool for investigating the host response and mechanisms of pathogenesis.

Mutational dynamics of the SARS coronavirus in cell culture and human populations isolated in 2003.

BACKGROUND: The SARS coronavirus is the etiologic agent for the epidemic of the Severe Acute Respiratory Syndrome. The recent emergence of this new pathogen, the careful tracing of its transmission patterns, and the ability to propagate in culture allows the exploration of the mutational dynamics of the SARS-CoV in human populations. METHODS: We sequenced complete SARS-CoV genomes taken from primary human tissues (SIN3408, SIN3725V, SIN3765V), cultured isolates (SIN848, SIN846, SIN842, SIN845, SIN847, SIN849, SIN850, SIN852, SIN3408L), and five consecutive Vero cell passages (SIN2774_P1, SIN2774_P2, SIN2774_P3, SIN2774_P4, SIN2774_P5) arising from SIN2774 isolate. These represented individual patient samples, serial in vitro passages in cell culture, and paired human and cell culture isolates. Employing a refined mutation filtering scheme and constant mutation rate model, the mutation rates were estimated and the possible date of emergence was calculated. Phylogenetic analysis was used to uncover molecular relationships between the isolates. RESULTS: Close examination of whole genome sequence of 54 SARS-CoV isolates identified before 14th October 2003, including 22 from patients in Singapore, revealed the mutations engendered during human-to-Vero and Vero-to-human transmission as well as in multiple Vero cell passages in order to refine our analysis of human-to-human transmission. Though co-infection by different quasipecies in individual tissue samples is observed, the in vitro mutation rate of the SARS-CoV in Vero cell passage is negligible. The in vivo mutation rate, however, is consistent with estimates of other RNA viruses at approximately 5.7 x 10-6 nucleotide substitutions per site per day (0.17 mutations per genome per day), or two mutations per human passage (adjusted R-square = 0.4014). Using the immediate Hotel M contact isolates as roots, we observed that the SARS epidemic has generated four major genetic groups that are geographically associated: two Singapore isolates, one Taiwan isolate, and one North China isolate which appears most closely related to the putative SARS-CoV isolated from a palm civet. Non-synonymous mutations are centered in non-essential ORFs especially in structural and antigenic genes such as the S and M proteins, but these mutations did not distinguish the geographical groupings. However, no non-synonymous mutations were found in the 3CLpro and the polymerase genes. CONCLUSIONS: Our results show that the SARS-CoV is well adapted to growth in culture and did not appear to undergo specific selection in human populations. We further assessed that the putative origin of the SARS epidemic was in late October 2002 which is consistent with a recent estimate using cases from China. The greater sequence divergence in the structural and antigenic proteins and consistent deletions in the 3'--most portion of the viral genome suggest that certain selection pressures are interacting with the functional nature of these validated and putative ORFs.

Inherent signals in sequencing-based Chromatin-ImmunoPrecipitation control libraries.

BACKGROUND: The growth of sequencing-based Chromatin Immuno-Precipitation studies call for a more in-depth understanding of the nature of the technology and of the resultant data to reduce false positives and false negatives. Control libraries are typically constructed to complement such studies in order to mitigate the effect of systematic biases that might be present in the data. In this study, we explored multiple control libraries to obtain better understanding of what they truly represent. METHODOLOGY: First, we analyzed the genome-wide profiles of various sequencing-based libraries at a low resolution of 1 Mbp, and compared them with each other as well as against aCGH data. We found that copy number plays a major influence in both ChIP-enriched as well as control libraries. Following that, we inspected the repeat regions to assess the extent of mapping bias. Next, significantly tag-rich 5 kbp regions were identified and they were associated with various genomic landmarks. For instance, we discovered that gene boundaries were surprisingly enriched with sequenced tags. Further, profiles between different cell types were noticeably distinct although the cell types were somewhat related and similar. CONCLUSIONS: We found that control libraries bear traces of systematic biases. The biases can be attributed to genomic copy number, inherent sequencing bias, plausible mapping ambiguity, and cell-type specific chromatin structure. Our results suggest careful analysis of control libraries can reveal promising biological insights.

Inferring direct regulatory targets of a transcription factor in the DREAM2 challenge.

In the DREAM2 community-wide experiment on regulatory network inference, one of the challenges was to identify which genes, in a list of 200, are direct regulatory targets of the transcription factor BCL6. The organizers of the challenge defined targets based on gene expression and chromatin immunoprecipitation experiments (ChIP-chip). The expression data were publicly available; the ChIP-chip data were not. In order to assess the likelihood that a gene is a BCL6 target, we used three classes of information: expression-level differences, over-representation of sequence motifs in promoter regions, and gene ontology annotations. A weight was attached to each analysis based on how well it identified BCL6-bound genes as defined by publicly available ChIP-chip data. By the organizers' criteria, our group, GenomeSingapore, performed best. However, our retrospective analysis indicates that this success was dominated by a gene expression analysis that was predicated on a regulatory model known to be favored by the organizers. We also noted that the 200-gene test set was enriched only in genes that are upregulated, while genes bound by BCL6 are enriched in both upregulated and downregulated genes. Together, these observations suggest possible model biases in the selection of the gold-standard gene set and imply that our success was attained in part by adhering to the same assumptions. We argue that model biases of this type are unavoidable in the inference of regulatory networks and, for that reason, we suggest that future community-wide experiments of this type should focus on the prediction of data, rather than models.

De-novo identification of PPARgamma/RXR binding sites and direct targets during adipogenesis.

BACKGROUND: The pathophysiology of obesity and type 2 diabetes mellitus is associated with abnormalities in endocrine signaling in adipose tissue and one of the key signaling affectors operative in these disorders is the nuclear hormone transcription factor peroxisome proliferator-activated receptor-gamma (PPARgamma). PPARgamma has pleiotropic functions affecting a wide range of fundamental biological processes including the regulation of genes that modulate insulin sensitivity, adipocyte differentiation, inflammation and atherosclerosis. To date, only a limited number of direct targets for PPARgamma have been identified through research using the well established pre-adipogenic cell line, 3T3-L1. In order to obtain a genome-wide view of PPARgamma binding sites, we applied the pair end-tagging technology (ChIP-PET) to map PPARgamma binding sites in 3T3-L1 preadipocyte cells. METHODOLOGY/PRINCIPAL FINDINGS: Coupling gene expression profile analysis with ChIP-PET, we identified in a genome-wide manner over 7700 DNA binding sites of the transcription factor PPARgamma and its heterodimeric partner RXR during the course of adipocyte differentiation. Our validation studies prove that the identified sites are bona fide binding sites for both PPARgamma and RXR and that they are functionally capable of driving PPARgamma specific transcription. Our results strongly indicate that PPARgamma is the predominant heterodimerization partner for RXR during late stages of adipocyte differentiation. Additionally, we find that PPARgamma/RXR association is enriched within the proximity of the 5' region of the transcription start site and this association is significantly associated with transcriptional up-regulation of genes involved in fatty acid and lipid metabolism confirming the role of PPARgamma as the master transcriptional regulator of adipogenesis. Evolutionary conservation analysis of these binding sites is greater when adjacent to up-regulated genes than down-regulated genes, suggesting the primordial function of PPARgamma/RXR is in the induction of genes. Our functional validations resulted in identifying novel PPARgamma direct targets that have not been previously reported to promote adipogenic differentiation. CONCLUSIONS/SIGNIFICANCE: We have identified in a genome-wide manner the binding sites of PPARgamma and RXR during the course of adipogenic differentiation in 3T3L1 cells, and provide an important resource for the study of PPARgamma function in the context of adipocyte differentiation.

Reprogramming of fibroblasts into induced pluripotent stem cells with orphan nuclear receptor Esrrb.

The dominant effect of transcription factors in imparting expanded potency is best exemplified by the reprogramming of fibroblasts to pluripotent cells using retrovirus-mediated transduction of defined transcription factors. In the murine system, Oct4, Sox2, c-Myc and Klf4 are sufficient to convert fibroblasts to induced pluripotent stem (iPS) cells that have many characteristics of embryonic stem (ES) cells. Here we show that the orphan nuclear receptor Esrrb functions in conjunction with Oct4 and Sox2 to mediate reprogramming of mouse embryonic fibroblasts (MEFs) to iPS cells. Esrrb-reprogrammed cells share similar expression and epigenetic signatures as ES cells. These cells are also pluripotent and can differentiate in vitro and in vivo into the three major embryonic cell lineages. Furthermore, these cells contribute to mouse chimaeras and are germline transmissible. In ES cells, Esrrb targets many genes involved in self-renewal and pluripotency. This suggests that Esrrb may mediate reprogramming through the upregulation of ES-cell-specific genes. Our findings also indicate that it is possible to reprogram MEFs without exogenous Klf transcription factors and link a nuclear receptor to somatic cell reprogramming.

Evolution of the mammalian transcription factor binding repertoire via transposable elements.

Identification of lineage-specific innovations in genomic control elements is critical for understanding transcriptional regulatory networks and phenotypic heterogeneity. We analyzed, from an evolutionary perspective, the binding regions of seven mammalian transcription factors (ESR1, TP53, MYC, RELA, POU5F1, SOX2, and CTCF) identified on a genome-wide scale by different chromatin immunoprecipitation approaches and found that only a minority of sites appear to be conserved at the sequence level. Instead, we uncovered a pervasive association with genomic repeats by showing that a large fraction of the bona fide binding sites for five of the seven transcription factors (ESR1, TP53, POU5F1, SOX2, and CTCF) are embedded in distinctive families of transposable elements. Using the age of the repeats, we established that these repeat-associated binding sites (RABS) have been associated with significant regulatory expansions throughout the mammalian phylogeny. We validated the functional significance of these RABS by showing that they are over-represented in proximity of regulated genes and that the binding motifs within these repeats have undergone evolutionary selection. Our results demonstrate that transcriptional regulatory networks are highly dynamic in eukaryotic genomes and that transposable elements play an important role in expanding the repertoire of binding sites.

Genome-wide mapping of RELA(p65) binding identifies E2F1 as a transcriptional activator recruited by NF-kappaB upon TLR4 activation.

NF-kappaB is a key mediator of inflammation. Here, we mapped the genome-wide loci bound by the RELA subunit of NF-kappaB in lipopolysaccharide (LPS)-stimulated human monocytic cells, and together with global gene expression profiling, found an overrepresentation of the E2F1-binding motif among RELA-bound loci associated with NF-kappaB target genes. Knockdown of endogenous E2F1 impaired the LPS inducibility of the proinflammatory cytokines CCL3(MIP-1alpha), IL23A(p19), TNF-alpha, and IL1-beta. Upon LPS stimulation, E2F1 is rapidly recruited to the promoters of these genes along with p50/RELA heterodimer via a mechanism that is dependent on NF-kappaB activation. Together with the observation that E2F1 physically interacts with p50/RELA in LPS-stimulated cells, our findings suggest that NF-kappaB recruits E2F1 to fully activate the transcription of NF-kappaB target genes. Global gene expression profiling subsequently revealed a spectrum of NF-kappaB target genes that are positively regulated by E2F1, further demonstrating the critical role of E2F1 in the Toll-like receptor 4 pathway.

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

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.