A hierarchical network of transcription factors governs androgen receptor-dependent prostate cancer growth

Wang Q, Li W, Liu XS, Carroll JS, Jänne OA, Keeton EK, Chinnaiyan AM, Pienta KJ, Brown M.

Androgen receptor (AR) is a ligand-dependent transcription factor that plays a key role in prostate cancer. Little is known about the nature of AR cis-regulatory sites in the human genome. We have mapped the AR binding regions on two chromosomes in human prostate cancer cells by combining chromatin immunoprecipitation (ChIP) with tiled oligonucleotide microarrays. We find that the majority of AR binding regions contain noncanonical AR-responsive elements (AREs). Importantly, we identify a noncanonical ARE as a cis-regulatory target of AR action in TMPRSS2, a gene fused to ETS transcription factors in the majority of prostate cancers. In addition, through the presence of enriched DNA-binding motifs, we find other transcription factors including GATA2 and Oct1 that cooperate in mediating the androgen response. These collaborating factors, together with AR, form a regulatory hierarchy that governs androgen-dependent gene expression and prostate cancer growth and offer potential new opportunities for therapeutic intervention.

Network motif analysis of a multi-mode genetic-interaction network

R James Taylor , Andrew F Siegel and Timothy Galitski
http://genomebiology.com/2007/8/8/R160

Abstract:

Different modes of genetic interaction indicate different functional relationships between genes. The extraction of biological information from dense multi-mode genetic-interaction networks demands appropriate statistical and computational methods. We developed such methods and implemented them in open-source software. Motifs extracted from multi-mode genetic-interaction networks form functional subnetworks, highlight genes dominating these subnetworks, and reveal genetic reflections of the underlying biochemical system.

Small dsRNAs induce transcriptional activation in human cells

Long-Cheng Li^*,, Steven T. Okino, Hong Zhao, Deepa Pookot, Robert F. Place, Shinji Urakami, Hideki Enokida, and Rajvir Dahiya^*,

Department of Urology, Veterans Affairs Medical Center and University of California, San Francisco, CA 94121

…… In conclusion, we have identified several dsRNAs that activate gene expression by targeting noncoding regulatory regions in gene promoters. These findings reveal a more diverse role for small RNA molecules in the regulation of gene expression than previously recognized and identify a potential therapeutic use for dsRNA in targeted gene activation.

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Timothy R. Lezon^*, Jayanth R. Banavar^*, Marek Cieplak, Amos Maritan, and Nina V. Fedoroff^,¶,||

Pennsylvania State University.

We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higher-order interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.