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  • Review Article
  • Published:

Molecular diagnostics in transplantation

Abstract

The past few decades are characterized by an explosive evolution of genetics and molecular cell biology. Advances in chemistry and engineering have enabled increased data throughput, permitting the study of complete sets of molecules with increasing speed and accuracy using techniques such as genomics, transcriptomics, proteomics, and metabolomics. Prediction of long-term outcomes in transplantation is hampered by the absence of sufficiently robust biomarkers and a lack of adequate insight into the mechanisms of acute and chronic alloimmune injury and the adaptive mechanisms of immunological quiescence that may support transplantation tolerance. Here, we discuss some of the great opportunities that molecular diagnostic tools have to offer both basic scientists and translational researchers for bench-to-bedside clinical application in transplantation medicine, with special focus on genomics and genome-wide association studies, epigenetics (DNA methylation and histone modifications), gene expression studies and transcriptomics (including microRNA and small interfering RNA studies), proteomics and peptidomics, antibodyomics, metabolomics, chemical genomics and functional imaging with nanoparticles. We address the challenges and opportunities associated with the newer high-throughput sequencing technologies, especially in the field of bioinformatics and biostatistics, and demonstrate the importance of integrative approaches. Although this Review focuses on transplantation research and clinical transplantation, the concepts addressed are valid for all translational research.

Key Points

  • The omics approach has accelerated biomarker discovery and improved understanding of multifactorial biological processes and diseases

  • The complex interplay between the genome, transcriptome, proteome and metabolome has mostly been overlooked in traditional translational research and in the field of transplantation

  • In transplantation research, data from high-throughput molecular diagnostic tools are shedding new light on many pathogenic processes (for example, acute cellular rejection, ischemia–reperfusion injury and chronic allograft damage)

  • Upcoming high-throughput techniques, such as massively parallel sequencing for genotyping and transcriptomics, are expected to further accelerate the pace at which translational research is performed

  • The challenge for the future is to translate the progress that is being made in molecular biology into health gains, an outcome that will only be possible with a holistic approach and integrative data analysis

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Figure 1: Current paradigm of molecular biology.
Figure 2: Application of omics in renal transplantation.
Figure 3: An integrative omics approach for translational research.

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References

  1. Merrill, J., Murray, J., Harrison, J. & Guild, W. R. Successful homotransplantation of the human kidney between identical twins. J. Am. Med. Assoc. 160, 277–282 (1956).

    Article  CAS  PubMed  Google Scholar 

  2. Morris, P. J. Transplantation—a medical miracle of the 20th century. N. Engl. J. Med. 351, 2678–2680 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Starzl, T. E. & Zinkernagel, R. M. Transplantation tolerance from a historical perspective. Nat. Rev. Immunol. 1, 233–239 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kahan, B. D. Individuality: the barrier to optimal immunosuppression. Nat. Rev. Immunol. 3, 831–838 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Watson, J. D. & Crick, F. H. Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953).

    Article  CAS  PubMed  Google Scholar 

  6. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 431, 931–945 (2004).

  7. Kidd, J. M. et al. Mapping and sequencing of structural variation from eight human genomes. Nature 453, 56–64 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Robertson, K. D. DNA methylation and human disease. Nat. Rev. Genet. 6, 597–610 (2005).

    Article  CAS  PubMed  Google Scholar 

  9. Sultan, M. et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008).

    CAS  PubMed  Google Scholar 

  10. Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kim, D. H. & Rossi, J. J. Strategies for silencing human disease using RNA interference. Nat. Rev. Genet. 8, 173–184 (2007).

    Article  CAS  PubMed  Google Scholar 

  12. Cohen, P. The regulation of protein function by multisite phosphorylation—-a 25 year update. Trends Biochem. Sci. 25, 596–601 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Jensen, O. N. Interpreting the protein language using proteomics. Nat. Rev. Mol. Cell Biol. 7, 391–403 (2006).

    Article  CAS  PubMed  Google Scholar 

  14. Fiehn, O. Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).

    Article  CAS  PubMed  Google Scholar 

  16. Zerhouni, E. A. Translational and clinical science—time for a new vision. N. Engl. J. Med. 353, 1621–1623 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Hemminki, K., Lorenzo, B. J. & Forsti, A. The balance between heritable and environmental aetiology of human disease. Nat. Rev. Genet. 7, 958–965 (2006).

    Article  CAS  PubMed  Google Scholar 

  18. Nicholson, J. K., Holmes, E., Lindon, J. C. & Wilson, I. D. The challenges of modeling mammalian biocomplexity. Nat. Biotechnol. 22, 1268–1274 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Farber, D. L. Biochemical signaling pathways for memory T cell recall. Semin. Immunol. 21, 84–91 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Adams, A. B. et al. Heterologous immunity provides a potent barrier to transplantation tolerance. J. Clin. Invest. 111, 1887–1895 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Smith, J. B. Frequency in human peripheral blood of T cells which respond to self, modified self and alloantigens. Immunology 50, 181–187 (1983).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Erlich, H. A., Opelz, G. & Hansen, J. HLA DNA typing and transplantation. Immunity 14, 347–356 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Hurley, C. K., Maiers, M., Marsh, S. G. & Oudshoorn, M. Overview of registries, HLA typing and diversity, and search algorithms. Tissue Antigens 69 (Suppl. 1), 3–5 (2007).

    Article  PubMed  Google Scholar 

  24. Sasazuki, T. et al. Effect of matching of class I HLA alleles on clinical outcome after transplantation of hematopoietic stem cells from an unrelated donor. Japan Marrow Donor Program. N. Engl. J. Med. 339, 1177–1185 (1998).

    Article  CAS  PubMed  Google Scholar 

  25. Rubinstein, P. HLA matching for bone marrow transplantation—how much is enough? N. Engl. J. Med. 345, 1842–1844 (2001).

    Article  CAS  PubMed  Google Scholar 

  26. Nickerson, P. The impact of immune gene polymorphisms in kidney and liver transplantation. Clin. Lab. Med. 28, 455–468, vii (2008).

    Article  PubMed  Google Scholar 

  27. Girnita, D. M., Webber, S. A. & Zeevi, A. Clinical impact of cytokine and growth factor genetic polymorphisms in thoracic organ transplantation. Clin. Lab. Med. 28, 423–440 (2008).

    Article  PubMed  Google Scholar 

  28. St Peter, S. D. et al. Genetic determinants of delayed graft function after kidney transplantation. Transplantation 74, 809–813 (2002).

    Article  CAS  PubMed  Google Scholar 

  29. Ullrich, R. et al. Microsatellite polymorphism in the heme oxygenase-1 gene promoter and cardiac allograft vasculopathy. J. Heart Lung Transplant. 24, 1600–1605 (2005).

    Article  PubMed  Google Scholar 

  30. Haimila, K. et al. Association of genetic variation in inducible costimulator gene with outcome of kidney transplantation. Transplantation 87, 393–396 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Kruger, B. et al. Donor Toll-like receptor 4 contributes to ischemia and reperfusion injury following human kidney transplantation. Proc. Natl Acad. Sci. USA 106, 3390–3395 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Brown, K. M. et al. Influence of donor C3 allotype on late renal-transplantation outcome. N. Engl. J. Med. 354, 2014–2023 (2006).

    Article  CAS  PubMed  Google Scholar 

  33. Kruger, B., Schroppel, B. & Murphy, B. T. Genetic polymorphisms and the fate of the transplanted organ. Transplant. Rev. (Orlando) 22, 131–140 (2008).

    Article  Google Scholar 

  34. de Jonge, H. & Kuypers, D. R. Pharmacogenetics in solid organ transplantation: current status and future directions. Transplant. Rev. (Orlando) 22, 6–20 (2008).

    Article  Google Scholar 

  35. Lennard, L., Van Loon, J. A. & Weinshilboum, R. M. Pharmacogenetics of acute azathioprine toxicity: relationship to thiopurine methyltransferase genetic polymorphism. Clin. Pharmacol. Ther. 46, 149–154 (1989).

    Article  CAS  PubMed  Google Scholar 

  36. Naesens, M. et al. Donor age and renal P-glycoprotein expression associate with chronic histological damage in renal allografts. J. Am. Soc. Nephrol. 20, 2468–2480 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Burckart, G. J. & Amur, S. Update on the clinical pharmacogenomics of organ transplantation. Pharmacogenomics 11, 227–236 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Thervet, E. et al. Optimization of initial tacrolimus dose using pharmacogenetic testing. Clin. Pharmacol. Ther. 87, 721–726 (2010).

    CAS  PubMed  Google Scholar 

  39. Kruglyak, L. The road to genome-wide association studies. Nat. Rev. Genet. 9, 314–318 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Hirschhorn, J. N. Genomewide association studies—illuminating biologic pathways. N. Engl. J. Med. 360, 1699–1701 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Frazer, K. A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. International HapMap Project [online], (2010).

  43. Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Manolio, T. A., Brooks, L. D. & Collins, F. S. A HapMap harvest of insights into the genetics of common disease. J. Clin. Invest. 118, 1590–1605 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hindorff, L. A., Junkins, H. A., Hall, P. N., Mehta, J. P. & Manolio, T. A. A Catalog of Published Genome-Wide Association Studies [online], (2010).

    Google Scholar 

  46. Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cantor, R. M., Lange, K. & Sinsheimer, J. S. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am. J. Hum. Genet. 86, 6–22 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Goldstein, D. B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Genomes: a Deep Catalog of Human Genetic Variation [online], (2010).

  50. The United Kingdom and Ireland Renal Transplant Consortium. WTCCC3—Wellcome Trust Case-Control Consortium 3: Defining the genetic basis of interactions between donor and recipient DNA that determine early and late renal transplant dysfunction [online], (2010).

  51. Jirtle, R. L. & Skinner, M. K. Environmental epigenomics and disease susceptibility. Nat. Rev. Genet. 8, 253–262 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Park, P. J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Esteller, M. Epigenetics in cancer. N. Engl. J. Med. 358, 1148–1159 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  55. Cuddapah, S., Barski, A. & Zhao, K. Epigenomics of T cell activation, differentiation, and memory. Curr. Opin. Immunol. 22, 341–347 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Araki, Y. et al. Genome-wide analysis of histone methylation reveals chromatin state-based regulation of gene transcription and function of memory CD8+ T cells. Immunity. 30, 912–925 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wilson, C. B., Rowell, E. & Sekimata, M. Epigenetic control of T-helper-cell differentiation. Nat. Rev. Immunol. 9, 91–105 (2009).

    Article  CAS  PubMed  Google Scholar 

  58. Granger, A. et al. Histone deacetylase inhibition reduces myocardial ischemia-reperfusion injury in mice. FASEB J. 22, 3549–3560 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Uhrberg, M. Shaping the human NK cell repertoire: an epigenetic glance at KIR gene regulation. Mol. Immunol. 42, 471–475 (2005).

    Article  CAS  PubMed  Google Scholar 

  60. Parra, M. Epigenetic events during B lymphocyte development. Epigenetics 4, 462–468 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Laird, P. W. Principles and challenges of genome-wide DNA methylation analysis. Nat. Rev. Genet. 11, 191–203 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Floess, S. et al. Epigenetic control of the foxp3 locus in regulatory T cells. PLoS Biol. 5, e38 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Lal, G. & Bromberg, J. S. Epigenetic mechanisms of regulation of Foxp3 expression. Blood 114, 3727–3735 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Parker, M. D., Chambers, P. A., Lodge, J. P. & Pratt, J. R. Ischemia–reperfusion injury and its influence on the epigenetic modification of the donor kidney genome. Transplantation 86, 1818–1823 (2008).

    Article  PubMed  Google Scholar 

  65. Hoffmann, S. C. et al. Molecular and immunohistochemical characterization of the onset and resolution of human renal allograft ischemia-reperfusion injury. Transplantation 74, 916–923 (2002).

    Article  CAS  PubMed  Google Scholar 

  66. Avihingsanon, Y. et al. On the intraoperative molecular status of renal allografts after vascular reperfusion and clinical outcomes. J. Am. Soc. Nephrol. 16, 1542–1548 (2005).

    Article  CAS  PubMed  Google Scholar 

  67. Strehlau, J. et al. Quantitative detection of immune activation transcripts as a diagnostic tool in kidney transplantation. Proc. Natl Acad. Sci. USA 94, 695–700 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Shihab, F. et al. Transforming growth factor-beta and matrix protein expression in acute and chronic rejection of human renal allografts. J. Am. Soc. Nephrol. 6, 286–294 (1995).

    CAS  PubMed  Google Scholar 

  69. Sharma, V. K. et al. Intragraft TGF-beta 1 mRNA: a correlate of interstitial fibrosis and chronic allograft nephropathy. Kidney Int. 49, 1297–1303 (1996).

    Article  CAS  PubMed  Google Scholar 

  70. Strom, T. B. & Suthanthiran, M. Transcriptional profiling to assess the clinical status of kidney transplants. Nat. Clin. Pract. Nephrol. 2, 116–117 (2006).

    Article  PubMed  Google Scholar 

  71. Li, B. et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N. Engl. J. Med. 344, 947–954 (2001).

    Article  CAS  PubMed  Google Scholar 

  72. Simon, T., Opelz, G., Wiesel, M., Ott, R. C. & Susal, C. Serial peripheral blood perforin and granzyme B gene expression measurements for prediction of acute rejection in kidney graft recipients. Am. J. Transplant. 3, 1121–1127 (2003).

    Article  CAS  PubMed  Google Scholar 

  73. Aquino-Dias, E. C. et al. Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int. 73, 877–884 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Anglicheau, D. & Suthanthiran, M. Noninvasive prediction of organ graft rejection and outcome using gene expression patterns. Transplantation 86, 192–199 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470 (1995).

    Article  CAS  PubMed  Google Scholar 

  76. Derisi, J. et al. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14, 457–460 (1996).

    Article  CAS  PubMed  Google Scholar 

  77. Shi, L. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

    Article  CAS  PubMed  Google Scholar 

  78. Weintraub, L. A. & Sarwal, M. M. Microarrays: a monitoring tool for transplant patients? Transpl. Int. 19, 775–788 (2006).

    Article  CAS  PubMed  Google Scholar 

  79. Ying, L. & Sarwal, M. In praise of arrays. Pediatr. Nephrol. 24, 1643–1659 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Khatri, P. & Sarwal, M. M. Using gene arrays in diagnosis of rejection. Curr. Opin. Organ Transplant. 14, 34–39 (2009).

    Article  PubMed  Google Scholar 

  81. Mueller, T. F. et al. The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function. Am. J. Transplant. 8, 78–85 (2008).

    CAS  PubMed  Google Scholar 

  82. Naesens, M. et al. Expression of complement components differs between kidney allografts from living and deceased donors. J. Am. Soc. Nephrol. 20, 1839–1851 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sarwal, M. et al. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N. Engl. J. Med. 349, 125–138 (2003).

    Article  CAS  PubMed  Google Scholar 

  84. Mueller, T. F. et al. Microarray analysis of rejection in human kidney transplants using pathogenesis-based transcript sets. Am. J. Transplant. 7, 2712–2722 (2007).

    Article  CAS  PubMed  Google Scholar 

  85. Mas, V. et al. Establishing the molecular pathways involved in chronic allograft nephropathy for testing new noninvasive diagnostic markers. Transplantation 83, 448–457 (2007).

    Article  CAS  PubMed  Google Scholar 

  86. Park, W., Griffin, M., Grande, J. P., Cosio, F. & Stegall, M. D. Molecular evidence of injury and inflammation in normal and fibrotic renal allografts one year posttransplant. Transplantation 83, 1466–1476 (2007).

    Article  CAS  PubMed  Google Scholar 

  87. Mengel, M. et al. Molecular correlates of scarring in kidney transplants: the emergence of mast cell transcripts. Am. J. Transplant. 9, 169–178 (2009).

    Article  CAS  PubMed  Google Scholar 

  88. Nakorchevsky, A. et al. Molecular mechanisms of chronic kidney transplant rejection via large-scale proteogenomic analysis of tissue biopsies. J. Am. Soc. Nephrol. 21, 362–373 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Brouard, S. et al. Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc. Natl Acad. Sci. USA 104, 15448–15453 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Martinez-Llordella, M. et al. Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients. J. Clin. Invest. 118, 2845–2857 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Zarkhin, V. & Sarwal, M. M. Microarrays: monitoring for transplant tolerance and mechanistic insights. Clin. Lab. Med. 28, 385–410 (2008).

    Article  PubMed  Google Scholar 

  92. Li, L. et al. Interference of globin genes with biomarker discovery for allograft rejection in peripheral blood samples. Physiol. Genomics 32, 190–197 (2008).

    Article  CAS  PubMed  Google Scholar 

  93. Gunther, O. P. et al. Functional genomic analysis of peripheral blood during early acute renal allograft rejection. Transplantation 88, 942–951 (2009).

    Article  PubMed  CAS  Google Scholar 

  94. Lin, D. et al. Whole blood genomic biomarkers of acute cardiac allograft rejection. J. Heart Lung Transplant. 28, 927–935 (2009).

    Article  PubMed  Google Scholar 

  95. Kurian, S. M. et al. Biomarkers for early and late stage chronic allograft nephropathy by proteogenomic profiling of peripheral blood. PLoS ONE 4, e6212 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Kwan, T. et al. Genome-wide analysis of transcript isoform variation in humans. Nat. Genet. 40, 225–231 (2008).

    Article  CAS  PubMed  Google Scholar 

  97. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Richard, H. et al. Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments. Nucleic Acids Res. 38, e112 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Grosshans, H. & Filipowicz, W. Molecular biology: the expanding world of small RNAs. Nature 451, 414–416 (2008).

    Article  CAS  PubMed  Google Scholar 

  100. Heo, I. & Kim, V. N. Regulating the regulators: posttranslational modifications of RNA silencing factors. Cell 139, 28–31 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. Ghildiyal, M. & Zamore, P. D. Small silencing RNAs: an expanding universe. Nat. Rev. Genet. 10, 94–108 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. McManus, M. T. MicroRNAs and cancer. Semin. Cancer Biol. 13, 253–258 (2003).

    Article  CAS  PubMed  Google Scholar 

  103. McManus, M. T. Small RNAs and immunity. Immunity 21, 747–756 (2004).

    Article  CAS  PubMed  Google Scholar 

  104. Gantier, M. P., Sadler, A. J. & Williams, B. R. Fine-tuning of the innate immune response by microRNAs. Immunol. Cell Biol. 85, 458–462 (2007).

    Article  CAS  PubMed  Google Scholar 

  105. Lodish, H. F., Zhou, B., Liu, G. & Chen, C. Z. Micromanagement of the immune system by microRNAs. Nat. Rev. Immunol. 8, 120–130 (2008).

    Article  CAS  PubMed  Google Scholar 

  106. Anglicheau, D. et al. MicroRNA expression profiles predictive of human renal allograft status. Proc. Natl Acad. Sci. USA 106, 5330–5335 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Cohen, C. D. Will non-coding RNAs help to decipher renal allograft failure? Nephrol. Dial. Transplant. 24, 2325–2327 (2009).

    Article  PubMed  Google Scholar 

  108. Harris, A., Krams, S. M. & Martinez, O. M. MicroRNAs as immune regulators: implications for transplantation. Am. J. Transplant. 10, 713–719 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Rodriguez, A. et al. Requirement of bic/microRNA-155 for normal immune function. Science 316, 608–611 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Castanotto, D. & Rossi, J. J. The promises and pitfalls of RNA-interference-based therapeutics. Nature 457, 426–433 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Abbott, A. A post-genomic challenge: learning to read patterns of protein synthesis. Nature 402, 715–720 (1999).

    Article  CAS  PubMed  Google Scholar 

  112. Mann, M. & Jensen, O. N. Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255–261 (2003).

    Article  CAS  PubMed  Google Scholar 

  113. Cristea, I. M., Gaskell, S. J. & Whetton, A. D. Proteomics techniques and their application to hematology. Blood 103, 3624–3634 (2004).

    Article  CAS  PubMed  Google Scholar 

  114. Ludwig, J. A. & Weinstein, J. N. Biomarkers in cancer staging, prognosis and treatment selection. Nat. Rev. Cancer 5, 845–856 (2005).

    Article  CAS  PubMed  Google Scholar 

  115. Graham, D. R., Elliott, S. T. & Van Eyk, J. E. Broad-based proteomic strategies: a practical guide to proteomics and functional screening. J. Physiol. 563, 1–9 (2005).

    Article  CAS  PubMed  Google Scholar 

  116. Hanash, S. M., Pitteri, S. J. & Faca, V. M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).

    Article  CAS  PubMed  Google Scholar 

  117. Sigdel, T. K., Klassen, R. B. & Sarwal, M. M. Interpreting the proteome and peptidome in transplantation. Adv. Clin. Chem. 47, 139–169 (2009).

    Article  CAS  PubMed  Google Scholar 

  118. Jenkins, J. K., Huang, H., Ndebele, K. & Salahudeen, A. K. Vitamin E inhibits renal mRNA expression of COX II, HO I, TGFbeta, and osteopontin in the rat model of cyclosporine nephrotoxicity. Transplantation 71, 331–334 (2001).

    Article  CAS  PubMed  Google Scholar 

  119. O'Riordan, E., Gross, S. S. & Goligorsky, M. S. Technology Insight: renal proteomics—at the crossroads between promise and problems. Nat. Clin. Pract. Nephrol. 2, 445–458 (2006).

    Article  CAS  PubMed  Google Scholar 

  120. Clarke, W. et al. Characterization of renal allograft rejection by urinary proteomic analysis. Ann. Surg. 237, 660–664 (2003).

    PubMed  PubMed Central  Google Scholar 

  121. Schaub, S. et al. Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J. Am. Soc. Nephrol. 15, 219–227 (2004).

    Article  CAS  PubMed  Google Scholar 

  122. O'Riordan, E. et al. Bioinformatic analysis of the urine proteome of acute allograft rejection. J. Am. Soc. Nephrol. 15, 3240–3248 (2004).

    Article  PubMed  Google Scholar 

  123. Borozdenkova, S. et al. Use of proteomics to discover novel markers of cardiac allograft rejection. J. Proteome Res. 3, 282–288 (2004).

    Article  CAS  PubMed  Google Scholar 

  124. Ling, X. B. et al. Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection. J. Am. Soc. Nephrol. 21, 646–653 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Quintana, L. F. et al. Urine proteomics to detect biomarkers for chronic allograft dysfunction. J. Am. Soc. Nephrol. 20, 428–435 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Banon-Maneus, E. et al. Two-dimensional difference gel electrophoresis urinary proteomic profile in the search of nonimmune chronic allograft dysfunction biomarkers. Transplantation 89, 548–558 (2010).

    Article  CAS  PubMed  Google Scholar 

  127. Quintana, L. F., Banon-Maneus, E., Sole-Gonzalez, A. & Campistol, J. M. Urine proteomics biomarkers in renal transplantation: an overview. Transplantation 88, S45–S49 (2009).

    Article  CAS  PubMed  Google Scholar 

  128. Yu, X., Schneiderhan-Marra, N., Hsu, H. Y., Bachmann, J. & Joos, T. O. Protein microarrays: effective tools for the study of inflammatory diseases. Methods Mol. Biol. 577, 199–214 (2009).

    Article  CAS  PubMed  Google Scholar 

  129. Robinson, W. H. et al. Autoantigen microarrays for multiplex characterization of autoantibody responses. Nat. Med. 8, 295–301 (2002).

    Article  CAS  PubMed  Google Scholar 

  130. Hudson, M. E., Pozdnyakova, I., Haines, K., Mor, G. & Snyder, M. Identification of differentially expressed proteins in ovarian cancer using high-density protein microarrays. Proc. Natl Acad. Sci. USA 104, 17494–17499 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Wang, X. et al. Autoantibody signatures in prostate cancer. N. Engl. J. Med. 353, 1224–1235 (2005).

    Article  CAS  PubMed  Google Scholar 

  132. Li, L. et al. Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and “antibodyome” measures. Proc. Natl Acad. Sci. USA 106, 4148–4153 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Sutherland, S. M. et al. Protein microarrays identify antibodies to protein kinase Czeta that are associated with a greater risk of allograft loss in pediatric renal transplant recipients. Kidney Int. 76, 1277–1283 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Li, L. et al. Compartmental localization and clinical relevance of MICA antibodies after renal transplantation. Transplantation 89, 312–319 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Porcheray, F. et al. B-cell immunity in the context of T-cell tolerance after combined kidney and bone marrow transplantation in humans. Am. J. Transplant. 9, 2126–2135 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Wishart, D. S. Metabolomics: a complementary tool in renal transplantation. Contrib. Nephrol. 160, 76–87 (2008).

    Article  CAS  PubMed  Google Scholar 

  137. Wishart, D. S. et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, D603–D610 (2009).

    Article  CAS  PubMed  Google Scholar 

  138. Frolkis, A. et al. SMPDB: The small molecule pathway database. Nucleic Acids Res. 38, D480–D487 (2010).

    Article  CAS  PubMed  Google Scholar 

  139. Serkova, N., Fuller, T. F., Klawitter, J., Freise, C. E. & Niemann, C. U. H-NMR-based metabolic signatures of mild and severe ischemia/reperfusion injury in rat kidney transplants. Kidney Int. 67, 1142–1151 (2005).

    Article  CAS  PubMed  Google Scholar 

  140. Wang, J. N., Zhou, Y., Zhu, T. Y., Wang, X. & Guo, Y. L. Prediction of acute cellular renal allograft rejection by urinary metabolomics using MALDI-FTMS. J. Proteome Res. 7, 3597–3601 (2008).

    Article  CAS  PubMed  Google Scholar 

  141. Christians, U. et al. Toxicodynamic therapeutic drug monitoring of immunosuppressants: promises, reality, and challenges. Ther. Drug Monit. 30, 151–158 (2008).

    Article  CAS  PubMed  Google Scholar 

  142. Foxall, P. J., Mellotte, G. J., Bending, M. R., Lindon, J. C. & Nicholson, J. K. NMR spectroscopy as a novel approach to the monitoring of renal transplant function. Kidney Int. 43, 234–245 (1993).

    Article  CAS  PubMed  Google Scholar 

  143. Serkova, N. J. et al. Early detection of graft failure using the blood metabolic profile of a liver recipient. Transplantation 83, 517–521 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Butcher, R. A. & Schreiber, S. L. Using genome-wide transcriptional profiling to elucidate small-molecule mechanism. Curr. Opin. Chem. Biol. 9, 25–30 (2005).

    Article  CAS  PubMed  Google Scholar 

  145. Strausberg, R. L. & Schreiber, S. L. From knowing to controlling: a path from genomics to drugs using small molecule probes. Science 300, 294–295 (2003).

    Article  CAS  PubMed  Google Scholar 

  146. Seiler, K. P. et al. ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic Acids Res. 36, D351–D359 (2008).

    Article  CAS  PubMed  Google Scholar 

  147. Weissleder, R., Tung, C. H., Mahmood, U. & Bogdanov, A. Jr. In vivo imaging of tumors with protease-activated near-infrared fluorescent probes. Nat. Biotechnol. 17, 375–378 (1999).

    Article  CAS  PubMed  Google Scholar 

  148. Sosnovik, D. E., Nahrendorf, M. & Weissleder, R. Targeted imaging of myocardial damage. Nat. Clin. Pract. Cardiovasc. Med. 5 (Suppl. 2), S63–S70 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Harisinghani, M. G. et al. Noninvasive detection of clinically occult lymph-node metastases in prostate cancer. N. Engl. J. Med. 348, 2491–2499 (2003).

    Article  PubMed  Google Scholar 

  150. Kooi, M. E. et al. Accumulation of ultrasmall superparamagnetic particles of iron oxide in human atherosclerotic plaques can be detected by in vivo magnetic resonance imaging. Circulation 107, 2453–2458 (2003).

    Article  CAS  PubMed  Google Scholar 

  151. Radu, C. G. et al. Molecular imaging of lymphoid organs and immune activation by positron emission tomography with a new [18F]-labeled 2′-deoxycytidine analog. Nat. Med. 14, 783–788 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Wildgruber, M. et al. Monocyte subset dynamics in human atherosclerosis can be profiled with magnetic nano-sensors. PLoS ONE 4, e5663 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  153. Christen, T. et al. Molecular imaging of innate immune cell function in transplant rejection. Circulation 119, 1925–1932 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Rogers, Y. H. & Venter, J. C. Genomics: massively parallel sequencing. Nature 437, 326–327 (2005).

    Article  CAS  PubMed  Google Scholar 

  155. Rothberg, J. M. & Leamon, J. H. The development and impact of 454 sequencing. Nat. Biotechnol. 26, 1117–1124 (2008).

    Article  CAS  PubMed  Google Scholar 

  156. Tucker, T., Marra, M. & Friedman, J. M. Massively parallel sequencing: the next big thing in genetic medicine. Am. J. Hum. Genet. 85, 142–154 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Aparicio, S. A. & Huntsman, D. G. Does massively parallel DNA resequencing signify the end of histopathology as we know it? J. Pathol. 220, 307–315 (2010).

    CAS  PubMed  Google Scholar 

  158. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  159. Shendure, J. The beginning of the end for microarrays? Nat. Methods 5, 585–587 (2008).

    Article  CAS  PubMed  Google Scholar 

  160. Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    Article  CAS  PubMed  Google Scholar 

  161. Teutsch, S. M. et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet. Med. 11, 3–14 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Giallourakis, C., Henson, C., Reich, M., Xie, X. & Mootha, V. K. Disease gene discovery through integrative genomics. Annu. Rev. Genomics Hum. Genet. 6, 381–406 (2005).

    Article  CAS  PubMed  Google Scholar 

  163. Mootha, V. K. et al. Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics. Proc. Natl Acad. Sci. USA 100, 605–610 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Lee, I., Date, S. V., Adai, A. T. & Marcotte, E. M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004).

    Article  CAS  PubMed  Google Scholar 

  165. Somorjai, R. L., Dolenko, B. & Baumgartner, R. Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 19, 1484–1491 (2003).

    Article  CAS  PubMed  Google Scholar 

  166. Pawitan, Y., Michiels, S., Koscielny, S., Gusnanto, A. & Ploner, A. False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 21, 3017–3024 (2005).

    Article  CAS  PubMed  Google Scholar 

  167. Butte, A. The use and analysis of microarray data. Nat. Rev. Drug Discov. 1, 951–960 (2002).

    Article  CAS  PubMed  Google Scholar 

  168. Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    Article  CAS  PubMed  Google Scholar 

  170. Dahlquist, K. D., Salomonis, N., Vranizan, K., Lawlor, S. C. & Conklin, B. R. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31, 19–20 (2002).

    Article  CAS  PubMed  Google Scholar 

  171. Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  172. National Center for Biotechnology Information. Gene Expression Omnibus [online], (2010).

  173. Butte, A. J. & Kohane, I. S. Creation and implications of a phenome-genome network. Nat. Biotechnol. 24, 55–62 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Thorisson, G. A., Muilu, J. & Brookes, A. J. Genotype-phenotype databases: challenges and solutions for the post-genomic era. Nat. Rev. Genet. 10, 9–18 (2009).

    Article  CAS  PubMed  Google Scholar 

  175. Perez-Iratxeta, C. & Andrade, M. A. Inconsistencies over time in 5% of NetAffx probe-to-gene annotations. BMC Bioinformatics 6, 183 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  176. Noth, S. & Benecke, A. Avoiding inconsistencies over time and tracking difficulties in Applied Biosystems AB1700/Panther probe-to-gene annotations. BMC Bioinformatics 6, 307 (2005).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  177. Chen, R., Li, L. & Butte, A. J. AILUN: reannotating gene expression data automatically. Nat. Methods 4, 879 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Rhee, S. Y., Wood, V., Dolinski, K. & Draghici, S. Use and misuse of the gene ontology annotations. Nat. Rev. Genet. 9, 509–515 (2008).

    Article  CAS  PubMed  Google Scholar 

  179. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  CAS  Google Scholar 

  180. Schulze, A. & Downward, J. Navigating gene expression using microarrays—a technology review. Nat. Cell Biol. 3, E190–E195 (2001).

    Article  CAS  PubMed  Google Scholar 

  181. Lieberfarb, M. E. et al. Genome-wide loss of heterozygosity analysis from laser capture microdissected prostate cancer using single nucleotide polymorphic allele (SNP) arrays and a novel bioinformatics platform dChipSNP. Cancer Res. 63, 4781–4785 (2003).

    CAS  PubMed  Google Scholar 

  182. Shen-Orr, S. S. et al. Cell type-specific gene expression differences in complex tissues. Nat. Methods 7, 287–289 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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We apologize to colleagues whose work we were unable to cite due to space constraints.

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Naesens, M., Sarwal, M. Molecular diagnostics in transplantation. Nat Rev Nephrol 6, 614–628 (2010). https://doi.org/10.1038/nrneph.2010.113

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