Androgen receptor (AR) plays a central role in prostate cancer, and most patients respond to androgen deprivation therapies, but they invariably relapse with a more aggressive prostate cancer that has been termed hormone refractory or androgen independent. To identify proteins that mediate this tumor progression, gene expression in 33 androgen-independent prostate cancer bone marrow metastases versus 22 laser capture–microdissected primary prostate cancers was compared using Affymetrix oligonucleotide microarrays. Multiple genes associated with aggressive behavior were increased in the androgen-independent metastatic tumors (MMP9, CKS2, LRRC15, WNT5A, EZH2, E2F3, SDC1, SKP2, and BIRC5), whereas a candidate tumor suppressor gene (KLF6) was decreased. Consistent with castrate androgen levels, androgen-regulated genes were reduced 2- to 3-fold in the androgen-independent tumors. Nonetheless, they were still major transcripts in these tumors, indicating that there was partial reactivation of AR transcriptional activity. This was associated with increased expression of AR (5.8-fold) and multiple genes mediating androgen metabolism (HSD3B2, AKR1C3, SRD5A1, AKR1C2, AKR1C1, and UGT2B15). The increase in aldo-keto reductase family 1, member C3 (AKR1C3), the prostatic enzyme that reduces adrenal androstenedione to testosterone, was confirmed by real-time reverse transcription-PCR and immunohistochemistry. These results indicate that enhanced intracellular conversion of adrenal androgens to testosterone and dihydrotestosterone is a mechanism by which prostate cancer cells adapt to androgen deprivation and suggest new therapeutic targets. (Cancer Res 2006; 66(5): 2815-25)

A valuable approach to chemical safety assessment is the use of “read-across” chemicals to provide safety data to support the assessment of structurally similar chemicals. An inventory of over 6000 discrete organic chemicals used as fragrance materials in consumer products has been clustered into chemical class-based groups for efficient search of read-across sources. We developed a robust, tiered system for chemical classification based on 1) organic functional groups 2) structural similarity and reactivity features of the hydrocarbon skeletons, 3) predicted or experimentally verified Phase I and Phase II metabolism, and 4) expert pruning to consider these variables in the context of specific toxicity endpoints. The systematic combination of these four data tiers yielded clusters, which may be visualized as a top-down hierarchical clustering tree. In this tree, chemical classes are formed at the highest level according to organic functional groups. Each subsequent sub-cluster stemming from classes in this hierarchy of the cluster is a chemical cluster defined by common organic functional groups and close similarity in the hydrocarbon skeleton. By examining the available experimental data for a toxicological endpoint within each cluster, users can better identify potential read-across chemicals to support safety assessments.

The aldo–keto reductases metabolize a wide range of substrates and are potential drug targets. This protein superfamily includes aldose reductases, aldehyde reductases, hydroxysteroid dehydrogenases and dihydrodiol dehydrogenases. By combining multiple sequence alignments with known three-dimensional structures and the results of site-directed mutagenesis studies, we have developed a structure/function analysis of this superfamily. Our studies suggest that the (α/β)8-barrel fold provides a common scaffold for an NAD(P)(H)-dependent catalytic activity, with substrate specificity determined by variation of loops on the C-terminal side of the barrel. All the aldo–keto reductases are dependent on nicotinamide cofactors for catalysis and retain a similar cofactor binding site, even among proteins with less than 30% amino acid sequence identity. Likewise, the aldo–keto reductase active site is highly conserved. However, our alignments indicate that variation of a single residue in the active site may alter the reaction mechanism from carbonyl oxidoreduction to carbon–carbon double-bond reduction, as in the 3-oxo-5β-steroid 4-dehydrogenases (Δ4-3-ketosteroid 5β-reductases) of the superfamily. Comparison of the proposed substrate binding pocket suggests residues 54 and 118, near the active site, as possible discriminators between sugar and steroid substrates. In addition, sequence alignment and subsequent homology modelling of mouse liver 17β-hydroxysteroid dehydrogenase and rat ovary 20α-hydroxysteroid dehydrogenase indicate that three loops on the C-terminal side of the barrel play potential roles in determining the positional and stereo-specificity of the hydroxysteroid dehydrogenases. Finally, we propose that the aldo–keto reductase superfamily may represent an example of divergent evolution from an ancestral multifunctional oxidoreductase and an example of convergent evolution to the same active-site constellation as the short-chain dehydrogenase/reductase superfamily.