Advertisement

Molecular Medicine

, Volume 17, Issue 11–12, pp 1311–1322 | Cite as

Pretransplant Transcriptome Profiles Identify among Kidneys with Delayed Graft Function Those with Poorer Quality and Outcome

  • Valeria R. Mas
  • Mariano J. Scian
  • Kellie J. Archer
  • Jihee L. Suh
  • Krystle G. David
  • Qing Ren
  • Todd W. B. Gehr
  • Anne L. King
  • Marc P. Posner
  • Thomas F. Mueller
  • Daniel G. Maluf
Research Article

Abstract

Robust biomarkers are needed to identify donor kidneys with poor quality associated with inferior early and longer-term outcome. The occurrence of delayed graft function (DGF) is most often used as a clinical outcome marker to capture poor kidney quality. Gene expression profiles of 92 preimplantation biopsies were evaluated in relation to DGF and estimated glomerular filtration rate (eGFR) to identify preoperative gene transcript changes associated with short-term function. Patients were stratified into those who required dialysis during the first week (DGF group) versus those without (noDGF group) and subclassified according to 1-month eGFR of >45 mL/min (eGFRhi) versus eGFR of ≤45 mL/min (eGFRlo). The groups and subgroups were compared in relation to clinical donor and recipient variables and transcriptome-associated biological pathways. A validation set was used to confirm target genes. Donor and recipient characteristics were similar between the DGF versus noDGF groups. A total of 206 probe sets were significant between groups (P< 0.01), but the gene functional analyses failed to identify any significantly affected pathways. However, the subclassification of the DGF and noDGF groups identified 283 probe sets to be significant among groups and associated with biological pathways. Kidneys that developed postoperative DGF and sustained an impaired 1-month function (DGFlo group) showed a transcriptome profile of significant immune activation already preimplant. In addition, these kidneys maintained a poorer transplant function throughout the first-year posttransplant. In conclusion, DGF is a poor marker for organ quality and transplant outcome. In contrast, preimplant gene expression profiles identify “poor quality” grafts and may eventually improve organ allocation.

Notes

Acknowledgments

The research results included in this report were supported by a National Institute of Diabetes and Digestive and Kidney Diseases grant (R01DK080074).

VR Mas, RF Mueller and DG Maluf participated in the research design. MJ Scian, JL Suh, KG David, AL King and TWB Gehr performed research. VR Mas, MJ Scian, KJ Archer and TF Mueller performed data analysis. VR Mas, MJ Scian, KJ Archer, MP Posner, TF Mueller and DG Maluf prepared the manuscript.

Supplementary material

10020_2011_17111311_MOESM1_ESM.pdf (2.7 mb)
Supplementary material, approximately 2796 KB.

References

  1. 1.
    Veroux M, Corona D, Veroux P. (2009) Kidney transplantation: future challenges. Minerva Chir. 64:75–100.PubMedGoogle Scholar
  2. 2.
    Knoll G. (2008) Trends in kidney transplantation over the past decade. Drugs. 68Suppl 1:3–10.CrossRefGoogle Scholar
  3. 3.
    Schold JD, Kaplan B. (2010) The elephant in the room: failings of current clinical endpoints in kidney transplantation. Am. J. Transplant. 10:1163–6.CrossRefGoogle Scholar
  4. 4.
    Mueller TF, Solez K, Mas V (2011). Assessment of kidney organ quality and prediction of outcome at time of transplantation. Semin. Immunopathol. 33:185–99.CrossRefGoogle Scholar
  5. 5.
    Mas VR, Mueller TF, Archer KJ, Maluf DG. (2011) Identifying biomarkers as diagnostic tools in kidney transplantation. Expert Rev. Mol. Diagn. 11:183–96.CrossRefGoogle Scholar
  6. 6.
    Yarlagadda SG, et al. (2008) Marked variation in the definition and diagnosis of delayed graft function: a systematic review. Nephrol. Dial. Transplant. 23:2995–3003.CrossRefGoogle Scholar
  7. 7.
    Moore J, et al. (2010) Assessing and comparing rival definitions of delayed renal allograft function for predicting subsequent graft failure. Transplantation. 90:1113–6.CrossRefGoogle Scholar
  8. 8.
    Hauser P, et al. (2004) Genome-wide gene-expression patterns of donor kidney biopsies distinguish primary allograft function. Lab. Invest. 84:353–61.CrossRefGoogle Scholar
  9. 9.
    Kainz A, et al. (2004) Alterations in gene expression in cadaveric vs. live donor kidneys suggest impaired tubular counterbalance of oxidative stress at implantation. Am. J. Transplant. 4:1595–6004.CrossRefGoogle Scholar
  10. 10.
    Mueller TF, et al. (2008) The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function. Am. J. Transplant. 8:78–85.PubMedGoogle Scholar
  11. 11.
    Melk A, et al. (2005) Transcriptional analysis of the molecular basis of human kidney aging using cDNA microarray profiling. Kidney Int. 68:2667–79.CrossRefGoogle Scholar
  12. 12.
    Mas VR, et al. (2008) Gene expression patterns in deceased donor kidneys developing delayed graft function after kidney transplantation. Transplantation. 85:626–35.CrossRefGoogle Scholar
  13. 13.
    Levey AS, et al. (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann. Intern. Med. 130:461–70.CrossRefGoogle Scholar
  14. 14.
    Kainz A, et al. (2007) Gene-expression profiles and age of donor kidney biopsies obtained before transplantation distinguish medium term graft function. Transplantation. 83:1048–54.CrossRefGoogle Scholar
  15. 15.
    Archer KJ, Dumur CI, Joel SE, Ramakrishnan V. (2006) Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixedeffects models. Biostatistics. 7:198–212.CrossRefGoogle Scholar
  16. 16.
    Archer KJ, Guennel T. (2006) An application for assessing quality of RNA hybridized to Affymetrix GeneChips. Bioinformatics. 22:2699–701.CrossRefGoogle Scholar
  17. 17.
    Gentleman RC, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5:R80.CrossRefGoogle Scholar
  18. 18.
    R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2007.Google Scholar
  19. 19.
    Collini A, et al. (2006) Long-term outcome of renal transplantation from marginal donors. Transplant. Proc. 38:3398–99.CrossRefGoogle Scholar
  20. 20.
    Chen J, Bardes EE, Aronow BJ, Jegga AG. (2009) ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37 (Web Server issue):W305.CrossRefGoogle Scholar
  21. 21.
    Fraser SM, et al. (2010) Acceptable outcome after kidney transplantation using “expanded criteria donor” grafts. Transplantation. 89:88–96.CrossRefGoogle Scholar
  22. 22.
    Ciancio G, et al. (2010) Favorable outcomes with machine perfusion and longer pump times in kidney transplantation: a single-center, observational study. Transplantation. 90:882–90.CrossRefGoogle Scholar
  23. 23.
    Mühlberger I, Perco P, Fechete R, Mayer B, Oberbauer R. (2009) Biomarkers in renal transplantation ischemia reperfusion injury. Transplantation. 88Suppl 3:S14–9.CrossRefGoogle Scholar
  24. 24.
    Yarlagadda SG, Klein CL, Jani A. (2008) Longterm renal outcomes after delayed graft function. Adv. Chronic Kidney Dis. 15:248–56.CrossRefGoogle Scholar
  25. 25.
    Tyson M, et al. (2010) Early graft function after laparoscopically procured living donor kidney transplantation. J. Urol. 184:1434–9.CrossRefGoogle Scholar
  26. 26.
    Suri D, Meyer TW. (1999) Influence of donor factors on early function of graft kidneys. J. Am. Soc. Nephrol. 10:1317–23.PubMedGoogle Scholar
  27. 27.
    Ciancio G, et al. (2010) Favorable outcomes with machine perfusion and longer pump times in kidney transplantation: a single-center, observational study. Transplantation. 90:882–90.CrossRefGoogle Scholar
  28. 28.
    Tyson M, et al. (2010) Early graft function after laparoscopically procured living donor kidney transplantation. J. Urol. 184:1434–9.CrossRefGoogle Scholar
  29. 29.
    Hawley CM, et al. (2007) Estimated donor glomerular filtration rate is the most important donor characteristic predicting graft function in recipients of kidneys from live donors. Transpl. Int. 20:64–72.CrossRefGoogle Scholar
  30. 30.
    Johnston O, et al. (2006) Reduced graft function (with or without dialysis) vs. immediate graft function: a comparison of long-term renal allograft survival. Nephrol. Dial. Transplant. 21:2270–4.CrossRefGoogle Scholar
  31. 31.
    Kainz A, et al. (2010) Steroid pretreatment of organ donors to prevent postischemic renal allograft failure: a randomized, controlled trial. Ann. Intern. Med. 153:222–30.CrossRefGoogle Scholar
  32. 32.
    Wilflingseder J, et al. (2010) Impaired metabolism in donor kidney grafts after steroid pretreatment. Transpl. Int. 23:796–804.CrossRefGoogle Scholar

Copyright information

© The Feinstein Institute for Medical Research 2011

Authors and Affiliations

  • Valeria R. Mas
    • 1
    • 2
  • Mariano J. Scian
    • 1
    • 2
  • Kellie J. Archer
    • 3
  • Jihee L. Suh
    • 1
    • 2
  • Krystle G. David
    • 1
    • 2
  • Qing Ren
    • 1
    • 2
  • Todd W. B. Gehr
    • 4
  • Anne L. King
    • 1
    • 4
  • Marc P. Posner
    • 1
  • Thomas F. Mueller
    • 5
  • Daniel G. Maluf
    • 1
    • 2
  1. 1.Department of Surgery, Hume-Lee Transplant CenterVirginia Commonwealth UniversityRichmondUSA
  2. 2.Molecular Transplant Research LaboratoryRichmondUSA
  3. 3.Department of BiostatisticsVirginia Commonwealth UniversityRichmondUSA
  4. 4.Internal MedicineVirginia Commonwealth UniversityRichmondUSA
  5. 5.Department of MedicineUniversity of AlbertaEdmontonCanada

Personalised recommendations