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Regulatory activity revealed by dynamic correlations in gene expression noise


Gene regulatory interactions are context dependent, active in some cellular states but not in others. Stochastic fluctuations, or 'noise', in gene expression propagate through active, but not inactive, regulatory links1,2. Thus, correlations in gene expression noise could provide a noninvasive means to probe the activity states of regulatory links. However, global, 'extrinsic', noise sources generate correlations even without direct regulatory links. Here we show that single-cell time-lapse microscopy, by revealing time lags due to regulation, can discriminate between active regulatory connections and extrinsic noise. We demonstrate this principle mathematically, using stochastic modeling, and experimentally, using simple synthetic gene circuits. We then use this approach to analyze dynamic noise correlations in the galactose metabolism genes of Escherichia coli. We find that the CRP-GalS-GalE feed-forward loop is inactive in standard conditions but can become active in a GalR mutant. These results show how noise can help analyze the context dependence of regulatory interactions in endogenous gene circuits.

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Figure 1: Using noise to analyze the activity of gene regulatory interactions.
Figure 2: Dynamic cross correlations in simulated regulatory interactions.
Figure 3: Time-lapse movies of gene expression fluctuations in a synthetic genetic circuit.
Figure 4: Experimental data and cross-correlation analysis of regulated and unregulated target genes.
Figure 5: Regulatory interactions in the endogenous gal feed-forward loop are dependent on cell state.

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  1. Pedraza, J.M. & van Oudenaarden, A. Noise propagation in gene networks. Science 307, 1965–1969 (2005).

    Article  CAS  Google Scholar 

  2. Rosenfeld, N. et al. Gene regulation at the single-cell level. Science 307, 1962–1965 (2005).

    Article  CAS  Google Scholar 

  3. Toledo, F. & Wahl, G.M. Regulating the p53 pathway: in vitro hypothesis, in vivo veritas. Nat. Rev. Cancer 6, 909–923 (2006).

    Article  CAS  Google Scholar 

  4. Piggot, P.J. & Hilbert, D.W. Sporulation of Bacillus subtilis. Curr. Opin. Microbiol. 7, 579–586 (2004).

    Article  CAS  Google Scholar 

  5. Suel, G.M. et al. An excitable gene regulatory circuit induces transient cellular differentiation. Nature 440, 545–550 (2006).

    Article  Google Scholar 

  6. Elowitz, M.B. et al. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    Article  CAS  Google Scholar 

  7. Raser, J.M. & O'Shea, E.K. Noise in gene expression: origins, consequences, and control. Science 309, 2010–2013 (2005).

    Article  CAS  Google Scholar 

  8. Kaern, M. et al. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Sigal, A. et al. Variability and memory of protein levels in human cells. Nature 444, 643–646 (2006).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Arkin, A.P. & Ross, J. Statistical construction of chemical-reaction mechanisms from measured time-series. J. Phys. Chem. 99, 970–979 (1995).

    Article  CAS  Google Scholar 

  13. Arkin, A.P., Shen, P. & Ross, J. A test case of correlation metric construction of a reaction pathway from measurements. Science 277, 1275–1279 (1997).

    Article  CAS  Google Scholar 

  14. Gillespie, D.T. Exact numerical simulation of the Ornstein-Uhlenbeck process and its integral. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 54, 2084–2091 (1996).

    CAS  PubMed  Google Scholar 

  15. Meyer, B.J., Maurer, R. & Ptashne, M. Gene regulation at the right operator (OR) of bacteriophage lambda. II. OR1, OR2, and OR3: their roles in mediating the effects of repressor and cro. J. Mol. Biol. 139, 163–194 (1980).

    Article  CAS  Google Scholar 

  16. Lutz, R. & Bujard, H. Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1–I2 regulatory elements. Nucleic Acids Res. 25, 1203–1210 (1997).

    Article  CAS  Google Scholar 

  17. 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 

  18. Mangan, S. et al. The incoherent feed-forward loop accelerates the response-time of the gal system of Escherichia coli. J. Mol. Biol. 356, 1073–1081 (2006).

    Article  CAS  Google Scholar 

  19. Kaplan, S. et al. The incoherent feed-forward loop can generate non-monotonic input functions for genes. Mol. Syst. Biol. 4, 203 (2008).

    Article  Google Scholar 

  20. Semsey, S. et al. Signal integration in the galactose network of Escherichia coli. Mol. Microbiol. 65, 465–476 (2007).

    Article  CAS  Google Scholar 

  21. Cox, C.D. et al. Using noise to probe and characterize gene circuits. Proc. Natl. Acad. Sci. USA 105, 10809–10814 (2008).

    Article  CAS  Google Scholar 

  22. Blake, W.J. et al. Phenotypic consequences of promoter-mediated transcriptional noise. Mol. Cell 24, 853–865 (2006).

    Article  CAS  Google Scholar 

  23. Maamar, H., Raj, A. & Dubnau, D. Noise in gene expression determines cell fate in Bacillus subtilis. Science 317, 526–529 (2007).

    Article  CAS  Google Scholar 

  24. Arkin, A., Ross, J. & McAdams, H.H. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Tsang, J. & van Oudenaarden, A. Exciting fluctuations: monitoring competence induction dynamics at the single-cell level. Mol. Syst. Biol. 2, 2006.0025 (2006).

    Article  Google Scholar 

  26. Megason, S.G. & Fraser, S.E. Imaging in systems biology. Cell 130, 784–795 (2007).

    Article  CAS  Google Scholar 

  27. Yu, D. et al. An efficient recombination system for chromosome engineering in Escherichia coli. Proc. Natl. Acad. Sci. USA 97, 5978–5983 (2000).

    Article  CAS  Google Scholar 

  28. Zaslaver, A. et al. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat. Methods 3, 623–628 (2006).

    Article  CAS  Google Scholar 

  29. Datsenko, K.A. & Wanner, B.L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640–6645 (2000).

    Article  CAS  Google Scholar 

  30. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

    Article  Google Scholar 

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We thank M. Fontes, F. Tan, L. Cai, E. Franco, and all members of the Elowitz and Murray groups for their feedback and suggestions. H. Garcia provided advice on the chromosomal integration and gene knockout experiments. We thank J. Garcia-Ojalvo, U. Alon, R. Kishony, N. Rosenfeld and B. Shraiman for discussions. M.J.D. and R.M.M. are supported by the Institute for Collaborative Biotechnologies through grant DAAD19-03-D-0004 from the US Army Research Office. M.J.D. was additionally supported by a Department of Energy Computational Science Graduate Fellowship. This research was supported by US National Institutes of Health grants R01GM079771, P50 GM068763, National Science Foundation CAREER Award 0644463 and the Packard Foundation.

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Correspondence to Michael B Elowitz.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 and Supplementary Note (PDF 157 kb)

Supplementary Movie 1

Movie of YFP (false colored in green) and RFP (red) for the chromosomally integrated synthetic circuit. Frames are spaced at a 10 minute interval. Selected frames from this movie are shown in Fig. 3B of the text. (AVI 264 kb)

Supplementary Movie 2

Movie of YFP and CFP for the chromosomally integrated synthetic circuit. (AVI 265 kb)

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Dunlop, M., Cox, R., Levine, J. et al. Regulatory activity revealed by dynamic correlations in gene expression noise. Nat Genet 40, 1493–1498 (2008).

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