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Influence of Complex Promoter Structure on Gene Expression

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Abstract

A gene is often regulated by a variety of transcription factors, leading to complex promoter structure. However, how this structure affects gene expression remains elusive. Here, this paper studies a stochastic gene model with the promoter containing arbitrarily many active and inactive states. First, the authors use the binomial moment method to derive analytical steady-state distributions of the mRNA and protein numbers. Then, the authors analytically investigate how the promoter structure impacts the mean expression levels and the expression noise. Third, numerical simulation finds interesting phenomena, e.g., the common on-off model overestimates the expression noise in contrast to multiple-state models; the multi-on mechanism can reduce the expression noise more than the multi-off mechanism if the mean expression level is kept the same; and multiple exits of transcription can result in multimodal distributions.

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References

  1. Sanchez A, Garcia H G, Jones D, et al., Effect of promoter architecture on the cell-to-cell variability in gene expression, PLoS Comput. Biol., 2011, 7: e1001100.

    Google Scholar 

  2. Pedraza J M and Paulsson J, Effects of molecular memory and bursting on fluctuations in gene expression, Science, 2008, 319: 339–343.

    Article  Google Scholar 

  3. Boeger H, Griesenbeck J, and Kornberg R D, Nucleosome retention and the stochastic nature of promoter chromatin remodeling for transcription, Cell, 2008, 133: 716–726.

    Article  Google Scholar 

  4. Larson D R, What do expression dynamics tell us about the mechanism of transcription? Curr. Opin. Genet. Dev., 2011, 21: 591–599.

    Article  Google Scholar 

  5. Blake W J, Kærn M, Cantor C R, et al., Noise in eukaryotic gene expression, Nature, 2003, 422: 633–637.

    Article  Google Scholar 

  6. Raser J and O’Shea E, Control of stochasticity in eukaryotic gene expression, Science, 2003, 304: 1811–1814.

    Article  Google Scholar 

  7. Chubb J R, Trcek T, Shenoy S M, et al., Transcriptional pulsing of a developmental gene, Curr. Biol., 2006, 16: 1018–1025.

    Article  Google Scholar 

  8. Sanchez A and Kondev J, Transcriptional control of noise in gene expression, Proc. Natl. Acad. Sci., 2008, 105: 5081–5086.

    Article  Google Scholar 

  9. Mariani L, Schulz E G, Lexberg M H, et al., Short-term memory in gene induction reveals the regulatory principle behind stochastic IL-4 expression, Mol. Syst. Biol., 2010, 6: 359–372.

    Article  Google Scholar 

  10. Qiu H H and Zhou T S, Exact distributions in the full models of gene expression, Sci. Sin. Math., 2017, 47: 1863–1878 (in Chinese).

    Article  Google Scholar 

  11. Eldar A and Elowitz M B, Functional roles for noise in genetic circuits, Nature, 2010, 467: 167–173.

    Article  Google Scholar 

  12. Yu J, Xiao J, Ren X J, et al., Probing gene expression in live cells, one protein molecule at a time, Science, 2006, 311: 1600–1603.

    Article  Google Scholar 

  13. Cai L, Friedman N, and Xie X S, Stochastic protein expression in individual cells at the single molecule level, Nature, 2006, 440: 358–362.

    Article  Google Scholar 

  14. Golding I, Paulsson J, Zawilski S M, et al., Real-time kinetics of gene activity in individual bacteria, Cell, 2005, 123: 1025–1036.

    Article  Google Scholar 

  15. Raj A, Peskin C S, Tranchina D, et al., Stochastic mRNA synthesis in mammalian cells, PLoS Biol., 2006, 4: e309.

    Google Scholar 

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

    Article  Google Scholar 

  17. Suter D M, Molina N, Gatfield D, et al., Mammalian genes are transcribed with widely different bursting kinetics, Science, 2011, 332: 472–474.

    Article  Google Scholar 

  18. Harper C V, Finkenstädt B, Woodcock D J, et al., Dynamic analysis of stochastic transcription cycles, PLoS Biol., 2011, 9: e1000607.

    Google Scholar 

  19. Dobrzynski M and Bruggeman F J, Elongation dynamics shape bursty transcription and translation, Proc. Natl. Acad. Sci., 2009, 106: 2583–2588.

    Article  Google Scholar 

  20. Rinott R, Jaimovich A, and Friedman N, Exploring transcription regulation through cell-to-cell variability, Proc. Natl. Acad. Sci., 2011, 108: 6329–6334.

    Article  Google Scholar 

  21. Huh D and Paulsson J, Non-genetic heterogeneity from stochastic partitioning at cell division, Nat. Genet., 2011, 43: 95–100.

    Article  Google Scholar 

  22. Bintu L, Buchler N E, and Garcia H G, Transcriptional regulation by the numbers: Models, Curr. Opin. Genet. Dev., 2005, 15: 116–124.

    Article  Google Scholar 

  23. Coulon A, Gandrillon O, and Beslon G, On the spontaneous stochastic dynamics of a single gene: Complexity of the molecular interplay at the promoter, BMC Syst. Biol., 2010, 4(1): 2.

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Höfer T and Rasch M J, On the kinetic design of transcription, Genome Inform., 2005, 16: 73–82.

    Google Scholar 

  26. Carey L B, van Dijk D, and Sloot P M, Promoter sequence determines the relationship between expression level andnoise, PLoS Biol., 2013, 11: e1001528.

    Google Scholar 

  27. Brown C R, Mao C, and Falkovskaia E, Linking stochastic fluctuations in chromatin structure and gene expression, PLoS Biol., 2013, 11: e1001621.

    Google Scholar 

  28. Zhou T S, Advance research of gene expression systems: Probability distribution, J. Jiangxi Norm. Uni. (Nat. Sci. Edit.), 2012, 36(3): 221–229.

    Google Scholar 

  29. Peccoud J and Ycart B, Markovian modelling of gene product synthesis, Theor. Popul. Biol., 1995, 48: 222–234.

    Article  MATH  Google Scholar 

  30. Kepler T B and Elston T C, Stochasticity in transcriptional regulation: Origins, consequences, and mathematical representations, Biophys. J., 2001, 81: 3116–3036.

    Article  Google Scholar 

  31. Paulsson J, Models of stochastic gene expression, Phys. Life Rev., 2005, 2: 157–175.

    Article  Google Scholar 

  32. Shahrezaei V and Swain P S, Analytical distributions for stochastic gene expression, Proc. Natl. Acad. Sci., 2008, 105: 17256–17261.

    Article  Google Scholar 

  33. Karmakar R and Bose I, Graded and binary responses in stochastic gene expression, Phys. Biol., 2004, 1: 197–204.

    Article  Google Scholar 

  34. Iyer-Biswas S, Hayot F, and Jayaprakash C, Stochasticity of gene products from transcriptional pulsing, Phys. Rev. E, 2009, 79: 031911.

    Article  Google Scholar 

  35. Mugler A, Walczak A M, and Wiggins C H, Spectral solutions to stochastic models of gene expression with bursts and regulation, Phys. Rev. E, 2009, 80: 041921.

    Article  Google Scholar 

  36. Zhang J J, Chen L N, and Zhou T S, Analytical distribution and tunability of noise in a model of promoter progress, Biophys. J., 2012, 102: 1247–1257.

    Article  Google Scholar 

  37. Zhou T S and Zhang J J, Analytical results for a multistate gene model, SIAM J. Appl. Math., 2012, 72: 789–818.

    Article  MathSciNet  MATH  Google Scholar 

  38. Sanchez A, Choubey S, and Kondev J, Stochastic models of transcription: From single molecules to single cells, Methods, 2013, 62(1): 13–25.

    Article  Google Scholar 

  39. To T L and Maheshri N, Noise can induce bimodality in positive transcriptional feedback loops without bistability, Science, 2010, 327: 1142–1145.

    Article  Google Scholar 

  40. Blake W J, Balázsi G, and Kohanski M A, Phenotypic consequences of promoter-mediated transcriptional noise, Mol. Cell., 2006, 6: 853–865.

    Article  Google Scholar 

  41. Zhang J J, Nie Q, and Zhou T S, A moment-convergence method for stochastic analysis of biochemical reaction networks, J. Chem. Phys., 2016, 144(19): 018620.

    Google Scholar 

  42. Zhou T S, Binomial moments and attribute factors for biochemical reaction systems, J. Jiangxi Norm. Uni. (Nat. Sci. Edit.), 2016, 40(1): 1–4.

    Google Scholar 

  43. Gillespie D T, Exact stochastic simulation of coupled chemical reactions, J. Phys. Chem., 1977, 81: 2340–2361.

    Article  Google Scholar 

  44. Zhang J J and Zhou T S, Promoter architecture-mediated transcriptional dynamics, Biophys. J., 2014, 106: 479–488.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mathematics and Computers, Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, 430200, China

    Huahai Qiu, Bengong Zhang & Tianshou Zhou

Authors
  1. Huahai Qiu
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  2. Bengong Zhang
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  3. Tianshou Zhou
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Corresponding authors

Correspondence to Bengong Zhang or Tianshou Zhou.

Additional information

This research was supported by Science and Technology Department under Grant No. 2014CB964703, the Natural Science Foundation under Grant Nos. 91530320, 11401448, 61573011, the Hubei Province Education Department under Grant No. B2016062, and the Science and Technology Department of Hubei Province under Grant Nos. 2017CFB682 and 2018CFB688.

This paper was recommended for publication by Editor SUN Jian.

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Qiu, H., Zhang, B. & Zhou, T. Influence of Complex Promoter Structure on Gene Expression. J Syst Sci Complex 32, 600–614 (2019). https://doi.org/10.1007/s11424-018-7224-7

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  • DOI: https://doi.org/10.1007/s11424-018-7224-7

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