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Phosphoproteomics-Based Network Analysis of Cancer Cell Signaling Systems

  • Hiroko Kozuka-Hata
  • Masaaki OyamaEmail author

Abstract

Signal transduction systems are known to regulate complex biological events such as cell proliferation and differentiation via sequential phosphorylation/dephosphorylation reactions over all cellular networks. Recent technological advances regarding high-resolution mass spectrometry-based quantitative proteomics, in combination with phosphorylation-directed protein/peptide enrichment methodology, have enabled us to grasp the comprehensive status of phosphorylated cellular signaling molecules in a time-resolved manner. Phosphotyrosine-targeted sample enrichment by anti-phosphotyrosine antibodies allows us to describe key regulatory signaling dynamics triggered by tyrosine kinases, including epidermal growth factor receptor, in various contexts of cancer cell signaling. Furthermore, chemistry-based phosphopeptide enrichment technologies such as immobilized metal affinity chromatography and metal oxide chromatography lead us to obtain a serine/threonine/tyrosine-phosphorylation dependent global landscape of cellular signaling at the network level. In this chapter, we introduce recent technological advances regarding phosphoproteomics-based computational analyses of signaling regulation and discuss the future directions of cancer research toward theoretical exploration of drug targets from a system-level point of view.

Keywords

Signal transduction NanoLC-MS/MS Phosphoproteomics Quantitative proteomics Computational modeling Network analysis Systems biology 

Notes

Acknowledgments

We gratefully acknowledge our colleagues at Medical Proteomics Laboratory, the Institute of Medical Science, the University of Tokyo for helpful discussions and comments. This work was supported by Grants-in-Aid for Scientific Research on Innovative Areas from Japan Society for the Promotion of Science (JSPS) and The Ministry of Education, Culture, Sports, Science and Technology (MEXT).

References

  1. Blagoev B, Ong SE, Kratchmarova I, Mann M (2004) Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nat Biotechnol 22:1139–1145CrossRefPubMedGoogle Scholar
  2. Bose R, Molina H, Patterson AS, Bitok JK, Periaswamy B, Bader JS, Pandey A, Cole PA (2006) Phosphoproteomic analysis of Her2/neu signaling and inhibition. Proc Natl Acad Sci U S A 103:9773–9778CrossRefPubMedCentralPubMedGoogle Scholar
  3. Brunner E, Ahrens CH, Mohanty S, Baetschmann H, Loevenich S, Potthast F, Deutsch EW, Panse C, de Lichtenberg U, Rinner O, Lee H, Pedrioli PG, Malmstrom J, Koehler K, Schrimpf S, Krijgsveld J, Kregenow F, Heck AJ, Hafen E, Schlapbach R, Aebersold R (2007) A high-quality catalog of the Drosophila melanogaster proteome. Nat Biotechnol 25:576–583CrossRefPubMedGoogle Scholar
  4. Choudhary C, Mann M (2010) Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol 11:427–439CrossRefPubMedGoogle Scholar
  5. Cohen P (2006) The twentieth century struggle to decipher insulin signalling. Nat Rev Mol Cell Biol 7:867–873CrossRefPubMedGoogle Scholar
  6. Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299CrossRefPubMedGoogle Scholar
  7. Cuesta N, Martín-Cófreces NB, Murga C, van Santen HM (2011) Receptors, signaling networks, and disease. Sci Signal 4:mr3Google Scholar
  8. de Godoy LM, Olsen JV, Cox J, Nielsen ML, Hubner NC, Fröhlich F, Walther TC, Mann M (2008) Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455:1251–1254CrossRefPubMedGoogle Scholar
  9. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4:P3CrossRefPubMedGoogle Scholar
  10. Gangloff YG, Mueller M, Dann SG, Svoboda P, Sticker M, Spetz JF, Um SH, Brown EJ, Cereghini S, Thomas G, Kozma SC (2004) Disruption of the mouse mTOR gene leads to early postimplantation lethality and prohibits embryonic stem cell development. Mol Cell Biol 24:9508–9516CrossRefPubMedCentralPubMedGoogle Scholar
  11. Guha U, Chaerkady R, Marimuthu A, Patterson AS, Kashyap MK, Harsha HC, Sato M, Bader JS, Lash AE, Minna JD, Pandey A, Varmus HE (2008) Comparisons of tyrosine phosphorylated proteins in cells expressing lung cancer-specific alleles of EGFR and KRAS. Proc Natl Acad Sci U S A 105:14112–14117CrossRefPubMedCentralPubMedGoogle Scholar
  12. 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 17:994–999CrossRefPubMedGoogle Scholar
  13. 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 19:946–951CrossRefPubMedCentralPubMedGoogle Scholar
  14. Hanke S, Besir H, Oesterhelt D, Mann M (2008) Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level. J Proteome Res 7:1118–1130CrossRefPubMedGoogle Scholar
  15. Hinsby AM, Olsen JV, Mann M (2004) Tyrosine phosphoproteomics of fibroblast growth factor signaling: a role for insulin receptor substrate-4. J Biol Chem 279:46438–46447CrossRefPubMedGoogle Scholar
  16. Hunter T (2000) Signaling: 2000 and beyond. Cell 100:113–127Google Scholar
  17. Jones RB, Gordus A, Krall JA, Macbeath G (2006) A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439:168–174CrossRefPubMedGoogle Scholar
  18. Kozuka-Hata H, Nasu-Nishimura Y, Koyama-Nasu Y, Ao-Kondo H, Tsumoto K, Akiyama T, Oyama M (2012a) Global proteome analysis of glioblastoma stem cells by high-resolution mass spectrometry. Curr Topics Pept Protein Res 13:1–47CrossRefGoogle Scholar
  19. Kozuka-Hata H, Nasu-Nishimura Y, Koyama-Nasu Y, Ao-Kondo H, Tsumoto K, Akiyama T, Oyama M (2012b) Phosphoproteome of human glioblastoma initiating cells reveals novel signaling regulators encoded by the transcriptome. PLoS One 7:e43398CrossRefPubMedCentralPubMedGoogle Scholar
  20. Kumar N, Wolf-Yadlin A, White FM, Lauffenburger DA (2007) Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data. PLoS Comput Biol 3:e4CrossRefPubMedCentralPubMedGoogle Scholar
  21. Larsen MR, Thingholm TE, Jensen ON, Roepstorff P, Jorgensen TJ (2005) Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol Cell Proteomics 4:873–886CrossRefPubMedGoogle Scholar
  22. Mani SA, Guo W, Liao MJ, Eaton EN, Ayyanan A, Zhou AY, Brooks M, Reinhard F, Zhang CC, Shipitsin M, Campbell LL, Polyak K, Brisken C, Yang J, Weinberg RA (2008) The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell 133:704–715CrossRefPubMedCentralPubMedGoogle Scholar
  23. Mann M (2006) Functional and quantitative proteomics using SILAC. Nat Rev Mol Cell Biol 7:952–958CrossRefPubMedGoogle Scholar
  24. Morandell S, Stasyk T, Skvortsov S, Ascher S, Huber LA (2008) Quantitative proteomics and phosphoproteomics reveal novel insights into complexity and dynamics of the EGFR signaling network. Proteomics 8:4383–4401CrossRefPubMedGoogle Scholar
  25. Murakami M, Ichisaka T, Maeda M, Oshiro N, Hara K, Edenhofer F, Kiyama H, Yonezawa K, Yamanaka S (2004) mTOR is essential for growth and proliferation in early mouse embryos and embryonic stem cells. Mol Cell Biol 24:6710–6718CrossRefPubMedCentralPubMedGoogle Scholar
  26. Oda K, Matsuoka Y, Funahashi A, Kitano H (2005) A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol 1:0010CrossRefPubMedGoogle Scholar
  27. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127:635–648CrossRefPubMedGoogle Scholar
  28. Olsen JV, Schwartz JC, Griep-Raming J, Nielsen ML, Damoc E, Denisov E, Lange O, Remes P, Taylor D, Splendore M, Wouters ER, Senko M, Makarov A, Mann M, Horning S (2009) A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed. Mol Cell Proteomics 8:2759–2769CrossRefPubMedCentralPubMedGoogle Scholar
  29. Olsen JV, Vermeulen M, Santamaria A, Kumar C, Miller ML, Jensen LJ, Gnad F, Cox J, Jensen TS, Nigg EA, Brunak S, Mann M (2010) Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal 3:ra3Google Scholar
  30. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386CrossRefPubMedGoogle Scholar
  31. Ong SE, Kratchmarova I, Mann M (2003) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res 2:173–181CrossRefPubMedGoogle Scholar
  32. Oyama M, Kozuka-Hata H, Tasaki S, Semba K, Hattori S, Sugano S, Inoue J, Yamamoto T (2009) Temporal perturbation of tyrosine phosphoproteome dynamics reveals the system-wide regulatory networks. Mol Cell Proteomics 8:226–231CrossRefPubMedGoogle Scholar
  33. Oyama M, Nagashima T, Suzuki T, Kozuka-Hata H, Yumoto N, Shiraishi Y, Ikeda K, Kuroki Y, Gotoh N, Ishida T, Inoue S, Kitano H, Okada-Hatakeyama M (2011) Integrated quantitative analysis of the phosphoproteome and transcriptome in tamoxifen-resistant breast cancer. J Biol Chem 286:818–829CrossRefPubMedCentralPubMedGoogle Scholar
  34. Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, Nardone J, Lee K, Reeves C, Li Y, Hu Y, Tan Z, Stokes M, Sullivan L, Mitchell J, Wetzel R, Macneill J, Ren JM, Yuan J, Bakalarski CE, Villen J, Kornhauser JM, Smith B, Li D, Zhou X, Gygi SP, Gu TL, Polakiewicz RD, Rush J, Comb MJ (2007) Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131:1190–1203CrossRefPubMedGoogle Scholar
  35. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169CrossRefPubMedGoogle Scholar
  36. Rush J, Moritz A, Lee KA, Guo A, Goss VL, Spek EJ, Zhang H, Zha XM, Polakiewicz RD, Comb MJ (2005) Immunoaffinity profiling of tyrosine phosphorylation in cancer cells. Nat Biotechnol 23:94–101CrossRefPubMedGoogle Scholar
  37. Sadygov R, Wohlschlegel J, Park SK, Xu T, Yates JR 3rd (2006) Central limit theorem as an approximation for intensity-based scoring function. Anal Chem 78:89–95CrossRefPubMedGoogle Scholar
  38. Salomon AR, Ficarro SB, Brill LM, Brinker A, Phung QT, Ericson C, Sauer K, Brock A, Horn DM, Schultz PG, Peters EC (2003) Profiling of tyrosine phosphorylation pathways in human cells using mass spectrometry. Proc Natl Acad Sci U S A 100:443–448CrossRefPubMedCentralPubMedGoogle Scholar
  39. Schlessinger J (2000) Cell signaling by receptor tyrosine kinases. Cell 103:211–225CrossRefPubMedGoogle Scholar
  40. Schulze WX, Deng L, Mann M (2005) Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol Syst Biol 1:2005.0008CrossRefPubMedCentralPubMedGoogle Scholar
  41. Singh S, Springer M, Steen J, Kirschner MW, Steen H (2009) FLEXIQuant: a novel tool for the absolute quantification of proteins, and the simultaneous identification and quantification of potentially modified peptides. J Proteome Res 8:2201–2210CrossRefPubMedCentralPubMedGoogle Scholar
  42. Steen H, Jebanathirajah JA, Springer M, Kirschner MW (2005) Stable isotope-free relative and absolute quantitation of protein phosphorylation stoichiometry by MS. Proc Natl Acad Sci U S A 102:3948–3953CrossRefPubMedCentralPubMedGoogle Scholar
  43. Stensballe A, Andersen S, Jensen ON (2001) Characterization of phosphoproteins from electrophoretic gels by nanoscale Fe(III) affinity chromatography with off-line mass spectrometry analysis. Proteomics 1:207–222CrossRefPubMedGoogle Scholar
  44. Tasaki S, Nagasaki M, Oyama M, Hata H, Ueno K, Yoshida R, Higuchi T, Sugano S, Miyano S (2006) Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data. Genome Inform 17:226–238PubMedGoogle Scholar
  45. Tasaki S, Nagasaki M, Kozuka-Hata H, Semba K, Gotoh N, Hattori S, Inoue J, Yamamoto T, Miyano S, Sugano S, Oyama M (2010) Phosphoproteomics-based modeling defines the regulatory mechanism underlying aberrant EGFR signaling. PLoS One 5:e13926CrossRefPubMedCentralPubMedGoogle Scholar
  46. Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904CrossRefPubMedGoogle Scholar
  47. Tran JC, Doucette AA (2009) Multiplexed size separation of intact proteins in solution phase for mass spectrometry. Anal Chem 81:6201–6209CrossRefPubMedGoogle Scholar
  48. Walther TC, Mann M (2010) Mass spectrometry-based proteomics in cell biology. J Cell Biol 190:491–500CrossRefPubMedCentralPubMedGoogle Scholar
  49. Wolf-Yadlin A, Kumar N, Zhang Y, Hautaniemi S, Zaman M, Kim HD, Grantcharova V, Lauffenburger DA, White FM (2006) Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Mol Syst Biol 2:54CrossRefPubMedCentralPubMedGoogle Scholar
  50. Zhang Y, Wolf-Yadlin A, Ross PL, Pappin DJ, Rush J, Lauffenburger DA, White FM (2005) Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol Cell Proteomics 4:1240–1250CrossRefPubMedGoogle Scholar

Copyright information

© Springer Japan 2015

Authors and Affiliations

  1. 1.Medical Proteomics Laboratory, The Institute of Medical ScienceThe University of TokyoTokyoJapan

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