第一型主要組織相容性複合體(major histocompatibility complex class I, MHC class I)呈現在所有的細胞表面，功能為提供來自外來病原或腫瘤相關的胜肽片段作為抗原，使免疫系統進行辨識。實驗使用同重元素相對和絕對定量(isobaric tags for relative and absolute quantitation, iTRAQ)的標定策略及質譜技術分析，經檸檬酸脫附後的人類乳突病毒(human papillomavirus, HPV) 轉染癌細胞之腫瘤相關抗原性胜肽。為了降低樣品複雜度並提高鑑定的動態範圍，使用奈米級液相層析質譜儀分析前會經由強陽離子交換層析(strong cationic exchange chromatography, SCX)、親水性交互作用層析(hydrophilic interaction chromatography, HILIC)及等電聚焦分級分離(solution isoelectric focusing, sIEF)對iTRAQ標定胜肽分離。數據分析上，首先以Swiss-Prot資料庫來確認，經質譜分析後的胜肽序列及其可信程度，再進一步使用MHC胜肽鍵結預測分析系統SYFPEITHI與immune epitope database (IEDB) 去預測胜肽與MHC class I分子間的鍵結親和性，最後以protein abundance across organisms (PaxDb) 與multi-omics profiling expression database (MOPED) 找尋MHC class I胜肽其來源蛋白質在各器官或疾病的表現量。癌細胞經由檸檬酸脫附後所得的樣品，以質譜鑑定並使用MHC胜肽鍵結預測分析系統，得到115段與MHC class I具有相關性的胜肽，並依據MHC的HLA*02:01型態篩選後得到78段胜肽，而其中FAG-、YVA-與YIP-此三段胜肽經由分析，確認會與MHC class I具有鍵結。本實驗提供ㄧ套以質譜分析為基礎的平台，去找尋MHC class I相關的胜肽，且T細胞若能對找尋到的MHC class I胜肽進行辨識並產生免疫反應，此胜肽對於腫瘤疫苗的發展將有極大的助益。

Major histocompatibility complex class I (MHC class I), which is present on the cell surface, play an important role in assisting immune system to recognize intracellular pathogens and tumor-derived peptide fragments. The goal of this study is to identify and to quantify tumor-associated peptides from HPV transformed cancer cell by citric acid treatment, isobaric tags for relative and absolute quantitation (iTRAQ) and mass spectrometric analysis. To reduce sample complexity for the quantitative dynamic range improvement, extracted MHC class I-bound peptides were fractionated by offline strong cationic exchange chromatography (SCX), hydrophilic interaction chromatography (HILIC) and solution isoelectric focusing (sIEF) before nanoLC mass spectrometric analysis. The tandem MS spectra were first searched against Swiss-Prot database for the possible MHC class I-associated peptide screening. Two algorithms, SYFPEITHI and immune epitope database (IEDB), were applied to calculate the binding affinity of MS-identified peptide sequence with MHC class I molecule. The results indicated that there were 115 MHC class I-associated peptides identified from the citric acid treated sample mixtures, and 78 of them were specific HLA*02:01-bound candidates. Among them, FAG-, YVA- and YIP- peptides were found to be stably bound with MHC class I by flow cytometry binding assay. Protein abundance across organisms (PaxDb) and multi-omics profiling expression database (MOPED) were also applied to validate the associate protein expression profiles of the predicted peptides in various organs and diseases. The proposed method provides an attractive alternative to discover native MHC class I-associated peptides by the MS-based platform. If these MHC class I-associated peptides can be recognized by T cells and be able to stimulate immune response, they will be of great assist in tumor vaccine development.

1. Medzhitov, R. and C.A. Janeway, Jr., Innate immunity: the virtues of a nonclonal system of recognition. Cell, 1997. 91(3): p. 295-8.
2. Medzhitov, R. and C.A. Janeway, Jr., Innate immunity: impact on the adaptive immune response. Curr Opin Immunol, 1997. 9(1): p. 4-9.
3. Medzhitov, R. and C.A. Janeway, Jr., Innate immune recognition and control of adaptive immune responses. Semin Immunol, 1998. 10(5): p. 351-3.
4. Cassier, M., Appropriation and commercialization of the Pasteur anthrax vaccine. Stud Hist Philos Biol Biomed Sci, 2005. 36(4): p. 722-42.
5. Hu, X., et al., Enhancement of cytolytic T lymphocyte precursor frequency in melanoma patients following immunization with the MAGE-1 peptide loaded antigen presenting cell-based vaccine. Cancer Res, 1996. 56(11): p. 2479-83.
6. Yamada, A., et al., Next-generation peptide vaccines for advanced cancer. Cancer Sci, 2013. 104(1): p. 15-21.
7. JAN M. M. WALBOOMERS1*, M.V.J., M. MICHELE MANOS2, F. XAVIER BOSCH3, J. ALAIN KUMMER1, KEERTI V. SHAH2, PETER J. F. SNIJDERS1, JULIAN PETO4, CHRIS J. L. M. MEIJER1 AND NUBIA MUN˜OZ5, HUMAN PAPILLOMAVIRUS IS A NECESSARY CAUSE OF INVASIVE CERVICAL CANCER WORLDWIDE. JOURNAL OF PATHOLOGY, 1999. 189: p. 12–19.
8. Durst, M., et al., A papillomavirus DNA from a cervical carcinoma and its prevalence in cancer biopsy samples from different geographic regions. Proc Natl Acad Sci U S A, 1983. 80(12): p. 3812-5.
9. Boshart, M., et al., A new type of papillomavirus DNA, its presence in genital cancer biopsies and in cell lines derived from cervical cancer. EMBO J, 1984. 3(5): p. 1151-7.
10. Janeway CA Jr, T.P., Walport M, et al., Immunobiology: The Immune System in Health and Disease. 5th edition. 2001.
11. Meydan, C., H.H. Otu, and O.U. Sezerman, Prediction of peptides binding to MHC class I and II alleles by temporal motif mining. BMC Bioinformatics, 2013. 14 Suppl 2: p. S13.
12. Mehra, N.K., et al., Molecular diversity of HLA-A*02 in Asian Indians: predominance of A*0211. Tissue Antigens, 2001. 57(6): p. 502-7.
13. Rammensee, H.G., T. Friede, and S. Stevanoviic, MHC ligands and peptide motifs: first listing. Immunogenetics, 1995. 41(4): p. 178-228.
14. Rammensee, H., et al., SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 1999. 50(3-4): p. 213-9.
15. Nielsen, M., et al., Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science, 2003. 12(5): p. 1007-1017.
16. Peters, B. and A. Sette, Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. Bmc Bioinformatics, 2005. 6.
17. Lundegaard, C., et al., NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Research, 2008. 36: p. W509-W512.
18. Sidney, J., et al., Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res, 2008. 4: p. 2.
19. Kim, Y., et al., Immune epitope database analysis resource. Nucleic Acids Res, 2012. 40(Web Server issue): p. W525-30.
20. Dessen, A., et al., X-ray crystal structure of HLA-DR4 (DRA*0101, DRB1*0401) complexed with a peptide from human collagen II. Immunity, 1997. 7(4): p. 473-81.
21. Madden, D.R., The Three-Dimensional Structure of Peptide-MHC Complexes. Annual Reviews, 1995. 13: p. 587-622.
22. Momburg, F., et al., Peptide Size Selection by the Major Histocompatibility Complex-Encoded Peptide Transporter. Journal of Experimental Medicine, 1994. 179(5): p. 1613-1623.
23. Lawrence J Stern1, D.C.W., Antigenic peptide binding by class I and class II histocompatibility proteins. Structure, 1994. 2(4): p. 245–251.
24. Motoyama, A. and J.R. Yates, 3rd, Multidimensional LC separations in shotgun proteomics. Anal Chem, 2008. 80(19): p. 7187-93.
25. Wang, X., et al., Peak capacity optimization of peptide separations in reversed-phase gradient elution chromatography: fixed column format. Anal Chem, 2006. 78(10): p. 3406-16.
26. Gilar, M., et al., Two-dimensional separation of peptides using RP-RP-HPLC system with different pH in first and second separation dimensions. J Sep Sci, 2005. 28(14): p. 1694-703.
27. Gilar, M., et al., Orthogonality of separation in two-dimensional liquid chromatography. Anal Chem, 2005. 77(19): p. 6426-34.
28. Hao, P., et al., Electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) versus strong cation exchange (SCX) for fractionation of iTRAQ-labeled peptides. J Proteome Res, 2011. 10(12): p. 5568-74.
29. Boersema, P.J., S. Mohammed, and A.J. Heck, Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal Bioanal Chem, 2008. 391(1): p. 151-9.
30. Alpert, A.J., Hydrophilic-interaction chromatography for the separation of peptides, nucleic acids and other polar compounds. J Chromatogr, 1990. 499: p. 177-96.
31. Buszewski, B. and S. Noga, Hydrophilic interaction liquid chromatography (HILIC)--a powerful separation technique. Anal Bioanal Chem, 2012. 402(1): p. 231-47.
32. Cargile, B.J., et al., Immobilized pH gradient isoelectric focusing as a first-dimension separation in shotgun proteomics. J Biomol Tech, 2005. 16(3): p. 181-9.
33. Lengqvist, J., K. Uhlen, and J. Lehtio, iTRAQ compatibility of peptide immobilized pH gradient isoelectric focusing. Proteomics, 2007. 7(11): p. 1746-52.
34. Horth, P., et al., Efficient fractionation and improved protein identification by peptide OFFGEL electrophoresis. Mol Cell Proteomics, 2006. 5(10): p. 1968-74.
35. Ernoult, E., E. Gamelin, and C. Guette, Improved proteome coverage by using iTRAQ labelling and peptide OFFGEL fractionation. Proteome Sci, 2008. 6: p. 27.
36. Chenau, J., et al., Peptides OFFGEL electrophoresis: a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with quantitative iTRAQ labeling. Proteome Sci, 2008. 6: p. 9.
37. Agilent 3100 OFFGEL Fractionator.
38. Fales, H.M., et al., Biological applications of electron ionization and chemical ionization mass spectrometry. Recent Prog Horm Res, 1972. 28: p. 591-626.
39. Fenn, J.B., et al., Electrospray ionization for mass spectrometry of large biomolecules. Science, 1989. 246(4926): p. 64-71.
40. Karas, M., D. Bachmann, and F. Hillenkamp, Influence of the Wavelength in High-Irradiance Ultraviolet-Laser Desorption Mass-Spectrometry of Organic-Molecules. Analytical Chemistry, 1985. 57(14): p. 2935-2939.
41. Karas, M. and F. Hillenkamp, Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem, 1988. 60(20): p. 2299-301.
42. Ikonomou, M.G., A.T. Blades, and P. Kebarle, Electrospray Ion Spray - a Comparison of Mechanisms and Performance. Analytical Chemistry, 1991. 63(18): p. 1989-1998.
43. Yi, E.C., et al., A microcapillary trap cartridge-microcapillary high-performance liquid chromatography electrospray ionization emitter device capable of peptide tandem mass spectrometry at the attomole level on an ion trap mass spectrometer with automated routine operation. Rapid Communications in Mass Spectrometry, 2003. 17(18): p. 2093-2098.
44. Nesvizhskii, A.I., et al., A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem, 2003. 75(17): p. 4646-58.
45. Peng, J., et al., Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Proteome Res, 2003. 2(1): p. 43-50.
46. Hunt, D.F., et al., Protein sequencing by tandem mass spectrometry. Proc Natl Acad Sci U S A, 1986. 83(17): p. 6233-7.
47. Wells, J.M. and S.A. McLuckey, Collision-induced dissociation (CID) of peptides and proteins. Methods Enzymol, 2005. 402: p. 148-85.
48. Edman, P., A method for the determination of amino acid sequence in peptides. Arch Biochem, 1949. 22(3): p. 475.
49. Edman, P. and G. Begg, A protein sequenator. Eur J Biochem, 1967. 1(1): p. 80-91.
50. Sagliocco, F., et al., Identification of proteins of the yeast protein map using genetically manipulated strains and peptide-mass fingerprinting. Yeast, 1996. 12(15): p. 1519-33.
51. Henzel, W.J., et al., Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc Natl Acad Sci U S A, 1993. 90(11): p. 5011-5.
52. O'Farrell, P.H., High resolution two-dimensional electrophoresis of proteins. J Biol Chem, 1975. 250(10): p. 4007-21.
53. Gygi, S.P., et al., Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U S A, 2000. 97(17): p. 9390-5.
54. Rabilloud, T., Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics, 2002. 2(1): p. 3-10.
55. Gorg, A., W. Weiss, and M.J. Dunn, Current two-dimensional electrophoresis technology for proteomics. Proteomics, 2004. 4(12): p. 3665-85.
56. Merril, C.R., R.C. Switzer, and M.L. Van Keuren, Trace polypeptides in cellular extracts and human body fluids detected by two-dimensional electrophoresis and a highly sensitive silver stain. Proc Natl Acad Sci U S A, 1979. 76(9): p. 4335-9.
57. Unlu, M., M.E. Morgan, and J.S. Minden, Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 1997. 18(11): p. 2071-7.
58. Tonge, R., et al., Validation and development of fluorescence two-dimensional differential gel electrophoresis proteomics technology. Proteomics, 2001. 1(3): p. 377-96.
59. Ong, S.E., et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics, 2002. 1(5): p. 376-86.
60. Gygi, S.P., et al., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol, 1999. 17(10): p. 994-9.
61. 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(8): p. 1895-904.
62. Hsu, J.L., et al., Stable-isotope dimethyl labeling for quantitative proteomics. Anal Chem, 2003. 75(24): p. 6843-52.
63. Ross, P.L., et al., Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics, 2004. 3(12): p. 1154-69.
64. Zieske, L.R., A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J Exp Bot, 2006. 57(7): p. 1501-8.
65. Shetty, V., et al., Quantitative immunoproteomics analysis reveals novel MHC class I presented peptides in cisplatin-resistant ovarian cancer cells. J Proteomics, 2012. 75(11): p. 3270-90.
66. Mueller, L.N., et al., An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res, 2008. 7(1): p. 51-61.
67. Simpson, K.L., A.D. Whetton, and C. Dive, Quantitative mass spectrometry-based techniques for clinical use: biomarker identification and quantification. J Chromatogr B Analyt Technol Biomed Life Sci, 2009. 877(13): p. 1240-9.
68. Zhu, W., J.W. Smith, and C.M. Huang, Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol, 2010. 2010: p. 840518.
69. Collas, P., The current state of chromatin immunoprecipitation. Mol Biotechnol, 2010. 45(1): p. 87-100.
70. Van Dilla, M.A., et al., Cell microfluorometry: a method for rapid fluorescence measurement. Science, 1969. 163(3872): p. 1213-4.
71. Epstein, L.B., H.W. Kreth, and Herzenbe.La, Fluorescence-Activated Cell Sorting of Human T-Lymphocytes and B-Lymphocytes .2. Identification of Cell Type Responsible for Interferon-Production and Cell-Proliferation in Response to Mitogens. Cellular Immunology, 1974. 12(3): p. 407-421.
72. Kreth, H.W. and Herzenbe.La, Fluorescence-Activated Cell Sorting of Human T-Lymphocytes and B-Lymphocytes .1. Direct Evidence That Lymphocytes with a High-Density of Membrane-Bound Immunoglobulin Are Precursors of Plasmacytes. Cellular Immunology, 1974. 12(3): p. 396-406.
73. Kohler, G. and C. Milstein, Continuous Cultures of Fused Cells Secreting Antibody of Predefined Specificity. Nature, 1975. 256(5517): p. 495-497.
74. Paczesny, S., et al., Measuring melanoma-specific cytotoxic T lymphocytes elicited by dendritic cell vaccines with a tumor inhibition assay in vitro. Journal of Immunotherapy, 2005. 28(2): p. 148-157.
75. Ljunggren, H.G., et al., Empty Mhc Class-I Molecules Come out in the Cold. Nature, 1990. 346(6283): p. 476-480.
76. Testa, J.S., et al., MHC class I-presented T cell epitopes identified by immunoproteomics analysis are targets for a cross reactive influenza-specific T cell response. PLoS One, 2012. 7(11): p. e48484.
77. Gilar, M., et al., Comparison of 1-D and 2-D LC MS/MS methods for proteomic analysis of human serum. Electrophoresis, 2009. 30(7): p. 1157-67.
78. Waller, L.N., K. Shores, and D.R. Knapp, Shotgun proteomic analysis of cerebrospinal fluid using off-gel electrophoresis as the first-dimension separation. J Proteome Res, 2008. 7(10): p. 4577-84.
79. Chenau, J., et al., Peptides Offgel electrophoresis: a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with iTRAQ labeling. Bulletin Du Cancer, 2008. 95: p. S19-S19.
80. Mihailova, A., et al., Improving the resolution of neuropeptides in rat brain with on-line HILIC-RP compared to on-line SCX-RP. J Sep Sci, 2008. 31(3): p. 459-67.
81. Antwi, K., et al., Proteomic identification of an MHC-binding peptidome from pancreas and breast cancer cell lines. Mol Immunol, 2009. 46(15): p. 2931-7.
82. Havugimana, P.C., et al., A Census of Human Soluble Protein Complexes. Cell, 2012. 150(5): p. 1068-1081.