Literature DB >> 19167290

Translational repression contributes greater noise to gene expression than transcriptional repression.

Michał Komorowski1, Jacek Miekisz, Andrzej M Kierzek.   

Abstract

Stochastic effects in gene expression may result in different physiological states of individual cells, with consequences for pathogen survival and artificial gene network design. We studied the contributions of a regulatory factor to gene expression noise in four basic mechanisms of negative gene expression control: 1), transcriptional regulation by a protein repressor, 2), translational repression by a protein; 3), transcriptional repression by RNA; and 4), RNA interference with the translation. We investigated a general model of a two-gene network, using the chemical master equation and a moment generating function approach. We compared the expression noise of genes with the same effective transcription and translation initiation rates resulting from the action of different repressors, whereas previous studies compared the noise of genes with the same mean expression level but different initiation rates. Our results show that translational repression results in a higher noise than repression on the promoter level, and that this relationship does not depend on quantitative parameter values. We also show that regulation of protein degradation contributes more noise than regulated degradation of mRNA. These are unexpected results, because previous investigations suggested that translational regulation is more accurate. The relative magnitude of the noise introduced by protein and RNA repressors depends on the protein and mRNA degradation rates, and we derived expressions for the threshold below which the noise introduced by a protein repressor is higher than the noise introduced by an RNA repressor.

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Year:  2009        PMID: 19167290      PMCID: PMC2716471          DOI: 10.1016/j.bpj.2008.09.052

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  37 in total

1.  Engineering stability in gene networks by autoregulation.

Authors:  A Becskei; L Serrano
Journal:  Nature       Date:  2000-06-01       Impact factor: 49.962

2.  The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression.

Authors:  A M Kierzek; J Zaim; P Zielenkiewicz
Journal:  J Biol Chem       Date:  2000-11-02       Impact factor: 5.157

3.  Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

Authors:  T B Kepler; T C Elston
Journal:  Biophys J       Date:  2001-12       Impact factor: 4.033

4.  Intrinsic noise in gene regulatory networks.

Authors:  M Thattai; A van Oudenaarden
Journal:  Proc Natl Acad Sci U S A       Date:  2001-07-03       Impact factor: 11.205

5.  Attenuation of noise in ultrasensitive signaling cascades.

Authors:  Mukund Thattai; Alexander van Oudenaarden
Journal:  Biophys J       Date:  2002-06       Impact factor: 4.033

6.  Regulation of noise in the expression of a single gene.

Authors:  Ertugrul M Ozbudak; Mukund Thattai; Iren Kurtser; Alan D Grossman; Alexander van Oudenaarden
Journal:  Nat Genet       Date:  2002-04-22       Impact factor: 38.330

7.  Noise in eukaryotic gene expression.

Authors:  William J Blake; Mads KAErn; Charles R Cantor; J J Collins
Journal:  Nature       Date:  2003-04-10       Impact factor: 49.962

8.  Summing up the noise in gene networks.

Authors:  Johan Paulsson
Journal:  Nature       Date:  2004-01-29       Impact factor: 49.962

9.  Stochastic simulations of the repressilator circuit.

Authors:  Adiel Loinger; Ofer Biham
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2007-11-21

Review 10.  Regulation of mRNA translation by 5'- and 3'-UTR-binding factors.

Authors:  Gavin S Wilkie; Kirsten S Dickson; Nicola K Gray
Journal:  Trends Biochem Sci       Date:  2003-04       Impact factor: 13.807

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  15 in total

1.  Using a single fluorescent reporter gene to infer half-life of extrinsic noise and other parameters of gene expression.

Authors:  Michał Komorowski; Bärbel Finkenstädt; David Rand
Journal:  Biophys J       Date:  2010-06-16       Impact factor: 4.033

2.  Quantifying negative feedback regulation by micro-RNAs.

Authors:  Shangying Wang; Sridhar Raghavachari
Journal:  Phys Biol       Date:  2011-08-10       Impact factor: 2.583

Review 3.  Autoregulatory systems controlling translation factor expression: thermostat-like control of translational accuracy.

Authors:  Russell Betney; Eric de Silva; Jawahar Krishnan; Ian Stansfield
Journal:  RNA       Date:  2010-02-25       Impact factor: 4.942

4.  Gene expression in self-repressing system with multiple gene copies.

Authors:  Jacek Miekisz; Paulina Szymańska
Journal:  Bull Math Biol       Date:  2013-01-25       Impact factor: 1.758

5.  Toggle switch: noise determines the winning gene.

Authors:  Joanna Jaruszewicz; Tomasz Lipniacki
Journal:  Phys Biol       Date:  2013-06-04       Impact factor: 2.583

6.  Gene autoregulation via intronic microRNAs and its functions.

Authors:  Carla Bosia; Matteo Osella; Mariama El Baroudi; Davide Corà; Michele Caselle
Journal:  BMC Syst Biol       Date:  2012-10-10

7.  The role of incoherent microRNA-mediated feedforward loops in noise buffering.

Authors:  Matteo Osella; Carla Bosia; Davide Corá; Michele Caselle
Journal:  PLoS Comput Biol       Date:  2011-03-10       Impact factor: 4.475

8.  Sequence-based analysis uncovers an abundance of non-coding RNA in the total transcriptome of Mycobacterium tuberculosis.

Authors:  Kristine B Arnvig; Iñaki Comas; Nicholas R Thomson; Joanna Houghton; Helena I Boshoff; Nicholas J Croucher; Graham Rose; Timothy T Perkins; Julian Parkhill; Gordon Dougan; Douglas B Young
Journal:  PLoS Pathog       Date:  2011-11-03       Impact factor: 6.823

9.  Bayesian inference of biochemical kinetic parameters using the linear noise approximation.

Authors:  Michał Komorowski; Bärbel Finkenstädt; Claire V Harper; David A Rand
Journal:  BMC Bioinformatics       Date:  2009-10-19       Impact factor: 3.169

10.  Revealing of Mycobacterium marinum transcriptome by RNA-seq.

Authors:  Sen Wang; Xinran Dong; Yongqiang Zhu; Chuan Wang; Gang Sun; Tao Luo; Weidong Tian; Huajun Zheng; Qian Gao
Journal:  PLoS One       Date:  2013-09-30       Impact factor: 3.240

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