Abstract: TH-PO798
Noninvasive Urine Epigenomics Identifies Allograft Pathology in Kidney Allografts
Session Information
- Transplantation: Clinical - 2
October 24, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Transplantation
- 2102 Transplantation: Clinical
Authors
- Abuhelaiqa, Essa, Hamad Medical Corporation, Doha, Qatar
- Alkadi, Mohamad M., Hamad Medical Corporation, Doha, Qatar
- Belkadi, Aziz, Weill Cornell Medicine - Qatar, Doha, Qatar
- Thareja, Gaurav, Weill Cornell Medicine - Qatar, Doha, Qatar
- Muthukumar, Thangamani, Weill Cornell Medicine, New York, New York, United States
- Al-Malki, Hassan A., Hamad Medical Corporation, Doha, Qatar
- Suthanthiran, Manikkam, Weill Cornell Medicine, New York, New York, United States
Background
Development of noninvasive assay as a diagnostic alternative to allograft biopsy in kidney transplantation is critical and has the potential of prompt diagnosis and treatment of allograft dysfunction. We aim to detect kidney pathology via recognition of associated methylation signatures and cellular profiles in urine, hypothesizing that different disease states lead to unique and measurable epigenetic and cellular changes.
Methods
Urine samples were obtained at time of for-cause kidney allograft biopsy of 138 kidney allograft recipients. Urinary pellet DNA was processed with the Infinium MethylationEPIC kit and categorized into “phenotypes” from biopsy findings. The resulting methylation data was filtered with the “ewastools” package in R. Average cellular composition for each phenotype was generated through a reference atlas and deconvolution algorithm from the literature. Differentially methylated regions (DMR) were compared between non-rejection (NR) and acute rejection (AR) specimens via the chip analysis methylation pipeline package.
Results
From a total of 138 unique samples and 11 technical replicates, methylation data for 650,719 CpG sites per analyte was available after filtering. Deconvolution analysis demonstrated immune cell predominance in AR and micro-vascular injury, particularly neutrophils, with only BKVN samples showing relative renal cell prevalence. Borderline samples showed a cellular profile intermediate between NR and AR. DMR analysis yielded a panel of 197 genes containing differentially methylated CpG sites between AR and NR samples.
Conclusion
Epigenomic analysis of urine is a feasible modality to assess graft health, with cellular composition and DMRs acting as surrogate biomarkers.
Funding
- Government Support – Non-U.S.