Literature DB >> 19955089

Quantitative proteomics by metabolic labeling of model organisms.

Joost W Gouw1, Jeroen Krijgsveld, Albert J R Heck.   

Abstract

In the biological sciences, model organisms have been used for many decades and have enabled the gathering of a large proportion of our present day knowledge of basic biological processes and their derailments in disease. Although in many of these studies using model organisms, the focus has primarily been on genetics and genomics approaches, it is important that methods become available to extend this to the relevant protein level. Mass spectrometry-based proteomics is increasingly becoming the standard to comprehensively analyze proteomes. An important transition has been made recently by moving from charting static proteomes to monitoring their dynamics by simultaneously quantifying multiple proteins obtained from differently treated samples. Especially the labeling with stable isotopes has proved an effective means to accurately determine differential expression levels of proteins. Among these, metabolic incorporation of stable isotopes in vivo in whole organisms is one of the favored strategies. In this perspective, we will focus on methodologies to stable isotope label a variety of model organisms in vivo, ranging from relatively simple organisms such as bacteria and yeast to Caenorhabditis elegans, Drosophila, and Arabidopsis up to mammals such as rats and mice. We also summarize how this has opened up ways to investigate biological processes at the protein level in health and disease, revealing conservation and variation across the evolutionary tree of life.

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Year:  2009        PMID: 19955089      PMCID: PMC2808257          DOI: 10.1074/mcp.R900001-MCP200

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  140 in total

Review 1.  The origin and evolution of model organisms.

Authors:  S Blair Hedges
Journal:  Nat Rev Genet       Date:  2002-11       Impact factor: 53.242

2.  Amino acid residue specific stable isotope labeling for quantitative proteomics.

Authors:  Haining Zhu; Songqin Pan; Sheng Gu; E Morton Bradbury; Xian Chen
Journal:  Rapid Commun Mass Spectrom       Date:  2002       Impact factor: 2.419

3.  Quantitative mass spectrometry identifies insulin signaling targets in C. elegans.

Authors:  Meng-Qiu Dong; John D Venable; Nora Au; Tao Xu; Sung Kyu Park; Daniel Cociorva; Jeffrey R Johnson; Andrew Dillin; John R Yates
Journal:  Science       Date:  2007-08-03       Impact factor: 47.728

Review 4.  Top-down MS, a powerful complement to the high capabilities of proteolysis proteomics.

Authors:  Fred W McLafferty; Kathrin Breuker; Mi Jin; Xuemei Han; Giuseppe Infusini; Honghai Jiang; Xianglei Kong; Tadhg P Begley
Journal:  FEBS J       Date:  2007-11-16       Impact factor: 5.542

Review 5.  Controlling morpholino experiments: don't stop making antisense.

Authors:  Judith S Eisen; James C Smith
Journal:  Development       Date:  2008-04-09       Impact factor: 6.868

6.  Comparative phosphoproteomics of zebrafish Fyn/Yes morpholino knockdown embryos.

Authors:  Simone Lemeer; Chris Jopling; Joost Gouw; Shabaz Mohammed; Albert J R Heck; Monique Slijper; Jeroen den Hertog
Journal:  Mol Cell Proteomics       Date:  2008-06-11       Impact factor: 5.911

7.  Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway.

Authors:  Albrecht Gruhler; Jesper V Olsen; Shabaz Mohammed; Peter Mortensen; Nils J Faergeman; Matthias Mann; Ole N Jensen
Journal:  Mol Cell Proteomics       Date:  2005-01-22       Impact factor: 5.911

Review 8.  Research resources for Drosophila: the expanding universe.

Authors:  Kathleen A Matthews; Thomas C Kaufman; William M Gelbart
Journal:  Nat Rev Genet       Date:  2005-03       Impact factor: 53.242

9.  Analysis of quantitative proteomic data generated via multidimensional protein identification technology.

Authors:  Michael P Washburn; Ryan Ulaszek; Cosmin Deciu; David M Schieltz; John R Yates
Journal:  Anal Chem       Date:  2002-04-01       Impact factor: 6.986

Review 10.  Functional senescence in Drosophila melanogaster.

Authors:  Michael S Grotewiel; Ian Martin; Poonam Bhandari; Eric Cook-Wiens
Journal:  Ageing Res Rev       Date:  2005-08       Impact factor: 10.895

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  49 in total

1.  Cardioproteomics: advancing the discovery of signaling mechanisms involved in cardiovascular diseases.

Authors:  Ziyou Cui; Shannamar Dewey; Aldrin V Gomes
Journal:  Am J Cardiovasc Dis       Date:  2011-09-10

2.  Improving the TFold test for differential shotgun proteomics.

Authors:  Paulo C Carvalho; John R Yates; Valmir C Barbosa
Journal:  Bioinformatics       Date:  2012-04-26       Impact factor: 6.937

3.  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

Review 4.  Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry.

Authors:  Robyn M Kaake; Xiaorong Wang; Lan Huang
Journal:  Mol Cell Proteomics       Date:  2010-05-05       Impact factor: 5.911

5.  The SILAC fly allows for accurate protein quantification in vivo.

Authors:  Matthias D Sury; Jia-Xuan Chen; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2010-06-05       Impact factor: 5.911

Review 6.  Generating and navigating proteome maps using mass spectrometry.

Authors:  Christian H Ahrens; Erich Brunner; Ermir Qeli; Konrad Basler; Ruedi Aebersold
Journal:  Nat Rev Mol Cell Biol       Date:  2010-10-14       Impact factor: 94.444

Review 7.  Proteomic analysis of stem cell differentiation and early development.

Authors:  Dennis van Hoof; Jeroen Krijgsveld; Christine Mummery
Journal:  Cold Spring Harb Perspect Biol       Date:  2012-03-01       Impact factor: 10.005

8.  Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches.

Authors:  Borjana Arsova; Henrik Zauber; Waltraud X Schulze
Journal:  Mol Cell Proteomics       Date:  2012-05-05       Impact factor: 5.911

9.  Quantitative phosphoproteomics after auxin-stimulated lateral root induction identifies an SNX1 protein phosphorylation site required for growth.

Authors:  Hongtao Zhang; Houjiang Zhou; Lidija Berke; Albert J R Heck; Shabaz Mohammed; Ben Scheres; Frank L H Menke
Journal:  Mol Cell Proteomics       Date:  2013-01-17       Impact factor: 5.911

Review 10.  Quantification of histone modifications using ¹⁵N metabolic labeling.

Authors:  Chunchao Zhang; Yifan Liu; Philip C Andrews
Journal:  Methods       Date:  2013-02-27       Impact factor: 3.608

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