Quantitative phosphoproteomics revealswidespread full phosphorylation site occupancy during mitosis

Science Signaling

Volumn 3, Issue 104, 2010, Pages

  Olsen, Jesper V ^a,b   Vermeulen, Michiel ^a,c   Santamaria, Anna ^d   Kumar, Chanchal ^d,e   Miller, Martin L ^b,f   Jensen, Lars J ^b   Gnad, Florian ^a   Cox, Jürgen ^a   Jensen, Thomas S ^g   Nigg, Erich A ^d   Brunak, Søren ^b,g   Mann, Matthias ^a,b  

^a MAX PLANCK INSTITUTE OF BIOCHEMISTRY   (Germany)

^b University of Copenhagen   (Denmark)

^c UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^d UNIVERSITY OF BASEL   (Switzerland)

^e Eli Lilly Singapore Pte Ltd   (Singapore)

^f MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^g TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ATM PROTEIN; ATR PROTEIN; CYCLIN DEPENDENT KINASE 1; CYCLIN DEPENDENT KINASE 2; DNA DEPENDENT PROTEIN KINASE; MESSENGER RNA; PHOSPHOPROTEIN; PROTEOME; CDK2 PROTEIN, HUMAN;

AMINO ACID SEQUENCE; ARTICLE; CELL CULTURE; CELL CYCLE REGULATION; CELL CYCLE S PHASE; CONTROLLED STUDY; DEREGULATION; DNA DAMAGE; HUMAN; HUMAN CELL; MASS SPECTROMETRY; MITOSIS; PRIORITY JOURNAL; PROTEIN DEGRADATION; PROTEIN PHOSPHORYLATION; QUANTITATIVE ANALYSIS; STOICHIOMETRY; UPREGULATION; BINDING SITE; CELL CYCLE; CLUSTER ANALYSIS; DNA MICROARRAY; ENZYME SPECIFICITY; FLOW CYTOMETRY; GENE EXPRESSION PROFILING; GENETICS; HELA CELL; IMMUNOBLOTTING; METABOLISM; METHODOLOGY; PHOSPHORYLATION; PHYSIOLOGY; PROTEIN BINDING; PROTEIN MICROARRAY; PROTEOMICS; SIGNAL TRANSDUCTION;

EUKARYOTA;

BINDING SITES; CDC2 PROTEIN KINASE; CELL CYCLE; CLUSTER ANALYSIS; CYCLIN-DEPENDENT KINASE 2; FLOW CYTOMETRY; GENE EXPRESSION PROFILING; HELA CELLS; HUMANS; IMMUNOBLOTTING; MASS SPECTROMETRY; MITOSIS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHOSPHOPROTEINS; PHOSPHORYLATION; PROTEIN ARRAY ANALYSIS; PROTEIN BINDING; PROTEOME; PROTEOMICS; SIGNAL TRANSDUCTION; SUBSTRATE SPECIFICITY;

EID: 77951644400     PISSN: 19450877     EISSN: 19379145     Source Type: Journal    
DOI: 10.1126/scisignal.2000475     Document Type: Article

References (61)

1. M. B. Kastan, J. Bartek, Cell-cycle checkpoints and cancer. Nature 432, 316-323 (2004).
2. D. O. Morgan, The Cell Cycle: Principles of Control (New Science Press Ltd., London, 2007).
3. R. J. Cho, M. Huang, M. J. Campbell, H. Dong, L. Steinmetz, L. Sapinoso, G. Hampton, S. J. Elledge, R. W. Davis, D. J. Lockhart, Transcriptional regulation and function during the human cell cycle. Nat. Genet. 27, 48-54 (2001).
4. M. L.Whitfield, G. Sherlock, A. J. Saldanha, J. I. Murray, C. A. Ball, K. E. Alexander, J. C. Matese, C. M. Perou, M. M. Hurt, P. O. Brown, D. Botstein, Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977-2000 (2002).
5. B. Domon, R. Aebersold, Mass spectrometry and protein analysis. Science 312, 212-217 (2006).
6. S. Matsuoka, B. A. Ballif, A. Smogorzewska, E. R. McDonald III, K. E. Hurov, J. Luo, C. E. Bakalarski, Z. Zhao, N. Solimini, Y. Lerenthal, Y. Shiloh, S. P. Gygi, S. J. Elledge, ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 316, 1160-1166 (2007).
7. M. Mann, N. L. Kelleher, Precision proteomics: The case for high resolution and high mass accuracy. Proc. Natl. Acad. Sci. U.S.A. 105, 18132-18138 (2008).
8. S. Charbonnier, O. Gallego, A. C. Gavin, The social network of a cell: Recent advances in interactome mapping. Biotechnol. Annu. Rev. 14, 1-28 (2008).
9. B. Macek, M. Mann, J. V. Olsen, Global and site-specific quantitative phosphoproteomics: Principles and applications. Annu. Rev. Pharmacol. Toxicol. 49, 199-221 (2009).
10. S. E. Ong, B. Blagoev, I. Kratchmarova, D. B. Kristensen, H. Steen, A. Pandey, M. Mann, Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376-386 (2002).
11. J. V. Olsen, B. Blagoev, F. Gnad, B. Macek, C. Kumar, P. Mortensen, M. Mann, Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635-648 (2006).
12. L. M. de Godoy, J. V. Olsen, J. Cox, M. L. Nielsen, N. C. Hubner, F. Frohlich, T. C. Walther, M. Mann, Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251-1254 (2008).
13. J. Cox, M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367-1372 (2008).
14. C. Kumar, M. Mann, Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett. 583, 1703-1712 (2009).
15. H. Daub, J. V. Olsen, M. Bairlein, F. Gnad, F. S. Oppermann, R. Korner, Z. Greff, G. Keri, O. Stemmann, M. Mann, Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 31, 438-448 (2008).
16. N. Dephoure, C. Zhou, J. Villén, S. A. Beausoleil, C. E. Bakalarski, S. J. Elledge, S. P. Gygi, A quantitative atlas of mitotic phosphorylation. Proc. Natl. Acad. Sci. U.S.A. 105, 10762-10767 (2008).
17. M. Selbach, B. Schwanhäusser, N. Thierfelder, Z. Fang, R. Khanin, N. Rajewsky, Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58-63 (2008).
18. J. R. Wisniewski, A. Zougman, N. Nagaraj, M. Mann, Universal sample preparation method for proteome analysis. Nat. Methods 6, 359-362 (2009).
19. P. Cohen, The Croonian Lecture 1998. Identification of a protein kinase cascade of major importance in insulin signal transduction. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354, 485-495 (1999).
20. L. J. Jensen, T. S. Jensen, U. de Lichtenberg, S. Brunak, P. Bork, Co-evolution of transcriptional and post-translational cell-cycle regulation. Nature 443, 594-597 (2006).
21. R. Kittler, L. Pelletier, A. K. Heninger, M. Slabicki, M. Theis, L. Miroslaw, I. Poser, S. Lawo, H. Grabner, K. Kozak, J.Wagner, V. Surendranath, C. Richter, W. Bowen, A. L. Jackson, B. Habermann, A. A. Hyman, F. Buchholz, Genome-scale RNAi profiling of cell division in human tissue culture cells. Nat. Cell Biol. 9, 1401-1412 (2007).
22. C. Pan, C. Kumar, S. Bohl, U. Klingmueller, M. Mann, Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol. Cell. Proteomics 8, 443-450 (2009).
23. J. P. Carson, N. Zhang, G. M. Frampton, N. P. Gerry, M. E. Lenburg, M. F. Christman, Pharmacogenomic identification of targets for adjuvant therapy with the topoisomerase poison camptothecin. Cancer Res. 64, 2096-2104 (2004).
24. J. Graumann, N. C. Hubner, J. B. Kim, K. Ko, M. Moser, C. Kumar, J. Cox, H. Scholer, M. Mann, Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol. Cell. Proteomics 7, 672-683 (2008).
25. T. Bonaldi, T. Straub, J. Cox, C. Kumar, P. B. Becker, M. Mann, Combined use of RNAi and quantitative proteomics to study gene function in Drosophila. Mol. Cell 31, 762-772 (2008).
26. K. A. O'Donnell, E. A. Wentzel, K. I. Zeller, C. V. Dang, J. T. Mendell, c-Myc-regulated microRNAs modulate E2F1 expression. Nature 435, 839-843 (2005).
27. K. Furukawa, T. Kondo, Identification of the lamina-associated- polypeptide-2-binding domain of B-type lamin. Eur. J. Biochem. 251, 729-733 (1998).
28. R. Foisner, L. Gerace, Integral membrane proteins of the nuclear envelope interact with lamins and chromosomes, and binding is modulated by mitotic phosphorylation. Cell 73, 1267-1279 (1993).
29. M. L. Miller, L. J. Jensen, F. Diella, C. Jorgensen, M. Tinti, L. Li, M. Hsiung, S. A. Parker, J. Bordeaux, T. Sicheritz-Ponten, M. Olhovsky, A. Pasculescu, J. Alexander, S. Knapp, N. Blom, P. Bork, S. Li, G. Cesareni, T. Pawson, B. E. Turk, M. B. Yaffe, S. Brunak, R. Linding, Linear motif atlas for phosphorylation-dependent signaling. Sci. Signal. 1, ra2 (2008).
30. L. O'Regan, J. Blot, A. M. Fry, Mitotic regulation by NIMA-related kinases. Cell Div. 2, 25 (2007).
31. 2. J. Biol. Chem. 280, 42383-42390 (2005).
32. L. J. Jensen, M. Kuhn, M. Stark, S. Chaffron, C. Creevey, J. Muller, T. Doerks, P. Julien, A. Roth, M. Simonovic, P. Bork, C. von Mering, STRING8-A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412-D416 (2009).
33. G. D. Bader, C. W. Hogue, An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003).
34. R. Linding, L. J. Jensen, A. Pasculescu, M. Olhovsky, K. Colwill, P. Bork, M. B. Yaffe, T. Pawson, NetworKIN: A resource for exploring cellular phosphorylation networks. Nucleic Acids Res. 36, D695-D699 (2008).
35. J. W. Harper, S. J. Elledge, The DNA damage response: Ten years after. Mol. Cell 28, 739-745 (2007).
36. D. Branzei, M. Foiani, Regulation of DNA repair throughout the cell cycle. Nat. Rev. Mol. Cell Biol. 9, 297-308 (2008).
37. F. Diella, S. Cameron, C. Gemund, R. Linding, A. Via, B. Kuster, T. Sicheritz-Ponten, N. Blom, T. J. Gibson, Phospho.ELM: A database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 5, 79 (2004).
38. P. V. Hornbeck, I. Chabra, J. M. Kornhauser, E. Skrzypek, B. Zhang, PhosphoSite: A bioinformatics resource dedicated to physiological protein phosphorylation. Proteomics 4, 1551-1561 (2004).
39. H. Steen, J. A. Jebanathirajah, M. Springer, M. W. Kirschner, Stable isotope-free relative and absolute quantitation of protein phosphorylation stoichiometry by MS. Proc. Natl. Acad. Sci. U.S.A. 102, 3948-3953 (2005).
40. J. A. Steen, H. Steen, A. Georgi, K. Parker, M. Springer, M. Kirchner, F. Hamprecht, M. W. Kirschner, Different phosphorylation states of the anaphase promoting complex in response to antimitotic drugs: A quantitative proteomic analysis. Proc. Natl. Acad. Sci. U.S.A. 105, 6069-6074 (2008).
41. O. J. Shah, S. Ghosh, T. Hunter, Mitotic regulation of ribosomal S6 kinase 1 involves Ser/Thr, Pro phosphorylation of consensus and non-consensus sites by Cdc2. J. Biol. Chem. 278, 16433-16442 (2003).
42. F. Gnad, S. Ren, J. Cox, J. V. Olsen, B. Macek, M. Oroshi, M. Mann, PHOSIDA (phosphorylation site database): Management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol. 8, R250 (2007).
43. F. Gnad, M. Oroshi, E. Birney, M. Mann, MAPU 2.0: High-accuracy proteomes mapped to genomes. Nucleic Acids Res. 37, D902-D906 (2009).
44. P. Flicek, B. L. Aken, K. Beal, B. Ballester, M. Caccamo, Y. Chen, L. Clarke, G. Coates, F. Cunningham, T. Cutts, T. Down, S. C. Dyer, T. Eyre, S. Fitzgerald, J. Fernandez-Banet, S. Graf, S. Haider, M. Hammond, R. Holland, K. L. Howe, K. Howe, N. Johnson, A. Jenkinson, A. Kahari, D. Keefe, F. Kokocinski, E. Kulesha, D. Lawson, I. Longden, K. Megy, P.Meidl, B.Overduin, A. Parker, B. Pritchard, A. Prlic, S. Rice, D. Rios, M. Schuster, I. Sealy, G. Slater, D. Smedley, G. Spudich, S. Trevanion, A. J. Vilella, J. Vogel, S.White, M. Wood, E. Birney, T. Cox, V. Curwen, R. Durbin, X. M. Fernandez-Suarez, J. Herrero, T. J. Hubbard, A. Kasprzyk, G. Proctor, J. Smith, A. Ureta-Vidal, S. Searle, Ensembl 2008. Nucleic Acids Res. 36, D707-D714 (2008).
45. G. E. Lienhard, Non-functional phosphorylations? Trends Biochem. Sci. 33, 351-352 (2008).
46. A. Shevchenko, H. Tomas, J. Havlis, J. V. Olsen, M. Mann, In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856-2860 (2006).
47. M. W. Pinkse, P. M. Uitto, M. J. Hilhorst, B. Ooms, A. J. Heck, Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLCESI-MS/MS and titanium oxide precolumns. Anal. Chem. 76, 3935-3943 (2004).
48. M. R. Larsen, T. E. Thingholm, O. N. Jensen, P. Roepstorff, T. J. Jorgensen, Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol. Cell. Proteomics 4, 873-886 (2005).
49. A. Blangy, L. Arnaud, E. A. Nigg, Phosphorylation by p34cdc2 protein kinase regulates binding of the kinesin-related motor HsEg5 to the dynactin subunit p150. J. Biol. Chem. 272, 19418-19424 (1997).
50. T. Yamaguchi, H. Goto, T. Yokoyama, H. Sillje, A. Hanisch, A. Uldschmid, Y. Takai, T. Oguri, E. A. Nigg, M. Inagaki, Phosphorylation by Cdk1 induces Plk1-mediated vimentin phosphorylation during mitosis. J. Cell Biol. 171, 431-436 (2005).
51. M. J. Schroeder, J. Shabanowitz, J. C. Schwartz, D. F. Hunt, J. J. Coon, A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. Anal. Chem. 76, 3590-3598 (2004).
52. J. V. Olsen, L. M. de Godoy, G. Li, B. Macek, P. Mortensen, R. Pesch, A. Makarov, O. Lange, S. Horning, M. Mann, Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010-2021 (2005).
53. R. D. Finn, J. W. Stalker, D. K. Jackson, E. Kulesha, J. Clements, R. Pettett, ProServer: A simple, extensible Perl DAS server. Bioinformatics 23, 1568-1570 (2007).
54. M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, G. Sherlock, Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000).
55. K. V. Mardia, P. E. Jupp, Directional Statistics (John Wiley and Sons Ltd., ed. 2, 2000).
56. S. Falcon, R. Gentleman, Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257-258 (2007).
57. R Development Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2008).
58. A. Sturn, J. Quackenbush, Z. Trajanoski, Genesis: Cluster analysis of microarray data. Bioinformatics 18, 207-208 (2002).
59. M. Kanehisa, S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000).
60. P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, T. Ideker, Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
61. We thank members of the department of proteomics and signal transduction at the Max Planck Institute for Biochemistry for helpful discussions and comments on the manuscript. The Protein Research Center (CPR) is supported by a generous donation from the Novo Nordisk Foundation. This work is supported by a European Union research directorate sixth framework grant (Interaction Proteome grant LSHG-CT-2003-505520). M.V. is supported by a grant from the Dutch Cancer Society. A.S. is supported by a grant from the Spanish Education and Science Ministry. T.S.J is supported by an FP6 Network of Excellence grant (ENFIN LSHG-CT-2005-518254). This work was in part funded by the Bundesministerin für Bildung und Forschung QuantPro grant (0313831D).