Literature DB >> 21314131

Proteome scale turnover analysis in live animals using stable isotope metabolic labeling.

Yaoyang Zhang1, Stefan Reckow, Christian Webhofer, Michael Boehme, Philipp Gormanns, Wolfgang M Egge-Jacobsen, Christoph W Turck.   

Abstract

At present most quantitative proteomics investigations are focused on the analysis of protein expression differences between two or more sample specimens. With each analysis a static snapshot of a cellular state is captured with regard to protein expression. However, any information on protein turnover cannot be obtained using classic methodologies. Protein turnover, the result of protein synthesis and degradation, represents a dynamic process, which is of equal importance to understanding physiological processes. Methods employing isotopic tracers have been developed to measure protein turnover. However, applying these methods to live animals is often complicated by the fact that an assessment of precursor pool relative isotope abundance is required. Also, data analysis becomes difficult in case of low label incorporation, which results in a complex convolution of labeled and unlabeled peptide mass spectrometry signals. Here we present a protein turnover analysis method that circumvents this problem using a (15)N-labeled diet as an isotopic tracer. Mice were fed with the labeled diet for limited time periods and the resulting partially labeled proteins digested and subjected to tandem mass spectrometry. For the interpretation of the mass spectrometry data, we have developed the ProTurnyzer software that allows the determination of protein fractional synthesis rates without the need of precursor relative isotope abundance information. We present results validating ProTurnyzer with Escherichia coli protein data and apply the method to mouse brain and plasma proteomes for automated turnover studies.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21314131     DOI: 10.1021/ac102755n

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


  28 in total

1.  Protein turnover quantification in a multilabeling approach: from data calculation to evaluation.

Authors:  Christian Trötschel; Stefan P Albaum; Daniel Wolff; Simon Schröder; Alexander Goesmann; Tim W Nattkemper; Ansgar Poetsch
Journal:  Mol Cell Proteomics       Date:  2012-04-06       Impact factor: 5.911

2.  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

3.  Topograph, a software platform for precursor enrichment corrected global protein turnover measurements.

Authors:  Edward J Hsieh; Nicholas J Shulman; Dao-Fu Dai; Evelyn S Vincow; Pabalu P Karunadharma; Leo Pallanck; Peter S Rabinovitch; Michael J MacCoss
Journal:  Mol Cell Proteomics       Date:  2012-08-03       Impact factor: 5.911

Review 4.  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 5.  Protein analysis by shotgun/bottom-up proteomics.

Authors:  Yaoyang Zhang; Bryan R Fonslow; Bing Shan; Moon-Chang Baek; John R Yates
Journal:  Chem Rev       Date:  2013-02-26       Impact factor: 60.622

6.  Monitoring newly synthesized proteins over the adult life span of Caenorhabditis elegans.

Authors:  Krishna Vukoti; Xiaokun Yu; Quanhu Sheng; Sudipto Saha; Zhaoyang Feng; Ao-Lin Hsu; Masaru Miyagi
Journal:  J Proteome Res       Date:  2015-02-25       Impact factor: 4.466

Review 7.  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

8.  In Vivo Protein Lifetime Measurements Across Multiple Organs in the Zebrafish.

Authors:  Sunit Mandad; Gudrun Kracht; Eugenio F Fornasiero
Journal:  Methods Mol Biol       Date:  2021

9.  Measurement of apo(a) kinetics in human subjects using a microfluidic device with tandem mass spectrometry.

Authors:  Haihong Zhou; Jose Castro-Perez; Michael E Lassman; Tiffany Thomas; Wenyu Li; Theresa McLaughlin; Xie Dan; Patricia Jumes; John A Wagner; David E Gutstein; Brian K Hubbard; Daniel J Rader; John S Millar; Henry N Ginsberg; Gissette Reyes-Soffer; Michele Cleary; Stephen F Previs; Thomas P Roddy
Journal:  Rapid Commun Mass Spectrom       Date:  2013-06-30       Impact factor: 2.419

10.  Proteins with high turnover rate in barley leaves estimated by proteome analysis combined with in planta isotope labeling.

Authors:  Clark J Nelson; Ralitza Alexova; Richard P Jacoby; A Harvey Millar
Journal:  Plant Physiol       Date:  2014-07-31       Impact factor: 8.340

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.