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.

Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast

Nature Genetics volume 38, pages 1082–1087 (2006)Cite this article

Abstract

Transcriptional noise is known to be an important cause of cellular heterogeneity and phenotypic variation. The extent to which molecular interaction networks may have evolved to either filter or exploit transcriptional noise is a much debated question. The yeast genetic network regulating galactose metabolism involves two proteins, Gal3p and Gal80p, that feed back positively and negatively, respectively, on GAL gene expression. Using kinetic modeling and experimental validation, we demonstrate that these feedback interactions together are important for (i) controlling the cell-to-cell variability of GAL gene expression and (ii) ensuring that cells rapidly switch to an induced state for galactose uptake.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Figure 1: Schematic of the GAL network.
Figure 2: Reporter response to galactose induction.
Figure 3: Fluorescence distributions over time for mutant and wild-type cells pre-grown on 2% raffinose and introduced to 0.1% galactose.
Figure 4: Population heterogeneity of reporter expression.
Figure 5: Simulation results showing the steady-state dose-response of four strains: wild-type, mutant (both GAL3 and GAL80 feedback loops disabled), GAL3 feedback loop disabled and GAL80 feedback loop disabled.

References

  1. Blake, W.J., Kærn, M., Cantor, C.R. & Collins, J.J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003).

    Article  CAS  Google Scholar 

  2. Kærn, M., Elston, T.C., Blake, W.J. & Collins, J.J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    Article  Google Scholar 

  3. Ozbudak, E.M., Thattai, M., Kurtser, I., Grossman, A.D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    Article  CAS  Google Scholar 

  4. Raser, J.M. & O'Shea, E.K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004).

    Article  CAS  Google Scholar 

  5. Volfson, D. et al. Origins of extrinsic variability in eukaryotic gene expression. Nature 439, 861–864 (2006).

    Article  CAS  Google Scholar 

  6. Becskei, A. & Serrano, L. Engineering stability in gene networks by autoregulation. Nature 405, 590–593 (2000).

    Article  CAS  Google Scholar 

  7. Orrell, D. & Bolouri, H. Control of internal and external noise in genetic regulatory networks. J. Theor. Biol. 230, 301–312 (2004).

    Article  CAS  Google Scholar 

  8. Brandman, O., Ferrell, J.E., Jr., Li, R. & Meyer, T. Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496–498 (2005).

    Article  CAS  Google Scholar 

  9. Hasty, J., Pradines, J., Dolnik, M. & Collins, J.J. Noise-based switches and amplifiers for gene expression. Proc. Natl. Acad. Sci. USA 97, 2075–2080 (2000).

    Article  CAS  Google Scholar 

  10. Melcher, K. Galactose metabolism in Saccharomyces cerevisiae: a paradigm for eukaryotic gene regulation. in Yeast Sugar Metabolism (eds. Zimmermann, F.K. & Entian, K.-D.) (North Holland, Lancaster, Pennsylvania, 1997).

    Google Scholar 

  11. Peng, G. & Hopper, J.E. Gene activation by interaction of an inhibitor with a cytoplasmic signaling protein. Proc. Natl. Acad. Sci. USA 99, 8548–8553 (2002).

    Article  CAS  Google Scholar 

  12. Platt, A. & Reece, R.J. The yeast galactose genetic switch is mediated by the formation of Gal4p-Gal80p-Gal3p complex. EMBO J. 17, 4086–4091 (1998).

    Article  CAS  Google Scholar 

  13. Acar, M., Becskei, A. & van Oudenaarden, A. Enhancement of cellular memory by reducing stochastic transitions. Nature 435, 228–232 (2005).

    Article  CAS  Google Scholar 

  14. Verma, M., Bhat, P.J. & Venkatesh, K.V. Quantitative analysis of GAL genetic switch of Saccharomyces cerevisiae reveals that nuclearcytoplasmic shuttling of Gal80p results in a highly sensitive response to galactose. J. Biol. Chem. 278, 48764–48769 (2003).

    Article  CAS  Google Scholar 

  15. Ruhela, A. et al. Autoregulation of regulatory proteins is key for dynamic operation of GAL switch in Saccharomyces cerevisiae. FEBS Lett. 576, 119–126 (2004).

    Article  CAS  Google Scholar 

  16. Biggar, S.R. & Crabtree, G.R. Cell signaling can direct either binary or graded transcriptional responses. EMBO J. 20, 3167–3176 (2001).

    Article  CAS  Google Scholar 

  17. Verma, M., Bhat, P.J., Bhartiya, S. & Venkatesh, K.V. A steady-state modeling approach to validate an in vivo mechanism of the GAL regulatory network in Saccharomyces cerevisiae. Eur. J. Biochem. 271, 4064–4074 (2004).

    Article  CAS  Google Scholar 

  18. Rosenfeld, N., Elowitz, M.B. & Alon, U. Negative autoregulation speeds the response times of transcriptional networks. J. Mol. Biol. 323, 785–793 (2002).

    Article  CAS  Google Scholar 

  19. de Atauri, P., Orrell, D., Ramsey, S. & Bolouri, H. Evolution of 'design' principles in biochemical networks. IEE Proc. Sys. Biol. 1, 28–40 (2004).

    Article  CAS  Google Scholar 

  20. Orrell, D. et al. Feedback control of stochastic noise in the yeast galactose utilization pathway. Physica D. 217, 64–76 (2006).

    Article  CAS  Google Scholar 

  21. Brachmann, C.B. et al. Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications preference. Yeast 14, 115–132 (1998).

    Article  CAS  Google Scholar 

  22. Dilworth, D.J. et al. Nup2p dynamically associates with the distal regions of the yeast nuclear pore complex. J. Cell Biol. 153, 1465–1478 (2001).

    Article  CAS  Google Scholar 

  23. Scholz, O., Thiel, A., Hillen, W. & Niederweis, M. Quantitative analysis of gene expression with an improved green fluorescent protein. Eur. J. Biochem. 267, 1565–1570 (2000).

    Article  CAS  Google Scholar 

  24. Ostergaard, S., Olsson, L. & Nielsen, J. Metabolic engineering of Saccharomyces cerevisiae. Microbiol. Mol. Biol. Rev. 64, 34–50 (2000).

    Article  CAS  Google Scholar 

  25. Guarente, L., Yocum, R.R. & Gifford, P.A. GAL10–CYC1 hybrid yeast promoter identifies the GAL4 regulatory region as an upstream site. Proc. Natl. Acad. Sci. USA 79, 7410–7414 (1982).

    Article  CAS  Google Scholar 

  26. Rao, C.V. & Arkin, A.P. Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm. J. Chem. Phys. 118, 4999–5010 (2003).

    Article  CAS  Google Scholar 

  27. Ramsey, S., Orrell, D. & Bolouri, H. Dizzy: Stochastic simulations of large-scale genetic regulatory networks. J. Bioinform. Comput. Biol. 3, 437–454 (2005).

    Article  CAS  Google Scholar 

  28. Gasch, A.P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241–4257 (2000).

    Article  CAS  Google Scholar 

  29. Pédelacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T.C. & Waldo, G.S. Engineering and characterization of a superfolder green fluorescent protein. Nature Biotech. 24, 79–88 (2006).

    Article  Google Scholar 

  30. Lue, N.F., Chasman, D.I., Buchman, A.R. & Kornberg, R.D. Interaction of GAL4 and GAL80 gene regulatory proteins in vitro. Mol. Cell. Biol. 7, 3446–3451 (1987).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank D. Hwang for helpful advice on the statistical analysis, H. Kostner for assistance with the QPCR experiments and E. Schweighofer for assistance with the cluster computing infrastructure. This work was supported in part by grants from the US National Institutes of Health (GM076547, GM067228).

Author information

Authors and Affiliations

  1. Institute for Systems Biology, 1441 N 34th Street, Seattle, 98103, Washington, USA

    Stephen A Ramsey, Jennifer J Smith, David Orrell, Marcello Marelli, Timothy W Petersen, Pedro de Atauri, Hamid Bolouri & John D Aitchison

Authors
  1. Stephen A Ramsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer J Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Orrell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcello Marelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Timothy W Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pedro de Atauri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hamid Bolouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. John D Aitchison
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.A.R., J.J.S., D.O., H.B. and J.D.A. designed the study. J.J.S., M.M. and T.W.P. carried out the experimental validation. S.A.R., D.O. and P.A. performed the modeling and simulations. S.A.R., J.J.S., H.B. and J.D.A. wrote the paper.

Corresponding authors

Correspondence to Hamid Bolouri or John D Aitchison.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Control experiments. (PDF 70 kb)

Supplementary Fig. 2

Analytic model of galactose import. (PDF 25 kb)

Supplementary Fig. 3

Simulated distribution of the number of molecules of Gal80p homodimer within the nucleus, for a cell population of wild-type and mutant strains grown on raffinose (noninducing media). (PDF 42 kb)

Supplementary Fig. 4

Steady-state galactose dose-response. (PDF 63 kb)

Supplementary Fig. 5

Simulated growth of wild-type and mutant strains on alternating galactose and raffinose media. (PDF 55 kb)

Supplementary Table 1

Fractional activity level of the reporter at 6 h, for the wild-type and mutant strains on different initial concentrations of galactose. (PDF 10 kb)

Supplementary Table 2

Oligonucleotide primers used in strain construction and in QPCR measurement of GAL3 and GAL80 expression levels. (PDF 18 kb)

Supplementary Note (PDF 105 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ramsey, S., Smith, J., Orrell, D. et al. Dual feedback loops in the GAL regulon suppress cellular heterogeneity in yeast. Nat Genet 38, 1082–1087 (2006). https://doi.org/10.1038/ng1869

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng1869

This article is cited by

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