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. 2020 Oct;24(20):12154-12163.
doi: 10.1111/jcmm.15861. Epub 2020 Sep 11.

A circulating exosomal microRNA panel as a novel biomarker for monitoring post-transplant renal graft function

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A circulating exosomal microRNA panel as a novel biomarker for monitoring post-transplant renal graft function

Yimeng Chen et al. J Cell Mol Med. 2020 Oct.

Abstract

Accurate and effective biomarkers for continuous monitoring of graft function are needed after kidney transplantation. The aim of this study was to establish a circulating exosomal miRNA panel as non-invasive biomarker for kidney transplant recipients. Plasma exosomes of 58 kidney transplant recipients and 27 healthy controls were extracted by gel exclusion chromatography and characterized by transmission electron microscopy, nanoparticle tracking analysis and Western blotting. Post-transplant renal graft function was evaluated by estimated glomerular filtration rate (eGFR). Quantitative real-time polymerase chain reaction was used to determine the expression of exosomal microRNAs (miRNAs). Exosomal miR-21, miR-210 and miR-4639 showed negative correlations with eGFR in the training set and were selected for further analysis. In the validation set, miR-21, miR-210 and miR-4639 showed the capability to discriminate between subjects with chronic allograft dysfunction (eGFR < 60 mL/min/1.73 m2 ) and those with normal graft function (eGFR > 90 mL/min/1.73 m2 ). Three-miRNA panel exhibited higher accuracy compared with individual miRNAs or double indicators. One-year follow-up revealed a stable recovery of allograft function in subjects with low calculated score from three-miRNA panel (below the optimal cut-off value). In conclusion, a unique circulating exosomal miRNA panel was identified as an effective biomarker for monitoring post-transplant renal graft function in this study.

Keywords: MicroRNAs; biomarker; estimated glomerular filtration rate; exosomes; kidney transplant.

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Conflict of interest statement

The authors confirm that there are no conflicts of interest. The authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and the accuracy of the data analysis.

Figures

FIGURE 1
FIGURE 1
An overview of the experimental design
FIGURE 2
FIGURE 2
Characterization of plasma exosomes. A, Representative electron micrograph of plasma exosomes. B, Size distribution of plasma exosomes analysed by nanoparticle tracking system. C, Western blot analysis of common exosomal markers Alix, CD63 and CD81, and the endoplasmic reticulum marker calnexin. Plasma was used as a control.
FIGURE 3
FIGURE 3
Correlations of eGFR and miRNA expression in plasma exosomes. A, miR‐21‐5p; B, miR‐210‐3p; C, miR‐4639‐5p. Pearson's correlation coefficient (rho) is shown. eGFR, estimated glomerular filtration rate. ****P < .0001
FIGURE 4
FIGURE 4
Discrimination of eGFR level by exosomal miRNA expression. A‐C, Relative expression levels of miR‐21‐5p, miR‐210‐3p and miR‐4639‐5p in participants with different eGFR levels. Boxes represented the interquartile range of the data. The lines across the boxes and the numbers indicated the median values. The hash marks above and below the boxes indicated the 90th and 10th percentiles for each group, respectively. eGFR ≥ 90 (mL/min/1.73 m2): n = 26; 60 ≤ eGFR <90 (mL/min/1.73 m2): n = 33; eGFR < 60 (mL/min/1.73 m2): n = 26. D‐F, ROC curve analysis of exosomal miR‐21‐5p, miR‐210‐3p and miR‐4639‐5p in distinguishing subjects with eGFR < 60 (mL/min/1.73 m2) from those with eGFR ≥ 90 (mL/min/1.73 m2). eGFR, estimated glomerular filtration rate; ROC curve, receiver operating characteristic curve; AUC, area under the curve. *P < .05, **P < .01, ***P < .001
FIGURE 5
FIGURE 5
Establishment of a predictive diagnostic miRNA panel and longitudinal eGFR analysis. A, ROC curve of the 3‐miRNA panel in discriminating subjects with eGFR < 60 mL/min/1.73 m2 (n = 26) from those with eGFR ≥ 90 mL/min/1.73 m2 (n = 26) in the training and validation sets. B, Dot plot presenting the distributions of scores generated from 3‐miRNA panel in discriminating kidney transplant recipients with different eGFR levels. Scores ranging from 0 to 1 were generated for each sample according to logistic regression equation. C, Association between scores generated from 3‐miRNA panel and Δ eGFR in longitudinal analysis. Kidney transplant recipients with 60 ≤ eGFR <90 (mL/min/1.73 m2) were divided into two groups according to the optimal cut‐off value of 3‐miRNA panel. The change of eGFR levels in the following 12 mo was presented (mean ± SEM). eGFR, estimated glomerular filtration rate; ROC curve, receiver operating characteristic curve; AUC, area under the curve; *P < .05

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