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Large-Scale Proteome and Phosphoproteome Quantification by Using Dimethylation Isotope Labeling

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Applications of Monolithic Column and Isotope Dimethylation Labeling in Shotgun Proteome Analysis

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Protein characterization alone is usually not enough to elucidate most biological processes. Although thousands of proteins can be identified in one proteomic experiment, it is difficult to relate these proteins with their biological functions. The expression levels of proteins within a living organism are reflections of the different physiological and pathological states. Conventional protein quantification technologies such as western blot (WB) are low throughput and can only quantify one protein in each experiment. Therefore, comprehensive proteome quantification in certain depth is an important direction in the development of proteomic technologies and is considered as the bridge for the gap between proteins and their biological function [1–7]. On the other hand, there are more than 300 types of posttranslational modifications (PTMs) that can dynamically modify the whole proteome, which makes the protein sample even more complex. The occupancy level of a PTM on specific site of a protein is also critical to the biological function of the protein in the regulation of different physiological and pathological processes, such as the protein phosphorylation is related to the signal transduction in many pathways activation and protein glycosylation is related to cell-to-cell recognition [8–10]. Therefore, comprehensive quantification of proteome PTMs is also another important task for current proteomic analysis.

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References

  1. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207

    Article  CAS  Google Scholar 

  2. Mann M (2006) Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 7:952–958

    Article  CAS  Google Scholar 

  3. Gruhler A, Olsen JV, Mohammed S, Mortensen P, Faergeman NJ, Mann M, Jensen ON (2005) Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol Cell Proteomics 4:310–327

    Article  CAS  Google Scholar 

  4. Forner F, Foster LJ, Campanaro S, Valle G, Mann M (2006) Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver. Mol Cell Proteomics 5:608–619

    Article  CAS  Google Scholar 

  5. Wright ME, Han DK, Aebersold R (2005) Mass spectrometry-based expression profiling of clinical prostate cancer. Mol Cell Proteomics 4:545–554

    Article  CAS  Google Scholar 

  6. Kruger M, Moser M, Ussar S, Thievessen I, Luber CA, Forner F, Schmidt S, Zanivan S, Fassler R, Mann M (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134:353–364

    Article  Google Scholar 

  7. Ong S-E, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262

    Article  CAS  Google Scholar 

  8. Khoury GA, Baliban RC, Floudas CA (2011) Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Sci Rep 1

    Google Scholar 

  9. Mann M, Jensen ON (2003) Proteomic analysis of post-translational modifications. Nat Biotechnol 21:255–261

    Article  CAS  Google Scholar 

  10. Witze ES, Old WM, Resing KA, Ahn NG (2007) Mapping protein post-translational modifications with mass spectrometry. Nat Methods 4:798–806

    Article  CAS  Google Scholar 

  11. Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH (2003) Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem 75:4818–4826

    Article  CAS  Google Scholar 

  12. Radulovic D, Jelveh S, Ryu S, Hamilton TG, Foss E, Mao Y, Emili A (2004) Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 3:984–997

    Article  CAS  Google Scholar 

  13. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17:994–999

    Article  CAS  Google Scholar 

  14. Han DK, Eng J, Zhou H, Aebersold R (2001) Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat Biotechnol 19:946–951

    Article  CAS  Google Scholar 

  15. Kettenbach AN, Rush J, Gerber SA (2011) Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides. Nat Protocols 6:175–186

    Article  CAS  Google Scholar 

  16. Ong S-E, Mann M (2007) A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protocols 1:2650–2660

    Article  Google Scholar 

  17. Cox J, Matic I, Hilger M, Nagaraj N, Selbach M, Olsen JV, Mann M (2009) A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat Protocols 4:698–705

    Article  CAS  Google Scholar 

  18. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372

    Article  CAS  Google Scholar 

  19. Boersema PJ, Raijmakers R, Lemeer S, Mohammed S, Heck AJR (2009) Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat Protocols 4:484–494

    Article  CAS  Google Scholar 

  20. Hsu J-L, Huang S-Y, Chow N-H, Chen S-H (2003) Stable-isotope dimethyl labeling for quantitative proteomics. Anal Chem 75:6843–6852

    Article  CAS  Google Scholar 

  21. Huang S-Y, Tsai M-L, Wu C-J, Hsu J-L, Ho S-H, Chen S-H (2006) Quantitation of protein phosphorylation in pregnant rat uteri using stable isotope dimethyl labeling coupled with IMAC. Proteomics 6:1722–1734

    Article  CAS  Google Scholar 

  22. Boersema PJ, Aye TT, van Veen TAB, Heck AJR, Mohammed S (2008) Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to cell and tissue lysates. Proteomics 8:4624–4632

    Article  CAS  Google Scholar 

  23. Raijmakers R, Berkers CR, de Jong A, Ovaa H, Heck AJR, Mohammed S (2008) Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity. Mol Cell Proteomics 7:1755–1762

    Article  CAS  Google Scholar 

  24. Seow TK, Liang RC, Leow CK, Chung MC (2001) Hepatocellular carcinoma: from bedside to proteomics. Proteomics 1:1249–1263

    Article  CAS  Google Scholar 

  25. Zheng J, Gao X, Beretta L, He F (2006) The Human Liver Proteome Project (HLPP) workshop during the 4th HUPO World Congress. Proteomics 6:1716–1718

    Article  CAS  Google Scholar 

  26. Chaerkady R, Harsha HC, Nalli A et al (2008) A quantitative proteomic approach for identification of potential biomarkers in hepatocellular carcinoma. J Proteome Res 7:4289–4298

    Article  CAS  Google Scholar 

  27. Chen N, Sun W, Deng X et al (2008) Quantitative proteome analysis of HCC cell lines with different metastatic potentials by SILAC. Proteomics 8:5108–5118

    Article  CAS  Google Scholar 

  28. Selkoe DJ (2001) Alzheimer’s disease: genes, proteins, and therapy. Physiol Rev 81:741–766

    CAS  Google Scholar 

  29. Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer’s disease. Lancet 368:387–403

    Article  CAS  Google Scholar 

  30. Rhein V, Song X, Wiesner A et al (2009) Amyloid-beta and tau synergistically impair the oxidative phosphorylation system in triple transgenic Alzheimer’s disease mice. Proc Natl Acad Sci U S A 106:20057–20062

    Article  CAS  Google Scholar 

  31. Dierssen M, Fillat C, Crnic L, Arbones M, Florez J, Estivill X (2001) Murine models for Down syndrome. Physiol Behav 73:859–871

    Article  CAS  Google Scholar 

  32. David DC, Ittner LM, Gehrig P, Nergenau D, Shepherd C, Halliday G, Gotz J (2006) Beta-amyloid treatment of two complementary P301L tau-expressing Alzheimer’s disease models reveals similar deregulated cellular processes. Proteomics 6:6566–6577

    Article  CAS  Google Scholar 

  33. Gillardon F, Rist W, Kussmaul L, Vogel J, Berg M, Danzer K, Kraut N, Hengerer B (2007) Proteomic and functional alterations in brain mitochondria from Tg2576 mice occur before amyloid plaque deposition. Proteomics 7:605–616

    Article  CAS  Google Scholar 

  34. Chishti MA, Yang DS, Janus C et al (2001) Early-onset amyloid deposition and cognitive deficits in transgenic mice expressing a double mutant form of amyloid precursor protein 695. J Biol Chem 276:21562–21570

    Article  CAS  Google Scholar 

  35. Phinney AL, Drisaldi B, Schmidt SD et al (2003) In vivo reduction of amyloid-beta by a mutant copper transporter. Proc Natl Acad Sci U S A 100:14193–14198

    Article  CAS  Google Scholar 

  36. Ryan SD, Whitehead SN, Swayne LA et al (2009) Amyloid-beta42 signals tau hyperphosphorylation and compromises neuronal viability by disrupting alkylacylglycerophosphocholine metabolism. Proc Natl Acad Sci U S A 106:20936–20941

    Article  CAS  Google Scholar 

  37. Hawkes CA, McLaurin J (2009) Selective targeting of perivascular macrophages for clearance of beta-amyloid in cerebral amyloid angiopathy. Proc Natl Acad Sci U S A 106:1261–1266

    Article  CAS  Google Scholar 

  38. Wang F, Chen R, Zhu J, Sun D, Song C, Wu Y, Ye M, Wang L, Zou H (2010) A fully automated system with online sample loading, isotope dimethyl labeling and multidimensional separation for high-throughput quantitative proteome analysis. Anal Chem 82:3007–3015

    Article  CAS  Google Scholar 

  39. Neo SY, Leow CK, Vega VB, Long PM, Islam AFM, Lai PBS, Liu ET, Ren EC (2004) Identification of discriminators of hepatoma by gene expression profiling using a minimal dataset approach. Hepatology 39:944–953

    Article  CAS  Google Scholar 

  40. Liang CRMY, Leow CK, Neo JCH, Tan GS, Lo SL, Lim JWE, Seow TK, Lai PBS, Chung MCM (2005) Proteome analysis of human hepatocellular carcinoma tissues by two-dimensional difference gel electrophoresis and mass spectrometry. Proteomics 5:2258–2271

    Article  CAS  Google Scholar 

  41. Liang RCMY, Neo JCH, Lo SL, Tan GS, Seow TK, Chung MCM (2002) Proteome database of hepatocellular carcinoma. J Chromatogr B 771:303–328

    Google Scholar 

  42. Lemeer S, Jopling C, Gouw J, Mohammed S, Heck AJ, Slijper M, den Hertog J (2008) Comparative phosphoproteomics of zebrafish Fyn/Yes morpholino knockdown embryos. Mol Cell Proteomics 7:2176–2187

    Article  CAS  Google Scholar 

  43. Kuramitsu Y, Harada T, Takashima M et al (2006) Increased expression and phosphorylation of liver glutamine synthetase in well-differentiated hepatocellular carcinoma tissues from patients infected with hepatitis C virus. Electrophoresis 27:1651–1658

    Article  CAS  Google Scholar 

  44. Lee HJ, Na K, Kwon MS, Kim H, Kim KS, Paik YK (2009) Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellular carcinoma specimen. Proteomics 9:3395–3408

    Article  CAS  Google Scholar 

  45. Song C, Wang F, Ye M, Cheng K, Chen R, Zhu J, Tan Y, Wang H, Figeys D, Zou H (2011) Improvement of the quantification accuracy and throughput for phosphoproteome analysis by a pseudo triplex stable isotope dimethyl labeling approach. Anal Chem 83:7755–7762

    Article  CAS  Google Scholar 

  46. Schwartz D, Gygi SP (2005) An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol 23:1391–1398

    Article  CAS  Google Scholar 

  47. Wu CJ, Chen YW, Tai JH, Chen SH (2011) Quantitative phosphoproteomics studies using stable isotope dimethyl labeling coupled with IMAC-HILIC-nanoLC-MS/MS for estrogen-induced transcriptional regulation. J Proteome Res 10:1088–1097

    Article  CAS  Google Scholar 

  48. Dhillon AS, Hagan S, Rath O, Kolch W (2007) MAP kinase signalling pathways in cancer. Oncogene 26:3279–3290

    Article  CAS  Google Scholar 

  49. Reddy KB, Nabha SM, Atanaskova N (2003) Role of MAP kinase in tumor progression and invasion. Cancer Metastasis Rev 22:395–403

    Article  CAS  Google Scholar 

  50. Dhomen N, Marais R (2007) New insight into BRAF mutations in cancer. Curr Opin Genet Dev 17:31–39

    Article  CAS  Google Scholar 

  51. Ritt DA, Monson DM, Specht SI, Morrison DK (2010) Impact of feedback phosphorylation and Raf heterodimerization on normal and mutant B-Raf signaling. Mol Cell Biol 30:806–819

    Article  CAS  Google Scholar 

  52. Soutar MP, Thornhill P, Cole AR, Sutherland C (2009) Increased CRMP2 phosphorylation is observed in Alzheimer’s disease; does this tell us anything about disease development? Curr Alzheimer Res 6:269–278

    Article  CAS  Google Scholar 

  53. Pham E, Crews L, Ubhi K et al (2010) Progressive accumulation of amyloid-beta oligomers in Alzheimer’s disease and in amyloid precursor protein transgenic mice is accompanied by selective alterations in synaptic scaffold proteins. FEBS J 277:3051–3067

    Article  Google Scholar 

  54. Verpelli C, Dvoretskova E, Vicidomini C et al (2011) Importance of shank3 in regulating metabotropic glutamate receptor 5 (mGluR5) expression and signaling at synapses. J Biol Chem 286(40):34839–34850

    Google Scholar 

  55. Grabrucker AM, Schmeisser MJ, Udvardi PT et al (2011) Amyloid beta protein-induced zinc sequestration leads to synaptic loss via dysregulation of the ProSAP2/Shank3 scaffold. Mol Neurodegener 6:65

    Article  CAS  Google Scholar 

  56. Reifert J, Hartung-Cranston D, Feinstein SC (2011) Amyloid beta-mediated cell death of cultured hippocampal neurons reveals extensive Tau fragmentation without increased full-length tau phosphorylation. J Biol Chem 286:20797–20811

    Article  CAS  Google Scholar 

  57. Han GH, Ye ML, Zhou HJ, Jiang XN, Feng S, Jiang XG, Tian RJ, Wan DF, Zou HF, Gu JR (2008) Large-scale phosphoproteome analysis of human liver tissue by enrichment and fractionation of phosphopeptides with strong anion exchange chromatography. Proteomics 8:1346–1361

    Article  CAS  Google Scholar 

  58. Chen R, Jiang XN, Sun DG, Han GH, Wang FJ, Ye ML, Wang LM, Zou HF (2009) Glycoproteomics analysis of human liver tissue by combination of multiple enzyme digestion and hydrazide chemistry. J Proteome Res 8:651–661

    Article  CAS  Google Scholar 

  59. Wessel D, Flügge UI (1984) A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Anal Biochem 138:141–143

    Article  CAS  Google Scholar 

  60. Yu Z, Han G, Sun S, Jiang X, Chen R, Wang F, Ye M, Zou H (2009) Preparation of monodisperse immobilized Ti4+ affinity chromatography microspheres for specific enrichment of phosphopeptides. Anal Chim Acta 636:34–41

    Article  CAS  Google Scholar 

  61. Mortensen P, Gouw JW, Olsen JV et al (2009) MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 9(1):393–403

    Google Scholar 

  62. Lemeer S, Jopling C, Gouw J, Mohammed S, Heck AJR, Slijper M, den Hertog J (2008) Comparative phosphoproteomics of zebrafish Fyn/Yes morpholino knockdown embryos. Mol Cell Proteomics 7:2176–2187

    Article  CAS  Google Scholar 

  63. Chen N, Sun W, Deng XY et al (2008) Quantitative proteome analysis of HCC cell lines with different metastatic potentials by SILAC. Proteomics 8:5108–5118

    Article  CAS  Google Scholar 

  64. van Breukelen B, van den Toorn HWP, Drugan MM, Heck AJR (2009) StatQuant: a post-quantification analysis toolbox for improving quantitative mass spectrometry. Bioinformatics 25:1472–1473

    Article  Google Scholar 

  65. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10(4):1794–1805

    Google Scholar 

  66. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372

    Article  CAS  Google Scholar 

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Wang, F. (2014). Large-Scale Proteome and Phosphoproteome Quantification by Using Dimethylation Isotope Labeling. In: Applications of Monolithic Column and Isotope Dimethylation Labeling in Shotgun Proteome Analysis. Springer Theses. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42008-5_4

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