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  • Review Article
  • Published:

Molecular assessment of disease states in kidney transplant biopsy samples

Nature Reviews Nephrology volume 12, pages 534–548 (2016)Cite this article

Subjects

Key Points

  • Unmet needs in renal transplantation include not just accurate diagnosis but understanding and reclassifying of disease states, which requires molecular studies of biopsy samples

  • Benefits of molecular analysis over histology include the potential for centralized objective assessment of rejection and injury, identification of mechanisms and druggable targets and better prediction of outcomes

  • Conventional diagnostic classes guide the mapping of the molecular landscape of disease entities, and the creation of molecular classifiers, which in turn can be used to guide diagnosis

  • The molecular phenotype provides an opportunity to recalibrate the conventional histologic classifications

  • The molecular phenotype of disease states can only be reliably assessed as a centralized test as it relies on rigorous reproducible measurement, quantification and normalization

Abstract

Progress in renal transplantation requires improved understanding and assessment of rejection and injury. Study of the relationship between gene expression and clinical phenotypes in kidney transplant biopsy samples has led to the development of a system that enables diagnoses of specific disease states on the basis of messenger RNA levels in the biopsy sample. Using this system we have defined the molecular landscape of T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), acute kidney injury (AKI), and tubular atrophy and interstitial fibrosis. TCMR and ABMR share IFNγ-mediated effects and TCMR has emerged as a cognate T cell–antigen presenting cell process in the interstitium, whereas ABMR is a natural-killer-cell-mediated process that occurs in the microcirculation. The specific features of these different processes have led to the creation of classifiers to test for TCMR and ABMR, and revealed that ABMR is the principal cause of kidney transplant deterioration. The molecular changes associated with renal injury are often more extensive than suggested by histology and indicate that the progression to graft failure is caused by continuing nephron injury, rather than fibrogenesis. In summary, advances in the molecular assessment of disease states in biopsy samples has improved understanding of specific processes involved in kidney graft outcomes.

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Figure 1: Pathogenesis based transcript (PBT) scores reveal large-scale molecular disturbances in biopsy samples with different disease diagnoses.
Figure 2: Emerging models of antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR) mechanisms.
Figure 3: Time-dependent effects on disease states after transplantation.
Figure 4: The relationship between the time-dependent changes in the expression of immunoglobulin, mast cell, acute kidney injury (AKI), and fibrillar collagen transcripts and the progression of tubular atrophy and interstitial fibrosis (TA/IF).
Figure 5: The nephron-centric model of renal transplant fibrosis based on the injury-related molecular events observed in biopsy samples in the first year post-transplantation.
Figure 6: Schematic of an analysis of a new biopsy sample in relation to a reference set of samples from indication biopsies.

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References

  1. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    CAS  PubMed  Google Scholar 

  2. Solin, L. J. et al. A multigene expression assay to predict local recurrence risk for ductal carcinoma in situ of the breast. J. Natl Cancer Inst. 105, 701–710 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Sparano, J. A. et al. Prospective validation of a 21-gene expression assay in breast cancer. N. Engl. J. Med. 373, 2005–2014 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Haas, M. et al. Banff 2013 meeting report: inclusion of C4d-negative antibody-mediated rejection and antibody-associated arterial lesions. Am. J. Transplant. 14, 272–283 (2014).

    CAS  PubMed  Google Scholar 

  5. Einecke, G. et al. Antibody-mediated microcirculation injury is the major cause of late kidney transplant failure. Am. J. Transplant. 9, 2520–2531 (2009).

    CAS  PubMed  Google Scholar 

  6. Loupy, A. et al. Molecular microscope strategy to improve risk stratification in early antibody-mediated kidney allograft rejection. J. Am. Soc. Nephrol. 52, 2267–2277 (2014).

    Google Scholar 

  7. Halloran, P. F., Merino Lopez, M., Salazar, I. D. R. & Chang, J. Clinical subclassifiation of ABMR phenotypes: recognizing variation in presentation [abstract]. Am. J. Transplant. 5 (Suppl. 3), 1458 (2015).

    Google Scholar 

  8. Mengel, M. et al. Banff 2011 meeting report: new concepts in antibody-mediated rejection. Am. J. Transplant. 12, 563–570 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Sis, B. et al. Isolated endarteritis and kidney transplant survival: a multicenter collaborative study. J. Am. Soc. Nephrol. 26, 1216–1227 (2015).

    CAS  PubMed  Google Scholar 

  10. Salazar, I. D. R., Lopez, M. M., Chang, J. & Halloran, P. F. Reassessing the significance of v-lesions in kidney transplant biopsies. J. Am. Soc. Nephrol. 26, 3190–3198 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Racusen, L. C. et al. The Banff 97 working classification of renal allograft pathology. Kidney Int. 55, 713–723 (1999).

    CAS  PubMed  Google Scholar 

  12. Halloran, P. F., Langone, A. J., Helderman, J. H. & Kaplan, B. Assessing long-term nephron loss: is it time to kick the CAN grading system? Am. J. Transplant. 4, 1729–1730 (2004).

    PubMed  Google Scholar 

  13. Solez, K. et al. Banff '05 meeting report: differential diagnosis of chronic allograft injury and elimination of chronic allograft nephropathy ('CAN'). Am. J. Transplant. 7, 518–526 (2007).

    CAS  PubMed  Google Scholar 

  14. Martin-Gandul, C., Mueller, N. J., Pascual, M. & Manuel, O. The impact of infection on chronic allograft dysfunction and allograft survival after solid organ transplantation. Am. J. Transplant. 15, 3024–3040 (2015).

    CAS  PubMed  Google Scholar 

  15. Furness, P. N. et al. International variation in histologic grading is large, and persistent feedback does not improve reproducibility. Am. J. Surg. Pathol. 27, 805–810 (2003).

    PubMed  Google Scholar 

  16. Furness, P. N. & Taub, N. International variation in the interpretation of renal transplant biopsies: report of the CERTPAP project. Kidney Int. 60, 1998–2012 (2001).

    CAS  PubMed  Google Scholar 

  17. Reeve, J. et al. Molecular diagnosis of T cell-mediated rejection in human kidney transplant biopsies. Am. J. Transplant. 13, 645–655 (2013).

    CAS  PubMed  Google Scholar 

  18. Middleton, D., Jones, J. & Lowe, D. Nothing's perfect: the art of defining HLA-specific antibodies. Transplant. Immunol. 30, 115–121 (2014).

    CAS  Google Scholar 

  19. Visentin, J. et al. Denatured class I human leukocyte antigen antibodies in sensitized kidney recipients: prevalence, relevance, and impact on organ allocation. Transplantation 98, 738–744 (2014).

    CAS  PubMed  Google Scholar 

  20. Gombos, P. et al. Influence of test technique on sensitization status of patients on the kidney transplant waiting list. Am. J. Transplant. 13, 2075–2082 (2013).

    CAS  PubMed  Google Scholar 

  21. Gebel, H. M. & Bray, R. A. In search of perfection. Am. J. Transplant. 13, 1951–1952 (2013).

    CAS  PubMed  Google Scholar 

  22. Lefaucheur, C. et al. IgG donor-specific anti-human HLA antibody subclasses and kidney allograft antibody-mediated injury. J. Am. Soc. Nephrol. 27, 293–304 (2015).

    PubMed  PubMed Central  Google Scholar 

  23. Loupy, A. et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N. Engl. J. Med. 369, 1215–1226 (2013).

    CAS  PubMed  Google Scholar 

  24. Hidalgo, L. G. et al. De novo donor specific antibody at the time of kidney transplant biopsy associates with microvascular pathology and late graft failure. Am. J. Transplant. 9, 2532–2541 (2009).

    CAS  PubMed  Google Scholar 

  25. Lachmann, N. et al. Anti-human leukocyte antigen and donor-specific antibodies detected by luminex posttransplant serve as biomarkers for chronic rejection of renal allografts. Transplantation 87, 1505–1513 (2009).

    CAS  PubMed  Google Scholar 

  26. Zou, Y., Stastny, P., Susal, C., Dohler, B. & Opelz, G. Antibodies against MICA antigens and kidney-transplant rejection. N. Engl. J. Med. 357, 1293–1300 (2007).

    CAS  PubMed  Google Scholar 

  27. Halloran, P. F. Transplantation: autoantibodies-epiphenomena or biological clues. Nat. Rev. Nephrol. 9, 705–706 (2013).

    CAS  PubMed  Google Scholar 

  28. Menon, M. C., Keung, K. L., Murphy, B. & O'Connell, P. J. The use of genomics and pathway analysis in our understanding and prediction of clinical renal transplant injury. Transplantation http://dx.doi.org/10.1097/TP.0000000000000943 (2015).

  29. Lo, D. J., Kaplan, B. & Kirk, A. D. Biomarkers for kidney transplant rejection. Nat. Rev. Nephrol. 10, 215–225 (2014).

    CAS  PubMed  Google Scholar 

  30. Flechner, S. M. et al. Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes. Am. J. Transplant. 4, 1475–1489 (2004).

    PubMed  PubMed Central  Google Scholar 

  31. Kurian, S. M. et al. Molecular classifiers for acute kidney transplant rejection in peripheral blood by whole genome gene expression profiling. Am. J. Transplant. 14, 1164–1172 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Roedder, S. et al. A three-gene assay for monitoring immune quiescence in kidney transplantation. J. Am. Soc. Nephrol. 26, 2042–2053 (2015).

    CAS  PubMed  Google Scholar 

  33. Li, L. et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am. J. Transplant. 12, 2710–2718 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Roedder, S. et al. The kSORT assay to detect renal transplant patients at high risk for acute rejection: results of the multicenter AART study. PLoS Med. 11, e1001759 (2014).

    PubMed  PubMed Central  Google Scholar 

  35. Suthanthiran, M. et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N. Engl. J. Med. 369, 20–31 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Anglicheau, D. et al. Discovery and validation of a molecular signature for the noninvasive diagnosis of human renal allograft fibrosis. Transplantation 93, 1136–1146 (2012).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  38. Naesens, M. et al. Progressive histological damage in renal allografts is associated with expression of innate and adaptive immunity genes. Kidney Int. 80, 1364–1376 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Park, W. D., Griffin, M. D., Cornell, L. D., Cosio, F. G. & Stegall, M. D. Fibrosis with inflammation at one year predicts transplant functional decline. J. Am. Soc. Nephrol. 21, 1987–1997 (2010).

    PubMed  PubMed Central  Google Scholar 

  40. Vitalone, M. J. et al. Transcriptome changes of chronic tubulointerstitial damage in early kidney transplantation. Transplantation 89, 537–547 (2010).

    CAS  PubMed  Google Scholar 

  41. Dosanjh, A. et al. Genomic meta-analysis of growth factor and integrin pathways in chronic kidney transplant injury. BMC Genomics 14, 275 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Einecke, G. et al. A molecular classifier for predicting future graft loss in late kidney transplant biopsies. J. Clin. Invest. 120, 1862–1872 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Mengel, M. et al. The molecular phenotype of six-week protocol biopsies from human renal allografts: reflections of prior injury but not future course. Am. J. Transplant. 11, 708–718 (2011).

    CAS  PubMed  Google Scholar 

  44. Reeve, J., Halloran, P. F. & Kaplan, B. Common errors in the implementation and interpretation of microarray studies. Transplantation 99, 470–475 (2015).

    PubMed  Google Scholar 

  45. Sellares, J. et al. Predictors of response to treatment in biopsy-diagnosed T cell-mediated rejection [abstract]. Am. J. Transplant. 12 (Suppl. 3), 322 (2012).

    Google Scholar 

  46. Halloran, P. F. et al. Disappearance of T cell-mediated rejection despite continued antibody-mediated rejection in late kidney transplant recipients. J. Am. Soc. Nephrol. 26, 1711–1720 (2015).

    CAS  PubMed  Google Scholar 

  47. Sellares, J. et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and non-adherence. Am. J. Transplant. 12, 388–399 (2012).

    CAS  PubMed  Google Scholar 

  48. Limmathurotsakul, D. et al. Fool's gold: why imperfect reference tests are undermining the evaluation of novel diagnostics: a reevaluation of 5 diagnostic tests for leptospirosis. Clin. Infect. Dis. 55, 322–331 (2012).

    PubMed  PubMed Central  Google Scholar 

  49. Brealey, S. D., Scally, A. J., Hahn, S. & Godfrey, C. Evidence of reference standard related bias in studies of plain radiograph reading performance: a meta-regression. Br. J. Radiol. 80, 406–413 (2007).

    CAS  PubMed  Google Scholar 

  50. Waikar, S. S., Betensky, R. A., Emerson, S. C. & Bonventre, J. V. Imperfect gold standards for kidney injury biomarker evaluation. J. Am. Soc. Nephrol. 23, 13–21 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Rutjes, A. W., Reitsma, J. B., Coomarasamy, A., Khan, K. S. & Bossuyt, P. M. Evaluation of diagnostic tests when there is no gold standard. A review of methods. Health Technol. Assess. 11, 50 (2007).

    Google Scholar 

  52. Reitsma, J. B., Rutjes, A. W., Khan, K. S., Coomarasamy, A. & Bossuyt, P. M. A review of solutions for diagnostic accuracy studies with an imperfect or missing reference standard. J. Clin. Epidemiol. 62, 797–806 (2009).

    PubMed  Google Scholar 

  53. Sis, B. et al. Endothelial gene expression in kidney transplants with alloantibody indicates antibody-mediated damage despite lack of C4d staining. Am. J. Transplant. 9, 2312–2323 (2009).

    CAS  PubMed  Google Scholar 

  54. Famulski, K. S. et al. Molecular phenotypes of acute kidney injury in human kidney transplants. J. Am. Soc. Nephrol. 23, 948–958 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Halloran, P. F. et al. Microarray diagnosis of antibody-mediated rejection in kidney transplant biopsies: an international prospective study (INTERCOM). Am. J. Transplant. 13, 2865–2874 (2013).

    CAS  PubMed  Google Scholar 

  56. Madill-Thomsen, K. S., Reeve, J., Bohmig, G., Eskandary, F. & Halloran, P. F. Molecular assessment of kidney transplant biopsies performs similarly in medulla and cortex [abstract 1006]. Am. J. Transplant. 16 (Suppl. 3), 16 (2016).

    Google Scholar 

  57. Hodgin, J. B. et al. A molecular profile of focal segmental glomerulosclerosis from formalin-fixed, paraffin-embedded tissue. Am. J. Pathol. 177, 1674–1686 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Vitalone, M. J. et al. Transcriptional perturbations in graft rejection. Transplant 99, 1882–1893 (2015).

    CAS  Google Scholar 

  59. Scian, M. J. et al. MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA. Am. J. Transplant. 11, 2110–2122 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Szczesniak, M. W. & Makalowska, I. lncRNA–RNA interactions across the human transcriptome. PLoS ONE 11, e0150353 (2016).

    PubMed  PubMed Central  Google Scholar 

  61. Lorenzen, J. M. et al. Long noncoding RNAs in urine are detectable and may enable early detection of acute T cell-mediated rejection of renal allografts. Clin. Chem. 61, 1505–1514 (2015).

    CAS  PubMed  Google Scholar 

  62. Mimura, I., Kanki, Y., Kodama, T. & Nangaku, M. Revolution of nephrology research by deep sequencing: ChIP-seq and RNA-seq. Kidney Int. 85, 31–38 (2014).

    CAS  PubMed  Google Scholar 

  63. Broin, O. et al. A pathogenesis-based transcript signature in donor-specific antibody-positive kidney transplant patients with normal biopsies. Genom. Data 2, 357–360 (2014).

    Google Scholar 

  64. Gupta, A. et al. Clinical and molecular significance of microvascular inflammation in transplant kidney biopsies. Kidney Int. 89, 217–225 (2016).

    CAS  PubMed  Google Scholar 

  65. 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).

    CAS  PubMed  Google Scholar 

  66. Venner, J. M. et al. Molecular landscape of T cell-mediated rejection in human kidney transplants: prominence of CTLA4 and PD ligands. Am. J. Transplant. 14, 2565–2576 (2014).

    CAS  PubMed  Google Scholar 

  67. Venner, J. M., Hidalgo, L. G., Famulski, K. S., Chang, J. & Halloran, P. F. The molecular landscape of antibody-mediated kidney transplant rejection: evidence for NK involvement through CD16a Fc receptors. Am. J. Transplant. 15, 1336–1348 (2015).

    CAS  PubMed  Google Scholar 

  68. Venner, J. M., Famulski, K. S., Reeve, J., Chang, J. & Halloran, P. F. Relationships among injury, fibrosis, and time in human kidney transplants. JCI Insight 1, e85323 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. Mueller, C. G. et al. Polymerase chain reaction selects a novel disintegrin proteinase from CD40-activated germinal center dendritic cells. J. Exp. Med. 186, 655–663 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Mueller, C. G. et al. Mannose receptor ligand-positive cells express the metalloprotease decysin in the B cell follicle. J. Immunol. 167, 5052–5060 (2001).

    CAS  PubMed  Google Scholar 

  71. Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Pauken, K. E. & Wherry, E. J. SnapShot: T cell exhaustion. Cell 163, 1038 (2015).

    CAS  PubMed  Google Scholar 

  73. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Lipson, E. J. et al. Tumor regression and allograft rejection after administration of anti-PD-1. N. Engl. J. Med. 378, 896–898 (2016).

    Google Scholar 

  75. Parkes, M. D., Halloran, P. F. & Hidalgo, L. G. Gene expression microarray analysis of purified CD16-stimulated human NK cells and indication biopsies supports a CD16-mediated role for NK cells in antibody-mediated kidney rejection [abstract 1997]. Am. J. Transplant. 16 (Suppl. 3), 16 (2016).

    Google Scholar 

  76. Min, X. et al. Expression and regulation of complement receptors by human natural killer cells. Immunobiology 219, 671–679 (2014).

    CAS  PubMed  Google Scholar 

  77. Ross, G. D. & Vetvicka, V. CR3 (CD11b, CD18): a phagocyte and NK cell membrane receptor with multiple ligand specificities and functions. Clin. Exp. Immunol. 92, 181–184 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Halloran, P. F., Merino, L. M. & Barreto, P. A. Identifying subphenotypes of antibody-mediated rejection in kidney transplants. Am. J. Transplant. 16, 908–920 (2016).

    CAS  PubMed  Google Scholar 

  79. Cosio, F. G., Gloor, J. M., Sethi, S. & Stegall, M. D. Transplant glomerulopathy. Am. J. Transplant. 8, 492–496 (2008).

    CAS  PubMed  Google Scholar 

  80. Aubert, O. et al. Phenotype and outcome of antibody-mediated rejection due to pre-existing and de novo DSA in kidney recipients [abstract 1336]. Am. J. Transplant. 16 (Suppl. 3), 16 (2016).

    Google Scholar 

  81. Platt, J. L. Accommodation: how you see it, how you don't. Am. J. Transplant. 11, 2007–2008 (2011).

    CAS  PubMed  Google Scholar 

  82. Cohen, D. et al. Pros and cons for C4d as a biomarker. Kidney Int. 81, 628–639 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Reeve, J. et al. Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches. Am. J. Transplant. 9, 1802–1810 (2009).

    CAS  PubMed  Google Scholar 

  84. Goes, N. et al. Disturbed MHC regulation in the interferon-γ knockout mouse. J. Immunol. 155, 4559–4566 (1995).

    CAS  PubMed  Google Scholar 

  85. Goes, N., Urmson, J., Ramassar, V. & Halloran, P. F. Ischemic acute tubular necrosis induces an extensive local cytokine response: evidence for induction of interferon-γ, transforming growth factorβ-1, granulocyte-macrophage colony-stimulating factor, interleukin-2 and interleukin-10. Transplantation 59, 565–572 (1995).

    CAS  PubMed  Google Scholar 

  86. Einecke, G. et al. Loss of solute carriers in T cell mediated rejection in mouse and human kidneys: an active epithelial injury — repair response. Am. J. Transplant. 10, 2241–2251 (2010).

    CAS  PubMed  Google Scholar 

  87. Einecke, G. et al. The early course of renal allograft rejection: defining the time when rejection begins. Am. J. Transplant. 9, 483–493 (2009).

    CAS  PubMed  Google Scholar 

  88. Famulski, K. S. et al. Kidney transplants with progressing chronic kidney diseases express high levels of acute kidney injury transcripts. Am. J. Transplant. 13, 634–644 (2013).

    CAS  PubMed  Google Scholar 

  89. Famulski, K. S. et al. Transcriptome analysis reveals heterogeneity in the injury response of kidney transplants. Am. J. Transplant. 7, 2483–2495 (2007).

    CAS  PubMed  Google Scholar 

  90. Einecke, G. et al. Expression of B cell and immunoglobulin transcripts is a feature of inflammation in late allografts. Am. J. Transplant. 8, 1434–1443 (2008).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  92. Fukuda, A. et al. Urine podocin:nephrin mRNA ratio (PNR) as a podocyte stress biomarker. Nephrol. Dial. Transplant. 27, 4079–4087 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Yang, Y. et al. The two kidney to one kidney transition and transplant glomerulopathy: a podocyte perspective. J. Am. Soc. Nephrol. 26, 1450–1465 (2015).

    PubMed  Google Scholar 

  94. Wickman, L. et al. Urine podocyte mRNAs, proteinuria, and progression in human glomerular diseases. J. Am. Soc. Nephrol. 24, 2081–2095 (2015).

    Google Scholar 

  95. Naik, A. S. et al. Quantitative podocyte parameters predict human native kidney and allograft half-lives. JCI Insight 1, e86943 (2016).

    PubMed Central  Google Scholar 

  96. Schnermann, J. Homer, W. The juxtaglomerular apparatus: from anatomical peculiarity to physiological relevance. J. Am. Soc. Nephrol. 14, 1681–1694 (2003).

    PubMed  Google Scholar 

  97. Schnermann, J. & Levine, D. Z. Paracrine factors in tubuloglomerular feedback: adenosine, ATP, and nitric oxide. Annu. Rev. Physiol. 65, 501–529 (2003).

    CAS  PubMed  Google Scholar 

  98. Schnermann, J. The juxtaglomerular apparatus: from anatomical peculiarity to physiological relevance. J. Am. Soc. Nephrol. 14, 1681–1694 (2003).

    PubMed  Google Scholar 

  99. Komlosi, P., Fintha, A. & Bell, P. D. Unraveling the relationship between macula densa cell volume and luminal solute concentration/osmolality. Kidney Int. 70, 865–871 (2006).

    CAS  PubMed  Google Scholar 

  100. Sellares, J. et al. Molecular diagnosis of antibody-mediated rejection in human kidney transplants. Am. J. Transplant. 13, 971–983 (2013).

    CAS  PubMed  Google Scholar 

  101. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).

    CAS  PubMed  Google Scholar 

  102. Tarca, A. L., Carey, V. J., Chen, X. W., Romero, R. & Draghici, S. Machine learning and its applications to biology. PLoS Comput. Biol. 3, e116 (2007).

    PubMed  PubMed Central  Google Scholar 

  103. Flach, P. A. Machine Learning: The Art and Science of Algorithms That Make Sense of Data (Cambridge Univ. Press, 2012).

    Google Scholar 

  104. Halloran, P. F. et al. Potential impact of microarray diagnosis of T cell-mediated rejection in kidney transplants: the INTERCOM study. Am. J. Transplant. 13, 2352–2363 (2013).

    CAS  PubMed  Google Scholar 

  105. Reeve, J., Chang, J., Salazar, I. D. R., Lopez, M. M. & Halloran, P. F. Using molecular phenotyping to guide improvements in the histologic diagnosis of T cell-mediated rejection. Am. J. Transplant. 16, 1183–1192 (2016).

    CAS  PubMed  Google Scholar 

  106. Gill, J. S. & Tonelli, M. Penny wise, pound foolish? Coverage limits on immunosuppression after kidney transplantation. N. Engl. J. Med. 366, 586–589 (2012).

    CAS  PubMed  Google Scholar 

  107. Wiebe, C. et al. Rates and determinants of progression to graft failure in kidney allograft recipients with de novo donor-specific antibody. Am. J. Transplant. 15, 2921–2930 (2015).

    CAS  PubMed  Google Scholar 

  108. Wiebe, C. et al. Evolution and clinical pathologic correlations of de novo donor-specific HLA antibody post kidney transplant. Am. J. Transplant. 12, 1157–1167 (2012).

    CAS  PubMed  Google Scholar 

  109. Modena, B. D. et al. Gene expression in biopsies of acute rejection and interstitial fibrosis/tubular atrophy reveals highly shared mechanisms that correlate with worse long-term outcomes. Am. J. Transplant. http://dx.doi.org/10.1111/ajt.13728 (2016).

  110. Halloran, K. et al. Microarray analysis of endobronchial lung transplant biopsies: detection of T-cell mediated inflammation in a safer biopsy type. J. Heart Lung Transplant. 35, S155–S156 (2016).

    Google Scholar 

  111. Halloran, K. et al. Microarray analysis of transbronchial biopsies in lung transplant recipients detect expression signatures of T-cell mediated inflammation. J. Heart Lung Transplant. 35, S234–S235 (2016).

    Google Scholar 

  112. Loupy, A. et al. The molecular landscape of antibody-mediated rejection in heart transplant patients: insights for mechanisms, activity and stage. Circulation (in press).

  113. Halloran, B. P. et al. Molecular patterns in human ulcerative colitis and correlation with response to infliximab. Inflamm. Bowel Dis. 20, 2353–2363 (2014).

    PubMed  PubMed Central  Google Scholar 

  114. Boor, P. & Floege, J. Renal allograft fibrosis: biology and therapeutic targets. Am. J. Transplant. 15, 863–886 (2015).

    CAS  PubMed  Google Scholar 

  115. Rockey, D. C., Bell, P. D. & Hill, J. A. Fibrosis — a common pathway to organ injury and failure. N. Engl. J. Med. 372, 1138–1149 (2015).

    CAS  PubMed  Google Scholar 

  116. El-Zoghby, Z. M. et al. Identifying specific causes of kidney allograft loss. Am. J. Transplant. 9, 527–535 (2009).

    CAS  PubMed  Google Scholar 

  117. Bunnag, S. et al. FOXP3 expression in human kidney transplant biopsies is associated with rejection and time post transplant but not with favorable outcomes. Am. J. Transplant. 8, 1423–1433 (2008).

    CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Alberta Transplant Applied Genomics Centre, University of Alberta,

    Philip F. Halloran, Konrad S. Famulski & Jeff Reeve

  2. Department of Medicine, Division of Nephrology and Transplant Immunology, 250 Heritage Medical Research Centre, University of Alberta, Edmonton, T6G 2S2, Alberta, Canada

    Philip F. Halloran

  3. Department of Laboratory Medicine & Pathology, 4B1.21 Walter Mackenzie Centre, University of Alberta, Edmonton, T6G 2B7, Alberta, Canada

    Konrad S. Famulski & Jeff Reeve

Authors
  1. Philip F. Halloran
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  2. Konrad S. Famulski
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  3. Jeff Reeve
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Contributions

P.F.H. researched data for article. All authors made substantial contributions to discussing the article's content, writing the article and reviewing or editing the article before submission.

Corresponding author

Correspondence to Philip F. Halloran.

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Competing interests

P.F.H. holds shares in Transcriptome Sciences Inc., a company with an interest in molecular diagnostics in transplantation, and has received research support from Transcriptome Sciences Inc., Roche Molecular Systems, Hoffmann-La Roche Canada Ltd., the Roche Organ Transplant Research Foundation, Novartis Pharma AG, and Astellas. The other authors declare no competing interests.

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DATABASES

Alberta Transplant Applied Genomics Centre Gene Lists

Ingenuity Pathway Analysis

Kyoto Encyclopedia of Genes and Genomes pathways

Broad Institute gene sets

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Halloran, P., Famulski, K. & Reeve, J. Molecular assessment of disease states in kidney transplant biopsy samples. Nat Rev Nephrol 12, 534–548 (2016). https://doi.org/10.1038/nrneph.2016.85

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