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Cell. 2015 Jul 16;162(2):441-451. doi: 10.1016/j.cell.2015.05.056.

Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Author information

1
Department of Biomedical Informatics (DBMI), Columbia University, New York, NY 10032, USA.
2
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.
3
Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
4
Center for Genomic Science of IIT@SEMM, Fondazione Istituto Italiano di Tecnologia (IIT), 20139 Milano, Italy.
5
Columbia Genome Center, High Throughput Screening Facility, Columbia University, New York, NY 10032, USA.
6
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 10027, USA.
7
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.
8
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 and Molecular Biophysics, Columbia University, New York, NY 10032, USA; Institute for Cancer Genetics, Columbia University, New York, NY 10032, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA. Electronic address: califano@cumc.columbia.edu.

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.

Comment in

PMID:
26186195
PMCID:
PMC4506491
DOI:
10.1016/j.cell.2015.05.056
[Indexed for MEDLINE]
Free PMC Article

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