IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1001136.html
   My bibliography  Save this article

Transcriptional Regulation: Effects of Promoter Proximal Pausing on Speed, Synchrony and Reliability

Author

Listed:
  • Alistair N Boettiger
  • Peter L Ralph
  • Steven N Evans

Abstract

Recent whole genome polymerase binding assays in the Drosophila embryo have shown that a substantial proportion of uninduced genes have pre-assembled RNA polymerase-II transcription initiation complex (PIC) bound to their promoters. These constitute a subset of promoter proximally paused genes for which mRNA elongation instead of promoter access is regulated. This difference can be described as a rearrangement of the regulatory topology to control the downstream transcriptional process of elongation rather than the upstream transcriptional initiation event. It has been shown experimentally that genes with the former mode of regulation tend to induce faster and more synchronously, and that promoter-proximal pausing is observed mainly in metazoans, in accord with a posited impact on synchrony. However, it has not been shown whether or not it is the change in the regulated step per se that is causal. We investigate this question by proposing and analyzing a continuous-time Markov chain model of PIC assembly regulated at one of two steps: initial polymerase association with DNA, or release from a paused, transcribing state. Our analysis demonstrates that, over a wide range of physical parameters, increased speed and synchrony are functional consequences of elongation control. Further, we make new predictions about the effect of elongation regulation on the consistent control of total transcript number between cells. We also identify which elements in the transcription induction pathway are most sensitive to molecular noise and thus possibly the most evolutionarily constrained. Our methods produce symbolic expressions for quantities of interest with reasonable computational effort and they can be used to explore the interplay between interaction topology and molecular noise in a broader class of biochemical networks. We provide general-purpose code implementing these methods.Author Summary: Gene activation is an inherently random process because numerous diffusing proteins and DNA must first interact by random association before transcription can begin. For many genes the necessary protein–DNA associations only begin after activation, but it has recently been noted that a large class of genes in multicellular organisms can assemble the initiation complex of proteins on the core promoter prior to activation. For these genes, activation merely releases polymerase from the preassembled complex to transcribe the gene. It has been proposed on the basis of experiments that such a mechanism, while possibly costly, increases both the speed and the synchrony of the process of gene transcription. We study a realistic model of gene transcription, and show that this conclusion holds for all but a tiny fraction of the space of physical rate parameters that govern the process. The improved control of cell-to-cell variations afforded by regulation through a paused polymerase may help multicellular organisms achieve the high degree of coordination required for development. Our approach has also generated tools with which one can study the effects of analogous changes in other molecular networks and determine the relative importance of various molecular binding rates to particular system properties.

Suggested Citation

  • Alistair N Boettiger & Peter L Ralph & Steven N Evans, 2011. "Transcriptional Regulation: Effects of Promoter Proximal Pausing on Speed, Synchrony and Reliability," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
  • Handle: RePEc:plo:pcbi00:1001136
    DOI: 10.1371/journal.pcbi.1001136
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001136
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1001136&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1001136?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nicholas J. Fuda & M. Behfar Ardehali & John T. Lis, 2009. "Defining mechanisms that regulate RNA polymerase II transcription in vivo," Nature, Nature, vol. 461(7261), pages 186-192, September.
    2. Arjun Raj & Charles S Peskin & Daniel Tranchina & Diana Y Vargas & Sanjay Tyagi, 2006. "Stochastic mRNA Synthesis in Mammalian Cells," PLOS Biology, Public Library of Science, vol. 4(10), pages 1-13, September.
    3. Fitzsimmons, P. J. & Pitman, Jim, 1999. "Kac's moment formula and the Feynman-Kac formula for additive functionals of a Markov process," Stochastic Processes and their Applications, Elsevier, vol. 79(1), pages 117-134, January.
    4. Mark Ptashne & Alexander Gann, 1997. "Transcriptional activation by recruitment," Nature, Nature, vol. 386(6625), pages 569-577, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jonathan Liu & Donald Hansen & Elizabeth Eck & Yang Joon Kim & Meghan Turner & Simon Alamos & Hernan Garcia, 2021. "Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonathan Liu & Donald Hansen & Elizabeth Eck & Yang Joon Kim & Meghan Turner & Simon Alamos & Hernan Garcia, 2021. "Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-26, May.
    2. Mohammad Soltani & Cesar A Vargas-Garcia & Duarte Antunes & Abhyudai Singh, 2016. "Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-23, August.
    3. Till D Frank & Aimée M Carmody & Boris N Kholodenko, 2012. "Versatility of Cooperative Transcriptional Activation: A Thermodynamical Modeling Analysis for Greater-Than-Additive and Less-Than-Additive Effects," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-15, April.
    4. Dell'Era Mario, M.D., 2008. "Pricing of the European Options by Spectral Theory," MPRA Paper 17429, University Library of Munich, Germany.
    5. Amy L. Hughes & Aleksander T. Szczurek & Jessica R. Kelley & Anna Lastuvkova & Anne H. Turberfield & Emilia Dimitrova & Neil P. Blackledge & Robert J. Klose, 2023. "A CpG island-encoded mechanism protects genes from premature transcription termination," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    6. Matthieu Wyart & David Botstein & Ned S Wingreen, 2010. "Evaluating Gene Expression Dynamics Using Pairwise RNA FISH Data," PLOS Computational Biology, Public Library of Science, vol. 6(11), pages 1-14, November.
    7. Qiwen Sun & Zhaohang Cai & Chunjuan Zhu, 2022. "A Novel Dynamical Regulation of mRNA Distribution by Cross-Talking Pathways," Mathematics, MDPI, vol. 10(9), pages 1-14, May.
    8. Stuart Aitken & Marie-Cécile Robert & Ross D Alexander & Igor Goryanin & Edouard Bertrand & Jean D Beggs, 2010. "Processivity and Coupling in Messenger RNA Transcription," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-12, January.
    9. Masaaki Fukasawa, 2010. "Asymptotic analysis for stochastic volatility: Edgeworth expansion," Papers 1004.2106, arXiv.org.
    10. Singh, Abhyudai & Vahdat, Zahra & Xu, Zikai, 2019. "Time-triggered stochastic hybrid systems with two timer-dependent resets," OSF Preprints u8fzg, Center for Open Science.
    11. Zhdanov, Vladimir P., 2011. "Periodic perturbation of the bistable kinetics of gene expression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 57-64.
    12. Krishanpal Anamika & Àkos Gyenis & Laetitia Poidevin & Olivier Poch & Làszlò Tora, 2012. "RNA Polymerase II Pausing Downstream of Core Histone Genes Is Different from Genes Producing Polyadenylated Transcripts," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    13. Muir Morrison & Manuel Razo-Mejia & Rob Phillips, 2021. "Reconciling kinetic and thermodynamic models of bacterial transcription," PLOS Computational Biology, Public Library of Science, vol. 17(1), pages 1-30, January.
    14. Lala M Motlhabi & Gary D Stormo, 2011. "Assessing the Effects of Symmetry on Motif Discovery and Modeling," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-8, September.
    15. Michael E Wall & David A Markowitz & Judah L Rosner & Robert G Martin, 2009. "Model of Transcriptional Activation by MarA in Escherichia coli," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, December.
    16. Masaaki Fukasawa, 2010. "Central limit theorem for the realized volatility based on tick time sampling," Finance and Stochastics, Springer, vol. 14(2), pages 209-233, April.
    17. Elijah Roberts & Andrew Magis & Julio O Ortiz & Wolfgang Baumeister & Zaida Luthey-Schulten, 2011. "Noise Contributions in an Inducible Genetic Switch: A Whole-Cell Simulation Study," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-21, March.
    18. Jackson Loper, 2020. "Uniform Ergodicity for Brownian Motion in a Bounded Convex Set," Journal of Theoretical Probability, Springer, vol. 33(1), pages 22-35, March.
    19. Ross D. Jones & Yili Qian & Katherine Ilia & Benjamin Wang & Michael T. Laub & Domitilla Del Vecchio & Ron Weiss, 2022. "Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    20. Marc S Sherman & Barak A Cohen, 2014. "A Computational Framework for Analyzing Stochasticity in Gene Expression," PLOS Computational Biology, Public Library of Science, vol. 10(5), pages 1-13, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1001136. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.