Dynamical effects of transcriptional pause-prone sites

Ribeiro, André S.; Häkkinen, Antti; Healy, Shannon; Yli‐Harja, Olli

doi:10.1016/j.compbiolchem.2010.04.003

Cited by 14 publications

(19 citation statements)

References 18 publications

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“…The effects of several pause sites on the same strain are cumulative, namely, the higher the number of pause sites, the higher the noise in RNA levels [32]. Combined with the present results, this leads us to the conclusion that the sequence-dependent transcriptional pausing mechanism likely exists to allow a wide variation of both RNA and protein noise levels.…”

Section: Resultssupporting

confidence: 71%

Stochastic sequence-level model of coupled transcription and translation in prokaryotes

et al. 2011

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BackgroundIn prokaryotes, transcription and translation are dynamically coupled, as the latter starts before the former is complete. Also, from one transcript, several translation events occur in parallel. To study how events in transcription elongation affect translation elongation and fluctuations in protein levels, we propose a delayed stochastic model of prokaryotic transcription and translation at the nucleotide and codon level that includes the promoter open complex formation and alternative pathways to elongation, namely pausing, arrests, editing, pyrophosphorolysis, RNA polymerase traffic, and premature termination. Stepwise translation can start after the ribosome binding site is formed and accounts for variable codon translation rates, ribosome traffic, back-translocation, drop-off, and trans-translation.ResultsFirst, we show that the model accurately matches measurements of sequence-dependent translation elongation dynamics. Next, we characterize the degree of coupling between fluctuations in RNA and protein levels, and its dependence on the rates of transcription and translation initiation. Finally, modeling sequence-specific transcriptional pauses, we find that these affect protein noise levels.ConclusionsFor parameter values within realistic intervals, transcription and translation are found to be tightly coupled in Escherichia coli, as the noise in protein levels is mostly determined by the underlying noise in RNA levels. Sequence-dependent events in transcription elongation, e.g. pauses, are found to cause tangible effects in the degree of fluctuations in protein levels.

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Section: Resultssupporting

confidence: 71%

Stochastic sequence-level model of coupled transcription and translation in prokaryotes

et al. 2011

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“…Transcriptional bursting – Dynamics and frequency of the pre-initiation complex (PIC) assembly at a promoter site can be perturbed in many ways, thereby causing bursts in transcription initiation [112–114]. In eukaryotes, this particularly depends on:

TATA-box – DNA motif with affinity for the TATA-box binding protein (TBP), found in a large number of gene promoters.

…”

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confidence: 99%

“…Transcriptional pausing – The presence of polymerase pause-sites can fine-tune noise, by either stalling polymerases or causing premature termination [112]. …”

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confidence: 99%

Interplay between gene expression noise and regulatory network architecture

et al. 2012

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Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate, or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of, or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.

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“…It is unknown to what extent this noise is evolvable. One mechanism that likely contributes to transcriptional noise in prokaryotes is RNA polymerase (RNAP) pausing during elongation [1,2]. Pausing enhances the propensity for collisions between consecutive RNAPs in the template [3] and, in some cases, of premature terminations [4], particularly when hairpin loops form in the transcript, facilitating the recruitment of Rho-factor, a protein that dissociates the RNA from the DNA template and RNA polymerase [5].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, it was suggested that pauses affect transcriptional noise [1]. Also, the location of the pause-prone sequence, the duration, and the proneness for pausing influence the extent to which the pause affects the kinetics of RNA production [2]. These effects on RNA numbers may be of relevance in prokaryotes, particularly because RNAs usually exist in very small amounts (from one to a few molecules) [13,14].…”

Section: Introductionmentioning

confidence: 99%

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

2012

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BackgroundEvidence suggests that in prokaryotes sequence-dependent transcriptional pauses affect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identified from in vitro measurements of transcription elongation kinetics.ResultsUsing a stochastic model of gene expression at the nucleotide and codon levels with realistic parameter values, we investigate three different but related questions and present statistical methods for their analysis. First, we show that information from in vivo RNA and protein temporal numbers is sufficient to discriminate between models with and without a pause site in their coding sequence. Second, we demonstrate that it is possible to separate a large variety of models from each other with pauses of various durations and locations in the template by means of a hierarchical clustering and a random forest classifier. Third, we introduce an approximate likelihood function that allows to estimate the location of a pause site.ConclusionsThis method can aid in detecting unknown pause-prone sequences from temporal measurements of RNA and protein numbers at a genome-wide scale and thus elucidate possible roles that these sequences play in the dynamics of genetic networks and phenotype.

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Dynamical effects of transcriptional pause-prone sites

Cited by 14 publications

References 18 publications

Stochastic sequence-level model of coupled transcription and translation in prokaryotes

Stochastic sequence-level model of coupled transcription and translation in prokaryotes

Interplay between gene expression noise and regulatory network architecture

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

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