The action of nuclear hormone receptors is tripartite, involving the receptor, its ligands, and its coregulator proteins. The estrogen receptor (ER), a member of this superfamily, is a hormone-activated transcription factor that mediates the stimulatory effects of estrogens and the inhibitory effects of antiestrogens such as tamoxifen in breast cancer and other estrogen target cells. To understand how antiestrogens and dominant negative ERs suppress ER activity, we used a dominant negative ER as bait in two-hybrid screening assays from which we isolated a clone from breast cancer cells that potentiates the inhibitory activities of dominant negative ERs and antiestrogen-liganded ER. At higher concentrations, it also represses the transcriptional activity of the estradiol-liganded ER, while having no effect on other nuclear hormone receptors. This clone, denoted REA for ''repressor of estrogen receptor activity,'' encodes a 37-kDa protein that is an ER-selective coregulator. Its competitive reversal of steroid receptor coactivator 1 enhancement of ER activity and its direct interaction with liganded ER suggest that it may play an important role in determining the sensitivity of estrogen target cells, including breast cancer cells, to antiestrogens and estrogens.

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Gene set analysis using biological pathways has become a widely used statistical approach for gene expression analysis. A biological pathway can be represented through a graph where genes and their interactions are, respectively, nodes and edges of the graph. From a biological point of view only some portions of a pathway are expected to be altered; however, few methods using pathway topology have been proposed and none of them tries to identify the signal paths, within a pathway, mostly involved in the biological problem. Here, we present a novel algorithm for pathway analysis , that tries to fill in this gap. implements a two-step empirical approach based on the exploitation of graph decomposition into a junction tree to reconstruct the most relevant signal path. In the first step selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it identifies within these pathways the signal paths having the greatest association with a specific phenotype. We test our approach on simulated and two real expression datasets. Our results demonstrate the efficacy of in the identification of signal transduction paths totally coherent with the biological problem.

Estrogen plays an important role in the regulation of vascular tone and in the pathophysiology of cardiovascular disease. Physiological effects of estrogen are mediated through estrogen receptors alpha (ERalpha) and beta (ERbeta), which are both expressed in vascular smooth muscle and endothelial cells. However, the molecular pathways mediating estrogen effects in blood vessels are not well defined. We have performed gene expression profiling in the mouse aorta to identify comprehensive gene sets the expression of which is regulated by long-term (1 wk) estrogen treatment. The ER subtype dependence of the alterations in gene expression was characterized by parallel gene expression profiling experiments in ERalpha-deficient [ERalpha knockout (ERalphaKO)] and ERbeta-deficient (ERbetaKO) mice. Importantly, these data revealed that ERalpha- and ERbeta-dependent pathways regulate distinct and largely nonoverlapping sets of genes. Whereas ERalpha is essential for most of the estrogen-mediated increase in gene expression in wild-type aortas, ERbeta mediates the large majority (nearly 90%) of estrogen-mediated decreases in gene expression. Biological functions of the estrogen-regulated genes include extracellular matrix synthesis, in addition to electron transport in the mitochondrion and reactive oxygen species pathways. Of note, the estrogen/ERbeta pathway mediates down-regulation of mRNAs for nuclear-encoded subunits in each of the major complexes of the mitochondrial respiratory chain. Several estrogen-regulated genes also encode transcription factors. Computational analysis of promoters from coexpressed genes revealed overrepresentation of binding sites for such factors, lending support for an estrogen-regulatory transcriptional network in the vasculature. Overall, these findings provide a foundation for understanding the molecular basis for estrogen effects on vasculature gene expression.