Note to users. If you're seeing this message, it means that your browser cannot find this page's style/presentation instructions -- or possibly that you are using a browser that does not support current Web standards. Find out more about why this message is appearing, and what you can do to make your experience of our site the best it can be.
Control of Stochasticity in Eukaryotic Gene Expression
Jonathan M. Raser and
Erin K. O'Shea*
Noise, or random fluctuations, in gene expression may producevariability in cellular behavior. To measure the noise intrinsicto eukaryotic gene expression, we quantified the differencesin expression of two alleles in a diploid cell. We found thatsuch noise is gene-specific and not dependent on the regulatorypathway or absolute rate of expression. We propose a model inwhich the balance between promoter activation and transcriptioninfluences the variability in messenger RNA levels. To confirmthe predictions of our model, we identified both cis- and trans-actingmutations that alter the noise of gene expression. These mutationssuggest that noise is an evolvable trait that can be optimizedto balance fidelity and diversity in eukaryotic gene expression.
Department of Biochemistry and Biophysics, Howard Hughes Medical Institute, University of CaliforniaSan Francisco (UCSF), 600 16th Street, Room S472D, San Francisco, CA 941432240, USA.
* To whom correspondence should be addressed. E-mail: oshea{at}biochem.ucsf.edu
ADD66, a Gene Involved in the Endoplasmic Reticulum-associated Degradation of {alpha}-1-Antitrypsin-Z in Yeast, Facilitates Proteasome Activity and Assembly.
C. M. Scott, K. B. Kruse, B. Z. Schmidt, D. H. Perlmutter, A. A. McCracken, and J. L. Brodsky (2007)
Mol. Biol. Cell
18, 3776-3787
|Abstract »|Full Text »|PDF »
Haploinsufficient Prostate Tumor Suppression by Nkx3.1: A ROLE FOR CHROMATIN ACCESSIBILITY IN DOSAGE-SENSITIVE GENE REGULATION.
A. P. Mogal, R. van der Meer, P. S. Crooke, and S. A. Abdulkadir (2007)
J. Biol. Chem.
282, 25790-25800
|Abstract »|Full Text »|PDF »
Chromosome-specific and noisy IFNB1 transcription in individual virus-infected human primary dendritic cells.
J. Hu, S. C. Sealfon, F. Hayot, C. Jayaprakash, M. Kumar, A. C. Pendleton, A. Ganee, A. Fernandez-Sesma, T. M. Moran, and J. G. Wetmur (2007)
Nucleic Acids Res.
|Abstract »|Full Text »|PDF »
Combinatorial promoter design for engineering noisy gene expression.
CD8 single-cell gene coexpression reveals three different effector types present at distinct phases of the immune response.
A. Peixoto, C. Evaristo, I. Munitic, M. Monteiro, A. Charbit, B. Rocha, and H. Veiga-Fernandes (2007)
J. Exp. Med.
204, 1193-1205
|Abstract »|Full Text »|PDF »
Non-monotonic dose-response relationship in steroid hormone receptor-mediated gene expression.
L. Li, M. E Andersen, S. Heber, and Q. Zhang (2007)
J. Mol. Endocrinol.
38, 569-585
|Abstract »|Full Text »|PDF »
Adaptive Divergence in Experimental Populations of Pseudomonas fluorescens. III. Mutational Origins of Wrinkly Spreader Diversity.
E. Bantinaki, R. Kassen, C. G. Knight, Z. Robinson, A. J. Spiers, and P. B. Rainey (2007)
Genetics
176, 441-453
|Abstract »|Full Text »|PDF »
Noisy information processing through transcriptional regulation.
Dynamic partitioning for hybrid simulation of the bistable HIV-1 transactivation network.
M. Griffith, T. Courtney, J. Peccoud, and W. H. Sanders (2006)
Bioinformatics
22, 2782-2789
|Abstract »|Full Text »|PDF »
Single-Cell Analysis of Glucocorticoid Receptor Action Reveals that Stochastic Post-Chromatin Association Mechanisms Regulate Ligand-Specific Transcription.
T. C. Voss, S. John, and G. L. Hager (2006)
Mol. Endocrinol.
20, 2641-2655
|Abstract »|Full Text »|PDF »
An Optimal Number of Molecules for Signal Amplification and Discrimination in a Chemical Cascade.
Y. Morishita, T. J. Kobayashi, and K. Aihara (2006)
Biophys. J.
91, 2072-2081
|Abstract »|Full Text »|PDF »
Predicting stochastic gene expression dynamics in single cells.
J. T. Mettetal, D. Muzzey, J. M. Pedraza, E. M. Ozbudak, and A. van Oudenaarden (2006)
PNAS
103, 7304-7309
|Abstract »|Full Text »|PDF »
Rules for biological regulation based on error minimization..