Abstract Title: DISTINCTIVE GENE EXPRESSION PATTERNS IN DONOR KIDNEYS DEVELOPING DELAYED GRAFT FUNCTION.
Daniel Maluf, MD1, Valeria Mas, PhD1, Robert Fisher, MD1, Kenneth Yanek, MS1, Kellie Archer, PhD2, Eric Gibney, MD1, Anne King, MD1, Adrian Cotterell, MD1, Catherine Dumur, PhD3 and Marc Posner, MD1. 1Surgery, Hume-Lee Transplant Center., VCUHS, Richmond, VA, United States; 2Biostatistics, VCUHS, Richmond, VA, United States and 3Pathology, VCUHS, Richmond, VA, United States.
Body: Background: Delayed graft function (DGF) after kidney transplantation (KT) range between 30% and 50%. As DGF exerts negative influences in long-term outcome, the key mechanisms leading to DGF deserves special interest. We aimed to identify differentially expressed genes in deceased donor kidney (DDK) biopsies with and without DGF.
Methods: Gene expression profiling was performed in kidney tissues from 34 DDK using high-density oligonucleotide microarray. DDK were classified as grafts with immediate function (IGF) and grafts with DGF. DGF was defined as dialysis requirement in the first week post-transplantation. Demographic donor and recipient information was collected. Serum BUN/Creatinine levels were evaluated at 1week, 1 and 3 months post-KT. The robust-multiarray average method was used to estimate probe set (Pset) expression summaries. Average linkage hierarchical clustering was performed. The significance analysis of microarrays method was used to identify Pset differentially expressed while controlling for the false discovery rate (FDR). Microarray results were confirmed using qRT-real time PCR.
Results: Patients (Pt) were followed for 3 months post-KT. Recipient demographics included 57% AA, 55.6% male, mean age: 42.3 +/-14.3 yo. Donor characteristics included 66% Caucasian, 62% male, mean age: 40.1+/-15.0 yo. DGF was present in 35.3% of the KT Pt. Serum BUN/Creatinine levels were statistically significant different between groups at 1 week and 1 month post-KT (P=0.01, P0.045). Fifty-five probe sets were differentially expressed in DDK with DGF when compared with DDK with IGF (=0.01). Gene ontology terms classified the over expressed genes in DDK with DGF as principally related to cell cycle/growth (FTH1, IFITM1), signal transduction (STRNA, RALB), immune response (XCL2, CD74, TAPBLP, MX1), and metabolism (TPMT, APOBEC1, PLCE1, PCCA, ACAA2). TNFRSF6B, an anti-apoptotic gene, was down regulated in DDK with DGF. Genes involved in xenobiotic metabolism (CYP3A7, CYP3A5) were down regulated in DDK with DGF.
Conclusions: We identified two different gene expression patterns between DDK with DGF and DDK with IGF. A better knowledge of the molecular mechanisms leading to DGF will be useful for designing studies to improve post-operative management and long-term outcomes.