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Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression

Overview of attention for article published in PLoS Computational Biology, July 2013
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Title
Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression
Published in
PLoS Computational Biology, July 2013
DOI 10.1371/journal.pcbi.1003161
Pubmed ID
Authors

C. J. Zopf, Katie Quinn, Joshua Zeidman, Narendra Maheshri

Abstract

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.

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Mendeley readers

The data shown below were compiled from readership statistics for 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 3%
United Kingdom 5 2%
Switzerland 2 <1%
Netherlands 2 <1%
Spain 2 <1%
Portugal 2 <1%
Germany 1 <1%
France 1 <1%
Denmark 1 <1%
Other 0 0%
Unknown 219 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 80 33%
Researcher 53 22%
Student > Master 30 12%
Student > Bachelor 15 6%
Professor 14 6%
Other 33 14%
Unknown 18 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 46%
Biochemistry, Genetics and Molecular Biology 53 22%
Physics and Astronomy 17 7%
Engineering 9 4%
Computer Science 6 2%
Other 20 8%
Unknown 26 11%