Validation of systems biology derived molecular markers of renal donor organ status associated with long term allograft function

Paul Perco,Silvia Wagner,Andreas Heinzel,Stefan Schneeberger,Rupert Oberhuber,Claudia Bösmüller,Johannes Leierer,Franziska Engler,Gert Mayer

doi:10.1038/s41598-018-25163-8

Paul Perco, Silvia Wagner + Show 7 more

Open Access

https://doi.org/10.1038/s41598-018-25163-8

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Abstract

Donor organ quality affects long term outcome after renal transplantation. A variety of prognostic molecular markers is available, yet their validity often remains undetermined. A network-based molecular model reflecting donor kidney status based on transcriptomics data and molecular features reported in scientific literature to be associated with chronic allograft nephropathy was created. Significantly enriched biological processes were identified and representative markers were selected. An independent kidney pre-implantation transcriptomics dataset of 76 organs was used to predict estimated glomerular filtration rate (eGFR) values twelve months after transplantation using available clinical data and marker expression values. The best-performing regression model solely based on the clinical parameters donor age, donor gender, and recipient gender explained 17% of variance in post-transplant eGFR values. The five molecular markers EGF, CD2BP2, RALBP1, SF3B1, and DDX19B representing key molecular processes of the constructed renal donor organ status molecular model in addition to the clinical parameters significantly improved model performance (p-value = 0.0007) explaining around 33% of the variability of eGFR values twelve months after transplantation. Collectively, molecular markers reflecting donor organ status significantly add to prediction of post-transplant renal function when added to the clinical parameters donor age and gender.

Highlights

Short-term renal allograft survival increased continuously during the last decades but the rate, at which transplants are lost long term remained disappointingly stable at a high level[1]
A set of 34 Gene Ontology (GO) biological processes could be identified as being enriched based on the set of 89 proteins being part of the donor organ status molecular model
In this study we used a systematic data integration approach to develop a molecular model of highly interconnected proteins being associated with renal function as assessed by estimated glomerular filtration rate (eGFR) 12 months after transplantation

Summary

Introduction

Short-term renal allograft survival increased continuously during the last decades but the rate, at which transplants are lost long term remained disappointingly stable at a high level[1]. Studied the proteomic signature of the preservation fluid to derive biomarkers to predict immediate postoperative transplant function[6] Another intuitively superior way is to use information obtained from pre-implantation biopsies. Researchers have started to look into molecular signatures in the biopsy tissue that predict immediate, and mid- to long term post-transplant renal function[9,10,11,12,13,14,15,16] Some of these studies are hypothesis driven. In this study we used a data integration approach to build a molecular model reflecting pre-implantation donor organ status This model was based on features associated with mid- to long term allograft function from. Transcriptomics study on renal zero-hour biopsies reporting differentially regulated genes associated with histopathological characteristics of the donor organ

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