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.

Transcription of functionally related constitutive genes is not coordinated

Subjects

This article has been updated

Abstract

Expression of an individual gene can vary considerably among genetically identical cells because of stochastic fluctuations in transcription. However, proteins comprising essential complexes or pathways have similar abundances and lower variability. It is not known whether coordination in the expression of subunits of essential complexes occurs at the level of transcription, mRNA abundance or protein expression. To directly measure the level of coordination in the expression of genes, we used highly sensitive fluorescence in situ hybridization (FISH) to count individual mRNAs of functionally related and unrelated genes within single Saccharomyces cerevisiae cells. Our results revealed that transcript levels of temporally induced genes are highly correlated in individual cells. In contrast, transcription of constitutive genes encoding essential subunits of complexes is not coordinated because of stochastic fluctuations. The coordination of these functional complexes therefore must occur post-transcriptionally, and likely post-translationally.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Highly coordinated transcription of genes in the galactose network.
Figure 2: Correlation between cytoplasmic mRNA abundance of GAL genes in individual cells.
Figure 3: Anti-correlation between cytoplasmic mRNA abundance of genes expressed during different cell cycle stages.
Figure 4: Correlation between cytoplasmic mRNA abundance of functionally unrelated constitutively active genes.
Figure 5: Correlation between cytoplasmic mRNA abundance of essential genes encoding subunits of multi-protein complexes.
Figure 6: Correlation between transcripts from two alleles of a constitutively active gene, MDN1, in diploid cells.
Figure 7: Stochastic model predicts correlation coefficients from mean mRNA abundance and half-life times.

Change history

  • 12 December 2010

    In the version of this article initially published online, the color key in Figure 7c,d was incorrect. The error has been corrected for all versions of this article.

References

  1. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

  2. Krogan, N.J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    Article  CAS  Google Scholar 

  3. Gavin, A.C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).

    Article  CAS  Google Scholar 

  4. Holstege, F.C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998).

    Article  CAS  Google Scholar 

  5. Lee, T.I. et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799–804 (2002).

    Article  CAS  Google Scholar 

  6. Harbison, C.T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).

    Article  CAS  Google Scholar 

  7. Carmi, S., Levanon, E.Y. & Eisenberg, E. Efficiency of complex production in changing environment. BMC Syst. Biol. 3, 3 (2009).

    Article  Google Scholar 

  8. Carmi, S., Levanon, E.Y., Havlin, S. & Eisenberg, E. Connectivity and expression in protein networks: proteins in a complex are uniformly expressed. Phys. Rev. E 73, 031909 (2006).

    Article  Google Scholar 

  9. Tuller, T., Kupiec, M. & Ruppin, E. Determinants of protein abundance and translation efficiency in S. cerevisiae. PLOS Comput. Biol. 3, e248 (2007).

    Article  Google Scholar 

  10. Lockhart, D.J. & Winzeler, E.A. Genomics, gene expression and DNA arrays. Nature 405, 827–836 (2000).

    Article  CAS  Google Scholar 

  11. Kaern, 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  CAS  Google Scholar 

  12. Elowitz, M.B., Levine, A.J., Siggia, E.D. & Swain, P.S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    Article  CAS  Google Scholar 

  13. Kaufmann, B.B. & van Oudenaarden, A. Stochastic gene expression: from single molecules to the proteome. Curr. Opin. Genet. Dev. (2007).

  14. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

    Article  CAS  Google Scholar 

  15. Swain, P.S., Elowitz, M.B. & Siggia, E.D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA 99, 12795–12800 (2002).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nat. Genet. 38, 636–643 (2006).

    Article  CAS  Google Scholar 

  19. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).

    Article  CAS  Google Scholar 

  20. Newman, J.R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).

    Article  CAS  Google Scholar 

  21. Fraser, H.B., Hirsh, A.E., Giaever, G., Kumm, J. & Eisen, M.B. Noise minimization in eukaryotic gene expression. PLoS Biol. 2, e137 (2004).

    Article  Google Scholar 

  22. Femino, A.M., Fay, F.S., Fogarty, K. & Singer, R.H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).

    Article  CAS  Google Scholar 

  23. Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  CAS  Google Scholar 

  24. Zenklusen, D., Larson, D.R. & Singer, R.H. Single-RNA counting reveals alternative modes of gene expression in yeast. Nat. Struct. Mol. Biol. 15, 1263–1271 (2008).

    Article  CAS  Google Scholar 

  25. Larson, D.R., Singer, R.H. & Zenklusen, D. A single molecule view of gene expression. Trends Cell Biol. 19, 630–637 (2009).

    Article  CAS  Google Scholar 

  26. Thompson, R.E., Larson, D.R. & Webb, W.W. Precise nanometer localization analysis for individual fluorescent probes. Biophys. J. 82, 2775–2783 (2002).

    Article  CAS  Google Scholar 

  27. Lohr, D., Venkov, P. & Zlatanova, J. Transcriptional regulation in the yeast GAL gene family: a complex genetic network. FASEB J. 9, 777–787 (1995).

    Article  CAS  Google Scholar 

  28. Traven, A., Jelicic, B. & Sopta, M. Yeast Gal4: a transcriptional paradigm revisited. EMBO Rep. 7, 496–499 (2006).

    Article  CAS  Google Scholar 

  29. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).

    Article  CAS  Google Scholar 

  30. Loy, C.J., Lydall, D. & Surana, U. NDD1, a high-dosage suppressor of cdc28–1N, is essential for expression of a subset of late-S-phase-specific genes in Saccharomyces cerevisiae. Mol. Cell. Biol. 19, 3312–3327 (1999).

    Article  CAS  Google Scholar 

  31. Veis, J., Klug, H., Koranda, M. & Ammerer, G. Activation of the G2/M-specific gene CLB2 requires multiple cell cycle signals. Mol. Cell. Biol. 27, 8364–8373 (2007).

    Article  CAS  Google Scholar 

  32. Mannhaupt, G., Schnall, R., Karpov, V., Vetter, I. & Feldmann, H. Rpn4p acts as a transcription factor by binding to PACE, a nonamer box found upstream of 26S proteasomal and other genes in yeast. FEBS Lett. 450, 27–34 (1999).

    Article  CAS  Google Scholar 

  33. Xie, Y. & Varshavsky, A. RPN4 is a ligand, substrate, and transcriptional regulator of the 26S proteasome: a negative feedback circuit. Proc. Natl. Acad. Sci. USA 98, 3056–3061 (2001).

    Article  CAS  Google Scholar 

  34. Peccoud, J. & Ycart, B. Markovian modeling of gene-product synthesis. Theor. Popul. Biol. 48, 222–234 (2002).

    Article  Google Scholar 

  35. Raj, A., Peskin, C.S., Tranchina, D., Vargas, D.Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006).

    Article  Google Scholar 

  36. Becskei, A., Kaufmann, B.B. & van Oudenaarden, A. Contributions of low molecule number and chromosomal positioning to stochastic gene expression. Nat. Genet. 37, 937–944 (2005).

    Article  CAS  Google Scholar 

  37. Berg, O.G. A model for the statistical fluctuations of protein numbers in a microbial population. J. Theor. Biol. 71, 587–603 (1978).

    Article  CAS  Google Scholar 

  38. Brauer, M.J. et al. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol. Biol. Cell 19, 352–367 (2008).

    Article  CAS  Google Scholar 

  39. Sánchez, A. & Kondev, J. Transcriptional control of noise in gene expression. Proc. Natl. Acad. Sci. USA 105, 5081–5086 (2008).

    Article  Google Scholar 

  40. Bryant, G.O. et al. Activator control of nucleosome occupancy in activation and repression of transcription. PLoS Biol. 6, 2928–2939 (2008).

    Article  CAS  Google Scholar 

  41. Guillemette, B. et al. Variant histone H2A.Z is globally localized to the promoters of inactive yeast genes and regulates nucleosome positioning. PLoS Biol. 3, e384 (2005).

    Article  Google Scholar 

  42. Pedraza, J.M. & Paulsson, J. Effects of molecular memory and bursting on fluctuations in gene expression. Science 319, 339–343 (2008).

    Article  CAS  Google Scholar 

  43. Wang, Y. et al. Precision and functional specificity in mRNA decay. Proc. Natl. Acad. Sci. USA 99, 5860–5865 (2002).

    Article  CAS  Google Scholar 

  44. Belle, A., Tanay, A., Bitincka, L., Shamir, R. & O′Shea, E.K. Quantification of protein half-lives in the budding yeast proteome. Proc. Natl. Acad. Sci. USA 103, 13004–13009 (2006).

    Article  CAS  Google Scholar 

  45. Li, X., Kusmierczyk, A.R., Wong, P., Emili, A. & Hochstrasser, M. β-Subunit appendages promote 20S proteasome assembly by overcoming an Ump1-dependent checkpoint. EMBO J. 26, 2339–2349 (2007).

    Article  CAS  Google Scholar 

  46. Le Tallec, B. et al. 20S proteasome assembly is orchestrated by two distinct pairs of chaperones in yeast and in mammals. Mol. Cell 27, 660–674 (2007).

    Article  CAS  Google Scholar 

  47. Gerber, A.P., Herschlag, D. & Brown, P.O. Extensive association of functionally and cytotopically related mRNAs with Puf family RNA-binding proteins in yeast. PLoS Biol. 2, E79 (2004).

    Article  Google Scholar 

  48. Hogan, D.J., Riordan, D.P., Gerber, A.P., Herschlag, D. & Brown, P.O. Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system. PLoS Biol. 6, e255 (2008).

    Article  Google Scholar 

  49. Pullmann, R. Jr. et al. Analysis of turnover and translation regulatory RNA-binding protein expression through binding to cognate mRNAs. Mol. Cell. Biol. 27, 6265–6278 (2007).

    Article  CAS  Google Scholar 

  50. Wilson, C.J., Zhan, H., Swint-Kruse, L. & Matthews, K.S. The lactose repressor system: paradigms for regulation, allosteric behavior and protein folding. Cell. Mol. Life Sci. 64, 3–16 (2007).

    Article  CAS  Google Scholar 

  51. Paulsson, J. & Ehrenberg, M. Noise in a minimal regulatory network: plasmid copy number control. Q. Rev. Biophys. 34, 1–59 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank M. Keogh from Albert Einstein College of Medicine for helpful discussions of GAL experiments and for providing reagents to create the HTZ1 deletion strain. We thank D. Botstein and S.J. Silverman from Princeton University for helpful discussions and for providing cells with a doubling time of 14 h, respectively. This work was supported by the United States National Institutes of Health (GM 57071) and the Human Frontier Science Program (T.L.).

Author information

Authors and Affiliations

Authors

Contributions

S.J.G. and D.Z. initiated the project. S.J.G. conducted the experiments and data analysis. S.J.G. and T.L. did the numerical simulations and T.L. derived the analytical solution. D.Z. and R.H.S. supervised the project. S.J.G. wrote the paper with editorial help from D.Z., T.L. and R.H.S.

Corresponding author

Correspondence to Robert H Singer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Table 1 and Supplementary Methods (PDF 2233 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Gandhi, S., Zenklusen, D., Lionnet, T. et al. Transcription of functionally related constitutive genes is not coordinated. Nat Struct Mol Biol 18, 27–34 (2011). https://doi.org/10.1038/nsmb.1934

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nsmb.1934

This article is cited by

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