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Elucidating Compound Mechanism of Action by Network Perturbation Analysis.
Woo, Jung Hoon; Shimoni, Yishai; Yang, Wan Seok; Subramaniam, Prem; Iyer, Archana; Nicoletti, Paola; Rodríguez Martínez, María; López, Gonzalo; Mattioli, Michela; Realubit, Ronald; Karan, Charles; Stockwell, Brent R; Bansal, Mukesh; Califano, Andrea.
Afiliación
  • Woo JH; Department of Biomedical Informatics (DBMI), Columbia University, New York, NY 10032, USA.
  • Shimoni Y; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • Yang WS; Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
  • Subramaniam P; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • Iyer A; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • Nicoletti P; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • Rodríguez Martínez M; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • López G; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA.
  • Mattioli M; Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milano, Italy.
  • Realubit R; Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, NY 10032, USA.
  • Karan C; Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, NY 10032, USA.
  • Stockwell BR; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Department of Biological Sciences, Columbia University, New York, NY 10027, USA; Department of Chemistry, Columbia University, New York, NY 10027, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 1002
  • Bansal M; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA. Electronic address: mb3113@cumc.columbia.edu.
  • Califano A; Department of Biomedical Informatics (DBMI), Columbia University, New York, NY 10032, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA; Center for Computational Biology and Bioinformatics (C2B2), Columbia University, New York, NY 10032, USA; Department of Biochemistry
Cell ; 162(2): 441-451, 2015 Jul 16.
Article en En | MEDLINE | ID: mdl-26186195
ABSTRACT
Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Terapia Molecular Dirigida / Antineoplásicos Idioma: En Revista: Cell Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Terapia Molecular Dirigida / Antineoplásicos Idioma: En Revista: Cell Año: 2015 Tipo del documento: Article País de afiliación: Estados Unidos