Analysis of Individual Protein Turnover in Live Animals on a Proteome-Wide Scale

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1156)

Abstract

Classical quantitative proteomics studies focus on the relative or absolute concentration of proteins at a given time. In contrast, the investigation of protein turnover reveals the dynamics leading to these states. Analyzing the balance between synthesis and degradation of individual proteins provides insights into the regulation of protein concentration and helps understanding underlying biological processes. Comparing the half-lives of proteins allows detecting functional relationships and common regulation mechanisms. Moreover, comparing turnover of individual brain and plasma proteins between control- and treatment-groups indicates turnover changes induced by the treatment.

Here, we describe a procedure for determining turnover information of individual proteins in mice on a proteome-wide scale based on partial 15N metabolic labeling. We will outline the complete experimental workflow starting from 15N labeling the animals over sample preparation and mass spectrometric measurement up to the analysis of the data.

Key words

Protein turnover Synthesis Degradation Partial 15N metabolic labeling Mass spectrometry 

References

  1. 1.
    Pratt JM, Petty J, Riba-Garcia I et al (2002) Dynamics of protein turnover, a missing dimension in proteomics. Mol Cell Proteomics 1:579–591PubMedCrossRefGoogle Scholar
  2. 2.
    Schoenheimer R, Rittenberg D, Foster GL et al (1938) The application of the nitrogen isotope 15N for the study of protein metabolism. Science 88:599–600PubMedCrossRefGoogle Scholar
  3. 3.
    Doherty MK, Beynon RJ (2006) Protein turnover on the scale of the proteome. Expert Rev Proteomics 3:97–110PubMedCrossRefGoogle Scholar
  4. 4.
    Eng J, McCormack A, Yates J (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989PubMedCrossRefGoogle Scholar
  5. 5.
    Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392PubMedCrossRefGoogle Scholar
  6. 6.
    Deutsch EW, Mendoza L, Shteynberg D et al (2010) A guided tour of the trans-proteomic pipeline. Proteomics 10:1150–1159PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Nesvizhskii AI, Keller A, Kolker E, Aebersold R (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75:4646–4658PubMedCrossRefGoogle Scholar
  8. 8.
    Zhang Y, Reckow S, Webhofer C et al (2011) Proteome scale turnover analysis in live animals using stable isotope metabolic labeling. Anal Chem 83:1665–1672PubMedCrossRefGoogle Scholar
  9. 9.
    Prince JT, Marcotte EM (2008) mspire: mass spectrometry proteomics in Ruby. Bioinformatics 24(23):2796–2797PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Proteomics and Biomarkers, Max Planck Institute of PsychiatryMunichGermany

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