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General properties of transcriptional time series in Escherichia coli

Abstract

Gene activity is described by the time series of discrete, stochastic mRNA production events. This transcriptional time series shows intermittent, bursty behavior. One consequence of this temporal intricacy is that gene expression can be tuned by varying different features of the time series. Here we quantify copy-number statistics of mRNA from 20 Escherichia coli promoters using single-molecule fluorescence in situ hybridization in order to characterize the general properties of these transcriptional time series. We find that the degree of burstiness is correlated with gene expression level but is largely independent of other parameters of gene regulation. The observed behavior can be explained by the underlying variation in the duration of bursting events. Using Shannon's mutual information function, we estimate the mutual information transmitted between an outside stimulus, such as the extracellular concentration of inducer molecules, and intracellular levels of mRNA. This suggests that the outside stimulus transmits information reflected in the properties of transcriptional time series.

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Figure 1: Different features of the transcriptional time series can be modulated to vary gene expression level.
Figure 2: Single-molecule FISH (smFISH) used to characterize mRNA copy-number statistics.
Figure 3: Gene expression level in E. coli is varied by changing the gene off rate.
Figure 4: The transcriptional time series optimizes information representation by the cell.

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References

  1. Golding, I. & Cox, E.C. Eukaryotic transcription: what does it mean for a gene to be 'on'? Curr. Biol. 16, R371–R373 (2006).

    Article  CAS  Google Scholar 

  2. Golding, I., Paulsson, J., Zawilski, S.M. & Cox, E.C. Real-time kinetics of gene activity in individual bacteria. Cell 123, 1025–1036 (2005).

    Article  CAS  Google Scholar 

  3. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    Article  CAS  Google Scholar 

  4. Chubb, J.R. & Liverpool, T.B. Bursts and pulses: insights from single cell studies into transcriptional mechanisms. Curr. Opin. Genet. Dev. 20, 478–484 (2010).

    Article  CAS  Google Scholar 

  5. Chubb, J.R., Trcek, T., Shenoy, S.M. & Singer, R.H. Transcriptional pulsing of a developmental gene. Curr. Biol. 16, 1018–1025 (2006).

    Article  CAS  Google Scholar 

  6. Paré, A. et al. Visualization of individual Scr mRNAs during Drosophila embryogenesis yields evidence for transcriptional bursting. Curr. Biol. 19, 2037–2042 (2009).

    Article  Google Scholar 

  7. Yunger, S., Rosenfeld, L., Garini, Y. & Shav-Tal, Y. Single-allele analysis of transcription kinetics in living mammalian cells. Nat. Methods 7, 631–633 (2010).

    Article  CAS  Google Scholar 

  8. 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 

  9. Peccoud, J. & Ycart, B. Markovian modeling of gene-product synthesis. Theor. Popul. Biol. 48, 222–234 (1995).

    Article  Google Scholar 

  10. Shahrezaei, V. & Swain, P.S. Analytical distributions for stochastic gene expression. Proc. Natl. Acad. Sci. USA 105, 17256–17261 (2008).

    Article  CAS  Google Scholar 

  11. 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 

  12. Tan, R.Z. & van Oudenaarden, A. Transcript counting in single cells reveals dynamics of rDNA transcription. Mol. Syst. Biol. 6, 358 (2010).

    Article  Google Scholar 

  13. Mitarai, N., Dodd, I.B., Crooks, M.T. & Sneppen, K. The generation of promoter-mediated transcriptional noise in bacteria. PLOS Comput. Biol. 4, e1000109 (2008).

    Article  Google Scholar 

  14. Dobrzynski, M. & Bruggeman, F.J. Elongation dynamics shape bursty transcription and translation. Proc. Natl. Acad. Sci. USA 106, 2583–2588 (2009).

    Article  CAS  Google Scholar 

  15. van Zon, J.S., Morelli, M.J., Tanase-Nicola, S. & ten Wolde, P.R. Diffusion of transcription factors can drastically enhance the noise in gene expression. Biophys. J. 91, 4350–4367 (2006).

    Article  CAS  Google Scholar 

  16. Fang, F.C. Sigma cascades in prokaryotic regulatory networks. Proc. Natl. Acad. Sci. USA 102, 4933–4934 (2005).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  18. Bernstein, J.A., Khodursky, A.B., Lin, P.H., Lin-Chao, S. & Cohen, S.N. Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc. Natl. Acad. Sci. USA 99, 9697–9702 (2002).

    Article  CAS  Google Scholar 

  19. Selinger, D.W., Saxena, R.M., Cheung, K.J., Church, G.M. & Rosenow, C. Global RNA half-life analysis in Escherichia coli reveals positional patterns of transcript degradation. Genome Res. 13, 216–223 (2003).

    Article  CAS  Google Scholar 

  20. Singh, D. et al. Regulation of ribonuclease E activity by the L4 ribosomal protein of Escherichia coli. Proc. Natl. Acad. Sci. USA 106, 864–869 (2009).

    Article  CAS  Google Scholar 

  21. Thattai, M. & van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. USA 98, 8614–8619 (2001).

    Article  CAS  Google Scholar 

  22. Goh, K.I. & Barabasi, A. Burstiness and memory in complex systems. Epl 81, 48002 (2008).

    Article  Google Scholar 

  23. Kepler, T.B. & Elston, T.C. Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys. J. 81, 3116–3136 (2001).

    Article  CAS  Google Scholar 

  24. Simpson, M.L., Cox, C.D. & Sayler, G.S. Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation. J. Theor. Biol. 229, 383–394 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. 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 

  27. Kuhlman, T., Zhang, Z., Saier, M.H. Jr. & Hwa, T. Combinatorial transcriptional control of the lactose operon of Escherichia coli. Proc. Natl. Acad. Sci. USA 104, 6043–6048 (2007).

    Article  CAS  Google Scholar 

  28. Paulsson, J. & Ehrenberg, M. Random signal fluctuations can reduce random fluctuations in regulated components of chemical regulatory networks. Phys. Rev. Lett. 84, 5447–5450 (2000).

    Article  CAS  Google Scholar 

  29. Tokeson, J.P., Garges, S. & Adhya, S. Further inducibility of a constitutive system: ultrainduction of the gal operon. J. Bacteriol. 173, 2319–2327 (1991).

    Article  CAS  Google Scholar 

  30. Weickert, M.J. & Adhya, S. The galactose regulon of Escherichia coli. Mol. Microbiol. 10, 245–251 (1993).

    Article  CAS  Google Scholar 

  31. Alekshun, M.N. & Levy, S.B. The mar regulon: multiple resistance to antibiotics and other toxic chemicals. Trends Microbiol. 7, 410–413 (1999).

    Article  CAS  Google Scholar 

  32. Hernandez, V.J. & Bremer, H. Guanosine tetraphosphate (ppGpp) dependence of the growth rate control of rrnB P1 promoter activity in Escherichia coli. J. Biol. Chem. 265, 11605–11614 (1990).

    CAS  PubMed  Google Scholar 

  33. Potrykus, K. et al. Antagonistic regulation of Escherichia coli ribosomal RNA rrnB P1 promoter activity by GreA and DksA. J. Biol. Chem. 281, 15238–15248 (2006).

    Article  CAS  Google Scholar 

  34. Abdel-Hamid, A.M. & Cronan, J.E. Coordinate expression of the acetyl coenzyme A carboxylase genes, accB and accC, is necessary for normal regulation of biotin synthesis in Escherichia coli. J. Bacteriol. 189, 369–376 (2007).

    Article  CAS  Google Scholar 

  35. Barker, D.F. & Campbell, A.M. Use of bio-lac fusion strains to study regulation of biotin biosynthesis in Escherichia coli. J. Bacteriol. 143, 789–800 (1980).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Michalowski, C.B. & Little, J.W. Positive autoregulation of cI is a dispensable feature of the phage lambda gene regulatory circuitry. J. Bacteriol. 187, 6430–6442 (2005).

    Article  CAS  Google Scholar 

  37. Sauer, R.T., Jordan, S.R. & Pabo, C.O. Lambda repressor: a model system for understanding protein-DNA interactions and protein stability. Adv. Protein Chem. 40, 1–61 (1990).

    Article  CAS  Google Scholar 

  38. Lim, W.A. & Sauer, R.T. Alternative packing arrangements in the hydrophobic core of lambda repressor. Nature 339, 31–36 (1989).

    Article  CAS  Google Scholar 

  39. 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 

  40. Garcia, H.G., Sanchez, A., Kuhlman, T., Kondev, J. & Phillips, R. Transcription by the numbers redux: experiments and calculations that surprise. Trends Cell Biol. 20, 723–733 (2010).

    Article  CAS  Google Scholar 

  41. Kittisopikul, M. & Suel, G.M. Biological role of noise encoded in a genetic network motif. Proc. Natl. Acad. Sci. USA 107, 13300–13305 (2010).

    Article  CAS  Google Scholar 

  42. 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 

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

    Article  CAS  Google Scholar 

  44. Kittisopikul, M. & Suel, G.M. Biological role of noise encoded in a genetic network motif. Proc. Natl. Acad. Sci. USA 107, 13300–13305 (2010).

    Article  CAS  Google Scholar 

  45. Cox, C.D., McCollum, J.M., Allen, M.S., Dar, R.D. & Simpson, M.L. Using noise to probe and characterize gene circuits. Proc. Natl. Acad. Sci. USA 105, 10809–10814 (2008).

    Article  CAS  Google Scholar 

  46. 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 

  47. Cai, L., Friedman, N. & Xie, X.S. Stochastic protein expression in individual cells at the single molecule level. Nature 440, 358–362 (2006).

    Article  CAS  Google Scholar 

  48. Yu, J., Xiao, J., Ren, X., Lao, K. & Xie, X.S. Probing gene expression in live cells, one protein molecule at a time. Science 311, 1600–1603 (2006).

    Article  CAS  Google Scholar 

  49. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533–538 (2010).

    Article  CAS  Google Scholar 

  50. Kennell, D. & Riezman, H. Transcription and translation initiation frequencies of the Escherichia coli lac operon. J. Mol. Biol. 114, 1–21 (1977).

    Article  CAS  Google Scholar 

  51. Liang, S. et al. Activities of constitutive promoters in Escherichia coli. J. Mol. Biol. 292, 19–37 (1999).

    Article  CAS  Google Scholar 

  52. Neidhardt, F.C. Rererer. Escherichia coli and Salmonella typhimurium: Cellular and Molecular Biology (American Society for Microbiology, Washington, D.C., 1987).

  53. 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 

  54. Fusco, D. et al. Single mRNA molecules demonstrate probabilistic movement in living mammalian cells. Curr. Biol. 13, 161–167 (2003).

    Article  CAS  Google Scholar 

  55. Bertrand, E. et al. Localization of ASH1 mRNA particles in living yeast. Mol. Cell 2, 437–445 (1998).

    Article  CAS  Google Scholar 

  56. Golding, I. & Cox, E.C. RNA dynamics in live Escherichia coli cells. Proc. Natl. Acad. Sci. USA 101, 11310–11315 (2004).

    Article  CAS  Google Scholar 

  57. Alon, U. An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman & Hall/CRC, Boca Raton, Florida, USA, 2007).

  58. Tkacik, G., Callan, C.G. Jr. & Bialek, W. Information flow and optimization in transcriptional regulation. Proc. Natl. Acad. Sci. USA 105, 12265–12270 (2008).

    Article  CAS  Google Scholar 

  59. Tkacik, G., Walczak, A.M. & Bialek, W. Optimizing information flow in small genetic networks. Phys. Rev. E 80, 031920 (2009).

    Article  Google Scholar 

  60. Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423, 623–656 (1948).

    Article  Google Scholar 

  61. 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 

  62. Geva-Zatorsky, N. et al. Protein dynamics in drug combinations: a linear superposition of individual-drug responses. Cell 140, 643–651 (2010).

    Article  CAS  Google Scholar 

  63. Mao, C. et al. Quantitative analysis of the transcription control mechanism. Mol. Syst. Biol. 6, 431 (2010).

    Article  CAS  Google Scholar 

  64. Dekel, E. & Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature 436, 588–592 (2005).

    Article  CAS  Google Scholar 

  65. Eldar, A. & Elowitz, M.B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    Article  CAS  Google Scholar 

  66. Singh, A., Razooky, B., Cox, C.D., Simpson, M.L. & Weinberger, L.S. Transcriptional bursting from the HIV-1 promoter is a significant source of stochastic noise in HIV-1 gene expression. Biophys. J. 98, L32–L34 (2010).

    Article  CAS  Google Scholar 

  67. Kaufmann, B.B., Yang, Q., Mettetal, J.T. & van Oudenaarden, A. Heritable stochastic switching revealed by single-cell genealogy. PLoS Biol. 5, e239 (2007).

    Article  Google Scholar 

  68. Choi, P.J., Cai, L., Frieda, K. & Xie, X.S. A stochastic single-molecule event triggers phenotype switching of a bacterial cell. Science 322, 442–446 (2008).

    Article  CAS  Google Scholar 

  69. Zeng, L. et al. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141, 682–691 (2010).

    Article  CAS  Google Scholar 

  70. Zong, C., So, L.-h., Sepulveda, L.A., Skinner, S.O. & Golding, I. Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene. Mol. Syst. Biol. 6, 440 (2010).

    Article  Google Scholar 

  71. Austin, D.W. et al. Gene network shaping of inherent noise spectra. Nature 439, 608–611 (2006).

    Article  CAS  Google Scholar 

  72. Mooney, R.A. et al. Regulator trafficking on bacterial transcription units in vivo. Mol. Cell 33, 97–108 (2009).

    Article  CAS  Google Scholar 

  73. Reppas, N.B., Wade, J.T., Church, G.M. & Struhl, K. The transition between transcriptional initiation and elongation in E. coli is highly variable and often rate limiting. Mol. Cell 24, 747–757 (2006).

    Article  CAS  Google Scholar 

  74. Whitelaw, N.C., Chong, S. & Whitelaw, E. Tuning in to noise: epigenetics and intangible variation. Dev. Cell 19, 649–650 (2010).

    Article  CAS  Google Scholar 

  75. Gillespie, D.T. Exact stochastic simulation of coupled chemical-reactions. J. Phys. Chem. 81, 2340–2361 (1977).

    Article  CAS  Google Scholar 

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Acknowledgements

We are grateful to S. Adhya, W. Boos, M. Cashel, V. Chakravartty, L. Chubiz, D. Court, J. Cronan, M. Dreyfus, M. Elowitz, Y. Feng, H. Garcia, T. Hwa, J. Imlay, S. Jang, T. Kuhlman, J. Little, A. van Oudenaarden, A. Raj, C. Rao, R. Milo, V. Shahrezaei, A. Sokac and P. Swain for advice and for providing reagents. We thank members of the Golding laboratory for providing help with experiments. We thank H. Garcia, J. Kondev, R. Phillips, A. Raj, Á. Sánchez, S. Sawai and G. Tkacˇik for commenting on earlier versions of the manuscript. Work in the Golding laboratory was supported by grants from US National Institutes of Health (R01GM082837) and the National Science Foundation (082265, PFC: Center for the Physics of Living Cells). Work in the Segev and Golding laboratories was supported by a joint grant from the Human Frontier Science Program (RGY 70/2008).

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I.G., L.-h.S. and R.S. designed the project. L.-h.S. performed the majority of experiments and the theoretical analysis of gene activity. L.A.S. and C.Z. performed additional experiments and developed analysis tools for gene activity. R.S. and A.G. performed the information theory analysis. I.G., L.-h.S., L.A.S., R.S. and A.G. wrote the paper.

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Correspondence to Ido Golding.

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So, Lh., Ghosh, A., Zong, C. et al. General properties of transcriptional time series in Escherichia coli. Nat Genet 43, 554–560 (2011). https://doi.org/10.1038/ng.821

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