Plasma proteome analysis of patients with type 1 diabetes with diabetic nephropathy

详细信息    查看全文

• 作者：Julie Anne Overgaard (1)
  Henning Gram Hansen (1)
  Maria Lajer (2)
  Lykke Pedersen (3)
  Lise Tarnow (2)
  Peter Rossing (2)
  James N McGuire (1)
  Flemming Pociot (1)
  
• 刊名：Proteome Science
• 出版年：2010
• 出版时间：December 2010
• 年：2010
• 卷：8
• 期：1
• 全文大小：597KB
• 参考文献：1. Caramori ML, Fioretto P, Mauer M: Enhancing the Predictive Value of Urinary Albumin for Diabetic Nephropathy. / J Am Soc Nephrol 2006, 17: 339鈥?52. CrossRef
  2. Hovind P, Tarnow L, Rossing K, Rossing P, Eising S, Larsen N, Binder C, Parving H-H: Decreasing Incidence of Severe Diabetic Microangiopathy in Type 1 Diabetes. / Diabets Care 2003, 26: 1258鈥?264. CrossRef
  3. Finne P, Reunanen A, Stenman S, Groop P-H, Gronhagen-Riska C: Incidence of End-stage Renal Disease in Patients With Type 1 Diabetes. / JAMA 2005, 294: 1782鈥?787. CrossRef
  4. Daneman D: Type 1 diabetes. / The Lancet 2006, 367: 847鈥?58. CrossRef
  5. Cameron JS: The discovery of diabetic nephropathy: from small print to centre stage. / J Nephrol 2006, 75鈥?7.
  6. Kilpatrick ES, Rigby AS, Atkin SL: HbA1c variability and the risk of microvascular complications in type 1 diabetes: data from the DCCT. / Diabetes Care 2008, dc08鈥?864.
  7. Mogensen CE: Long-Term Antihypertensive Treatment Inhibiting Progression of Diabetic Nephropathy. / Brit Med J 1982, 285: 685鈥?88. CrossRef
  8. Ahmad J: Renin-angiotensin system blockade in diabetic nephropathy. / Diabetes and Metabolic Syndrome: Clinical Research and Reviews 2008, 2: 135鈥?58. CrossRef
  9. Lewis EJ, Hunsicker LG, Bain RP, Rohde RD, The Collaborative Study G: The Effect of Angiotensin-Converting-Enzyme Inhibition on Diabetic Nephropathy. / N Engl J Med 1993, 329: 1456鈥?462. CrossRef
  10. Otu HH, Can H, Spentzos D, Nelson RG, Hanson RL, Looker HC, Knowler WC, Monroy M, Libermann TA, Karumanchi SA, Thadhani R: Prediction of Diabetic Nephropathy Using Urine Proteomic Profiling 10 Years Prior to Development of Nephropathy. / Diabetes Care 2007, 30: 638鈥?43. CrossRef
  11. Rossing K, Mischak H, Dakna M, Zurbig P, Novak J, Julian BA, Good DM, Coon JJ, Tarnow L, Rossing P, on behalf of the PN: Urinary Proteomics in Diabetes and CKD. / J Am Soc Nephrol 2008, 19: 1283鈥?290. CrossRef
  12. Gu W, Zou LX, Shan PF, Chen YD: Analysis of urinary proteomic patterns for diabetic nephropathy by ProteinChip. / Proteom Clin APPL 2008, 2: 744鈥?50. CrossRef
  13. Hyun-Jung K, Hyun-Syuk Y, Chan-Wha K: Proteomics in diabetic nephropathy. / Proteom Clin APPL 2008, 2: 301鈥?11. CrossRef
  14. Kim HJ, Cho EH, Yoo JH, Kim PK, Shin JS, Kim MR, Kim CW: Proteome Analysis of Serum from Type 2 Diabetics with Nephropathy. / J Proteome Res 2007, 6: 735鈥?43. CrossRef
  15. Eun-Hee C, Mi-Ryung K, Hyun-Jung K, Do-Yeon L, Pan-Kyeom K, Kyung MC, Ohk-Hyun R, Chan-Wha K: The discovery of biomarkers for type 2 diabetic nephropathy by serum proteome analysis. / Proteom Clin APPL 2007, 1: 352鈥?61. CrossRef
  16. McGuire JN, Overgaard J, Pociot F: Mass spectrometry is only one piece of the puzzle in clinical proteomics. / Brief Funct Genomic Proteomic 2008, eln005.
  17. Boschetti E, Righetti PG: The ProteoMiner in the proteomic arena: A non-depleting tool for discovering low-abundance species. / J Proteomics 2008, 71: 255鈥?64. CrossRef
  18. Hyv盲rinen A, Oja E: Independent component analysis: algorithms and applications. / Neural Networks 2000, 13: 411鈥?30. CrossRef
  19. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. / Journal of the Royal Statistical Society 1995, 57: 289鈥?00.
  20. Zhang L, Krzentowski G, Albert A, Lef猫bvre PJ: Factors predictive of nephropathy in DCCT Type 1 diabetic patients with good or poor metabolic control. / Diabetic Med 2003, 20: 580鈥?85. CrossRef
  21. Vilarrasa N, Soler J, Montanya E: Regression of microalbuminuria in type 1 diabetic patients: results of a sequential intervention with improved metabolic control and ACE inhibitors. / Acta Diabetologica 2005, 42: 87鈥?4. CrossRef
  22. Hirano T, Sakaue T, Misaki A, Murayama S, Takahashi T, Okada K, Takeuchi H, Yoshino G, Adachi M: Very low-density lipoprotein-apoprotein CI is increased in diabetic nephropathy: Comparison with apoprotein CIII. / Kidney Int 2003, 63: 2171鈥?177. CrossRef
  23. Raila J, Henze A, Spranger J, Mohlig M, Pfeiffer AFH, Schweigert FJ: Microalbuminuria is a major determinant of elevated plasma retinol-binding protein 4 in type 2 diabetic patients. / Kidney Int 2007, 72: 505鈥?11. CrossRef
  24. Jenkins AJ, Lyons TJ, Zheng D, Otvos JD, Lackland DT, McGee D, Garvey WT, Klein RL: Lipoproteins in the DCCT/EDIC cohort: Associations with diabetic nephropathy. / Kidney Int 2003, 64: 817鈥?28. CrossRef
  25. Mojiminiyi OA, Abdella N: Evaluation of cystatin C and [beta]-2 microglobulin as markers of renal function in patients with type 2 diabetes mellitus. / Journal of Diabetes and its Complications 2003, 17: 160鈥?68. CrossRef
  26. Mussap M, Vestra MD, Fioretto P, Saller A, Varagnolo M, Nosadini R, Plebani M: Cystatin C is a more sensitive marker than creatinine for the estimation of GFR in type 2 diabetic patients. / Kidney Int 2002, 61: 1453鈥?461. CrossRef
  27. Mantini D, Petrucci F, Del Boccio P, Pieragostino D, Di Nicola M, Lugaresi A, Federici G, Sacchetta P, Di Ilio C, Urbani A: Independent component analysis for the extraction of reliable protein signal profiles from MALDI-TOF mass spectra. / Bioinformatics 2008, 24: 63鈥?0. CrossRef
  28. Bonsnes RW, Taussky HH: On the Colorimetric Determination of Creatinine by the Jaffe Reaction. / J Biol Chem 1945, 158: 581鈥?91.
  29. Maggie Merchant SRW: Recent advancements in surface-enhanced laser desorption/ionization-time of flight-mass spectrometry. / ELECTROPHORESIS 2000, 21: 1164鈥?177. CrossRef
  30. Hutchens TW, Yip TT: New desorption strategies for the mass spectrometric analysis of macromolecules. / Rapid Communications in Mass Spectrometry 1993, 7: 576鈥?80. CrossRef
  31. Perkins DN, Pappin DJC, Creasy DM, Cottrell JS: Probability-based protein identification by searching sequence databases using mass spectrometry data. / ELECTROPHORESIS 1999, 20: 3551鈥?567. CrossRef
  
• 作者单位：Julie Anne Overgaard (1)
  Henning Gram Hansen (1)
  Maria Lajer (2)
  Lykke Pedersen (3)
  Lise Tarnow (2)
  Peter Rossing (2)
  James N McGuire (1)
  Flemming Pociot (1)
  
  1. Department of Genome Biology, Hagedorn Research Institute, Gentofte, Denmark
  2. Department of Research in Complications, Steno Diabetes Center, Gentofte, Denmark
  3. Center for Models of Life, Niels Bohr Institute, Copenhagen, Denmark
  

文摘

Background As part of a clinical proteomics program focused on diabetes and its complications we are looking for new and better protein biomarkers for diabetic nephropathy. The search for new and better biomarkers for diabetic nephropathy has, with a few exceptions, previously focused on either hypothesis-driven studies or urinary based investigations. To date only two studies have investigated the proteome of blood in search for new biomarkers, and these studies were conducted in sera from patients with type 2 diabetes. This is the first reported in depth proteomic study where plasma from type 1 diabetic patients was investigated with the goal of finding improved candidate biomarkers to predict diabetic nephropathy. In order to reach lower concentration proteins in plasma a pre-fractionation step, either hexapeptide bead-based libraries or anion exchange chromatography, was performed prior to surface enhanced laser desorption/ionization time-of-flight mass spectrometry analysis. Results Proteomic analysis of plasma from a cross-sectional cohort of 123 type 1 diabetic patients previously diagnosed as normoalbuminuric, microalbuminuric or macroalbuminuric, gave rise to 290 peaks clusters of which 16 were selected as the most promising biomarker candidates based on statistical performance, including independent component analysis. Four of the peaks that were discovered have been identified as transthyretin, apolipoprotein A1, apolipoprotein C1 and cystatin C. Several yet unidentified proteins discovered by this novel approach appear to have more potential as biomarkers for diabetic nephropathy. Conclusion These results demonstrate the capacity of proteomic analysis of plasma, by confirming the presence of known biomarkers as well as revealing new biomarkers for diabetic nephropathy in plasma in type 1 diabetic patients.