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Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks

Nature Biotechnology volume 23, pages 377–383 (2005)Cite this article

Abstract

A 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 targets1,2,3,4,5,6,7,8. 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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Figure 1: Overview of the MNI method.
Figure 2: Structure of the network model.
Figure 3: Predicted targets of itraconazole.
Figure 4: Thioredoxin/thioredoxin reductase activity assay.

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Acknowledgements

Support for this work was provided by the Department of Energy, the National Institutes of Health, the National Heart, Lung and Blood Institute Proteomics Initiative, the Whitaker Foundation, the National Science Foundation, the Fondazione Telethon, Boston University and the Pharmaceutical Research and Manufacturers of America Foundation.

Author information

Author notes

  1. Diego di Bernardo, Michael J Thompson and Timothy S Gardner: These authors contributed equally to this work.

Authors and Affiliations

  1. Telethon Institute for Genetics and Medicine, Naples, Italy

    Diego di Bernardo

  2. Center for BioDynamics and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

    Michael J Thompson, Timothy S Gardner & James J Collins

  3. Department of Chemistry, Boston University, Boston, Massachusetts, USA

    Sarah E Chobot, Erin L Eastwood, Andrew P Wojtovich, Sean J Elliott & Scott E Schaus

  4. Center for Chemical Methodology and Library Development, Boston University, Boston, Massachusetts, USA

    Erin L Eastwood & Scott E Schaus

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  1. Diego di Bernardo
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Corresponding author

Correspondence to James J Collins.

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Competing interests

Provisional patent applications have been filed on the MNI algorithm and the PTSB compound.

Supplementary information

Supplementary Table 1

TET-inducible experiments: comparison between normal and modified z-scores (PDF 21 kb)

Supplementary Table 2

Treatment with drugs: comparison between normal and modified z-scores (PDF 23 kb)

Supplementary Table 3

Pathways involved in compound mode of action: MNI approach versus mRNA expression change (PDF 15 kb)

Supplementary Table 4

Pathways involved in compound mode of action: Association analysis approaches (PDF 15 kb)

Supplementary Table 5

Top 50 ranked genes (MNI) Drugs with known targets Drugs without well-established targets (PDF 24 kb)

Supplementary Notes (PDF 91 kb)

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di Bernardo, D., Thompson, M., Gardner, T. et al. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23, 377–383 (2005). https://doi.org/10.1038/nbt1075

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