Proteomics and systems biology for understanding diabetic nephropathy

Journal of Cardiovascular Translational Research

Volumn 5, Issue 4, 2012, Pages 479-490

  Starkey, Jonathan M ^a   Tilton, Ronald G ^a,b  

^a University of Texas Medical Branch School of Medicine   (United States)

^b UNIVERSITY OF TEXAS MEDICAL BRANCH   (United States)

Author keywords

Bioinformatics; Diabetes; Diabetic nephropathy; Mass spectrometry; Proteomics; Sytems biology

Indexed keywords

GAMMA INTERFERON INDUCIBLE PROTEIN 10; INTERLEUKIN 6; INTERLEUKIN 8; MACROPHAGE INFLAMMATORY PROTEIN 1; MONOCYTE CHEMOTACTIC PROTEIN 1; PROTEOME;

ARTICLE; BIOINFORMATICS; DIABETIC NEPHROPATHY; HUMAN; INTERNET; MASS SPECTROMETRY; MATHEMATICAL COMPUTING; MONITORING; NONHUMAN; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN PROCESSING; PROTEIN SECRETION; PROTEOMICS; QUANTITATIVE ANALYSIS; SYSTEMS BIOLOGY; TWO DIMENSIONAL GEL ELECTROPHORESIS; VALIDATION PROCESS;

ANIMALS; BIOLOGICAL MARKERS; DIABETIC NEPHROPATHIES; HUMANS; KIDNEY; PHENOTYPE; PROGNOSIS; PROTEINS; PROTEOMICS; SYSTEMS BIOLOGY; TRANSLATIONAL MEDICAL RESEARCH;

EID: 84866062801     PISSN: 19375387     EISSN: 19375395     Source Type: Journal    
DOI: 10.1007/s12265-012-9372-9     Document Type: Article

References (114)

1. The Diabetes Control and Complications Trial Research Group (1993). The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine, 329, 977-986.
2. Welsh, G. I., Hale, L. J., Eremina, V., Jeansson, M., Maezawa, Y., Lennon, R., et al. (2010). Insulin signaling to the glomerular podocyte is critical for normal kidney function. Cell Metabolism, 12, 329-340.
3. Oates, P. J., & Mylari, B. L. (1999). Aldose reductase inhibitors: therapeutic implications for diabetic complications. Expert Opinion on Investigational Drugs, 8, 2095-2119.
4. Brownlee, M., Cerami, A., & Vlassara, H. (1988). Advanced products of nonenzymatic glycosylation and the pathogenesis of diabetic vascular disease. Diabetes/Metabolism Reviews, 4, 437-451.
5. Bucala, R., & Vlassara, H. (1995). Advanced glycosylation end products in diabetic renal and vascular disease. American Journal of Kidney Diseases, 26, 875-888.
6. Singh, R., Barden, A., Mori, T., & Beilin, L. (2001). Advanced glycation end-products: a review. Diabetologia, 44, 129-146.
7. Schmidt, A. M., Hori, O., Chen, J. X., Li, J. F., Crandall, J., Zhang, J., et al. (1995). Advanced glycation endproducts interacting with their endothelial receptor induce expression of vascular cell adhesion molecule-1 (VCAM-1) in cultured human endothelial cells and in mice. A potential mechanism for the accelerated vasculopathy of diabetes. Journal of Clinical Investigation, 96, 1395-1403.
8. Bierhaus, A., Schiekofer, S., Schwaninger, M., Andrassy, M., Humpert, P. M., Chen, J., et al. (2001). Diabetes-associated sustained activation of the transcription factor nuclear factor-kappaB. Diabetes, 50, 2792-2808.
9. Craven, P. A., Studer, R. K., Negrete, H., & DeRubertis, F. R. (1995). Protein kinase C in diabetic nephropathy. Journal of Diabetes and its Complications, 9, 241-245.
10. DeRubertis, F. R., & Craven, P. A. (1994). Activation of protein kinase C in glomerular cells in diabetes. Mechanisms and potential links to the pathogenesis of diabetic glomerulopathy. Diabetes, 43, 1-8.
11. Koya, D., & King, G. L. (1998). Protein kinase C activation and the development of diabetic complications. Diabetes, 47, 859-866.
12. Baynes, J. W., & Thorpe, S. R. (1999). Role of oxidative stress in diabetic complications: a new perspective on an old paradigm. Diabetes, 48, 1-9.
13. Szabo, C. (2005). Roles of poly(ADP-ribose) polymerase activation in the pathogenesis of diabetes mellitus and its complications. Pharmacological Research, 52, 60-71.
14. Harding, H. P., & Ron, D. (2002). Endoplasmic reticulum stress and the development of diabetes: a review. Diabetes, 51(Suppl 3), S455-S461.
15. Araki, E., Oyadomari, S., & Mori, M. (2003). Endoplasmic reticulum stress and diabetes mellitus. Internal Medicine, 42, 7-14.
16. Wolf, G. (1998). Molecular mechanisms of angiotensin II in the kidney: emerging role in the progression of renal disease: beyond haemodynamics. Nephrology, Dialysis, Transplantation, 13, 1131-1142.
17. Nishikawa, T., Edelstein, D., Du, X. L., Yamagishi, S., Matsumura, T., Kaneda, Y., et al. (2000). Normalizing mitochondrial superoxide production blocks three pathways of hyperglycaemic damage. Nature, 404, 787-790.
18. Hammes, H. P., Du, X., Edelstein, D., Taguchi, T., Matsumura, T., Ju, Q., et al. (2003). Benfotiamine blocks three major pathways of hyperglycemic damage and prevents experimental diabetic retinopathy. Nature Medicine, 9, 294-299.
19. Williamson, J. R., Chang, K., Frangos, M., Hasan, K. S., Ido, Y., Kawamura, T., et al. (1993). Hyperglycemic pseudohypoxia and diabetic complications. Diabetes, 42, 801-813.
20. Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422, 198-207.
21. Domon, B., & Aebersold, R. (2006). Mass spectrometry and protein analysis. Science, 312, 212-217.
22. Cox, J., & Mann, M. (2011). Quantitative, high-resolution proteomics for data-driven systems biology. Annual Review of Biochemistry, 80, 273-299.
23. Schordan, S., Schordan, E., Endlich, N., Lindenmeyer, M. T., Meyer-Schwesinger, C., Meyer, T. N., et al. (2009). Alterations of the podocyte proteome in response to high glucose concentrations. Proteomics, 9, 4519-4528.
24. Li, Z., Zhang, H., Dong, X., Burczynski, F. J., Choy, P., Yang, F., et al. (2010). Proteomic profile of primary isolated rat mesangial cells in high-glucose culture condition and decreased expression of PSMA6 in renal cortex of diabetic rats. Biochemical Cell Biology, 88, 635-648.
25. Tilton, R. G., Haidacher, S. J., LeJeune,W. S., Zhao, Y., Kurosky, A., Brasier, A. R., et al. (2007). Diabetes-induced changes in the renal cortical proteome assessed with two-dimensional gel electrophoresis and mass spectrometry. Proteomics, 7, 1729-1742.
26. Zhang, D., Yang, H., Kong, X.,Wang, K., Mao, X., Yan, X., et al. (2011). Proteomics analysis reveals diabetic kidney as a ketogenic organ in type 2 diabetes. American Journal of Physiology. Endocrinology and Metabolism, 300, E287-E295.
27. Thongboonkerd, V., Barati, M. T., McLeish, K. R., Benarafa, C., Remold-O'Donnell, E., Zheng, S., et al. (2004). Alterations in the renal elastin-elastase system in type 1 diabetic nephropathy identified by proteomic analysis. Journal of the American Society of Nephrology, 15, 650-662.
28. Miyamoto, M., Yoshida, Y., Taguchi, I., Nagasaka, Y., Tasaki, M., Zhang, Y., et al. (2007). In-depth proteomic profiling of the normal human kidney glomerulus using two-dimensional protein prefractionation in combination with liquid chromatographytandem mass spectrometry. Journal of Proteome Research, 6, 3680-3690.
29. Almeida, J. S., Stanislaus, R., Krug, E., & Arthur, J. M. (2005). Normalization and analysis of residual variation in twodimensional gel electrophoresis for quantitative differential proteomics. Proteomics, 5, 1242-1249.
30. Merril, C. R., Creed, G. J., Joy, J., & Olson, A. D. (1993). Identification and use of constitutive proteins for the normalization of high resolution electrophoretograms. Applied and Theoretical Electrophoresis, 3, 329-333.
31. Nishihara, J. C., & Champion, K. M. (2002). Quantitative evaluation of proteins in one- and two-dimensional polyacrylamide gels using a fluorescent stain. Electrophoresis, 23, 2203-2215.
32. Molloy, M. P., Herbert, B. R., Walsh, B. J., Tyler, M. I., Traini, M., Sanchez, J. C., et al. (1998). Extraction of membrane proteins by differential solubilization for separation using two-dimensional gel electrophoresis. Electrophoresis, 19, 837-844.
33. Marouga, R., David, S., & Hawkins, E. (2005). The development of the DIGE system: 2D fluorescence difference gel analysis technology. Analytical and Bioanalytical Chemistry, 382, 669-678.
34. Friedman, D. B., & Lilley, K. S. (2008). Optimizing the difference gel electrophoresis (DIGE) technology. Methods in Molecular Biology, 428, 93-124.
35. Tao, W. A., & Aebersold, R. (2003). Advances in quantitative proteomics via stable isotope tagging and mass spectrometry. Current Opinion in Biotechnology, 14, 110-118.
36. Gygi, S. P., Rist, B., Gerber, S. A., Turecek, F., Gelb, M. H., & Aebersold, R. (1999). Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnology, 17, 994-999.
37. Heller, M., Mattou, H., Menzel, C., & Yao, X. (2003). Trypsin catalyzed 16O-to-18O exchange for comparative proteomics: tandem mass spectrometry comparison using MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers. Journal of the American Society for Mass Spectrometry, 14, 704-718.
38. Kostiainen, R., Kotiaho, T., Kuuranne, T., & Auriola, S. (2003). Liquid chromatography/atmospheric pressure ionization-mass spectrometry in drug metabolism studies. Journal of Mass Spectrometry, 38, 357-372.
39. Yost, R. A., & Enke, C. G. (1979). Triple quadrupole mass spectrometry for direct mixture analysis and structure elucidation. Analytical Chemistry, 51, 1251-1264.
40. Gallien, S., Duriez, E., & Domon, B. (2011). Selected reaction monitoring applied to proteomics. Journal of Mass Spectrometry, 46, 298-312.
41. Lange, V., Picotti, P., Domon, B., & Aebersold, R. (2008). Selected reaction monitoring for quantitative proteomics: a tutorial. Molecular Systems Biology, 4, 222.
42. Cutillas, P. R., Biber, J., Marks, J., Jacob, R., Stieger, B., Cramer, R., et al. (2005). Proteomic analysis of plasma membrane vesicles isolated from the rat renal cortex. Proteomics, 5, 101-112.
43. Magni, F., Sarto, C., Valsecchi, C., Casellato, S., Bogetto, S. F., Bosari, S., et al. (2005). Expanding the proteome twodimensional gel electrophoresis reference map of human renal cortex by peptide mass fingerprinting. Proteomics, 5, 816-825.
44. Yoshida, Y., Miyazaki, K., Kamiie, J., Sato, M., Okuizumi, S., Kenmochi, A., et al. (2005). Two-dimensional electrophoretic profiling of normal human kidney glomerulus proteome and construction of an extensible markup language (XML)-based database. Proteomics, 5, 1083-1096.
45. Barile, M., Pisitkun, T., Yu, M. J., Chou, C. L., Verbalis, M. J., Shen, R. F., et al. (2005). Large scale protein identification in intracellular aquaporin-2 vesicles from renal inner medullary collecting duct. Molecular & Cellular Proteomics, 4, 1095-1106.
46. Dihazi, H., Asif, A. R., Agarwal, N. K., Doncheva, Y., & Muller, G. A. (2005). Proteomic analysis of cellular response to osmotic stress in thick ascending limb of Henle's loop (TALH) cells. Molecular & Cellular Proteomics, 4, 1445-1458.
47. Pieper, R., Gatlin, C. L., McGrath, A. M., Makusky, A. J., Mondal, M., Seonarain, M., et al. (2004). Characterization of the human urinary proteome: a method for high-resolution display of urinary proteins on two-dimensional electrophoresis gels with a yield of nearly 1400 distinct protein spots. Proteomics, 4, 1159-1174.
48. Oh, J., Pyo, J. H., Jo, E. H., Hwang, S. I., Kang, S. C., Jung, J. H., et al. (2004). Establishment of a near-standard two-dimensional human urine proteomic map. Proteomics, 4, 3485-3497.
49. Thongboonkerd, V., McLeish, K. R., Arthur, J. M., & Klein, J. B. (2002). Proteomic analysis of normal human urinary proteins isolated by acetone precipitation or ultracentrifugation. Kidney International, 62, 1461-1469.
50. Castagna, A., Cecconi, D., Sennels, L., Rappsilber, J., Guerrier, L., Fortis, F., et al. (2005). Exploring the hidden human urinary proteome via ligand library beads. Journal of Proteome Research, 4, 1917-1930.
51. Adachi, J., Kumar, C., Zhang, Y., Olsen, J. V., & Mann, M. (2006). The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins. Genome Biology, 7, R80.
52. Hewitt, S. M., Dear, J., & Star, R. A. (2004). Discovery of protein biomarkers for renal diseases. Journal of the American Society of Nephrology, 15, 1677-1689.
53. He, J. C., Chuang, P. Y., Ma'ayan, A., & Iyengar, R. (2012). Systems biology of kidney diseases. Kidney International, 81, 22-39.
54. Barati, M. T., Merchant, M. L., Kain, A. B., Jevans, A. W., McLeish, K. R., & Klein, J. B. (2007). Proteomic analysis defines altered cellular redox pathways and advanced glycation endproduct metabolism in glomeruli of db/db diabetic mice. American Journal of Physiology. Renal Physiology, 293, F1157-F1165.
55. Thongboonkerd, V., Zheng, S., McLeish, K. R., Epstein, P. N., & Klein, J. B. (2005). Proteomic identification and immunolocalization of increased renal calbindin-D28k expression in OVE26 diabetic mice. The Review of Diabetic Studies, 2, 19-26.
56. Cummins, T. D., Barati, M. T., Coventry, S. C., Salyer, S. A., Klein, J. B., & Powell, D. W. (2010). Quantitative mass spectrometry of diabetic kidney tubules identifies GRAP as a novel regulator of TGF-beta signaling. Biochimica et Biophysica Acta, 1804, 653-661.
57. Chougale, A. D., Bhat, S. P., Bhujbal, S. V., Zambare, M. R., Puntambekar, S., Somani, R. S., et al. (2012). Proteomic analysis of glycated proteins from streptozotocin-induced diabetic rat kidney. Molecular Biotechnology, 50, 28-38.
58. Sharma, K., Lee, S., Han, S., Lee, S., Francos, B., McCue, P., et al. (2005). Two-dimensional fluorescence difference gel electrophoresis analysis of the urine proteome in human diabetic nephropathy. Proteomics, 5, 2648-2655.
59. Meier, M., Kaiser, T., Herrmann, A., Knueppel, S., Hillmann, M., Koester, P., et al. (2005). Identification of urinary protein pattern in type 1 diabetic adolescents with early diabetic nephropathy by a novel combined proteome analysis. Journal of Diabetes and its Complications, 19, 223-232.
60. Dihazi, H., Muller, G. A., Lindner, S., Meyer, M., Asif, A. R., Oellerich, M., et al. (2007). Characterization of diabetic nephropathy by urinary proteomic analysis: identification of a processed ubiquitin form as a differentially excreted protein in diabetic nephropathy patients. Clinical Chemistry, 53, 1636-1645.
61. Rossing, K., Mischak, H., Rossing, P., Schanstra, J. P., Wiseman, A., & Maahs, D. M. (2008). The urinary proteome in diabetes and diabetes-associated complications: New ways to assess disease progression and evaluate therapy. Proteomics. Clinical Applications, 2, 997-1007.
62. Wolkow, P. P., Niewczas, M. A., Perkins, B., Ficociello, L. H., Lipinski, B., Warram, J. H., et al. (2008). Association of urinary inflammatory markers and renal decline in microalbuminuric type 1 diabetics. Journal of the American Society of Nephrology, 19, 789-797.
63. Otu, H. H., Can, H., Spentzos, D., Nelson, R. G., Hanson, R. L., Looker, H. C., et al. (2007). Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy. Diabetes Care, 30, 638-643.
64. Rao, P. V., Lu, X., Standley, M., Pattee, P., Neelima, G., Girisesh, G., et al. (2007). Proteomic identification of urinary biomarkers of diabetic nephropathy. Diabetes Care, 30, 629-637.
65. Jiang, H., Guan, G., Zhang, R., Liu, G., Cheng, J., Hou, X., et al. (2009). Identification of urinary soluble E-cadherin as a novel biomarker for diabetic nephropathy. Diabetes/Metabolism Research and Reviews, 25, 232-241.
66. Papale, M., Di, P. S., Magistroni, R., Lamacchia, O., Di Palma, A. M., De, M. A., et al. (2010). Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy. Diabetes Care, 33, 2409-2415.
67. Ben, A. R., Molina, L., Bolvin, C., Kifagi, C., Jarraya, F., Ayadi, H., et al. (2010). Proteomic approaches for discovering biomarkers of diabetic nephropathy. Nephrology, Dialysis, Transplantation, 25, 2866-2875.
68. Zhi, W., Purohit, S., Carey, C., Wang, M., & She, J. X. (2010). Proteomic technologies for the discovery of type 1 diabetes biomarkers. Journal ofDiabetes Science and Technology, 4, 993-1002.
69. Alkhalaf, A., Zurbig, P., Bakker, S. J., Bilo, H. J., Cerna, M., Fischer, C., et al. (2010). Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy. PLoS One, 5, e13421.
70. Maahs, D. M., Siwy, J., Argiles, A., Cerna, M., Delles, C., Dominiczak, A. F., et al. (2010). Urinary collagen fragments are significantly altered in diabetes: a link to pathophysiology. PLoS One, 5, e13051.
71. Parving, H. H., Oxenboll, B., Svendsen, P. A., Christiansen, J. S., & Andersen, A. R. (1982). Early detection of patients at risk of developing diabetic nephropathy. A longitudinal study of urinary albumin excretion. Acta Endocrinol (Copenh), 100, 550-555.
72. Viberti, G. C., Jarrett, R. J., & Keen, H. (1982). Microalbuminuria as prediction of nephropathy in diabetics. Lancet, 2, 611.
73. Mogensen, C. E., & Christensen, C. K. (1984). Predicting diabetic nephropathy in insulin-dependent patients. The New England Journal of Medicine, 311, 89-93.
74. Mogensen, C. E. (1984). Microalbuminuria predicts clinical proteinuria and early mortality in maturity-onset diabetes. The New England Journal of Medicine, 310, 356-360.
75. Weir, M. R., & Bakris, G. L. (2010). Editorial perspective. Should microalbuminuria ever be considered as a renal endpoint in any clinical trial? American Journal of Nephrology, 31, 469-470.
76. Lambers Heerspink, H. J., & de Zeeuw, D. (2010). Debate: PRO position. Should microalbuminuria ever be considered as a renal endpoint in any clinical trial? American Journal of Nephrology, 31, 458-461.
77. Glassock, R. J. (2010). Debate: CON position. Should microalbuminuria ever be considered as a renal endpoint in any clinical trial? American Journal of Nephrology, 31, 462-465.
78. Perkins, B. A., Ficociello, L. H., Silva, K. H., Finkelstein, D. M., Warram, J. H., & Krolewski, A. S. (2003). Regression of microalbuminuria in type 1 diabetes. The New England Journal of Medicine, 348, 2285-2293.
79. Perkins, B. A., Ficociello, L. H., Ostrander, B. E., Silva, K. H., Weinberg, J., Warram, J. H., et al. (2007). Microalbuminuria and the risk for early progressive renal function decline in type 1 diabetes. Journal of the American Society of Nephrology, 18, 1353-1361.
80. Thongboonkerd, V., Songtawee, N., & Sritippayawan, S. (2007). Urinary proteome profiling using microfluidic technology on a chip. Journal of Proteome Research, 6, 2011-2018.
81. Thongboonkerd, V. (2007). Recent progress in urinary proteomics. Proteomics. Clinical Applications, 1, 780-791.
82. Coon, J. J., Ueberheide, B., Syka, J. E., Dryhurst, D. D., Ausio, J., Shabanowitz, J., et al. (2005). Protein identification using sequential ion/ion reactions and tandem mass spectrometry. Proceedings of the National Academy of Sciences of the United States of America, 102, 9463-9468.
83. Zerefos, P., Prados, J., Kossida, S., Kalousis, A., & Vlahou, A. (2007). Sample preparation and bioinformatics in MALDI profiling of urinary proteins. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 853, 20-30.
84. Zerefos, P. G., Vougas, K., Dimitraki, P., Kossida, S., Petrolekas, A., Stravodimos, K., et al. (2006). Characterization of the human urine proteome by preparative electrophoresis in combination with 2-DE. Proteomics, 6, 4346-4355.
85. Kushnir, M. M., Mrozinski, P., Rockwood, A. L., & Crockett, D. K. (2009). A depletion strategy for improved detection of human proteins from urine. Journal of Biomolecular Techniques, 20, 101-108.
86. Kolch, W., Neususs, C., Pelzing, M., & Mischak, H. (2005). Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery. Mass Spectrometry Reviews, 24, 959-977.
87. Fliser, D., Novak, J., Thongboonkerd, V., Argiles, A., Jankowski, V., Girolami, M. A., et al. (2007). Advances in urinary proteome analysis and biomarker discovery. Journal of the American Society of Nephrology, 18, 1057-1071.
88. Mischak, H., Julian, B. A., & Novak, J. (2007). High-resolution proteome/peptidome analysis of peptides and low-molecularweight proteins in urine. Proteomics Clinical Applications, 1, 792.
89. Gilbert, R. E., & Cooper, M. E. (1999). The tubulointerstitium in progressive diabetic kidney disease: more than an aftermath of glomerular injury? Kidney International, 56, 1627-1637.
90. Bohle, A., Wehrmann, M., Bogenschutz, O., Batz, C., Muller, C. A., & Muller, G. A. (1991). The pathogenesis of chronic renal failure in diabetic nephropathy. Investigation of 488 cases of diabetic glomerulosclerosis. Pathology, Research and Practice, 187, 251-259.
91. Wada, T., Furuichi, K., Sakai, N., Iwata, Y., Yoshimoto, K., Shimizu, M., et al. (2000). Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney International, 58, 1492-1499.
92. Merchant, M. L., Perkins, B. A., Boratyn, G. M., Ficociello, L. H., Wilkey, D. W., Barati, M. T., et al. (2009). Urinary peptidome may predict renal function decline in type 1 diabetes and microalbuminuria. Journal of the American Society of Nephrology, 20, 2065-2074.
93. Bernstam, E. V., Smith, J. W., & Johnson, T. R. (2010). What is biomedical informatics? Journal of Biomedical Informatics, 43, 104-110.
94. Nesvizhskii, A. I. (2010). A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. Journal of Proteomics, 73, 2092-2123.
95. Nagaraj, N., & Mann, M. (2011). Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome. Journal of Proteome Research, 10, 637-645.
96. Hunt, S. M., Thomas, M. R., Sebastian, L. T., Pedersen, S. K., Harcourt, R. L., Sloane, A. J., et al. (2005). Optimal replication and the importance of experimental design for gel-based quantitative proteomics. Journal of Proteome Research, 4, 809-819.
97. Levin, Y. (2011). The role of statistical power analysis in quantitative proteomics. Proteomics, 11, 2565-2567.
98. Moore, C. G., Carter, R. E., Nietert, P. J., & Stewart, P.W. (2011). Recommendations for planning pilot studies in clinical and translational research. Clinical and Translational Science, 4, 332-337.
99. Martens, L., Hermjakob, H., Jones, P., Adamski, M., Taylor, C., States, D., et al. (2005). PRIDE: the proteomics identifications database. Proteomics, 5, 3537-3545.
100. Kolker, E., Higdon, R., Haynes, W., Welch, D., Broomall, W., Lancet, D., et al. (2012). MOPED: Model Organism Protein Expression Database. Nucleic Acids Research, 40, D1093- D1099.
101. Orchard, S., & Hermjakob, H. (2008). The HUPO proteomics standards initiative-easing communication and minimizing data loss in a changing world. Briefings in Bioinformatics, 9, 166-173.
102. DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7, 177-188.
103. Jorge, I., Navarro, P., Martinez-Acedo, P., Nunez, E., Serrano, H., Alfranca, A., et al. (2009). Statistical model to analyze quantitative proteomics data obtained by 18O/16O labeling and linear ion trap mass spectrometry: application to the study of vascular endothelial growth factor-induced angiogenesis in endothelial cells. Molecular & Cellular Proteomics, 8, 1130-1149.
104. Bonzon-Kulichenko, E., Martinez-Martinez, S., Trevisan-Herraz, M., Navarro, P., Redondo, J. M., & Vazquez, J. (2011). Quantitative in-depth analysis of the dynamic secretome of activated Jurkat T-cells. Journal of Proteomics, 75, 561-571.
105. Fogle, R. L., Hollenbeak, C. S., Stanley, B. A., Vary, T. C., Kimball, S. R., & Lynch, C. J. (2011). Functional proteomic analysis reveals sex-dependent differences in structural and energy-producing myocardial proteins in rat model of alcoholic cardiomyopathy. Physiological Genomics, 43, 346-356.
106. Carbon, S., Ireland, A., Mungall, C. J., Shu, S., Marshall, B., & Lewis, S. (2009). AmiGO: online access to ontology and annotation data. Bioinformatics, 25, 288-289.
107. Zhao, Y., Denner, L., Haidacher, S. J., LeJeune, W. S., & Tilton, R. G. (2008). Comprehensive analysis of the mouse renal cortex using two-dimensional HPLC - tandem mass spectrometry. Proteome Sci, 6, 15.
108. Kanehisa, M., & Goto, S. (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27-30.
109. Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., et al. (2007). Integration of biological networks and gene expression data using Cytoscape. Nature Protocols, 2, 2366-2382.
110. Hu, Z., Hung, J. H., Wang, Y., Chang, Y. C., Huang, C. L., Huyck, M., et al. (2009). VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Research, 37, W115-W121.
111. Vidal, M., Cusick, M. E., & Barabasi, A. L. (2011). Interactome networks and human disease. Cell, 144, 986-998.
112. Pflieger, D., Gonnet, F., de la Fuente van Bentem, S., Hirt, H., & de la FA, A. (2011). Linking the proteins-elucidation of proteome-scale networks using mass spectrometry. Mass Spectrometry Reviews, 30, 268-297.
113. Starkey, J. M., Zhao, Y., Sadygov, R. G., Haidacher, S. J., LeJeune, W. S., Dey, N., et al. (2010). Altered retinoic acid metabolism in diabetic mouse kidney identified by O isotopic labeling and 2D mass spectrometry. PLoS One, 5, e11095.
114. Overgaard, A. J., Thingholm, T. E., Larsen, M. R., Tarnow, L., Rossing, P., McGuire, J. N., et al. (2010). Quantitative iTRAQbased proteomic identification of candidate biomarkers for diabetic nephropathy in plasma of type 1 diabetic patients. Clin Proteomics, 6, 105-114.