Condition Transition Analysis Reveals TF Activity Related to Nutrient-Limitation-Specific Effects of Oxygen Presence in Yeast

  • T. A. Knijnenburg
  • L. F. A. Wessels
  • M. J. T. Reinders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4210)


Regulatory networks are usually presented as graph structures showing the (combinatorial) regulatory effect of transcription factors (TF’s) on modules of similarly expressed or otherwise related genes. However, from these networks it is not clear when and how TF’s are activated. The actual conditions or perturbations that trigger a change in the activity of TF’s should be a crucial part of the generated regulatory network.

Here, we demonstrate the power to uncover TF activity by focusing on a small, homogeneous, yet well defined set of chemostat cultivation experiments, where the transcriptional response of yeast grown under four different nutrient limitations, both aerobically as well as anaerobically was measured. We define a condition transition as an instant change in yeast’s extracellular environment by comparing two cultivation conditions, where either the limited nutrient or the oxygen availability is different. Differential gene expression as a consequence of such a condition transition is represented in a tertiary matrix, where zero indicates no change in expression; 1 and -1 respectively indicate an increase and decrease in expression as a consequence of a condition transition. We uncover TF activity by assessing significant TF binding in the promotor region of genes that behave accordingly at a condition transition. The interrelatedness of the conditions in the combinatorial setup is exploited by performing specific hypergeometric tests that allow for the discovery of both individual and combined effects of the cultivation parameters on TF activity. Additionally, we create a weight-matrix indicating the involvement of each TF in each of the condition transitions by posing our problem as an orthogonal Procrustes problem. We show that the Procrustes analysis strengthens and broadens the uncovered relationships.

The resulting regulatory network reveals nutrient-limitation-specific effects of oxygen presence on expression behavior and TF activity. Our analysis identifies many TF’s that seem to play a very specific regulatory role at the nutrient and oxygen availability transitions.


Condition Transition Expression Behavior Nutrient Limitation Anaerobic Growth Aerobic Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Banerjee, N., Zhang, M.Q.: Functional genomics as applied to mapping transcription regulatory networks. Curr. Opin. Microbiol. 5, 313–317 (2002)CrossRefGoogle Scholar
  2. 2.
    Chua, G., Robinson, M.D., Morris, Q., Hughes, T.R.: Transcriptional networks: reverse-engineering gene regulation on a global scale. Curr. Opin. Microbiol. 7(6), 638–646 (2004)CrossRefGoogle Scholar
  3. 3.
    Pilpel, Y., Sudarsanam, P., Church, G.M.: Identifying regulatory networks by combinatorial analysis of promoter elements. Nat. Genet. 29(2), 153–159 (2001)CrossRefGoogle Scholar
  4. 4.
    Bar-Joseph, Z., Gerber, G.K., Lee, T.I., Rinaldi, N.J., Yoo, J.Y., Robert, F., Gorden, D.B., Fraenkel, E., Jaakkola, T.S., Young, R.A., Gifford, D.K.: Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. 21(11), 1337–1342 (2003)CrossRefGoogle Scholar
  5. 5.
    Wang, W., Cherry, J.M., Nochomovitz, Y., Jolly, E., Botstein, D., Li, H.: Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc. Natl. Acad. Sci. U.S.A. 102(6), 1998–2003 (2005)CrossRefGoogle Scholar
  6. 6.
    Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., Friedman, N.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34(2), 166–176 (2003)CrossRefGoogle Scholar
  7. 7.
    Ashburner, M., Ball, C.A., Rubin, G.M., Sherlock, G., et al.: Gene ontology: tool for the unification of biology the gene ontology consortium. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  8. 8.
    Tai, S.L., Boer, V.M., Daran-Lapujade, P., Walsh, M.C., de Winde, J.H., Daran, J.M., Pronk, J.T.: Two-dimensional transcriptome analysis in chemostat cultures. J. Biol. Chem. 280(1), 437–447 (2005)Google Scholar
  9. 9.
    Harbison, C.T., Gordon, D.B., Lee, T.I., Rinaldi, N.J., Fraenkel, E., Young, R.A., et al.: Transcriptional regulatory code of a eukaryotic genome. Nature 431(7004), 99–104 (2004)CrossRefGoogle Scholar
  10. 10.
    Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98(9), 5116–5121 (2001)MATHCrossRefGoogle Scholar
  11. 11.
    Knijnenburg, T.A., et al. (unpublished results)Google Scholar
  12. 12.
    Knijnenburg, T.A., Daran, J.M., Daran-Lapujade, P., Reinders, M.J.T., Wessels, L.F.A.: Relating transcription factors, modules of genes and cultivation conditions in saccharomyces cerevisiae. In: IEEE CSBW 2005, pp. 71–72 (2005)Google Scholar
  13. 13.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Patt. Anal. Machine Intell. PAMI-1, 224–227 (1979)CrossRefGoogle Scholar
  14. 14.
    Barash, Y., Bejerano, G., Friedman, N.: A simple hypergeometric approach for discovering putative transcription factor binding sites. In: Gascuel, O., Moret, B.M.E. (eds.) WABI 2001. LNCS, vol. 2149, pp. 278–293. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Ge, Y., Dudoit, S., Speed, T.P.: Resampling-based multiple testing for microarray data analysis. TEST 12(1), 1–77 (2003)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Bussemaker, H.J., Li, H., Siggia, E.D.: Regulatory element detection using correlation with expression. Nat. Genet. 27(2), 167–174 (2001)CrossRefGoogle Scholar
  17. 17.
    Gao, F., Foat, B.C., Bussemaker, H.J.: Defining transcriptional networks through integrative modeling of mrna expression and transcription factor binding data. BMC Bioinformatics 5(31) (2004)Google Scholar
  18. 18.
    Golub, G.H., Van Loan, C.F.: Matrix Computations. The John Hopkins University Press, Maryland (1996)MATHGoogle Scholar
  19. 19.
    Folsburg, S.L., Gaurente, L.: Identification and characterization of hap4: a third component of the ccaat-bound hap2/hap3 heteromer. Genes Dev. 3(8), 1166–1178 (1989)CrossRefGoogle Scholar
  20. 20.
    Yeast protein database,
  21. 21.
    Blaiseau, P.L., Thomas, D.: Multiple transcriptional activation complexes tether the yeast activator met4 to dna. EMBO J. 17(21), 6327–6336 (1998)CrossRefGoogle Scholar
  22. 22.
    Magasanik, B., Kaiser, C.A.: Nitrogen regulation in saccharomyces cerevisiae. Gene 290, 1–18 (2002)CrossRefGoogle Scholar
  23. 23.
    Passmore, S., Elbe, R., Tye, B.K.: A protein involved in minichromosome maintenance in yeast binds a transcriptional enhancer conserved in eukaryotes. Genes Dev. 3, 921–935 (1989)CrossRefGoogle Scholar
  24. 24.
    Chen, Y., Tye, B.K.: The yeast mcm1 protein is regulated posttranscriptionally by the flux of glycolysis. Mol. Cell. Biol. 15(8), 4631–4639 (1995)Google Scholar
  25. 25.
    Newcomb, L.L., Diderich, J.A., Slattery, M.G., Heideman, W.: Glucose regulation of saccharomyces cerevisiae cell cycle genes. Eukaryot. Cell. 2(1), 143–149 (2003)CrossRefGoogle Scholar
  26. 26.
    Wu, J., Zhang, N., Hayes, A., Panoutsopoulou, K., Oliver, S.G.: Global analysis of nutrient control of gene expression in saccharomyces cerevisiae during growth and starvation. Proc. Natl. Acad. Sci. U.S.A. 101(9), 3148–3153 (2004)CrossRefGoogle Scholar
  27. 27.
    Jamieson, D.J.: Oxidative stress responses of the yeast saccharomyces cerevisiae. Yeast 14(16), 1511–1527 (1998)CrossRefGoogle Scholar
  28. 28.
    Gasch, A.P., Werner-Washburne, M.: The genomics of yeast responses to environmental stress and starvation. Funct. Integr. Genomics (4-5), 181–192 (2002)CrossRefGoogle Scholar
  29. 29.
    Mewes, H.W., Albermann, K., Heumann, K., Liebl, S., Pfeiffer, F.: Mips: a database for protein sequences, homology data and yeast genome information. Nucleic Acids Research 25(1), 28–30 (1997)CrossRefGoogle Scholar
  30. 30.
    Kohlhaw, G.B.: Leucine biosynthesis in fungi: entering metabolism through the back door. Microbiol. Mol. Biol. Rev. 67(1), 1–15 (2003)CrossRefGoogle Scholar
  31. 31.
    Boer, V.M., Daran, J.M., Almering, M.J., de Winde, J.H., Pronk, J.T.: Contribution of the saccharomyces cerevisiae transcriptional regulator leu3p to physiology and gene expression in nitrogen-and carbon-limited chemostat cultures. FEMS Yeast Res. 5(10), 885–897 (2005)CrossRefGoogle Scholar
  32. 32.
    Mendoza-Cozatl, D., Loza-Tavera, H., Hernandez-Navarro, A., Moreno-Sanchez, R.: Sulfur assimilation and glutathione metabolism under cadmium stress in yeast, protists and plants. FEMS Microbiol. Rev. 29(4), 653–671 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • T. A. Knijnenburg
    • 1
    • 2
  • L. F. A. Wessels
    • 1
    • 3
  • M. J. T. Reinders
    • 1
  1. 1.Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
  2. 2.Kluyver Centre for Genomics of Industrial FermentationDelftThe Netherlands
  3. 3.Department of Molecular BiologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands

Personalised recommendations