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. Luciferase activity was increased 30- to 50-fold by 10 nM E2 relative to that in solvent control. Maximal reporter gene activity induced by 10 nM alpha-ZEA was 54% of that induced by E2; however, the activity did not increase following doses of up to 10 microM. A dose of 1 microM 4-t-OP induced 23% of the maximal reporter gene activity induced by E2, whereas 10 microM GEN induced activity to the same level as E2. A dose-dependent increase in vitellogenin (VTG) mRNA expression was observed in Xenopus treated intraperitoneally with E2 at 0.05 to 5 mg/kg/d for three consecutive days, with the maximal induction observed in the group receiving 1 mg/kg/d. The alpha-ZEA, GEN, and 4-t-OP also significantly induced VTG mRNA expression, although at higher doses. These results demonstrate the utility of X. laevis as an amphibian model to assess the estrogenic activity of endocrine disruptors.

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. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.