Literature DB >> 23009859

Consequences of mRNA transport on stochastic variability in protein levels.

Abhyudai Singh1, Pavol Bokes.   

Abstract

Homogeneous cell populations can exhibit considerable cell-to-cell variability in protein levels arising from the stochastic nature of the gene-expression process. In particular, transcriptional bursting of mRNAs from the promoter has been implicated as a major source of stochasticity in the expression of many genes. In eukaryotes, transcribed pre-mRNAs have to be exported outside the nucleus and in many cases, export rates can be slow and comparable to mRNA turnover rates. We investigate whether such export processes can be effective mechanisms in buffering protein levels from transcriptional bursting of pre-mRNAs in the nucleus. For a stochastic gene-expression model with both transcriptional bursting and export, we derive an exact solution of the steady-state probability-generating function for both the nuclear and the cytoplasmic mRNA levels. These formulas reveal that decreasing export rates can dramatically reduce variability in cytoplasmic mRNA levels. However, our results also show that decreasing export rates enhance mRNA autocorrelation times, which function to increase heterogeneity in protein levels. Our overall analysis concludes that under physiologically relevant parameter regimes, a pre-mRNA export step can decrease steady-state variability at the mRNA level but not at the protein level. Finally, we reinforce previous observations that saturation in the pre-mRNA transport machinery can be an important mechanism in suppressing protein variability from underlying transcriptional bursts.
Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Mesh:

Substances:

Year:  2012        PMID: 23009859      PMCID: PMC3433621          DOI: 10.1016/j.bpj.2012.07.015

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


  47 in total

1.  Linking stochastic dynamics to population distribution: an analytical framework of gene expression.

Authors:  Nir Friedman; Long Cai; X Sunney Xie
Journal:  Phys Rev Lett       Date:  2006-10-19       Impact factor: 9.161

2.  Noise in protein expression scales with natural protein abundance.

Authors:  Arren Bar-Even; Johan Paulsson; Narendra Maheshri; Miri Carmi; Erin O'Shea; Yitzhak Pilpel; Naama Barkai
Journal:  Nat Genet       Date:  2006-05-21       Impact factor: 38.330

3.  Increased cell-to-cell variation in gene expression in ageing mouse heart.

Authors:  Rumana Bahar; Claudia H Hartmann; Karl A Rodriguez; Ashley D Denny; Rita A Busuttil; Martijn E T Dollé; R Brent Calder; Gary B Chisholm; Brad H Pollock; Christoph A Klein; Jan Vijg
Journal:  Nature       Date:  2006-06-22       Impact factor: 49.962

Review 4.  Network motifs: theory and experimental approaches.

Authors:  Uri Alon
Journal:  Nat Rev Genet       Date:  2007-06       Impact factor: 53.242

5.  Modeling stochastic gene expression under repression.

Authors:  G C P Innocentini; J E M Hornos
Journal:  J Math Biol       Date:  2007-05-22       Impact factor: 2.259

6.  Transient-mediated fate determination in a transcriptional circuit of HIV.

Authors:  Leor S Weinberger; Roy D Dar; Michael L Simpson
Journal:  Nat Genet       Date:  2008-03-16       Impact factor: 38.330

7.  Effects of molecular memory and bursting on fluctuations in gene expression.

Authors:  Juan M Pedraza; Johan Paulsson
Journal:  Science       Date:  2008-01-18       Impact factor: 47.728

Review 8.  Stochasticity and cell fate.

Authors:  Richard Losick; Claude Desplan
Journal:  Science       Date:  2008-04-04       Impact factor: 47.728

Review 9.  Nature, nurture, or chance: stochastic gene expression and its consequences.

Authors:  Arjun Raj; Alexander van Oudenaarden
Journal:  Cell       Date:  2008-10-17       Impact factor: 41.582

10.  Transcriptome-wide noise controls lineage choice in mammalian progenitor cells.

Authors:  Hannah H Chang; Martin Hemberg; Mauricio Barahona; Donald E Ingber; Sui Huang
Journal:  Nature       Date:  2008-05-22       Impact factor: 49.962

View more
  16 in total

1.  Macromolecular crowding as a regulator of gene transcription.

Authors:  Hiroaki Matsuda; Gregory Garbès Putzel; Vadim Backman; Igal Szleifer
Journal:  Biophys J       Date:  2014-04-15       Impact factor: 4.033

2.  Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells.

Authors:  Sandeep Choubey; Jane Kondev; Alvaro Sanchez
Journal:  Biophys J       Date:  2018-05-08       Impact factor: 4.033

3.  Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase.

Authors:  Md Zulfikar Ali; Sandeep Choubey; Dipjyoti Das; Robert C Brewster
Journal:  Biophys J       Date:  2020-02-12       Impact factor: 4.033

4.  Modeling bursty transcription and splicing with the chemical master equation.

Authors:  Gennady Gorin; Lior Pachter
Journal:  Biophys J       Date:  2022-02-07       Impact factor: 4.033

5.  Pathway dynamics can delineate the sources of transcriptional noise in gene expression.

Authors:  Lucy Ham; Marcel Jackson; Michael Ph Stumpf
Journal:  Elife       Date:  2021-10-12       Impact factor: 8.140

6.  RNA velocity unraveled.

Authors:  Gennady Gorin; Meichen Fang; Tara Chari; Lior Pachter
Journal:  PLoS Comput Biol       Date:  2022-09-12       Impact factor: 4.779

Review 7.  Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method.

Authors:  Kevin A Janes
Journal:  Curr Opin Biotechnol       Date:  2016-04-01       Impact factor: 9.740

8.  Post-Transcriptional Noise Control.

Authors:  Maike M K Hansen; Leor S Weinberger
Journal:  Bioessays       Date:  2019-06-21       Impact factor: 4.345

9.  Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability.

Authors:  Maike M K Hansen; Ravi V Desai; Michael L Simpson; Leor S Weinberger
Journal:  Cell Syst       Date:  2018-09-19       Impact factor: 10.304

10.  Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Authors:  Sandeep Choubey; Jane Kondev; Alvaro Sanchez
Journal:  PLoS Comput Biol       Date:  2015-11-06       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.