• Title/Summary/Keyword: Biology and Life Science

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CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures

  • Jang, Su-Kyeong;Yoon, Byung-Ha;Kang, Seung Min;Yoon, Yeo-Gha;Kim, Seon-Young;Kim, Wankyu
    • Molecules and Cells
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    • v.42 no.3
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    • pp.237-244
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    • 2019

  • Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial-mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).

  • https://doi.org/10.14348/molcells.2018.0413 인용 PDF KSCI HTML

Pharmacophore Mapping and Virtual Screening for SIRT1 Activators

  • Sakkiah, Sugunadevi;Krishnamoorthy, Navaneethakrishnan;Gajendrarao, Poornima;Thangapandian, Sundarapandian;Lee, Yun-O;Kim, Song-Mi;Suh, Jung-Keun;Kim, Hyong-Ha;Lee, Keun-Woo
    • Bulletin of the Korean Chemical Society
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    • v.30 no.5
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    • pp.1152-1156
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    • 2009

  • Silent information regulator 2 (Sir2) or sirtuins are NAD(+)-dependent deacetylases, which hydrolyze the acetyllysine residues. In mammals, sirtuins are classified into seven different classes (SIRT1-7). SIRT1 was reported to be involved in age related disorders like obesity, metabolic syndrome, type II diabetes mellitus and Parkinson’s disease. Activation of SIRT1 is one of the promising approaches to treat these age related diseases. In this study, we have used HipHop module of CATALYST to identify a series of pharmacophore models to screen SIRT1 enhancing molecules. Three molecules from Sirtris Pharmaceuticals were selected as training set and 607 sirtuin activator molecules were used as test set. Five different hypotheses were developed and then validated using the training set and the test set. The results showed that the best pharmacophore model has four features, ring aromatic, positive ionization and two hydrogen-bond acceptors. The best hypothesis from our study, Hypo2, screened high number of active molecules from the test set. Thus, we suggest that this four feature pharmacophore model could be helpful to screen novel SIRT1 activator molecules. Hypo2-virtual screening against Maybridge database reveals seven molecules, which contains all the critical features. Moreover, two new scaffolds were identified from this study. These scaffolds may be a potent lead for the SIRT1 activation.

  • https://doi.org/10.5012/bkcs.2009.30.5.1152 인용 PDF KSCI