Description
Endocrine disruptors (ED) include plasticizers, pesticides, detergents and pharmaceuticals. Many of these chemicals enter rivers and the ocean, where they directly interact with fish and disrupt endocrine responses. Human are exposed to these chemicals from drinking water and consumption of fish. The detailed mechanisms by which ED compounds act is not completely understood, but many are known to bind to nuclear receptors, such as the estrogen receptor (ER), retinoid X receptor (RXR) and peroxisome proliferator-activated receptors (PPARs). Two important EDS are 17_-ethynylestradiol (EE2) and bisphenol-A (BPA). EE2 is an orally bio-active estragon used in almost all modern formulations of combined oral contraceptive pills. It is widely dispersed in municipal wastewater effluent discharges in US and Europe. EE2 is active in very small amounts (pM levels) and can alter sex ratios in fish. BPA serves as a chemical building block for the polycarbonate plastic and epoxy resins found in many consumer products. BPA's ubiquitous nature in the environment is highlighted by the fact that 92.6% of adults excrete BPA in their urine, with levels averaging 2.4 and 2.9 _g/L in females and males respectively. Evidence of endocrine-related effects has been reported in fish, aquatic invertebrates, amphibians and reptiles at environmentally relevant exposure levels, which are generally much lower than those required for acute toxicity. To understand in full detail the complexity of EDs and how they interact, with different cellular signaling programs, I have employed a pathway based discovery approach. Whole genome micro arrays and next generation sequencing data sets, which probe global gene expression changes in response to EDs (including EE2 and BPA) will provide an unbiased view of the entire transcriptome. I will exploit this property to allow the experiment itself to reveal the pathways involved, by combining the gene expression data with a database of gene-gene, gene-protein, and protein-protein interactions: the interactome. I will combine experimental gene expression results with the network. For instance, a measure of potential involvement of a gene can be calculated as the absolute value of the log-ratio between gene expression in the control tissue and that of the exposed. This information is then projected onto the network. By drawing each node of the network with a full circle whose radius is proportional to the experimentally-determined measure of involvement, sub-networks (pathways or groups of genes) potentially relevant to the phenotype of the exposed tissue will become evident. Based on this rationale the exposure experiments will relevant gene pathways. Since in reality one cannot visually inspect the complicated connections within the interactome, I will use percolation and invasion percolation theory, two well-studied paradigm of theoretical physics.
