Predicting delayed kidney graft function with gene expression in preimplantation biopsies and first-day posttransplant blood

Protein biomarkers in assessing kidney quality before transplantation‑current status and future perspectives (Review)

To meet the demand for kidney transplants (KTx), organs are frequently retrieved not only from standard criteria donors (SCD; a donor who is aged <50 years and suffered brain death from any number of causes, such as traumatic injuries or a stroke) but also from expanded criteria donors (any donor aged >60 years or donors aged >50 years with two of the following: A history of high blood pressure, a creatinine serum level ≥1.5 mg/dl or death resulting from a stroke). This comes at the cost of a higher risk of primary non‑function (the permanent hyperkalemia, hyperuremia and fluid overload that result in the need for continuous dialysis after KTx), delayed graft function (the need for dialysis session at least once during the first week after KTx), earlier graft loss and urinary complications (vesico‑ureteral reflux, obstruction of the vesico‑ureteral anastomosis, urine leakage). At present, there are no commercially available diagnostic tools for assessing kidney quality prior to KTx. Currently available predictive models based on clinical data, such as the Kidney Donor Profile Index, are insufficient. One promising option is the application of perfusion solutions for protein biomarkers of kidney quality and predictors of short‑ and long‑term outcomes. However, to date, protein markers that can be detected with ELISA, western blotting and cytotoxic assays have not been identified to be a beneficial predictors of kidney quality. These include lactate dehydrogenases, glutathione S‑transferases, fatty acid binding proteins, extracellular histones, IL‑18, neutrophil gelatinase‑associated lipocalin, MMPs and kidney injury molecule‑1. However, novel methods, including liquid chromatography‑mass spectrometry (LC‑MS) and microarrays, allow the analysis of all renal proteins suspended/dissolved in the acellular preservation solution used for kidney storage before KTx (including hypothermic machine perfusion as one of kidney storage methods) e.g. Belzer University of Wisconsin. Recent proteomic studies utilizing LC‑MS have identified complement pathway elements (C3, C1QB, C4BPA, C1S, C1R and C1RL), desmoplakin, blood coagulation pathway elements and immunoglobulin heavy variable 2‑26 to be novel predictors of kidney quality before transplantation. This was because they were found to correlate with estimated glomerular filtration rate at 3 and 12 months after kidney transplantation. However, further proteomic studies focusing on distinct markers obtained from hypothermic and normothermic machine perfusion are needed to confirm their predictive value and to improve kidney storage methods. Therefore, the present literature review from PubMed, Scopus, Embase and Web of Science was performed with the aims of summarizing the current knowledge on the most frequently studied single protein biomarkers. In addition, novel analytical methods and insights into organ injury during preservation were documented, where future directions in assessing organ quality before kidney transplantation were also discussed.

Emerging biomarkers of delayed graft function in kidney transplantation

Delayed Graft Function (DGF) is one of the most common early complications in kidney transplantation, associated with poor graft outcomes, prolonged post-operative hospitalization and higher rejection rates. Given the severe shortage of high-quality organs for transplantation, DGF incidence is expected to raise in the next years because of the use of nonstandard kidneys from Extended Criteria Donors (ECD) and from Donors after Circulatory Death (DCD). Alongside conventional methods for the evaluation of renal allograft [e.g. serum creatinine Glomerular Filtration Rate (GFR), needle biopsy], recent advancements in omics technologies, including proteomics, metabolomics and transcriptomics, may allow to discover novel biomarkers associated with DGF occurrence, in order to identify early preclinical signs of renal dysfunction and to improve the quality of graft management. Here, we gather contributions from basic scientists and clinical researchers to describe new omics studies in renal transplantation, reporting the emerging biomarkers of DGF that may implement and improve conventional approaches.

A novel genomic model for predicting the likelihood of delayed graft function in DCD kidney transplantation

Background

The high incidence of delayed graft function (DGF) following kidney transplantation with donation after cardiac death allografts (DCD-KT) poses great challenges to transplant clinicians. This study aimed to explore the DGF-related biomarkers and establish a genomic model for DGF prediction specific to DCD KT.

Methods

By data mining a public dataset (GSE43974), the key DGF-related genes in DCD kidney biopsies taken after short-time reperfusion (45–60 min) were identified by differential expression analysis and a LASSO-penalized logistic regression model. Their coefficients for modeling were calculated by multivariate logistic regression. Receiver operating characteristic curves and a nomogram were generated to evaluate its predictive ability for DGF occurrence. Gene set enrichment analysis (GSEA) was performed to explore biological pathways underlying DGF in DCD KT.

Results

Five key DGF-related genes (CHST3, GOLPH3, ZBED5, AKR1C4, and ERRFI1) were first identified, all of which displayed good discrimination for DGF occurrence after DCD KT (all P<0.05). A five-mRNA-based risk score was further established and showed excellent predictive ability (AUC =0.9708, P<0.0001), which was obviously higher than that of the five genes alone. Eight DGF-related biological pathways in DCD kidneys, such as “arachidonic acid metabolism”, “lysosome”, “proximal tubule bicarbonate reclamation”, “glutathione metabolism”, were identified by GSEA (all P<0.05). Moreover, a convenient and visual nomogram based on the genomic risk score was also constructed and displayed high accuracy for DGF prediction specific to DCD KT.

Conclusions

The novel genomic model may effectively predict the likelihood of DGF immediately after DCD KT or even prior to transplantation in the context of normothermic machine perfusion in the future.