Mapping in vivo signal transduction defects by phosphoproteomics

Trends in Molecular Medicine

Volumn 18, Issue 1, 2012, Pages 43-51

  Stasyk, Taras ^a   Huber, Lukas A ^a  

^a INNSBRUCK MEDICAL UNIVERSITY   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; EPIDERMAL GROWTH FACTOR; INSULIN; MAMMALIAN TARGET OF RAPAMYCIN; NEU DIFFERENTIATION FACTOR; PHOSPHOPEPTIDE; PROTEIN KINASE B; TITANIUM DIOXIDE; TOLL LIKE RECEPTOR; TRANSFORMING GROWTH FACTOR BETA;

AMINO ACID IN CELL CULTURE; ANION EXCHANGE CHROMATOGRAPHY; AUTOIMMUNE DISEASE; BIOINFORMATICS; CARDIOVASCULAR DISEASE; CELL TRANSFORMATION; ENZYME ACTIVITY; FRONTOTEMPORAL DEMENTIA; FUNCTIONAL PROTEOMICS; HUMAN; HYDROPHILIC INTERACTION CHROMATOGRAPHY; IMMOBILIZED METAL AFFINITY CHROMATOGRAPHY; IN VIVO STUDY; ISOTOPE LABELING; MASS SPECTROMETRY; NONHUMAN; PRIMARY CELL CULTURE; PROTEIN DEPHOSPHORYLATION; PROTEIN MODIFICATION; PROTEIN STABILITY; REVIEW; SIGNAL TRANSDUCTION;

ANIMALS; HUMANS; MASS SPECTROMETRY; PHOSPHOPROTEINS; PROTEOMICS; SIGNAL TRANSDUCTION;

ANIMALIA;

EID: 84855572697     PISSN: 14714914     EISSN: 1471499X     Source Type: Journal    
DOI: 10.1016/j.molmed.2011.11.001     Document Type: Review

References (81)

1. Tan C.S., et al. Positive selection of tyrosine loss in metazoan evolution. Science 2009, 325:1686-1688.
2. Khoury G.A., et al. Proteome-wide post-translational modification statistics: frequency analysis and curation of the Swiss-Prot database. Sci. Rep. 2011, 1. pii srep00090.
3. Cieśla J., et al. Phosphorylation of basic amino acid residues in proteins: important but easily missed. Acta Biochim. Pol. 2011, 58:137-148.
4. Tan C.S., et al. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Sci. Signal 2009, 2:ra39.
5. Harsha H.C., Pandey A. Phosphoproteomics in cancer. Mol. Oncol. 2010, 4:482-495.
6. Iwai L.K., et al. Quantitative phosphoproteomic analysis of T cell receptor signaling in diabetes prone and resistant mice. J. Proteome Res. 2010, 9:3135-3145.
7. López-Santalla M. Tyr(323)-dependent p38 activation is associated with rheumatoid arthritis and correlates with disease activity. Arthritis Rheum. 2011, 63:1833-1842.
8. Gaestel M., et al. Targeting innate immunity protein kinase signalling in inflammation. Nat. Rev. Drug Discov. 2009, 8:480-499.
9. Van Eyk J.E. Overview: the maturing of proteomics in cardiovascular research. Circ. Res. 2011, 108:490-498.
10. Xia Q., et al. Phosphoproteomic analysis of human brain by calcium phosphate precipitation and mass spectrometry. J. Proteome Res. 2008, 7:2845-2851.
11. Rask-Andersen M., et al. Trends in the exploitation of novel drug targets. Nat. Rev. Drug Discov. 2011, 10:579-590.
12. Morandell S., et al. Phosphoproteomics strategies for the functional analysis of signal transduction. Proteomics 2006, 6:4047-4056.
13. Cutillas P.R., Jørgensen C. Biological signalling activity measurements using mass spectrometry. Biochem. J. 2011, 434:189-199.
14. Morandell S., et al. Quantitative proteomics and phosphoproteomics reveal novel insights into complexity and dynamics of the EGFR signaling network. Proteomics 2008, 8:4383-4401.
15. Preisinger C., et al. Proteomics and phosphoproteomics for the mapping of cellular signalling networks. Proteomics 2008, 8:4402-4415.
16. Tedford N.C., et al. Quantitative analysis of cell signaling and drug action via mass spectrometry-based systems level phosphoproteomics. Proteomics 2009, 9:1469-1487.
17. Macek B., et al. Global and site-specific quantitative phosphoproteomics: principles and applications. Annu. Rev. Pharmacol. Toxicol. 2009, 49:199-221.
18. Kelstrup C.D., et al. Pinpointing phosphorylation sites: quantitative filtering and a novel site-specific x-ion fragment. J. Proteome Res. 2011, 10:2937-2948.
19. Michalski A., et al. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res. 2011, 10:1785-1793.
20. Ficarro S.B., et al. Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae. Nat. Biotechnol. 2002, 20:301-305.
21. Larsen M.R., et al. Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol. Cell. Proteomics 2005, 4:873-886.
22. Dunn J.D., et al. Techniques for phosphopeptide enrichment prior to analysis by mass spectrometry. Mass Spectrom. Rev. 2010, 29:29-54.
23. Larsen M.R., et al. Exploring the sialiome using titanium dioxide chromatography and mass spectrometry. Mol. Cell. Proteomics 2007, 6:1778-1787.
24. Ballif B.A., et al. Phosphoproteomic analysis of the developing mouse brain. Mol. Cell. Proteomics 2004, 3:1093-1101.
25. Han G., et al. Large-scale phosphoproteome analysis of human liver tissue by enrichment and fractionation of phosphopeptides with strong anion exchange chromatography. Proteomics 2008, 8:1346-1361.
26. McNulty D.E., Annan R.S. Hydrophilic interaction chromatography reduces the complexity of the phosphoproteome and improves global phosphopeptide isolation and detection. Mol. Cell. Proteomics 2008, 7:971-980.
27. Alpert A.J. Electrostatic repulsion hydrophilic interaction chromatography for isocratic separation of charged solutes and selective isolation of phosphopeptides. Anal. Chem. 2008, 80:62-76.
28. Zarei M., et al. Comparison of ERLIC-TiO2, HILIC-TiO2, and SCX-TiO2 for global phosphoproteomics approaches. J. Proteome Res. 2011, 10:3474-3483.
29. Di Palma, et al. Zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC and ZIC-cHILIC) provide high resolution separation and increase sensitivity in proteome analysis. Anal. Chem. 2011, 83:3440-3447.
30. Eyrich B., et al. Catch me if you can: mass spectrometry-based phosphoproteomics and quantification strategies. Proteomics 2011, 11:554-570.
31. Thingholm T.E., et al. Analytical strategies for phosphoproteomics. Proteomics 2009, 9:1451-1468.
32. Kosako H., Nagano K. Quantitative phosphoproteomics strategies for understanding protein kinase-mediated signal transduction pathways. Expert Rev. Proteomics 2011, 8:81-94.
33. Ozlu N., et al. Phosphoproteomics. Wiley Interdiscip. Rev. Syst. Biol. Med. 2011, 2:255-276.
34. Ong S.E. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1:376-386.
35. Ross P.L., et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 2004, 3:1154-1169.
36. Thompson A., et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 2003, 75:1895-1904.
37. Koehler C.J., et al. Isobaric peptide termini labeling for MS/MS-based quantitative proteomics. J. Proteome Res. 2009, 8:4333-4341.
38. Schmidt A., et al. A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 2005, 5:4-15.
39. Boersema P.J., et al. Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to cell and tissue lysates. Proteomics 2008, 8:4624-4632.
40. Osinalde N., et al. Interleukin-2 signaling pathway analysis by quantitative phosphoproteomics. J. Proteomics 2011, In press. 10.1016/j.jprot.2011.06.007.
41. Xiao K., et al. Global phosphorylation analysis of beta-arrestin-mediated signaling downstream of a seven transmembrane receptor (7TMR). Proc. Natl. Acad. Sci. U.S.A. 2010, 107:15299-15304.
42. Ali N.A., Molloy M.P. Quantitative phosphoproteomics of transforming growth factor-beta signalling in colon cancer cells. Proteomics 2011, 11:3390-3401.
43. Nagashima T., et al. Phosphoproteome and transcriptome analyses of ErbB ligand-stimulated MCF-7 cells. Cancer Genomics Proteomics 2008, 5:161-168.
44. Krüger M., et al. Dissection of the insulin signaling pathway via quantitative phosphoproteomics. Proc. Natl. Acad. Sci. U.S.A. 2008, 105:2451-2456.
45. Krüger M., et al. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 2008, 134:353-364.
46. Sury M.D., et al. The SILAC fly allows for accurate protein quantification in vivo. Mol. Cell. Proteomics 2010, 9:2173-2183.
47. Looso M., et al. Advanced identification of proteins in uncharacterized proteomes by pulsed in vivo stable isotope labeling-based mass spectrometry. Mol. Cell. Proteomics 2010, 5:1157-1166.
48. Weintz G., et al. The phosphoproteome of toll-like receptor-activated macrophages. Mol. Syst. Biol. 2010, 6:371.
49. Geiger T. Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 2011, 6:147-157.
50. Monetti M., et al. Large-scale phosphosite quantification in tissues by a spike-in SILAC method. Nat. Methods 2011, 8:655-658.
51. Wiśniewski J.R., et al. Universal sample preparation method for proteome analysis. Nat. Methods 2009, 6:359-362.
52. Manza L.L., et al. Sample preparation and digestion for proteomic analyses using spin filters. Proteomics 2005, 5:1742-1745.
53. Cox J., Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26:1367-1372.
54. Geiger T., et al. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 2010, 7:383-385.
55. Ishihama Y., et al. Quantitative mouse brain proteomics using culture-derived isotope tags as internal standards. Nat. Biotechnol. 2005, 23:617-621.
56. Pan C., et al. Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol. Cell. Proteomics 2009, 8:443-450.
57. Wu J., et al. Integrating titania enrichment, iTRAQ labeling, and Orbitrap CID-HCD for global identification and quantitative analysis of phosphopeptides. Proteomics 2010, 10:2224-2234.
58. Hsu P.P., et al. The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling. Science 2011, 332:1317-1322.
59. Pawar H. Quantitative tissue proteomics of esophageal squamous cell carcinoma for novel biomarker discovery. Cancer Biol. Ther. 2011, 12:510-522.
60. Pichler P., et al. Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell. Anal. Chem. 2011, 83:1469-1474.
61. Pichler P. Peptide labeling with isobaric tags yields higher identification rates using iTRAQ 4-plex compared to TMT 6-plex and iTRAQ 8-plex on LTQ Orbitrap. Anal. Chem. 2010, 82:6549-6558.
62. Thingholm T.E., et al. Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J. Proteome Res. 2010, 9:4045-4052.
63. Mirzaei H., et al. Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry. Mol. Cell. Proteomics 2009, 8:1934-1946.
64. Burkhart J.M., et al. Quality control of nano-LC-MS systems using stable isotope-coded peptides. Proteomics 2011, 11:1049-1057.
65. Soderblom E.J., et al. Quantitative label-free phosphoproteomics strategy for multifaceted experimental designs. Anal. Chem. 2011, 83:3758-3764.
66. Montoya A. Characterization of a TiO(2) enrichment method for label-free quantitative phosphoproteomics. Methods 2011, 54:370-378.
67. Herskowitz J.H., et al. Phosphoproteomic analysis reveals site-specific changes in GFAP and NDRG2 phosphorylation in frontotemporal lobar degeneration. J. Proteome Res. 2010, 9:6368-6379.
68. Ostasiewicz P., et al. Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry. J. Proteome Res. 2010, 9:3688-3700.
69. Eng J.K., et al. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 1994, 5:976-989.
70. Perkins D.N., et al. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999, 20:3551-3567.
71. Olsen J.V., et al. A dual pressure linear ion trap Orbitrap instrument with very high sequencing speed. Mol. Cell. Proteomics 2009, 8:2759-2769.
72. Nagaraj N., et al. Feasibility of large-scale phosphoproteomics with higher energy collisional dissociation fragmentation. J. Proteome Res. 2010, 9:6786-6794.
73. Bell A.W., et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat. Methods 2009, 6:423-430.
74. Elias J.E., Gygi S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 2007, 4:207-214.
75. Thingholm T.E., et al. SIMAC (sequential elution from IMAC), a phosphoproteomics strategy for the rapid separation of monophosphorylated from multiply phosphorylated peptides. Mol. Cell. Proteomics 2008, 7:661-671.
76. Friedel C.C., Dölken L. Metabolic tagging and purification of nascent RNA: implications for transcriptomics. Mol. Biosyst. 2010, 5:1271-1278.
77. Hsu J.L., et al. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem. 2003, 75:6843-6852.
78. Song C., et al. Improvement of the quantification accuracy and throughput for phosphoproteome analysis by a pseudo triplex stable isotope dimethyl labeling approach. Anal. Chem. 2011, 83:7755-7762.
79. Asara J.M., et al. A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics 2008, 8:994-999.
80. Steinberg T.H., et al. Global quantitative phosphoprotein analysis using multiplexed proteomics technology. Proteomics 2003, 3:1128-1144.
81. Di Domenico F., et al. Quantitative proteomics analysis of phosphorylated proteins in the hippocampus of Alzheimer's disease subjects. J. Proteomics 2011, 74:1091-1103.