Microarray applications in nephrology with special focus on transplantation

Journal of Nephrology

Volumn 25, Issue 5, 2012, Pages 589-602

  Mueller, Thomas F ^a   Mas, Valeria R ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF VIRGINIA HEALTH SYSTEM   (United States)

Author keywords

Gene expression; Kidney transplantation; Microarray technology; Nephrology; Transcriptome

Indexed keywords

MESSENGER RNA; TRANSCRIPTOME;

ANALYTICAL ERROR; DATA ANALYSIS; DATA PROCESSING; DNA MICROARRAY; DONOR; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; GENETIC TRANSCRIPTION; GENOMICS; HISTOPATHOLOGY; HUMAN; KIDNEY BIOPSY; KIDNEY INJURY; KIDNEY TRANSPLANTATION; MICROARRAY ANALYSIS; NEPHROLOGY; PATIENT ASSESSMENT; PROTEIN MICROARRAY; QUANTITATIVE ANALYSIS; REVERSE TRANSCRIPTION POLYMERASE CHAIN REACTION; REVIEW; RNA SEQUENCE; STRUCTURE ANALYSIS; TRANSCRIPTOMICS; VALIDATION PROCESS;

ANIMALS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETIC PREDISPOSITION TO DISEASE; HUMANS; KIDNEY FAILURE, CHRONIC; KIDNEY TRANSPLANTATION; NEPHROLOGY; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PHENOTYPE; PROGNOSIS; TRANSCRIPTOME;

EID: 84866978471     PISSN: 11218428     EISSN: 17246059     Source Type: Journal    
DOI: 10.5301/jn.5000205     Document Type: Review

References (195)

1. Barsoum RS. Chronic kidney disease in the developing world. N Engl J Med. 2006;354(10):997-999.
2. He JC, Chuang PY, Ma'ayan A, Iyengar R. Systems biology of kidney diseases. Kidney Int. 2012;81(1):22-39.
3. Schlondorff DO. Overview of factors contributing to the pathophysiology of progressive renal disease. Kidney Int. 2008;74(7):860-866.
4. Jevnikar AM, Mannon RB. Late kidney allograft loss: what we know about it, and what we can do about it. Clin J Am Soc Nephrol. 2008;3(Suppl.2):S56-S67.
5. Meier-Kriesche HU, Schold JD, Kaplan B. Long-term renal allograft survival: have we made significant progress or is it time to rethink our analytic and therapeutic strategies? Am J Transplant. 2004;4(8):1289-1295.
6. Stenvinkel P, Wanner C, Metzger T, et al. Inflammation and outcome in end-stage renal failure: does female gender constitute a survival advantage? Kidney Int. 2002;62(5): 1791-1798.
7. Carrero JJ, Stenvinkel P. Persistent inflammation as a catalyst for other risk factors in chronic kidney disease: a hypothesis proposal. Clin J Am Soc Nephrol. 2009;4(Suppl.1):S49-S55.
8. Dimény EM. Cardiovascular disease after renal transplantation. Kidney Int Suppl. 2002;61(80):78-84.
9. Faubel S. Acute kidney injury and multiple organ dysfunction syndrome. Minerva Urol Nefrol. 2009;61(3):171-188.
10. Grebe SO, Kuhlmann U, Fogl D, Luyckx VA, Mueller TF. Macrophage activation is associated with poorer long-term outcomes in renal transplant patients. Clin Transplant. 2011; 25(5):744-754.
11. Guarnieri G, Biolo G, Zanetti M, Barazzoni R. Chronic systemic inflammation in uremia: potential therapeutic approaches. Semin Nephrol. 2004;24(5):441-445.
12. Hobson CE, Yavas S, Segal MS, et al. Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation. 2009;119(18):2444-2453.
13. Eardley KS, Kubal C, Zehnder D, et al. The role of capillary density, macrophage infiltration and interstitial scarring in the pathogenesis of human chronic kidney disease. Kidney Int. 2008;74(4):495-504.
14. Roberts IS, Furness P, Cook HT. Beyond diagnosis: stage and grade in inflammatory renal disease. Curr Diagn Pathol. 2004;10(1):22-35.
15. Ju W, Brosius FC III. Understanding kidney disease: toward the integration of regulatory networks across species. Semin Nephrol. 2010;30(5):512-519.
16. Neusser MA, Lindenmeyer MT, Kretzler M, Cohen CD. Genomic analysis in nephrology: towards systems biology and systematic medicine? Nephrol Ther. 2008;4(5): 306-311.
17. Mueller TF, Solez K, Mas V. Assessment of kidney organ quality and prediction of outcome at time of transplantation. Semin Immunopathol. 2011;33(2):185-199.
18. Bunnag S, Einecke G, Reeve J, et al. Molecular correlates of renal function in kidney transplant biopsies. J Am Soc Nephrol. 2009;20(5):1149-1160.
19. Mas VR, Mueller TF, Archer KJ, Maluf DG. Identifying biomarkers as diagnostic tools in kidney transplantation. Expert Rev Mol Diagn. 2011;11(2):183-196.
20. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998;95(25):14863-14868.
21. Staudt LM, Brown PO. Genomic views of the immune system. Annu Rev Immunol. 2000;18(1):829-859.
22. Quackenbush J. Genomics. Microarrays: guilt by association. Science. 2003;302(5643):240-241.
23. Colvin RB. Getting out of flatland: into the third dimension of microarrays. Am J Transplant. 2007;7(12):2650-2651.
24. Stegall MD, Park W. What can be learned using microarrays? Kidney Int. 2007;72(7):783-784.
25. Brenchley PE, Lindholm B, Dekker FW, Navis G. Translating knowledge of the human genome into clinical practice in nephrology dialysis and transplantation: the renal genome network (ReGeNet). Nephrol Dial Transplant. 2006;21(10): 2681-2683.
26. Hudson T. Genome-sequencing anniversary: genomics and clinical relevance. Science. 2011;331(6017):547.
27. Naesens M, Sarwal MM. Molecular diagnostics in transplantation. Nat Rev Nephrol. 2010;6(10):614-628.
28. Perkins D, Verma M, Park KJ. Advances of genomic science and systems biology in renal transplantation: a review. Semin Immunopathol. 2011;33(2):211-218.
29. Sarwal MM. Deconvoluting the 'omics' for organ transplantation. Curr Opin Organ Transplant. 2009;14(5):544-551.
30. Chiang DY, Brown PO, Eisen MB. Visualizing associationsbetween genome sequences and gene expression data using genome-mean expression profiles. Bioinformatics. 2001;17(Suppl.1):S49-S55.
31. Ideker T, Thorsson V, Ranish JA, et al. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001;292(5518):929-934.
32. Ideker T, Thorsson V, Siegel AF, Hood LE. Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J Comput Biol. 2000;7(6):805-817.
33. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001;291(5507):1304-1351.
34. Bustin SA. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002;29(1):23-39.
35. Bustin SA, Dorudi S. The value of microarray techniques for quantitative gene profiling in molecular diagnostics. Trends Mol Med. 2002;8(6):269-272.
36. Hodgin JB, Cohen CD. Experimental approaches to the human renal transcriptome. Semin Nephrol. 2010;30(5):455-467.
37. Keller BJ, Martini S, Sedor JR, Kretzler M. A systems view of genetics in chronic kidney disease. Kidney Int. 2012;81(1): 14-21.
38. Cravedi P, Maggiore U, Mannon RB. Low-density array PCR analysis of reperfusion biopsies: an adjunct to histological analysis. Nephrol Dial Transplant. 2010;25(12):4077-4086.
39. Allanach K, Mengel M, Einecke G, et al. Comparing microarray versus RT-PCR assessment of renal allograft biopsies: similar performance despite different dynamic ranges. Am J Transplant. 2008;8(5):1006-1015.
40. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826.
41. Bustin SA. Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol. 2000;25(2):169-193.
42. Christopher K, Mueller TF, Ma C, Liang Y, Perkins DL. Analysis of the innate and adaptive phases of allograft rejection by cluster analysis of transcriptional profiles. J Immunol. 2002;169(1):522-530.
43. Freeman WM, Walker SJ, Vrana KE. Quantitative RT-PCR: pitfalls and potential. Biotechniques. 1999;26(1):112-122+124-5.
44. Giulietti A, Overbergh L, Valckx D, Decallonne B, Bouillon R, Mathieu C. An overview of real-time quantitative PCR: applications to quantify cytokine gene expression. Methods. 2001;25(4):386-401.
45. Harrison DC, Medhurst AD, Bond BC, Campbell CA, Davis RP, Philpott KL. The use of quantitative RT-PCR to measure mRNA expression in a rat model of focal ischemia: caspase-3 as a case study. Brain Res Mol Brain Res. 2000;75(1): 143-149.
46. Higuchi R, Fockler C, Dollinger G, Watson R. Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology (N Y). 1993;11(9):1026-1030.
47. Hoffmann SC, Kampen RL, Amur S, et al. Molecular and immunohistochemical characterization of the onset and resolution of human renal allograft ischemia-reperfusion injury. Transplantation. 2002;74(7):916-923.
48. Medhurst AD, Pangalos MN. Application of TaqMan RT-PCR for real-time semiquantitative analysis of gene expression in the striatum. Methods Mol Med. 2003;79:229-241.
49. Archer KJ, Mas VR, O'Brien TR, Pfeiffer R, Lum NL, Fisher RA. Quality assessment of microarray data in a multicenter study. Diagn Mol Pathol. 2009;18(1):34-43.
50. Gollub J, Ball CA, Binkley G, et al. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res. 2003;31(1):94-96.
51. Iyer VR, Eisen MB, Ross DT, et al. The transcriptional program in the response of human fibroblasts to serum. Science. 1999;283(5398):83-87.
52. Lee ML, Kuo FC, Whitmore GA, Sklar J. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci USA. 2000;97(18):9834-9839.
53. Mills JC, Roth KA, Cagan RL, Gordon JI. DNA microarrays and beyond: completing the journey from tissue to cell. Nat Cell Biol. 2001;3(8):E175-E178.
54. Schneider MD, Schwartz RJ. Chips ahoy: gene expression in failing hearts surveyed by high-density microarrays. Circulation. 2000;102(25):3026-3027.
55. Schulze A, Downward J. Navigating gene expression using microarrays: a technology review. Nat Cell Biol. 2001;3(8): E190-E195.
56. Shi L, Reid LH, Jones WD, et al; MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter and intraplatform reproducibility of gene expression measurements. Nat Biotechnol. 2006;24(9):1151-1161.
57. Zeaiter Z, Diaz H, Stein M, Huynh HQ. Helicobacter pylori induces expression and secretion of oncostatin M in macrophages in vitro. Dig Dis Sci. 2011;56(3):689-697.
58. Ying L, Sarwal M. In Praise of arrays. Pediatr Nephrol. 2009; 24:1643-1659 [quiz: 1655, 1659].
59. Sotiriou C, Piccart MJ. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer. 2007;7(7):545-553.
60. Ji H, Davis RW. Data quality in genomics and microarrays. Nat Biotechnol. 2006;24(9):1112-1113.
61. Mardis ER. The impact of next-generation sequencing tech-nology on genetics. Trends Genet. 2008;24(3):133-141.
62. Marguerat S, Bähler J. RNA-seq: from technology to biology. Cell Mol Life Sci. 2010;67(4):569-579.
63. Turner DJ, Keane TM, Sudbery I, Adams DJ. Next-generation sequencing of vertebrate experimental organisms. Mamm Genome. 2009;20(6):327-338.
64. Lander ES. Initial impact of the sequencing of the human genome. Nature. 2011;470(7333):187-197.
65. Corless CL. Medicine. Personalized cancer diagnostics. Science. 2011;334(6060):1217-1218.
66. Roychowdhury S, Iyer MK, Robinson DR, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3(111):111-121.
67. Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795): 1929-1935.
68. Tomlins SA, Rhodes DR, Perner S, et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science. 2005;310(5748):644-648.
69. Bild AH, Potti A, Nevins JR. Linking oncogenic pathways with therapeutic opportunities. Nat Rev Cancer. 2006;6(9): 735-741.
70. Chen BS, Yang SK, Lan CY, Chuang YJ. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining. BMC Med Genomics. 2008;1(1):46.
71. Chen R, Sigdel TK, Li L, et al. Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions. PLOS Comput Biol. 2010;6(9):e1000940.
72. Stojnev S, Pejcic M, Dolicanin Z, Velickovic LJ, Dimov I, Stefanovic V. Challenges of genomics and proteomics in nephrology. Ren Fail. 2009;31(8):765-772.
73. Yasuda Y, Cohen CD, Henger A, Kretzler M; European Renal cDNA Bank (ERCB) Consortium. Gene expression profiling analysis in nephrology: towards molecular definition of renal disease. Clin Exp Nephrol. 2006;10(2):91-98.
74. Hoffman EP, Awad T, Palma J, et al; Tumor Analysis BestPractices Working Group. Expression profiling: best practices for data generation and interpretation in clinical trials. Nat Rev Genet. 2004;5(3):229-237.
75. van't Veer LJ, Bernards R. Enabling personalized cancermedicine through analysis of gene-expression patterns. Nature. 2008;452(7187):564-570.
76. Quackenbush J. Computational analysis of microarray data. Nat Rev Genet. 2001;2(6):418-427.
77. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001;98(9):5116-5121.
78. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst. 2007;99(2): 147-157.
79. Bittner M, Meltzer P, Trent J. Data analysis and integration: of steps and arrows. Nat Genet. 1999;22(3):213-215.
80. Brazma A, Vilo J. Gene expression data analysis. Microbes Infect. 2001;3(10):823-829.
81. Dinu I, Potter JD, Mueller T, et al. Improving gene set analysis of microarray data by SAM-GS. BMC Bioinformatics. 2007;8(1):242.
82. Herrero J, Valencia A, Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics. 2001;17(2):126-136.
83. Ilyin SE, Pinhasov A, Vaidya AH, et al. Emerging paradigms in applied bioinformatics. Biosilico. 2003;1(3):86-88.
84. Vingron M. Bioinformatics needs to adopt statistical thinking. Bioinformatics. 2001;17(5):389-390.
85. Wu TD. Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol. 2001;195(1): 53-65.
86. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci USA. 2002;99(10):6567-6572.
87. Tilstone C. DNA microarrays: vital statistics. Nature. 2003; 424(6949):610-612.
88. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA. 2000;97(22):12182-12186.
89. Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA. 1999;96(6):2907-2912.
90. Halloran PF, Reeve J, Kaplan B. Lies, damn lies, and statistics: the perils of the P value. Am J Transplant. 2006;6(1):10-11.
91. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst. 2003;95(1):14-18.
92. Ioannidis JP, Khoury MJ. Improving validation practices in "omics" research. Science. 2011;334(6060):1230-1232.
93. Baggerly K. Disclose all data in publications. Nature. 2010; 467(7314):401.
94. Rabani M, Levin JZ, Fan L, et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat Biotechnol. 2011;29(5): 436-442.
95. Raghavan A, Ogilvie RL, Reilly C, et al. Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes. Nucleic Acids Res. 2002;30(24):5529-5538.
96. Suter DM, Molina N, Gatfield D, Schneider K, Schibler U, Naef F. Mammalian genes are transcribed with widely different bursting kinetics. Science. 2011;332(6028):472-474.
97. Holter NS, Mitra M, Maritan A, Cieplak M, Banavar JR, Fedoroff NV. Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc Natl Acad Sci USA. 2000;97(15):8409-8414.
98. Bagshaw SM, Laupland KB, Doig CJ, et al. Prognosis for long-term survival and renal recovery in critically ill patients with severe acute renal failure: a population-based study. Crit Care. 2005;9(6):R700-R709.
99. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute kidney injury, mortality, length of stay, and costs in hospitalized patients. J Am Soc Nephrol. 2005;16(11): 3365-3370.
100. Thadhani R, Pascual M, Bonventre JV. Acute renal failure. N Engl J Med. 1996;334(22):1448-1460.
101. Venkatachalam MA, Griffin KA, Lan R, Geng H, Saikumar P, Bidani AK. Acute kidney injury: a springboard for progression in chronic kidney disease. Am J Physiol Renal Physiol. 2010;298(5):F1078-F1094.
102. Wen X, Peng Z, Kellum JA. Pathogenesis of acute kidney injury: effects of remote tissue damage on the kidney. Contrib Nephrol. 2011;174:129-137.
103. Scheel PJ, Liu M, Rabb H. Uremic lung: new insights into a forgotten condition. Kidney Int. 2008;74(7):849-851.
104. Kinsey GR, Li L, Okusa MD. Inflammation in acute kidney injury. Nephron Exp Nephrol. 2008;109(4):e102-e107.
105. Rosen S, Heyman SN. Difficulties in understanding human "acute tubular necrosis": limited data and flawed animal models. Kidney Int. 2001;60(4):1220-1224.
106. Rosen S, Stillman IE. Acute tubular necrosis is a syndrome of physiologic and pathologic dissociation. J Am Soc Nephrol. 2008;19(5):871-875.
107. Solez K, Morel-Maroger L, Sraer JD. The morphology of "acute tubular necrosis" in man: analysis of 57 renal biopsies and a comparison with the glycerol model. Medicine (Baltimore). 1979;58(5):362-376.
108. Jang HR, Rabb H. The innate immune response in ischemic acute kidney injury. Clin Immunol. 2009;130(1):41-50.
109. Mühlberger I, Perco P, Fechete R, Mayer B, Oberbauer R. Biomarkers in renal transplantation ischemia reperfusion injury. Transplantation. 2009;88(3)(Suppl.):S14-S19.
110. Ascon M, Ascon DB, Liu M, et al. Renal ischemia-reperfusion leads to long term infiltration of activated and effector-memory T lymphocytes. Kidney Int. 2009;75(5):526-535.
111. Bolisetty S, Agarwal A. Neutrophils in acute kidney injury: not neutral any more. Kidney Int. 2009;75(7):674-676.
112. de Vries DK, Lindeman JH, Tsikas D, et al. Early renal ischemia-reperfusion injury in humans is dominated by IL-6 release from the allograft. Am J Transplant. 2009;9(7):1574-1584.
113. Boros P, Bromberg JS. New cellular and molecular immune pathways in ischemia/reperfusion injury. Am J Transplant. 2006;6(4):652-658.
114. Bromberg JS, Heeger PS, Li XC. Evolving paradigms that determine the fate of an allograft. Am J Transplant. 2010;10(5):1143-1148.
115. Famulski KS, Broderick G, Einecke G, et al. Transcriptome analysis reveals heterogeneity in the injury response of kidney transplants. Am J Transplant. 2007;7(11):2483-2495.
116. Pratschke J, Wilhelm MJ, Kusaka M, et al. Brain death and its influence on donor organ quality and outcome after transplantation. Transplantation. 1999;67(3):343-348.
117. Schold JD, Kaplan B, Baliga RS, Meier-Kriesche HU. The broad spectrum of quality in deceased donor kidneys. Am J Transplant. 2005;5(4 Pt 1):757-765.
118. Shoskes DA, Cecka JM. Deleterious effects of delayed graft function in cadaveric renal transplant recipients independent of acute rejection. Transplantation. 1998;66(12):1697-1701.
119. Suri D, Meyer TW. Influence of donor factors on early function of graft kidneys. J Am Soc Nephrol. 1999;10(6): 1317-1323.
120. Vazquez MA, Jeyarajah DR, Kielar ML, Lu CY. Long-term outcomes of renal transplantation: a result of the original endowment of the donor kidney and the inflammatory response to both alloantigens and injury. Curr Opin Nephrol Hypertens. 2000;9(6):643-648.
121. Shoskes DA, Halloran PF. Delayed graft function in renal transplantation: etiology, management and long-term significance. J Urol. 1996;155(6):1831-1840.
122. Ojo AO, Wolfe RA, Held PJ, Port FK, Schmouder RL. Delayed graft function: risk factors and implications for renal allograft survival. Transplantation. 1997;63(7):968-974.
123. Tapiawala SN, Tinckam KJ, Cardella CJ, et al. Delayed graft function and the risk for death with a functioning graft. J Am Soc Nephrol. 2010;21(1):153-161.
124. Yarlagadda SG, Klein CL, Jani A. Long-term renal outcomes after delayed graft function. Adv Chronic Kidney Dis. 2008; 15(3):248-256.
125. Peeters P, Vanholder R. Therapeutic interventions favorably influencing delayed and slow graft function in kidney transplantation: mission impossible? Transplantation. 2008; 85(7) (Suppl):S31-S37.
126. Irish WD, Wang J, Brennan DC. Utility of a mathematical nomogram to predict delayed graft function: a singlecenter experience-critique. Transplantation. 2007;83(4): 524-525.
127. Nyberg SL, Matas AJ, Kremers WK, et al. Improved scoring system to assess adult donors for cadaver renal transplantation. Am J Transplant. 2003;3(6):715-721.
128. Plata-Munoz JJ, Vazquez-Montes M, Friend PJ, Fuggle SV. The deceased donor score system in kidney transplants from deceased donors after cardiac death. Transpl Int. 2009.
129. Rao PS, Schaubel DE, Guidinger MK, et al. A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index. Transplantation. 2009;88(2): 231-236.
130. Irish WD, Ilsley JN, Schnitzler MA, Feng S, Brennan DC. A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am J Transplant. 2010;10(10):2279-2286.
131. Kaplan B, Schold J. Transplantation: neural networks for predicting graft survival. Nat Rev Nephrol. 2009;5(4):190-192.
132. Kubal C, Bhati C. Delayed graft function: utility of predictive models. Am J Transplant. 2006;6(11):2818-2819.
133. Grossberg JA, Reinert SE, Monaco AP, Gohh R, Morrissey PE. Utility of a mathematical nomogram to predict delayed graft function: a single-center experience. Transplantation. 2006;81(2):155-159.
134. Mengel M, Sis B. An appeal for zero-time biopsies in renal transplantation. Am J Transplant. 2008;8(11):2181-2182.
135. Nickeleit V. Pathology: donor biopsy evaluation at time of renal grafting. Nat Rev Nephrol. 2009;5(5):249-251.
136. Randhawa P. Role of donor kidney biopsies in renal transplantation. Transplantation. 2001;71(10):1361-1365.
137. Solez K, Colvin RB, Racusen LC, et al. Banff 07 classification of renal allograft pathology: updates and future directions. Am J Transplant. 2008;8(4):753-760.
138. Munivenkatappa RB, Schweitzer EJ, Papadimitriou JC, et al. The Maryland aggregate pathology index: a deceased donor kidney biopsy scoring system for predicting graft failure. Am J Transplant. 2008;8(11):2316-2324.
139. Anglicheau D, Loupy A, Lefaucheur C, et al. A simple clinicohistopathological composite scoring system is highly predictive of graft outcomes in marginal donors. Am J Transplant. 2008;8(11):2325-2334.
140. Pessione F, Cohen S, Durand D, et al. Multivariate analysis of donor risk factors for graft survival in kidney transplantation. Transplantation. 2003;75(3):361-367.
141. Hauser P, Schwarz C, Mitterbauer C, et al. Genome-wide gene-expression patterns of donor kidney biopsies distinguish primary allograft function. Lab Invest. 2004;84(3):353-361.
142. Kainz A, Mitterbauer C, Hauser P, et al. Alterations in gene expression in cadaveric vs. live donor kidneys suggest impaired tubular counterbalance of oxidative stress at implantation. Am J Transplant. 2004;4(10):1595-1604.
143. Mueller TF, Reeve J, Jhangri GS, et al. The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function. Am J Transplant. 2008;8(1):78-85.
144. Naesens M, Li L, Ying L, et al. Expression of complement components differs between kidney allografts from living and deceased donors. J Am Soc Nephrol. 2009;20(8):1839-1851.
145. Famulski KS, Einecke G, Reeve J, et al. Changes in the transcriptome in allograft rejection: IFN-gamma-induced transcripts in mouse kidney allografts. Am J Transplant. 2006;6(6):1342-1354.
146. Mueller TF, Einecke G, Reeve J, et al. Microarray analysis of rejection in human kidney transplants using pathogenesis based transcript sets. Am J Transplant. 2007;7(12):2712-2722.
147. Mueller TF, Ma C, Lederer JA, Perkins DL. Differentiation of stress, metabolism, communication, and defense responses following transplantation. J Leukoc Biol. 2003;73(3):379-390.
148. Gurtner GC, Werner S, Barrandon Y, Longaker MT. Wound repair and regeneration. Nature. 2008;453(7193):314-321.
149. Devarajan P. Cellular and molecular derangements in acute tubular necrosis. Curr Opin Pediatr. 2005;17(2):193-199.
150. Basile DP. The endothelial cell in ischemic acute kidney injury: implications for acute and chronic function. Kidney Int. 2007;72(2):151-156.
151. Basile DP, Fredrich K, Alausa M, et al. Identification of persistently altered gene expression in the kidney after functional recovery from ischemic acute renal failure. Am J Physiol Renal Physiol. 2005;288(5):F953-F963.
152. Maluf DG, Mas VR, Archer KJ, et al. Molecular pathways involved in loss of kidney graft function with tubular atrophy and interstitial fibrosis. Mol Med. 2008;14(5-6):276-285.
153. Park W, Griffin M, Grande JP, Cosio F, Stegall MD. Molecular evidence of injury and inflammation in normal and fibrotic renal allografts one year posttransplant. Transplantation. 2007;83(11):1466-1476.
154. Kurian SM, Flechner SM, Kaouk J, et al. Laparoscopic donor nephrectomy gene expression profiling reveals upregulation of stress and ischemia associated genes compared to control kidneys. Transplantation. 2005;80(8):1067-1071.
155. Naesens M, Lerut E, de Jonge H, Van Damme B, Vanrenterghem Y, Kuypers DR. Donor age and renal P-glycoprotein expression associate with chronic histological damage in renal allografts. J Am Soc Nephrol. 2009;20(11):2468-2480.
156. Mas VR, Archer KJ, Yanek K, et al. Gene expression patterns in deceased donor kidneys developing delayed graft function after kidney transplantation. Transplantation. 2008;85(4):626-635.
157. Mas VR, Archer KJ, Scian M, Maluf DG. Molecular pathways involved in loss of graft function in kidney transplant recipients. Expert Rev Mol Diagn. 2010;10(3):269-284.
158. Avihingsanon Y, Ma N, Pavlakis M, et al. On the intraoperative molecular status of renal allografts after vascular reperfusion and clinical outcomes. J Am Soc Nephrol. 2005;16(6): 1542-1548.
159. Kainz A, Perco P, Mayer B, et al. Gene-expression profiles and age of donor kidney biopsies obtained before transplantation distinguish medium term graft function. Transplantation. 2007;83(8):1048-1054.
160. Mancilla E, Avila-Casado C, Uribe-Uribe N, et al. Time-zero renal biopsy in living kidney transplantation: a valuable opportunity to correlate predonation clinical data with histological abnormalities. Transplantation. 2008;86(12):1684-1688.
161. Perco P, Kainz A, Wilflingseder J, Soleiman A, Mayer B, Oberbauer R. Histogenomics: association of gene expression patterns with histological parameters in kidney biopsies. Transplantation. 2009;87(2):290-295.
162. Coca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int. 2008;73(9):1008-1016.
163. Devarajan P. Review: neutrophil gelatinase-associated lipocalin: a troponin-like biomarker for human acute kidney injury. Nephrology (Carlton). 2010;15(4):419-428.
164. Mishra J, Ma Q, Kelly C, et al. Kidney NGAL is a novel early marker of acute injury following transplantation. Pediatr Nephrol. 2006;21(6):856-863.
165. Obeidat MA, Luyckx VA, Grebe SO, et al. Post-transplant nuclear renal scans correlate with renal injury biomarkers and early allograft outcomes. Nephrol Dial Transplant. 2011; 26(9):3038-3045.
166. Parikh CR, Jani A, Mishra J, et al. Urine NGAL and IL-18 are predictive biomarkers for delayed graft function following kidney transplantation. Am J Transplant. 2006;6(7):1639-1645.
167. Pickering JW, Endre ZH. Secondary prevention of acute kidney injury. Curr Opin Crit Care. 2009;15(6):488-497.
168. Liang M, Cowley AW Jr, Hessner MJ, Lazar J, Basile DP, Pietrusz JL. Transcriptome analysis and kidney research: toward systems biology. Kidney Int. 2005;67(6):2114-2122.
169. Chabardès-Garonne D, Mejéan A, Aude JC, et al. A panoramic view of gene expression in the human kidney. Proc Natl Acad Sci USA. 2003;100(23):13710-13715.
170. Soutourina O, Cheval L, Doucet A. Global analysis of gene expression in mammalian kidney. Pflugers Arch. 2005;450(1): 13-25.
171. Eikmans M, Baelde HJ, de Heer E, Bruijn JA. Messenger RNA assessment in clinical nephrology: perspectives and progress of methodology. Nephrol Dial Transplant. 2005; 20(12):2598-2601.
172. Henger A, Kretzler M, Doran P, et al. Gene expression fingerprints in human tubulointerstitial inflammation and fibrosis as prognostic markers of disease progression. Kidney Int. 2004;65(3):904-917.
173. Corwin HL, Schwartz MM, Lewis EJ. The importance of sample size in the interpretation of the renal biopsy. Am J Nephrol. 1988;8(2):85-89.
174. Lindenmeyer MT, Kretzler M, Boucherot A, et al. Interstitial vascular rarefaction and reduced VEGF-A expression in human diabetic nephropathy. J Am Soc Nephrol. 2007;18(6):1765-1776.
175. Martini S, Eichinger F, Nair V, Kretzler M. Defining human diabetic nephropathy on the molecular level: integration of transcriptomic profiles with biological knowledge. Rev Endocr Metab Disord. 2008;9(4):267-274.
176. Peterson KS, Huang JF, Zhu J, et al. Characterization of heterogeneity in the molecular pathogenesis of lupus nephritis from transcriptional profiles of laser-captured glomeruli. J Clin Invest. 2004;113(12):1722-1733.
177. Schwab K, Witte DP, Aronow BJ, Devarajan P, Potter SS, Patterson LT. Microarray analysis of focal segmental glomerulosclerosis. Am J Nephrol. 2004;24(4):438-447.
178. Parsons J, Wand Y. A question of class. Nature. 2008; 455(7216):1040-1041.
179. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403(6769):503-511.
180. Sørlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98(19):10869-10874.
181. Bhavnani SK, Eichinger F, Martini S, Saxman P, Jagadish HV, Kretzler M. Network analysis of genes regulated in renal diseases: implications for a molecular-based classification. BMC Bioinformatics. 2009;10(Suppl.9):S3.
182. Bhavnani SK, Ganesan A, Hall T, et al. Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations. BMC Res Notes. 2010;3(1):296.
183. Zhernakova A, van Diemen CC, Wijmenga C. Detecting shared pathogenesis from the shared genetics of immunerelated diseases. Nat Rev Genet. 2009;10(1):43-55.
184. Pittman J, Huang E, Dressman H, et al. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc Natl Acad Sci USA. 2004;101(22):8431-8436.
185. West M, Ginsburg GS, Huang AT, Nevins JR. Embracing the complexity of genomic data for personalized medicine. Genome Res. 2006;16(5):559-566.
186. Heng TS, Painter MW, Elpek K, et al; Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat Immunol. 2008;9(10):1091-1094.
187. Sun Y, Goodison S, Li J, Liu L, Farmerie W. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics. 2007;23(1):30-37.
188. Brenton JD, Carey LA, Ahmed AA, Caldas C. Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J Clin Oncol. 2005;23(29):7350-7360.
189. Finak G, Bertos N, Pepin F, et al. Stromal gene expression predicts clinical outcome in breast cancer. Nat Med. 2008;14(5):518-527.
190. Herbst RS, Lippman SM. Molecular signatures of lung cancer: toward personalized therapy. N Engl J Med. 2007;356(1): 76-78.
191. Shedden K, Taylor JM, Enkemann SA, et al; Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14(8):822-827.
192. Xie Y, Minna JD. Predicting the future for people with lung cancer. Nat Med. 2008;14(8):812-813.
193. Malumbres R, Chen J, Tibshirani R, et al. Paraffin-based 6-gene model predicts outcome in diffuse large B-cell lymphoma patients treated with R-CHOP. Blood. 2008;111(12):5509-5514.
194. Schold JD, Kaplan B. The elephant in the room: failings of current clinical endpoints in kidney transplantation. Am J Transplant. 2010;10(5):1163-1166.
195. Boitnott JK, Solez K. The use of ranking in the statistical assessment of morphologic data. In: Solez K, Whelton A, eds. Acute renal failure: correlations between morphology and function. NewYork: Marcel Dekker; 1984:13-16.