Literature DB >> 14512430

Quantitative analysis of GAL genetic switch of Saccharomyces cerevisiae reveals that nucleocytoplasmic shuttling of Gal80p results in a highly sensitive response to galactose.

Malkhey Verma1, Paike Jayadeva Bhat, K V Venkatesh.   

Abstract

The nucleocytoplasmic shuttling of the repressor Gal80p is known to play a pivotal role in the signal transduction process of GAL genetic switch of Saccharomyces cerevisiae (Peng, G., and Hopper, J. E. (2002) Proc. Natl. Acad. Sci. U. S. A. 99, 8548-8553). We have developed a comprehensive model of this GAL switch to quantify the expression from the GAL promoter containing one or two Gal4p-binding sites and to understand the biological significance of the shuttling process. Our experiments show that the expression of proteins from the GAL promoter containing one and two binding sites for Gal4p is ultrasensitive (a steep response to a given input). Furthermore, the model revealed that the shuttling of Gal80p is the key step in imparting ultrasensitive response to the inducer. During induction, free Gal80p concentration is altered by sequestration, without any change in the distribution coefficient across the nuclear membrane. Furthermore, the estimated concentrations of Gal80p and Gal3p allow basal expression of alpha-galactosidase, but not beta-galactosidase, from the GAL promoter containing one and two binding sites for Gal4p, respectively. Conversely, the expression from genes with two binding sites is more sensitive to inducer concentration as compared with one binding site. We show that autoregulation of Gal80p is coincidental to the autoregulation of Gal3p, and it does not impart ultrasensitivity. We conclude from our analysis that the ultrasensitivity of the GAL genetic switch is solely because of the shuttling phenomena of the repressor Gal80p across the nuclear membrane.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 14512430     DOI: 10.1074/jbc.M303526200

Source DB:  PubMed          Journal:  J Biol Chem        ISSN: 0021-9258            Impact factor:   5.157


  13 in total

1.  Multiple feedback loop design in the tryptophan regulatory network of Escherichia coli suggests a paradigm for robust regulation of processes in series.

Authors:  Sharad Bhartiya; Nikhil Chaudhary; K V Venkatesh; Francis J Doyle
Journal:  J R Soc Interface       Date:  2006-06-22       Impact factor: 4.118

2.  Derivation, identification and validation of a computational model of a novel synthetic regulatory network in yeast.

Authors:  Lucia Marucci; Stefania Santini; Mario di Bernardo; Diego di Bernardo
Journal:  J Math Biol       Date:  2010-06-12       Impact factor: 2.259

3.  Replacement of a conserved tyrosine by tryptophan in Gal3p of Saccharomyces cerevisiae reduces constitutive activity: implications for signal transduction in the GAL regulon.

Authors:  Anirudha Lakshminarasimhan; Paike Jayadeva Bhat
Journal:  Mol Genet Genomics       Date:  2005-09-14       Impact factor: 3.291

4.  Dynamic analysis of the KlGAL regulatory system in Kluyveromyces lactis: a comparative study with Saccharomyces cerevisiae.

Authors:  Venkat Reddy Pannala; K Y Ahammed Sherief; Sharad Bhartiya; K V Venkatesh
Journal:  Syst Synth Biol       Date:  2011-06-03

5.  Steady-state analysis of glucose repression reveals hierarchical expression of proteins under Mig1p control in Saccharomyces cerevisiae.

Authors:  Malkhey Verma; Paike J Bhat; K V Venkatesh
Journal:  Biochem J       Date:  2005-06-15       Impact factor: 3.857

Review 6.  Epigenetics of the yeast galactose genetic switch.

Authors:  Paike Jayadeva Bhat; Revathi S Iyer
Journal:  J Biosci       Date:  2009-10       Impact factor: 1.826

7.  Timing and Variability of Galactose Metabolic Gene Activation Depend on the Rate of Environmental Change.

Authors:  Truong D Nguyen-Huu; Chinmaya Gupta; Bo Ma; William Ott; Krešimir Josić; Matthew R Bennett
Journal:  PLoS Comput Biol       Date:  2015-07-22       Impact factor: 4.475

8.  Stochastic analysis of the GAL genetic switch in Saccharomyces cerevisiae: modeling and experiments reveal hierarchy in glucose repression.

Authors:  Vinay Prasad; K V Venkatesh
Journal:  BMC Syst Biol       Date:  2008-11-17

9.  Gene network requirements for regulation of metabolic gene expression to a desired state.

Authors:  Jan Berkhout; Bas Teusink; Frank J Bruggeman
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

10.  Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology.

Authors:  David A Ball; Matthew W Lux; Neil R Adames; Jean Peccoud
Journal:  PLoS One       Date:  2014-09-11       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.