Literature DB >> 15627957

Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates.

Mary K Doherty1, Colin Whitehead, Heather McCormack, Simon J Gaskell, Robert J Beynon.   

Abstract

The complete definition of changes in a proteome requires information about dynamics and specifically the rate at which the individual proteins are turned over intracellularly. Whilst this can be achieved in single-cell culture using stable isotope precursors, it is more challenging to develop methods for intact animals. In this study, we show how dietary administration of stable isotope-labelled amino acids can obtain information on the relative rates of synthesis and degradation of individual proteins in a proteome. The pattern of stable isotope-labelling in tryptic peptides can be deconstructed to yield a highly reliable measure of the isotope abundance of the precursor pool, a parameter that is often difficult to acquire. We demonstrate this approach using chickens fed a semisynthetic diet containing [(2)H(8)]valine at a calculated relative isotope abundance (RIA) of 0.5. When the labelling pattern of gel-resolved muscle proteins was analyzed, the intracellular precursor isotope abundance was 0.35, consistent with dilution of the amino acid precursor pool with unlabelled amino acids derived from degradation of pre-existing proteins. However, the RIA was stable over an extended labelling window, and permitted calculation of the rates of synthesis and degradation of individual proteins isolated by gel electrophoresis. For the first time, it is feasible to contemplate the analysis of turnover of individual proteins in intact animals.

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Year:  2005        PMID: 15627957     DOI: 10.1002/pmic.200400959

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  50 in total

1.  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

2.  Quantitative analysis of isotope distributions in proteomic mass spectrometry using least-squares Fourier transform convolution.

Authors:  Edit Sperling; Anne E Bunner; Michael T Sykes; James R Williamson
Journal:  Anal Chem       Date:  2008-06-04       Impact factor: 6.986

3.  Quantitative proteomics by metabolic labeling of model organisms.

Authors:  Joost W Gouw; Jeroen Krijgsveld; Albert J R Heck
Journal:  Mol Cell Proteomics       Date:  2009-11-19       Impact factor: 5.911

Review 4.  Quantitative proteomic analysis of histone modifications.

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Journal:  Chem Rev       Date:  2015-02-17       Impact factor: 60.622

5.  Quantitative proteomics: measuring protein synthesis using 15N amino acid labeling in pancreatic cancer cells.

Authors:  Yingchun Zhao; Wai-Nang Paul Lee; Shu Lim; Vay Liang Go; Jing Xiao; Rui Cao; Hengwei Zhang; Robert Roy Recker; Gary Guishan Xiao
Journal:  Anal Chem       Date:  2009-01-15       Impact factor: 6.986

6.  Measuring the dynamics of E. coli ribosome biogenesis using pulse-labeling and quantitative mass spectrometry.

Authors:  Stephen S Chen; Edit Sperling; Josh M Silverman; Joseph H Davis; James R Williamson
Journal:  Mol Biosyst       Date:  2012-10-30

7.  Quantitative proteomic analysis of primary neurons reveals diverse changes in synaptic protein content in fmr1 knockout mice.

Authors:  Lujian Liao; Sung Kyu Park; Tao Xu; Peter Vanderklish; John R Yates
Journal:  Proc Natl Acad Sci U S A       Date:  2008-09-30       Impact factor: 11.205

8.  Quantitative proteomic profiling of host-pathogen interactions: the macrophage response to Mycobacterium tuberculosis lipids.

Authors:  Wenqing Shui; Sarah A Gilmore; Leslie Sheu; Jun Liu; Jay D Keasling; Carolyn R Bertozzi
Journal:  J Proteome Res       Date:  2009-01       Impact factor: 4.466

9.  Proteomic profiling of glucocorticoid-exposed myogenic cells: Time series assessment of protein translocation and transcription of inactive mRNAs.

Authors:  Erica K M Reeves; Heather Gordish-Dressman; Eric P Hoffman; Yetrib Hathout
Journal:  Proteome Sci       Date:  2009-07-30       Impact factor: 2.480

10.  Envelope: interactive software for modeling and fitting complex isotope distributions.

Authors:  Michael T Sykes; James R Williamson
Journal:  BMC Bioinformatics       Date:  2008-10-20       Impact factor: 3.169

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