Nature Biotechnology
23, 377 - 383 (2005)
Published online: 4 March 2005; | doi:10.1038/nbt1075
Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networksDiego di Bernardo1, 5, Michael J Thompson2, 5, Timothy S Gardner2, 5, Sarah E Chobot3, Erin L Eastwood3, 4, Andrew P Wojtovich3, Sean J Elliott3, Scott E Schaus3, 4
& James J Collins21
Telethon Institute for Genetics and Medicine, Naples, Italy. 2
Center for BioDynamics and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA. 3
Department of Chemistry, Boston University, Boston, Massachusetts, USA. 4
Center for Chemical Methodology and Library Development, Boston University, Boston, Massachusetts, USA. 5
These authors contributed equally to this work.
Correspondence should be addressed to James J Collins jcollins@bu.eduA major challenge in drug discovery is to distinguish the molecular targets of a bioactive compound from the hundreds to thousands of additional gene products that respond indirectly to changes in the activity of the targets1,
2,
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4,
5,
6,
7,
8. Here, we present an integrated computational-experimental approach for computing the likelihood that gene products and associated pathways are targets of a compound. This is achieved by filtering the mRNA expression profile of compound-exposed cells using a reverse-engineered model of the cell's gene regulatory network. We apply the method to a set of 515 whole-genome yeast expression profiles resulting from a variety of treatments (compounds, knockouts and induced expression), and correctly enrich for the known targets and associated pathways in the majority of compounds examined. We demonstrate our approach with PTSB, a growth inhibitory compound with a previously unknown mode of action, by predicting and validating thioredoxin and thioredoxin reductase as its target.
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