References
- Anderson NL, Anderson NG. Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis19(11), 1853–1861 (1998).
- Elliott MH, Smith DS, Parker CE, Borchers C. Current trends in quantitative proteomics. J. Mass Spectrom.44(12), 1637–1660 (2009).
- Doherty MK, Beynon RJ. Protein turnover on the scale of the proteome. Expert Rev. Proteomics3(1), 97–110 (2006).
- Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol.17(10), 994–999 (1999).
- Beynon RJ, Doherty MK, Pratt JM, Gaskell SJ. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods2(8), 587–589 (2005).
- Eagle H, Piez KA, Fleischman R, Oyama VI. Protein turnover in mammaliar cell cultures. J. Biol. Chem.234(3), 592–597 (1959).
- Anderson L, Seilhamer J. A comparison of selected mRNA and protein abundances in human liver. Electrophoresis18(3–4), 533–537 (1997).
- Chen G, Gharib TG, Huang CC et al. Discordant protein and mRNA expression in lung adenocarcinomas. Mol. Cell Proteomics1(4), 304–313 (2002).
- Mehra A, Lee KH, Hatzimanikatis V. Insights into the relation between mRNA and protein expression patterns: I. Theoretical considerations. Biotechnol. Bioeng.84(7), 822–833 (2003).
- Lim KL. Ubiquitin–proteasome system dysfunction in Parkinson’s disease: current evidence and controversies. Expert Rev. Proteomics4(6), 769–781 (2007).
- Yao D, Gu Z, Nakamura T et al. Nitrosative stress linked to sporadic Parkinson’s disease: S-nitrosylation of parkin regulates its E3 ubiquitin ligase activity. Proc. Natl Acad. Sci. USA101(29), 10810–10814 (2004).
- Riederer IM, Schiffrin M, Kovari E, Bouras C, Riederer BM. Ubiquitination and cysteine nitrosylation during aging and Alzheimer’s disease. Brain Res. Bull.80(4–5), 233–241 (2009).
- Upadhya SC, Hegde AN. Role of the ubiquitin proteasome system in Alzheimer’s disease. BMC Biochem.8(Suppl. 1), S12 (2007).
- Deng S, Zhou H, Xiong R et al. Over-expression of genes and proteins of ubiquitin specific peptidases (USPs) and proteasome subunits (PSs) in breast cancer tissue observed by the methods of RFDD-PCR and proteomics. Breast Cancer Res. Treat.104(1), 21–30 (2007).
- White E, DiPaola RS. The double-edged sword of autophagy modulation in cancer. Clin. Cancer Res.15(17), 5308–5316 (2009).
- Caso G, Ford GC, Nair KS, Garlick PJ, McNurlan MA. Aminoacyl-tRNA enrichment after a flood of labeled phenylalanine: insulin effect on muscle protein synthesis. Am. J. Physiol. Endocrinol. Metab.282(5), E1029–E1038 (2002).
- Johnson HA, Baldwin RL, France J, Calvert CC. Recycling, channeling and heterogeneous protein turnover estimation using a model of whole-body protein turnover based on leucine kinetics in rodents. J. Nutr.129(3), 740–750 (1999).
- Johnson HA, Baldwin RL, France J, Calvert CC. A model of whole-body protein turnover based on leucine kinetics in rodents. J. Nutr.129(3), 728–739 (1999).
- Davis TA, Fiorotto ML, Nguyen HV, Burrin DG. Aminoacyl-tRNA and tissue free amino acid pools are equilibrated after a flooding dose of phenylalanine. Am. J. Physiol.277(1 Pt 1), E103–E109 (1999).
- Papageorgopoulos C, Caldwell K, Shackleton C, Schweingrubber H, Hellerstein MK. Measuring protein synthesis by mass isotopomer distribution analysis (MIDA). Anal. Biochem.267(1), 1–16 (1999).
- Reeds PJ, Davis TA. Of flux and flooding: the advantages and problems of different isotopic methods for quantifying protein turnover in vivo: I. Methods based on the dilution of a tracer. Curr. Opin. Clin. Nutr. Metab. Care2(1), 23–28 (1999).
- Rennie MJ. An introduction to the use of tracers in nutrition and metabolism. Proc. Nutr. Soc.58(4), 935–944 (1999).
- Buse MG, Reid SS. Leucine. A possible regulator of protein turnover in muscle. J. Clin. Invest.56(5), 1250–1261 (1975).
- Pannemans DL, Wagenmakers AJ, Westerterp KR, Schaafsma G, Halliday D. The effect of an increase of protein intake on whole-body protein turnover in elderly women is tracer dependent. J. Nutr.127(9), 1788–1794 (1997).
- Ong SE, Blagoev B, Kratchmarova I et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell Proteomics1(5), 376–386 (2002).
- Andersen JS, Lam YW, Leung AK et al. Nucleolar proteome dynamics. Nature433(7021), 77–83 (2005).
- Geiger T, Cox J, Ostasiewicz P, Wisniewski JR, Mann M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods7(5), 383–385 (2010).
- Hanke S, Besir H, Oesterhelt D, Mann M. Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level. J. Proteome Res.7(3), 1118–1130 (2008).
- Mann M. Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol.7(12), 952–958 (2006).
- He CY, Merrick BA, Mansfield BK, Hite MC, Daluge DR, Selkirk JK. Comparison of 14C-amino acid mixture and[35S]methionine labeling of cellular proteins from mouse fibroblast C3H10T1/2 cells by two-dimensional gel electrophoresis. Electrophoresis12(9), 658–666 (1991).
- Pratt JM, Petty J, Riba-Garcia I et al. Dynamics of protein turnover, a missing dimension in proteomics. Mol. Cell Proteomics1(8), 579–591 (2002).
- Bouwman F, Renes J, Mariman E. A combination of protein profiling and isotopomer analysis using matrix-assisted laser desorption/ionization-time of flight mass spectrometry reveals an active metabolism of the extracellular matrix of 3T3-L1 adipocytes. Proteomics4(12), 3855–3863 (2004).
- Cargile BJ, Bundy JL, Grunden AM, Stephenson JL Jr. Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover. Anal. Chem.76(1), 86–97 (2004).
- Doherty MK, Whitehead C, McCormack H, Gaskell SJ, Beynon RJ. Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates. Proteomics5(2), 522–533 (2005).
- Busch R, Kim YK, Neese RA et al. Measurement of protein turnover rates by heavy water labeling of nonessential amino acids. Biochim. Biophys. Acta1760(5), 730–744 (2006).
- Hellerstein MK, Neese RA. Mass isotopomer distribution analysis: a technique for measuring biosynthesis and turnover of polymers. Am. J. Physiol.263(5 Pt 1), E988–E1001 (1992).
- Hellerstein MK, Neese RA. Mass isotopomer distribution analysis at eight years: theoretical, analytic, and experimental considerations. Am. J. Physiol.276(6 Pt 1), E1146–E1170 (1999).
- Kruger M, Moser M, Ussar S et al. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell134(2), 353–364 (2008).
- Price JC, Guan S, Burlingame A, Prusiner SB, Ghaemmaghami S. Analysis of proteome dynamics in the mouse brain. Proc. Natl Acad. Sci. USA107(32), 14508–14513 (2010).
- Krijgsveld J, Ketting RF, Mahmoudi T et al. Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics. Nat. Biotechnol.21(8), 927–931 (2003).
- Gouw JW, Krijgsveld J, Heck AJR. Quantitative proteomics by metabolic labeling of model organisms. Mol. Cell. Proteomics9(1), 11–24 (2010).
- Doherty MK, Hammond DE, Clague MJ, Gaskell SJ, Beynon RJ. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res.8(1), 104–112 (2009).
- Schwanhäusser B, Gossen M, Dittmar G, Selbach M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics9(1), 205–209 (2009).
- Yee JC, Jacob NM, Jayapal KP et al. Global assessment of protein turnover in recombinant antibody producing myeloma cells. J. Biotechnol.148(4), 182–193 (2010).
- Bunner AE, Williamson JR. Stable isotope pulse-chase monitored by quantitative mass spectrometry applied to E. coli 30S ibosome assembly kinetics. Methods49(2), 136–141 (2009).
- Jayapal KP, Sui S, Philp RJ et al. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. J. Proteome Res.9(5), 2087–2097 (2010).
- Rao PK, Roxas BAP, Li Q. Determination of global protein turnover in stressed mycobacterium cells using hybrid-linear ion trap-fourier transform mass spectrometry. Anal. Chem.80(2), 396–406 (2008).
- De Riva A, Deery MJ, McDonald S, Lund T, Busch R. Measurement of protein synthesis using heavy water labeling and peptide mass spectrometry: discrimination between major histocompatibility complex allotypes. Anal. Biochem.403(1–2), 1–12 (2010).
- Rachdaoui N, Austin L, Kramer E et al. Measuring proteome dynamics in vivo.Mol. Cell. Proteomics8(12), 2653–2663 (2009).
- Yang XY, Chen WP, Rendahl AK, Hegeman AD, Gray WM, Cohen JD. Measuring the turnover rates of Arabidopsis proteins using deuterium oxide: an auxin signaling case study. Plant J.63(4), 680–695 (2010).
- Belle A, Tanay A, Bitincka L, Shamir R, O’Shea EK. Quantification of protein half lifes in the budding yeast proteome. Proc. Natl Acad. Sci. USA103(35), 13004–13009 (2006).
- Tompa P, Prilusky J, Silman I, Sussman JL. Structural disorder serves as a weak signal for intracellular protein degradation. Proteins71(2), 903–909 (2008).
- Yen HC, Elledge SJ. Identification of SCF ubiquitin ligase substrates by global protein stability profiling. Science322(5903), 923–929 (2008).
- Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods1(1), 39–45 (2004).
- MacCoss MJ, Wu CC, Liu H, Sadygov R, Yates JR 3rd. A correlation algorithm for the automated quantitative analysis of shotgun proteomics data. Anal. Chem.75(24), 6912–6921 (2003).
- Mortensen P, Gouw JW, Olsen JV et al. MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J. Proteome Res.9(1), 393–403 (2010).
- Cox J, Matic I, Hilger M et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc.4(5), 698–705 (2009).
- Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol.26(12), 1367–1372 (2008).
- Yewdell JW, Lacsina JR, Rechsteiner MC, Nicchitta CV. Out with the old, in with the new? Comparing methods for measuring protein degradation. Cell Biol. Int.35, 457–462 (2011).
Websites
- Matrix Science www.matrixscience.com
- MaxQuant www.maxquant.org
- MSQuant http://msquant.alwaysdata.net/msq
- Yates Lab, the Scripps Research Institute. Research tools http://fields.scripps.edu/researchtools.php
- Software: trans-proteomic pipeline http://tools.proteomecenter.org/wiki/index.php?title=Software:TPP