Systematic determination of genetic network architecture (original) (raw)

Nature Genetics volume 22, pages 281–285 (1999)Cite this article

Abstract

Technologies to measure whole-genome mRNA abundances1,2,3 and methods to organize and display such data4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes5. We have previously shown that hierarchical clustering can group together genes whose _cis_-regulatory elements are bound by the same proteins in vivo6. Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions7. The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected _cis_-regulatory elements8. Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative _cis_-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.

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

Access options

Subscribe to this journal

Receive 12 print issues and online access

$209.00 per year

only $17.42 per issue

Buy this article

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

Additional access options:

Similar content being viewed by others

References

  1. Velculescu, V.E. et al. Characterization of the yeast transcriptome. Cell 88, 243–251 ( 1997).
    Article CAS Google Scholar
  2. Lockhart, D.J. et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol. 14, 1675– 1680 (1996).
    Article CAS Google Scholar
  3. DeRisi, J.L., Iyer, V.R. & Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680– 686 (1997).
    Article CAS Google Scholar
  4. Weinstein, J.N. et al. An information-intensive approach to the molecular pharmacology of cancer. Science 275, 343– 349 (1997).
    Article CAS Google Scholar
  5. Wen, X. et al. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl Acad. Sci. USA 95, 334–339 (1998).
    Article CAS Google Scholar
  6. Tavazoie, S. & Church, G.M. Quantitative whole-genome analysis of DNA-protein interactions by in vivo methylase protection in E. coli. Nature Biotechnol. 16, 566– 571 (1998).
    Article CAS Google Scholar
  7. Eisen, M.B., Spellman, P.T., Brown, P.O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863– 14868 (1998).
    Article CAS Google Scholar
  8. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiase by microarray hybridization. Mol. Biol. Cell 9, 3273– 3297 (1998).
    Article CAS Google Scholar
  9. Holstege, F.C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998).
    Article CAS Google Scholar
  10. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl Acad. Sci. USA 96, 2907– 2912 (1999).
    Article CAS Google Scholar
  11. Cho, R.J. et al. A genome wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2, 65–73 (1998).
    Article CAS Google Scholar
  12. Wodicka, L., Dong, H., Mittmann, M., Ho, M.H. & Lockhart, D.J. Genome-wide expression monitoring in Saccharomyces cerevisiae. Nature Biotechnol. 15, 1359 –1366 (1997).
    Article CAS Google Scholar
  13. Everitt, B. Cluster Analysis 122 (Heinemann, London, 1974).
    Google Scholar
  14. Hartigan, J.A. Clustering Algorithms 351 (Wiley, New York, 1975).
    Google Scholar
  15. Mewes, H.W. et al. Overview of the yeast genome. Nature 387, 7–65 (1997).
    Article Google Scholar
  16. Roth, F.P., Hughes, J.D., Estep, P.W. & Church, G.M. Finding DNA-regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nature Biotechnol. 16, 949–945 (1998).
    Article Google Scholar
  17. Schneider, T.D. & Stephens, R.M. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 18, 6097–6100 (1990).
    Article CAS Google Scholar
  18. Berg, O.G. & von Hippel, P.H. Selection of DNA-binding sites by regulatory proteins. Statistical-mechanical theory and application to operators and promoters. J. Mol. Biol. 193, 723–750 ( 1987).
    Article CAS Google Scholar
  19. Koch, C. & Nasmyth, K. Cell cycle regulated transcription in yeast. Curr. Opin. Cell Biol. 6, 451– 459 (1994).
    Article CAS Google Scholar
  20. McInerny, C.J., Partridge, J.F., Mikesell, G.E., Creemer, D.P. & Breeden, L.L. A novel Mcm1-dependent element in the SWI4, CLN3, CDC6, and CDC47 promoters activates M/G1-specific transcription. Genes Dev. 11, 1277–1288 (1997).
    Article CAS Google Scholar
  21. Kuo, M. & Grayhack, E. A library of yeast genomic MCM1 binding sites contains genes involved in cell cycle control, cell wall and membrane structure, and metabolism. Mol. Cell. Biol. 14, 348–359 (1994).
    Article CAS Google Scholar
  22. Planta, R.J., Goncalves, M. & Mager, W.H. Global regulators of ribosome biosynthesis in yeast. Biochem. Cell Biol. 73, 825– 834 (1995).
    Article CAS Google Scholar
  23. Moskovina, E. et al. A search in the genome of Saccharomyces cerevisiae for genes regulated via stress response elements. Yeast 14, 1041–1050 (1998).
    Article Google Scholar
  24. Thomas, D. & Surdin-Kerjan, Y. Metabolism of sulfur amino acids in Saccharomyces cerevisiae. Microbiol. Mol. Biol. Rev. 61, 503–532 ( 1997).
    CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

We thank D. Lockhart and L. Wodicka for support, and B. Gewurz, V. Mootha, S. Tavazoie, M. Tavazoie and members of the Church lab, especially P. Estep, R. Mitra, B. Cohen, J. Johnson, M. Bulyk and J. Aach, for discussions and critical readings of the manuscript. This work was supported by the US Department of Energy (grant DE-FG02-87-ER60565), the office of Naval Research and DARPA (grant N00014-97-1-0865), the Lipper Foundation and Hoechst Marion Roussel.

Author information

Authors and Affiliations

  1. Department of Genetics, Harvard Medical School, 200 Longwood Ave, Boston, 02115, Massachusetts, USA
    Saeed Tavazoie, Jason D. Hughes & George M. Church
  2. Graduate Program in Biophysics, 200 Longwood Ave
    Jason D. Hughes
  3. Harvard University, Boston, 02115, Massachusetts , USA
    Jason D. Hughes
  4. Molecular Applications Group, 607 Hansen Way, Building One, Palo Alto, 94303-1110, California, USA
    Michael J. Campbell
  5. Department of Genetics, B400 Beckman Center, 279 Campus Drive
    Raymond J. Cho
  6. Stanford Medical Center, Palo Alto , 94304, California, USA
    Raymond J. Cho

Authors

  1. Saeed Tavazoie
    You can also search for this author inPubMed Google Scholar
  2. Jason D. Hughes
    You can also search for this author inPubMed Google Scholar
  3. Michael J. Campbell
    You can also search for this author inPubMed Google Scholar
  4. Raymond J. Cho
    You can also search for this author inPubMed Google Scholar
  5. George M. Church
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toGeorge M. Church.

Rights and permissions

About this article

Cite this article

Tavazoie, S., Hughes, J., Campbell, M. et al. Systematic determination of genetic network architecture.Nat Genet 22, 281–285 (1999). https://doi.org/10.1038/10343

Download citation

Associated content