Abstract: TH-PO1005

Dynamics of DNA Methylation in Renal Allograft: From Early Ischemia/Reperfusion Injury to Late Fibrosis Response

Session Information

Category: Transplantation

  • 1702 Transplantation: Clinical and Translational

Authors

  • Bontha, Sai Vineela, University of Virginia, Charlottesville, Virginia, United States
  • Mas, Valeria, University of Virginia, Charlottesville, Virginia, United States
Background

The mechanisms of development of fibrosis post-transplantation are not completely understood. Early graft insults like ischemia/reperfusion injury followed by course of its response/repair could involve changes in molecular determinants including DNA methylation (DNAm) which influence long term allograft function. In the current study we assessed the dynamics of DNAm across 1) pre-implant biopsies post-ischemic injury (PI) 2) post-reperfusion (PR) and 3) >24 months post kidney transplantation (KT).

Methods

Infinium 450K methylation (n = 96) and gene expression (n = 182) arrays were performed in PI, PR and KT renal allograft biopsies and analyzed. Genome runner was used to assess distribution and enrichment of Dme CpG sites along regulatory features. Integrative analyses of differentially methylated (Dme) CpGs and corresponding differential gene expression were performed at each matched time points.

Results

PI allografts classified based on progression to allograft dysfunction showed 1,188 Dme CpGs mapped to genes involved in inflammation and metabolism. When paired PI and PR allografts were compared there was apparent change in DNAm of genes involved in pathways like NRF2 mediated oxidative stress response and functions like cellular assembly and organization, cell death and survival. Integration analysis showed Dme and expression of genes involved in energy metabolism, transporters and transcription factors important in regulation of immune response. Further, comparison of post-KT allografts with differential outcomes revealed 21,351 Dme CpG sites. The Dme CpGs observed at early time points were mostly hypomethylated and promoter associated. However, a shift in the pattern was observed in later stages. Dme CpGs were interestingly located in gene bodies and in evolutionarily conserved tissue specific regions. Integration analysis corroborated with findings as gene expression changes in kidney tissue specific regions like tubular epithelium was observed.

Conclusion

Shift in DNAm pattern over time in renal allograft is evident with corresponding change in gene expression pattern. It is important to study closely the sequential changes to evaluate timely therapeutic intervention based on the pathways dysregulated at different molecular levels.

Funding

  • NIDDK Support