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 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.

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

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.

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.

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.

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.

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.

The cause of severe acute respiratory syndrome (SARS) has been identified as a new coronavirus. Whole genome sequence analysis of various isolates might provide an indication of potential strain differences of this new virus. Moreover, mutation analysis will help to develop effective vaccines.

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.

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.

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.