Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Functional classification of drugs by properties of their pairwise interactions

Nature Genetics volume 38, pages 489–494 (2006)Cite this article

Abstract

Multidrug treatments are increasingly important in medicine and for probing biological systems1,2,3,4,5,6. Although many studies have focused on interactions between specific drugs, little is known about the system properties of a full drug interaction network6. Like their genetic counterparts, two drugs may have no interaction, or they may interact synergistically or antagonistically to increase or suppress their individual effects. Here we use a sensitive bioluminescence technique7,8 to provide quantitative measurements of pairwise interactions among 21 antibiotics that affect growth rate in Escherichia coli. We find that the drug interaction network possesses a special property: it can be separated into classes of drugs such that any two classes interact either purely synergistically or purely antagonistically. These classes correspond directly to the cellular functions affected by the drugs. This network approach provides a new conceptual framework for understanding the functional mechanisms of drugs and their cellular targets and can be applied in systems intractable to mutant screening, biochemistry or microscopy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Clustering of individual drugs into functional classes solely on the basis of properties of their mutual interaction network.
Figure 2: Experimental classification of drug interactions into four types using bioluminescence measurements of bacterial growth in the presence of sublethal concentrations of antibiotics.
Figure 3: Systematic measurements of pairwise interactions between antibiotics.
Figure 4: Unsupervised classification of the antibiotic network into monochromatically interacting classes of drugs with similar mechanisms of action.

Similar content being viewed by others

References

  1. Walsh, C. Molecular mechanisms that confer antibacterial drug resistance. Nature 406, 775–781 (2000).

    Article  CAS  Google Scholar 

  2. Hurley, L.H. DNA and its associated processes as targets for cancer therapy. Nat. Rev. Cancer 2, 188–200 (2002).

    Article  CAS  Google Scholar 

  3. Leeb, M. Antibiotics: a shot in the arm. Nature 431, 892–893 (2004).

    Article  CAS  Google Scholar 

  4. Levy, S.B. & Marshall, B. Antibacterial resistance worldwide: causes, challenges and responses. Nat. Med. 10, S122–S129 (2004).

    Article  CAS  Google Scholar 

  5. Nathan, C. Antibiotics at the crossroads. Nature 431, 899–902 (2004).

    Article  CAS  Google Scholar 

  6. Keith, C.T., Borisy, A.A. & Stockwell, B.R. Multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov. 4, 71–78 (2005).

    Article  CAS  Google Scholar 

  7. Bjarnason, J., Southward, C.M. & Surette, M.G. Genomic profiling of iron-responsive genes in Salmonella enterica serovar typhimurium by high-throughput screening of a random promoter library. J. Bacteriol. 185, 4973–4982 (2003).

    Article  CAS  Google Scholar 

  8. Kishony, R. & Leibler, S. Environmental stresses can alleviate the average deleterious effect of mutations. J. Biol. 2, 14 (2003).

    Article  Google Scholar 

  9. Schreiber, S.L. The small-molecule approach to biology. Chem. Eng. News 81, 51–61 (2003).

    Article  Google Scholar 

  10. Hartman, J.L., Garvik, B. & Hartwell, L. Cell biology - Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).

    Article  CAS  Google Scholar 

  11. Tong, A.H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

    Article  CAS  Google Scholar 

  12. Davierwala, A.P. et al. The synthetic genetic interaction spectrum of essential genes. Nat. Genet. 37, 1147–1152 (2005).

    Article  CAS  Google Scholar 

  13. Loewe, S. The problem of synergism and antagonism of combined drugs. Arzneimittel-Forschung-Drug Research 3, 285–290 (1953).

    CAS  Google Scholar 

  14. Bliss, C.I. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615 (1939).

    Article  CAS  Google Scholar 

  15. Parsons, A.B. et al. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat. Biotechnol. 22, 62–69 (2004).

    Article  CAS  Google Scholar 

  16. Segre, D., DeLuna, A., Church, G.M. & Kishony, R. Modular epistasis in yeast metabolism. Nat. Genet. 37, 77–83 (2005).

    Article  CAS  Google Scholar 

  17. Borisy, A.A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl. Acad. Sci. USA 100, 7977–7982 (2003).

    Article  CAS  Google Scholar 

  18. Scott, G.M. & Kyi, M.S. Handbook of Essential Antibiotics (Harwood Academic, Amsterdam, 2001).

    Book  Google Scholar 

  19. Walsh, C. Antibiotics: Actions, Origins, Resistance (American Society for Microbiology, Washington, D.C., 2003).

    Book  Google Scholar 

  20. Hoffman, L.R. et al. Aminoglycoside antibiotics induce bacterial biofilm formation. Nature 436, 1171–1175 (2005).

    Article  CAS  Google Scholar 

  21. Perlman, Z. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    Article  CAS  Google Scholar 

  22. Hecht, S.M. Bleomycin: New perspectives on the mechanism of action. J. Nat. Prod. 63, 158–168 (2000).

    Article  CAS  Google Scholar 

  23. Gardner, T.S., di Bernardo, D., Lorenz, D. & Collins, J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301, 102–105 (2003).

    Article  CAS  Google Scholar 

  24. Swinney, D.C. Biochemical mechanisms of drug action: what does it take for success? Nat. Rev. Drug Discov. 3, 801–808 (2004).

    Article  CAS  Google Scholar 

  25. Milo, R. et al. Network motifs: Simple building blocks of complex networks. Science 298, 824–827 (2002).

    Article  CAS  Google Scholar 

  26. Shen-Orr, S.S., Milo, R., Mangan, S. & Alon, U. Network motifs in the transcriptional regulation network of Escherichia coli. Nat. Genet. 31, 64–68 (2002).

    Article  CAS  Google Scholar 

  27. Barabasi, A.L. & Oltvai, Z.N. Network biology: Understanding the cell's functional organization. Nat. Rev. Genet. 5, 101–113 (2004).

    Article  CAS  Google Scholar 

  28. Remold, S.K. & Lenski, R.E. Pervasive joint influence of epistasis and plasticity on mutational effects in Escherichia coli. Nat. Genet. 36, 423–426 (2004).

    Article  CAS  Google Scholar 

  29. Balaban, N.Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    Article  CAS  Google Scholar 

  30. Komarova, N.L. & Wodarz, D. Drug resistance in cancer: Principles of emergence and prevention. Proc. Natl. Acad. Sci. USA 102, 9714–9719 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank N. Barkai, J. Clardy, A. De Luna, M. Elowitz, L. Garwin, M. Hegreness, H. Hofmann, D. Kahne, G. Lahav, M. Laub, T. Mitchison, A. Murray, E. O'Shea, S. Renn, V. Savage, D. Segrè, N. Shoresh and C. Walsh for helpful suggestions and for comments on the manuscript. We acknowledge support from the Bauer Center for Genomics Research.

Author information

Authors and Affiliations

  1. Bauer Center for Genomics Research, Harvard University, 7 Divinity Avenue, Cambridge, 02138, Massachusetts, USA

    Pamela Yeh, Ariane I Tschumi & Roy Kishony

  2. Department of Systems Biology, Harvard Medical School, 240 Longwood Avenue, Boston, 02115, Massachusetts, USA

    Roy Kishony

Authors
  1. Pamela Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ariane I Tschumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roy Kishony
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roy Kishony.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Reproducibility of growth rate measurements using the bioluminescence technique. (PDF 40 kb)

Supplementary Fig. 2

Single-drug dose-reponse measurements. (PDF 52 kb)

Supplementary Fig. 3

Examining dose dependence drug interactions. (PDF 57 kb)

Supplementary Fig. 4

Monochromaticity exhibited by the drug network is a special property not exhibited in random networks. (PDF 21 kb)

Supplementary Note (PDF 64 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yeh, P., Tschumi, A. & Kishony, R. Functional classification of drugs by properties of their pairwise interactions. Nat Genet 38, 489–494 (2006). https://doi.org/10.1038/ng1755

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng1755

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing