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Abstract: FR-PO873

Differential Expression of Plasma miRNA in Acute Transplant Rejection

Session Information

Category: Transplantation

  • 1802 Transplantation: Clinical

Authors

  • Van Craenenbroeck, Amaryllis H., University Hospital of Antwerp (UZA), Edegem, Belgium
  • Van laere, Steven J., University of Antwerp, Antwerp, Belgium
  • Gielis, Els M., University of Antwerp, Antwerp, Belgium
  • Smet, Annemieke, University of Antwerp, Antwerp, Belgium
  • Dendooven, Amélie, University Hospital of Antwerp (UZA), Edegem, Belgium
  • Hellemans, Rachel, University Hospital of Antwerp (UZA), Edegem, Belgium
  • Couttenye, Marie M., University Hospital of Antwerp (UZA), Edegem, Belgium
  • De winter, Benedicte, University of Antwerp, Antwerp, Belgium
  • Van craenenbroeck, Emeline M., University Hospital of Antwerp (UZA), Edegem, Belgium
  • Ledeganck, Kristien J., University of Antwerp, Antwerp, Belgium
  • Abramowicz, Daniel, University Hospital of Antwerp (UZA), Edegem, Belgium
Background

Transplant rejection remains a major clinical problem in nephrology. miRNAs, negative epigenetic regulators of gene expression at the posttranscriptional level, are involved in various conditions including kidney disease. We performed next-generation sequencing (NGS-)based miRNA profiling of plasma to discover a miRNA signature of acute graft rejection in kidney transplant recipients.

Methods

6 stable (STA, 4 male, 46 ± 8 years) and 4 patients with biopsy-proven acute rejection (AR, 1 male, 44 ± 10 yrs) within the first 3 months after transplantation were studied. AR was defined as a rise in creatinine ≥ 0.3 mg/dl and a score ≥ Banff 2 on biopsy. STA patients had normal biopsies and stable creatinine levels. NGS was performed on isolated miRNA from EDTA plasma. Obtained read counts were normalized for library size, log2 transformed and subjected to differential miRNA expression analysis (limma-package BioConductor). Support Vector Machine (SVM) classification was applied to build and cross-validate (i.e. leave-one-out method) a predictive model for graft rejection.

Results

68 miRNA (26 novel) were differentially expressed in AR vs STA (p<0.05) with 16 miRNA being downregulated and 52 miRNA upregulated (Fig1). The SVM classification model revealed a signature of 4 miRNAs (miR-16-2, miR-361, miR-629 and miR-199a) that could predict AR with 90% accuracy, and a positive and negative predictive value of 100% and 86%, respectively.

Conclusion

We found a novel and unique plasma signature of 4 miRNA in AR patients. Prior to clinical implementation, prospective validation in an independent cohort with sensitivity and specificity analysis is mandatory. In addition, study of the involved miRNA and their mRNA targets can offer more insights into underlying disease mechanisms.

Volcano plot: plasma miRNA expression profiles AR vs STA

Funding

  • Government Support - Non-U.S.