Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome

Abstract

The process and evolution of an organ transplant procedure has evolved in terms of the prevention of immunological rejection with the improvement in the determination of immune response genes. These techniques include considering more important genes, more polymorphism detection, more refinement of the response motifs, as well as the analysis of epitopes and eplets, its capacity to fix complement, the PIRCHE algorithm and post-transplant monitoring with promising new biomarkers that surpass the classic serum markers such as creatine and other similar parameters of renal function. Among these new biomarkers, we analyze new serological, urine, cellular, genomic and transcriptomic biomarkers and computational prediction, with particular attention to the analysis of donor free circulating DNA as an optimal marker of kidney damage.

Keywords: cell-free DNA; cfDNA; chronic rejection; computational prediction; donor-specific antibody (DSA); human leukocyte antigen (HLA); kidney transplantation; long-term graft survival; monitoring biomarkers; regulatory cell.

Figure 1

Biomarkers in allotransplantation with a virtual example of hypothetical promising biomarkers and their eventual pre- and post-transplant monitoring time. cfDNA, cell-free DNA; ddcfDNA, donor-derived cell-free DNA; DSA, donor-specific antibodies; HLA, human leukocyte antigens; miRNA, microRNA. C3d: Final degradation product of the third component of complement (C3). m-cfDNA: mitocondrial cfDNA.

Figure 2

Design of approximate strategies for future biomarkers in donor and recipient pairs in an organ allotransplantation with a small blood sample and/or urine extraction. cfDNA, cell-free DNA; ddcfDNA, derived-donor cell-free DNA; DSA, donor-specific antibodies; miRNA, microRNA; mtDNA, mitochondrial DNA; qPCR, quantitative PCR; NGS, next-generation sequencing; m-cfDNA, mitocondrial cfDNA.