Literature DB >> 22114196

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range.

Ty M Thomson1, Kirsten R Benjamin, Alan Bush, Tonya Love, David Pincus, Orna Resnekov, Richard C Yu, Andrew Gordon, Alejandro Colman-Lerner, Drew Endy, Roger Brent.   

Abstract

Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.

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Year:  2011        PMID: 22114196      PMCID: PMC3250143          DOI: 10.1073/pnas.1004042108

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  42 in total

1.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles.

Authors:  C J Roberts; B Nelson; M J Marton; R Stoughton; M R Meyer; H A Bennett; Y D He; H Dai; W L Walker; T R Hughes; M Tyers; C Boone; S H Friend
Journal:  Science       Date:  2000-02-04       Impact factor: 47.728

2.  Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties.

Authors:  A Levchenko; J Bruck; P W Sternberg
Journal:  Proc Natl Acad Sci U S A       Date:  2000-05-23       Impact factor: 11.205

3.  Multiplex detection and quantitation of proteins on western blots using fluorescent probes.

Authors:  J C Gingrich; D R Davis; Q Nguyen
Journal:  Biotechniques       Date:  2000-09       Impact factor: 1.993

4.  Pheromone induction promotes Ste11 degradation through a MAPK feedback and ubiquitin-dependent mechanism.

Authors:  R K Esch; B Errede
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-20       Impact factor: 11.205

5.  A quantitative characterization of the yeast heterotrimeric G protein cycle.

Authors:  Tau-Mu Yi; Hiroaki Kitano; Melvin I Simon
Journal:  Proc Natl Acad Sci U S A       Date:  2003-09-05       Impact factor: 11.205

Review 6.  Regulation of G protein-initiated signal transduction in yeast: paradigms and principles.

Authors:  H G Dohlman; J W Thorner
Journal:  Annu Rev Biochem       Date:  2001       Impact factor: 23.643

7.  Nuclear-specific degradation of Far1 is controlled by the localization of the F-box protein Cdc4.

Authors:  M Blondel; J M Galan; Y Chi; C Lafourcade; C Longaretti; R J Deshaies; M Peter
Journal:  EMBO J       Date:  2000-11-15       Impact factor: 11.598

8.  Reagents for investigating MAPK signalling in model yeast species.

Authors:  David Pincus; Kirsten Benjamin; Ian Burbulis; Annie E Tsong; Orna Resnekov
Journal:  Yeast       Date:  2010-07       Impact factor: 3.239

9.  Global analysis of protein expression in yeast.

Authors:  Sina Ghaemmaghami; Won-Ki Huh; Kiowa Bower; Russell W Howson; Archana Belle; Noah Dephoure; Erin K O'Shea; Jonathan S Weissman
Journal:  Nature       Date:  2003-10-16       Impact factor: 49.962

10.  The yeast G protein alpha subunit Gpa1 transmits a signal through an RNA binding effector protein Scp160.

Authors:  Ming Guo; Christopher Aston; Scott A Burchett; Christine Dyke; Stanley Fields; S Johannes R Rajarao; Peter Uetz; Yuqi Wang; Kathleen Young; Henrik G Dohlman
Journal:  Mol Cell       Date:  2003-08       Impact factor: 17.970

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  33 in total

1.  Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories.

Authors:  Rory M Donovan; Andrew J Sedgewick; James R Faeder; Daniel M Zuckerman
Journal:  J Chem Phys       Date:  2013-09-21       Impact factor: 3.488

Review 2.  Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.

Authors:  Lily A Chylek; Leonard A Harris; Chang-Shung Tung; James R Faeder; Carlos F Lopez; William S Hlavacek
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2013-09-30

3.  Quantitative measurement of protein relocalization in live cells.

Authors:  Alan Bush; Alejandro Colman-Lerner
Journal:  Biophys J       Date:  2013-02-05       Impact factor: 4.033

4.  A quantitative framework for the forward design of synthetic miRNA circuits.

Authors:  Ryan J Bloom; Sally M Winkler; Christina D Smolke
Journal:  Nat Methods       Date:  2014-09-14       Impact factor: 28.547

5.  An Analytical Framework for Studying Small-Number Effects in Catalytic Reaction Networks: A Probability Generating Function Approach to Chemical Master Equations.

Authors:  Masaki Nakagawa; Yuichi Togashi
Journal:  Front Physiol       Date:  2016-03-24       Impact factor: 4.566

6.  Push-Pull and Feedback Mechanisms Can Align Signaling System Outputs with Inputs.

Authors:  Steven S Andrews; William J Peria; Richard C Yu; Alejandro Colman-Lerner; Roger Brent
Journal:  Cell Syst       Date:  2016-10-27       Impact factor: 10.304

7.  Compartmentalization of a bistable switch enables memory to cross a feedback-driven transition.

Authors:  Andreas Doncic; Oguzhan Atay; Ervin Valk; Alicia Grande; Alan Bush; Gustavo Vasen; Alejandro Colman-Lerner; Mart Loog; Jan M Skotheim
Journal:  Cell       Date:  2015-03-12       Impact factor: 41.582

8.  Detailed simulations of cell biology with Smoldyn 2.1.

Authors:  Steven S Andrews; Nathan J Addy; Roger Brent; Adam P Arkin
Journal:  PLoS Comput Biol       Date:  2010-03-12       Impact factor: 4.475

9.  Single-cell dynamics and variability of MAPK activity in a yeast differentiation pathway.

Authors:  Patrick Conlon; Rita Gelin-Licht; Ambhighainath Ganesan; Jin Zhang; Andre Levchenko
Journal:  Proc Natl Acad Sci U S A       Date:  2016-09-20       Impact factor: 11.205

10.  Preserving Single Cells in Space and Time for Analytical Assays.

Authors:  Luke A Gallion; Matthew M Anttila; David H Abraham; Angela Proctor; Nancy L Allbritton
Journal:  Trends Analyt Chem       Date:  2019-11-07       Impact factor: 12.296

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