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      Chemical genomics of cancer chemopreventive dithiolethiones

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          Abstract

          3 H-1,2-dithiole-3-thione (D3T) and its analogues 4-methyl-5-pyrazinyl-3 H-1,2-dithiole-3-thione (OLT) and 5-tert-butyl-3 H-1,2-dithiole-3-thione (TBD) are chemopreventive agents that block or diminish early stages of carcinogenesis by inducing activities of detoxication enzymes. While OLT has been used in clinical trials, TBD has been shown to be more efficacious and possibly less toxic than OLT in animals. Here, we utilize a robust and high-resolution chemical genomics procedure to examine the pharmacological structure–activity relationships of these compounds in livers of male rats by microarray analyses. We identified 226 differentially expressed genes that were common to all treatments. Functional analysis identified the relation of these genes to glutathione metabolism and the nuclear factor, erythroid derived 2-related factor 2 pathway (Nrf2) that is known to regulate many of the protective actions of dithiolethiones. OLT and TBD were shown to have similar efficacies and both were weaker than D3T. In addition, we identified 40 genes whose responses were common to OLT and TBD, yet distinct from D3T. As inhibition of cytochrome P450 (CYP) has been associated with the effects of OLT on CYP expression, we determined the half maximal inhibitory concentration (IC 50) values for inhibition of CYP1A2. The rank order of inhibitor potency was OLT ≫ TBD ≫ D3T, with IC 50 values estimated as 0.2, 12.8 and >100 μM, respectively. Functional analysis revealed that OLT and TBD, in addition to their effects on CYP, modulate liver lipid metabolism, especially fatty acids. Together, these findings provide new insight into the actions of clinically relevant and lead dithiolethione analogues.

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          Most cited references43

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              The KEGG databases at GenomeNet.

              The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the primary database resource of the Japanese GenomeNet service (http://www.genome.ad.jp/) for understanding higher order functional meanings and utilities of the cell or the organism from its genome information. KEGG consists of the PATHWAY database for the computerized knowledge on molecular interaction networks such as pathways and complexes, the GENES database for the information about genes and proteins generated by genome sequencing projects, and the LIGAND database for the information about chemical compounds and chemical reactions that are relevant to cellular processes. In addition to these three main databases, limited amounts of experimental data for microarray gene expression profiles and yeast two-hybrid systems are stored in the EXPRESSION and BRITE databases, respectively. Furthermore, a new database, named SSDB, is available for exploring the universe of all protein coding genes in the complete genomes and for identifying functional links and ortholog groups. The data objects in the KEGG databases are all represented as graphs and various computational methods are developed to detect graph features that can be related to biological functions. For example, the correlated clusters are graph similarities which can be used to predict a set of genes coding for a pathway or a complex, as summarized in the ortholog group tables, and the cliques in the SSDB graph are used to annotate genes. The KEGG databases are updated daily and made freely available (http://www.genome.ad.jp/kegg/).
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                Author and article information

                Journal
                Carcinogenesis
                carcin
                carcin
                Carcinogenesis
                Oxford University Press
                0143-3334
                1460-2180
                March 2009
                6 January 2009
                6 January 2009
                : 30
                : 3
                : 480-486
                Affiliations
                [1 ]Department of Mathematical Sciences
                [2 ]Department of Biology, University of Memphis, Memphis, TN 38152, USA
                [3 ]W. Harry Feinstone Center for Genomic Research
                [4 ]Department of Computer Science, University of Memphis, Memphis, TN 38152, USA
                [5 ]Department of Pharmacology and Toxicology, Dartmouth Medical School, Hanover, NH 03755, USA
                [6 ]Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
                Author notes
                [* ]To whom correspondence should be addressed. Tel: +1 901 678 8391; Fax: +1 901 678 2458; Email: tsutter@ 123456memphis.edu
                Article
                10.1093/carcin/bgn292
                2650797
                19126641
                9913d7a3-36ab-4ccf-9d0e-d75c57081e02
                © The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

                History
                : 25 September 2008
                : 17 December 2008
                : 20 December 2008
                Categories
                Molecular Epidemiology

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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