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Processivity and Coupling in Messenger RNA Transcription

Author

Listed:
  • Stuart Aitken
  • Marie-Cécile Robert
  • Ross D Alexander
  • Igor Goryanin
  • Edouard Bertrand
  • Jean D Beggs

Abstract

Background: The complexity of messenger RNA processing is now being uncovered by experimental techniques that are capable of detecting individual copies of mRNA in cells, and by quantitative real-time observations that reveal the kinetics. This processing is commonly modelled by permitting mRNA to be transcribed only when the promoter is in the on state. In this simple on/off model, the many processes involved in active transcription are represented by a single reaction. These processes include elongation, which has a minimum time for completion and processing that is not captured in the model. Methodology: In this paper, we explore the impact on the mRNA distribution of representing the elongation process in more detail. Consideration of the mechanisms of elongation leads to two alternative models of the coupling between the elongating polymerase and the state of the promoter: Processivity allows polymerases to complete elongation irrespective of the promoter state, whereas coupling requires the promoter to be active to produce a full-length transcript. We demonstrate that these alternatives have a significant impact on the predicted distributions. Models are simulated by the Gillespie algorithm, and the third and fourth moments of the resulting distribution are computed in order to characterise the length of the tail, and sharpness of the peak. By this methodology, we show that the moments provide a concise summary of the distribution, showing statistically-significant differences across much of the feasible parameter range. Conclusions: We conclude that processivity is not fully consistent with the on/off model unless the probability of successfully completing elongation is low—as has been observed. The results also suggest that some form of coupling between the promoter and a rate-limiting step in transcription may explain the cell's inability to maintain high mRNA levels at low noise—a prediction of the on/off model that has no supporting evidence.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0008845
    DOI: 10.1371/journal.pone.0008845
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    References listed on IDEAS

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    1. 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.
    2. Dmitri Volfson & Jennifer Marciniak & William J. Blake & Natalie Ostroff & Lev S. Tsimring & Jeff Hasty, 2006. "Origins of extrinsic variability in eukaryotic gene expression," Nature, Nature, vol. 439(7078), pages 861-864, February.
    3. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
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