Human Transplant Kidneys on Normothermic Machine Perfusion Display Endocrine Activity

Background. Normothermic machine perfusion (NMP) is an alternative to hypothermic machine perfusion (HMP) for donor kidney preservation before transplantation. Contrary to HMP, NMP allows for functional assessment of donor kidneys because normothermic conditions allow for metabolic activity. The kidneys are key producers of hormones. Yet, it remains unknown whether donor kidneys during NMP display endocrine functions. Methods. Fifteen donor kidneys were subjected to HMP followed by 2 h of NMP before transplantation. NMP perfusate was collected at 3 time points (0, 1, 2 h) for the measurements of prorenin/renin, erythropoietin (EPO), and vitamin D, and urine samples were collected at 1 h and 2 h for urodilatin measurement. Fifteen HMP perfusate samples were collected for the same measurements. Results. Kidneys on NMP secreted significantly more prorenin, renin, EPO, and active vitamin D than during HMP. EPO and vitamin D secretion remained stable during 2 h of NMP, whereas the prorenin release rate increased and renin release rate decreased after 1 h. Donation after brain death kidneys secreted more vitamin D and less EPO during NMP than donation after circulatory death kidneys. Twelve donor kidneys produced urine during NMP and released detectable levels of urodilatin. Kidneys exhibited a large variation in hormone release rates. No significant differences were found in hormone release capacity between delayed graft function (DGF) and non-DGF kidneys, and no significant correlations were found between hormone release rates and the duration of DGF or 1-mo posttransplant serum creatinine levels. Conclusions. Human transplant kidneys display endocrine activity during NMP. To explore whether correlations exist between hormone release rates and posttransplant kidney function, large numbers of kidneys are required.</p

A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel

Introduction: A decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario. Materials and Methods: Gene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version. Results: The best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively. Discussion: This kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel

The applications of DNA methylation as a biomarker in kidney transplantation: a systematic review

Background: Although kidney transplantation improves patient survival and quality of life, long-term results are hampered by both immune- and non-immune-mediated complications. Current biomarkers of post-transplant complications, such as allograft rejection, chronic renal allograft dysfunction, and cutaneous squamous cell carcinoma, have a suboptimal predictive value. DNA methylation is an epigenetic modification that directly affects gene expression and plays an important role in processes such as ischemia/reperfusion injury, fibrosis, and alloreactive immune response. Novel techniques can quickly assess the DNA methylation status of multiple loci in different cell types, allowing a deep and interesting study of cells' activity and function. Therefore, DNA methylation has the potential to become an important biomarker for prediction and monitoring in kidney transplantation. Purpose of the study: The aim of this study was to evaluate the role of DNA methylation as a potential biomarker of graft survival and complications development in kidney transplantation. MATERIAL AND METHODS: A systematic review of several databases has been conducted. The Newcastle-Ottawa scale and the Jadad scale have been used to assess the risk of bias for observational and randomized studies, respectively. Results: Twenty articles reporting on DNA methylation as a biomarker for kidney transplantation were included, all using DNA methylation for prediction and monitoring. DNA methylation pattern alterations in cells isolated from different tissues, such as kidney biopsies, urine, and blood, have been associated with ischemia-reperfusion injury and chronic renal allograft dysfunction. These alterations occurred in different and specific loci. DNA methylation status has also proved to be important for immune response modulation, having a crucial role in regulatory T cell definition and activity. Research also focused on a better understanding of the role of this epigenetic modification assessment for regulatory T cells isolation and expansion for future tolerance induction-oriented therapies. Conclusions: Studies included in this review are heterogeneous in study design, biological samples, and outcome. More coordinated investigations are needed to affirm DNA methylation as a clinically relevant biomarker important for prevention, monitoring, and intervention. Keywords: Biomarker; DNA methylation; Kidney transplantation; Reperfusion injury; Systematic review

Feasibility and Potential of Transcriptomic Analysis Using the NanoString nCounter Technology to Aid the Classification of Rejection in Kidney Transplant Biopsies

Background. Transcriptome analysis could be an additional diagnostic parameter in diagnosing kidney transplant (KTx) rejection. Here, we assessed feasibility and potential of NanoString nCounter analysis of KTx biopsies to aid the classification of rejection in clinical practice using both the Banff-Human Organ Transplant (B-HOT) panel and a customized antibody-mediated rejection (AMR)-specific NanoString nCounter Elements (Elements) panel. Additionally, we explored the potential for the classification of KTx rejection building and testing a classifier within our dataset. Methods. Ninety-six formalin-fixed paraffin-embedded KTx biopsies were retrieved from the archives of the ErasmusMC Rotterdam and the University Hospital Cologne. Biopsies with AMR, borderline or T cell-mediated rejections (BLorTCMR), and no rejection were compared using the B-HOT and Elements panels. Results. High correlation between gene expression levels was found when comparing the 2 chemistries pairwise (r = 0.76-0.88). Differential gene expression (false discovery rate; P < 0.05) was identified in biopsies diagnosed with AMR (B-HOT: 294; Elements: 76) and BLorTCMR (B-HOT: 353; Elements: 57) compared with no rejection. Using the most predictive genes from the B-HOT analysis and the Element analysis, 2 least absolute shrinkage and selection operators-based regression models to classify biopsies as AMR versus no AMR (BLorTCMR or no rejection) were developed achieving an receiver-operating-characteristic curve of 0.994 and 0.894, sensitivity of 0.821 and 0.480, and specificity of 1.00 and 0.979, respectively, during cross-validation. Conclusions. Transcriptomic analysis is feasible on KTx biopsies previously used for diagnostic purposes. The B-HOT panel has the potential to differentiate AMR from BLorTCMR or no rejection and could prove valuable in aiding kidney transplant rejection classification