Estrogen induces c-myc gene expression via an upstream enhancer activated by the estrogen receptor and the AP-1 transcription factor.

c-myc oncogene is implicated in tumorigenesis of many cancers, including breast cancer. Although c-myc is a well-known estrogen-induced gene, its promoter has no estrogen-response element, and the underlying mechanism by which estrogen induces its expression remains obscure. Recent genome-wide studies by us and others suggested that distant elements may mediate estrogen induction of gene expression.

Genome-wide analysis of estrogen receptor binding sites.

The estrogen receptor is the master transcriptional regulator of breast cancer phenotype and the archetype of a molecular therapeutic target. We mapped all estrogen receptor and RNA polymerase II binding sites on a genome-wide scale, identifying the authentic cis binding sites and target genes, in breast cancer cells. Combining this unique resource with gene expression data demonstrates distinct temporal mechanisms of estrogen-mediated gene regulation, particularly in the case of estrogen-suppressed genes.

Use of Xenopus laevis as a model for investigating in vitro and in vivo endocrine disruption in amphibians.

The estrogenic activity of 17beta-estradiol (E2), alpha-zearalenol (alpha-ZEA), genistein (GEN), and 4-t-octylphenol (4-t-OP) was investigated using Xenopus laevis-based assays. All test compounds competed with [3H]E2 for binding to a recombinant Xenopus estrogen receptor (xER) with the following relative affinities: E2 > alpha-ZEA > 4-t-OP > GEN. The ability of these compounds to induce xER-mediated reporter gene expression was then assessed in MCF-7 human breast cancer cells cotransfected with a Gal4-xERdef chimeric estrogen receptor and a Gal4-regulated luciferase reporter gene.

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity.