Literature DB >> 22444387

Compartment modeling for mammalian protein turnover studies by stable isotope metabolic labeling.

Shenheng Guan1, John C Price, Sina Ghaemmaghami, Stanley B Prusiner, Alma L Burlingame.   

Abstract

Protein turnover studies on a proteome scale based on metabolic isotopic labeling can provide a systematic understanding of mechanisms for regulation of protein abundances and their transient behaviors. At this time, these large-scale studies typically utilize a simple kinetic model to extract protein dynamic information. Although many high-quality, protein isotope incorporation data are available from those experiments, accurate and additionally useful protein dynamic information cannot be extracted from the experimental data by use of the simple kinetic models. In this paper, we describe a formal connection between data obtained from elemental isotope labeling experiments and the well-known compartment modeling, and we demonstrate that an appropriate application of a compartment model to turnover of proteins from mammalian tissues can indeed lead to a better fitting of the experimental data.

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Year:  2012        PMID: 22444387      PMCID: PMC3923578          DOI: 10.1021/ac203330z

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  8 in total

1.  Dynamics of protein turnover, a missing dimension in proteomics.

Authors:  Julie M Pratt; June Petty; Isabel Riba-Garcia; Duncan H L Robertson; Simon J Gaskell; Stephen G Oliver; Robert J Beynon
Journal:  Mol Cell Proteomics       Date:  2002-08       Impact factor: 5.911

Review 2.  De novo generation of prion strains.

Authors:  David W Colby; Stanley B Prusiner
Journal:  Nat Rev Microbiol       Date:  2011-09-26       Impact factor: 60.633

3.  Analysis of proteome dynamics in the mouse brain.

Authors:  John C Price; Shenheng Guan; Alma Burlingame; Stanley B Prusiner; Sina Ghaemmaghami
Journal:  Proc Natl Acad Sci U S A       Date:  2010-08-10       Impact factor: 11.205

4.  Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions.

Authors:  M W Senko; S C Beu; F W McLaffertycor
Journal:  J Am Soc Mass Spectrom       Date:  1995-04       Impact factor: 3.109

5.  Protein turnover methods in single-celled organisms: dynamic SILAC.

Authors:  Amy J Claydon; Robert J Beynon
Journal:  Methods Mol Biol       Date:  2011

6.  A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling.

Authors:  Shenheng Guan; John C Price; Stanley B Prusiner; Sina Ghaemmaghami; Alma L Burlingame
Journal:  Mol Cell Proteomics       Date:  2011-09-21       Impact factor: 5.911

7.  Measurement of protein turnover in rat liver. Analysis of the complex curve for decay of label in a mixture of proteins.

Authors:  P J Garlick; J C Waterlow; R W Swick
Journal:  Biochem J       Date:  1976-06-15       Impact factor: 3.857

8.  Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC.

Authors:  Mary K Doherty; Dean E Hammond; Michael J Clague; Simon J Gaskell; Robert J Beynon
Journal:  J Proteome Res       Date:  2009-01       Impact factor: 4.466

  8 in total
  16 in total

Review 1.  Proteome dynamics: revisiting turnover with a global perspective.

Authors:  Amy J Claydon; Robert Beynon
Journal:  Mol Cell Proteomics       Date:  2012-11-02       Impact factor: 5.911

Review 2.  Mitochondrial protein turnover: methods to measure turnover rates on a large scale.

Authors:  X'avia C Y Chan; Caitlin M Black; Amanda J Lin; Peipei Ping; Edward Lau
Journal:  J Mol Cell Cardiol       Date:  2014-11-11       Impact factor: 5.000

3.  A Novel Quantitative Mass Spectrometry Platform for Determining Protein O-GlcNAcylation Dynamics.

Authors:  Xiaoshi Wang; Zuo-Fei Yuan; Jing Fan; Kelly R Karch; Lauren E Ball; John M Denu; Benjamin A Garcia
Journal:  Mol Cell Proteomics       Date:  2016-04-25       Impact factor: 5.911

4.  Gaussian Process Modeling of Protein Turnover.

Authors:  Mahbubur Rahman; Stephen F Previs; Takhar Kasumov; Rovshan G Sadygov
Journal:  J Proteome Res       Date:  2016-06-09       Impact factor: 4.466

5.  Proteome Scale-Protein Turnover Analysis Using High Resolution Mass Spectrometric Data from Stable-Isotope Labeled Plants.

Authors:  Kai-Ting Fan; Aaron K Rendahl; Wen-Ping Chen; Dana M Freund; William M Gray; Jerry D Cohen; Adrian D Hegeman
Journal:  J Proteome Res       Date:  2016-01-29       Impact factor: 4.466

6.  A mass spectrometry workflow for measuring protein turnover rates in vivo.

Authors:  Mihai Alevra; Sunit Mandad; Till Ischebeck; Henning Urlaub; Silvio O Rizzoli; Eugenio F Fornasiero
Journal:  Nat Protoc       Date:  2019-11-04       Impact factor: 13.491

7.  Protein turnover models for LC-MS data of heavy water metabolic labeling.

Authors:  Rovshan G Sadygov
Journal:  Brief Bioinform       Date:  2022-03-10       Impact factor: 11.622

8.  JUMPt: Comprehensive Protein Turnover Modeling of In Vivo Pulse SILAC Data by Ordinary Differential Equations.

Authors:  Surendhar Reddy Chepyala; Xueyan Liu; Ka Yang; Zhiping Wu; Alex M Breuer; Ji-Hoon Cho; Yuxin Li; Ariana Mancieri; Yun Jiao; Hui Zhang; Junmin Peng
Journal:  Anal Chem       Date:  2021-09-29       Impact factor: 6.986

Review 9.  Using stable isotope labeling to advance our understanding of Alzheimer's disease etiology and pathology.

Authors:  Timothy J Hark; Jeffrey N Savas
Journal:  J Neurochem       Date:  2021-02-02       Impact factor: 5.546

10.  Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS.

Authors:  Vugar R Sadygov; William Zhang; Rovshan G Sadygov
Journal:  J Proteome Res       Date:  2020-04-02       Impact factor: 4.466

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