Accelerated Communication A NOVEL METHOD FOR VISUALIZING NUCLEAR HORMONE RECEPTOR NETWORKS RELEVANT TO DRUG METABOLISM

The increasing generation of biological data represents a challenge to understanding the complexity of systems, resulting in scientists increasingly focused on a relatively narrow area of study, thereby limiting insight that can be gained from a broader perspective. In the field of drug metabolism and toxicology we are witnessing the characterization of many proteins. Most of the key enzymes and transporters are recognized as transcriptionally regulated by the nuclear hormone receptors such as pregnane X receptor, constitutive androstane receptor, vitamin D receptor, glucocorticoid receptor, and others. There is apparent cross talk in regulation, since multiple receptors may modulate expression of a single enzyme or transporter, representing one of many areas of active research interest. We have used published data on nuclear hormone receptors, enzymes, ligands, and other biological information to manually annotate an Oracle database, forming the basis of a platform for querying (MetaDrug). Using algorithms, we have demonstrated how nuclear hormone receptors alone can form a network of direct interactions, and when expanded, this network increases in complexity to describe the interactions with target genes as well as small molecules known to bind a receptor, enzyme, or transporter. We have also described how the database can be used for visualizing high-throughput microarray data derived from a published study of MCF-7 cells treated with 4-hydroxytamoxifen, to highlight potential downstream effects of molecule treatment. The database represents a novel knowledge mining and analytical tool that, to be relevant, requires continual updating to evolve alongside other key storage systems and sources of biological knowledge. The increasing generation of biological data using high-throughput methods in drug discovery necessitates the use of computational technologies including databases to store, analyze, interpret, and learn from this information (Navarro et al., 2003). Within drug disposition and toxicology, in vitro approaches for generating data with drugmetabolizing enzymes, transporters, ion channels and receptors can be used for predictive computer model generation (Ekins and Swaan, 2004). Many of these proteins are known to be regulated by nuclear hormone receptors (NHRs) or other transcription factors (Waxman, 1999; Moore and Kliewer, 2000; Xie et al., 2000; Goodwin et al., 2001; LeCluyse, 2001a,b; Staudinger et al., 2001a,b; Akiyama and Gonzalez, 2003; Mankowski and Ekins, 2003) affecting endogenous metabolism, cell growth, proliferation, and oxidative stress (Ulrich, 2003; Ulrich et al., 2004). The effect of these NHRs and other transcriptional factors on the toxic response and drug metabolism is complex and overlapping in a species-specific manner (Sonoda et al., 2003), with the same compounds working as agonists and antagonists on different receptors (Ulrich, 2003). Understanding the interactions of diverse ligands with these receptors (Sueyoshi et al., 1999; Spink et al., 2002; Mimura and Fujii-Kuriyama, 2003; Hartley et al., 2004; Tabb et al., 2004) and their impact on regulation of proteins has resulted in a simplistic schematic of the cross talk (Ekins et al., 2002). There have been considerable advances in the availability of software for visualizing complex gene networks. To date, several algorithms have been described in the literature for combining protein interaction information and expression data to find condition-specific modules in protein networks. These include different statistical methods to analyze data prior to mapping onto interaction networks (Tornow and Mewes, 2003), network clustering algorithms such as superparamagnetic clustering to identify tightly connected sets of nodes (objects connected to each other on a network, a component that can be a gene, small molecule, etc.) (Hanisch et al., 2002), simulated annealing (Ideker et al., 2002), probabilistic graphical models (Segal et al., 2003), and finding tightly connected clusters of nodes (cliques) using Monte Carlo optimization (Spirin and Mirny, 2003). These different approaches are useful when dealing with the idea of modular organization of large-scale networks of biological processes in which various types of cellular functionality are provided by relatively small, transient, but tightly connected networks of proteins (5–25 nodes) that are engaged in performing specific functions (Hartwell et al., 1999). In the present study we describe the development of software with This work was supported by National Institutes of Health Grant 1-R43GM069124-01 “In Silico Assessment of Drug Metabolism and Toxicity”. Article, publication date, and citation information can be found at http://dmd.aspetjournals.org. doi:10.1124/dmd.104.002717. ABBREVIATIONS: NHR, nuclear hormone receptor; ACC, acetyl-CoA; PPAR, peroxisome proliferator-activated receptor; AHR, aryl hydrocarbon receptor; FXR, farnesoid X receptor; LXR, liver X receptor; PXR, pregnane X receptor; RXR, retinoid X receptor; OHT, hydroxytamoxifen. 0090-9556/05/3303-474–481$20.00 DRUG METABOLISM AND DISPOSITION Vol. 33, No. 3 Copyright © 2005 by The American Society for Pharmacology and Experimental Therapeutics 2717/1196256 DMD 33:474–481, 2005 Printed in U.S.A. 474 at A PE T Jornals on Jne 5, 2017 dm d.aspurnals.org D ow nladed from