The urine microRNA profile may help monitor post-transplant renal graft function
2013; Elsevier BV; Volume: 85; Issue: 2 Linguagem: Inglês
10.1038/ki.2013.338
ISSN1523-1755
AutoresDaniel G. Maluf, Catherine I. Dumur, Jihee L. Suh, Mariano J. Scian, Anne L. King, Helen P. Cathro, Jae K. Lee, Ricardo C. Gehrau, Kenneth L. Brayman, Lorenzo Gallon, Valeria R. Mas,
Tópico(s)Renal and Vascular Pathologies
ResumoNoninvasive, cost-effective biomarkers that allow accurate monitoring of graft function are needed in kidney transplantation. Since microRNAs (miRNAs) have emerged as promising disease biomarkers, we sought to establish an miRNA signature in urinary cell pellets comparing kidney transplant patients diagnosed with chronic allograft dysfunction (CAD) with interstitial fibrosis and tubular atrophy and those recipients with normal graft function. Overall, we evaluated 191 samples from 125 deceased donor primary kidney transplant recipients in the discovery, initial validation, and the longitudinal validation studies for noninvasive monitoring of graft function. Of 1733 mature miRNAs studied using microarrays, 22 were found to be differentially expressed between groups. Ontology and pathway analyses showed inflammation as the principal biological function associated with these miRNAs. Twelve selected miRNAs were longitudinally evaluated in urine samples of an independent set of 66 patients, at two time points after kidney transplant. A subset of these miRNAs was found to be differentially expressed between groups early after kidney transplant before histological allograft injury was evident. Thus, a panel of urine miRNAs was identified as potential biomarkers for monitoring graft function and anticipating progression to CAD in kidney transplant patients. Noninvasive, cost-effective biomarkers that allow accurate monitoring of graft function are needed in kidney transplantation. Since microRNAs (miRNAs) have emerged as promising disease biomarkers, we sought to establish an miRNA signature in urinary cell pellets comparing kidney transplant patients diagnosed with chronic allograft dysfunction (CAD) with interstitial fibrosis and tubular atrophy and those recipients with normal graft function. Overall, we evaluated 191 samples from 125 deceased donor primary kidney transplant recipients in the discovery, initial validation, and the longitudinal validation studies for noninvasive monitoring of graft function. Of 1733 mature miRNAs studied using microarrays, 22 were found to be differentially expressed between groups. Ontology and pathway analyses showed inflammation as the principal biological function associated with these miRNAs. Twelve selected miRNAs were longitudinally evaluated in urine samples of an independent set of 66 patients, at two time points after kidney transplant. A subset of these miRNAs was found to be differentially expressed between groups early after kidney transplant before histological allograft injury was evident. Thus, a panel of urine miRNAs was identified as potential biomarkers for monitoring graft function and anticipating progression to CAD in kidney transplant patients. A major obstacle in the management of kidney transplant recipients is the lack of specific biomarkers for continuous monitoring of graft function after kidney transplantation (KT). The current gold standard is the histological evaluation of biopsies. Additional markers such as serum creatinine (Cr), estimated glomerular filtration rate (eGFR), and/or proteinuria1.Al-Awwa I.A. Hariharan S. First M.R. Importance of allograft biopsy in renal transplant recipients: correlation between clinical and histological diagnosis.Am J Kidney Dis. 1998; 31: S15-S18Abstract Full Text PDF PubMed Scopus (50) Google Scholar, 2.John R. Herzenberg A.M. Our approach to a renal transplant biopsy.J Clin Pathol. 2010; 63: 26-37Crossref PubMed Scopus (17) Google Scholar, 3.Kozakowski N. Regele H. Biopsy diagnostics in renal allograft rejection: from histomorphology to biological function.Transpl Int. 2009; 22: 945-953Crossref PubMed Scopus (20) Google Scholar, 4.Mannon R.B. Kirk A.D. 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Lower complexity than mRNAs, no postprocessing modification, tissue-specific expression, and amplifiable signals make miRNAs in the urine ideal candidates as noninvasive biomarkers of kidney disease. As a biofluid, the urine allows repeated and noninvasive collection, and its molecular composition highly reflects intrarenal events.19.Beltrami C. Clayton A. Phillips A.O. et al.Analysis of urinary microRNAs in chronic kidney disease.Biochem Soc Trans. 2012; 40: 875-879Crossref PubMed Scopus (39) Google Scholar, 20.Matheson A. Willcox M.D. Flanagan J. et al.Urinary biomarkers involved in type 2 diabetes: a review.Diabetes Metab Res Rev. 2010; 26: 150-171Crossref PubMed Scopus (110) Google Scholar, 21.Caubet C. Lacroix C. Decramer S. et al.Advances in urinary proteome analysis and biomarker discovery in pediatric renal disease.Pediatr Nephrol. 2010; 25: 27-35Crossref PubMed Scopus (62) Google Scholar We and others have published the utility of assessing mRNA levels in urinary pellet for the evaluation of acute cellular rejection (ACR),22.Afaneh C. Muthukumar T. Lubetzky M. et al.Urinary cell levels of mRNA for OX40,OX40L, PD-1, PD-L1, or PD-L2 and acute rejection of human renal allografts.Transplantation. 2010; 90: 1381-1387Crossref PubMed Scopus (48) Google Scholar,23.Muthukumar T. Dadhania D. Ding R. et al.Messenger RNA for FOXP3 in the urine of renal-allograft recipients.N Engl J Med. 2005; 353: 2342-2351Crossref PubMed Scopus (490) Google Scholar BK virus nephropathy,24.Dadhania D. Snopkowski C. Ding R. et al.Validation of noninvasive diagnosis of BK virus nephropathy and identification of prognostic biomarkers.Transplantation. 2010; 90: 189-197Crossref PubMed Scopus (51) Google Scholar and chronic allograft dysfunction (CAD) with interstitial fibrosis and tubular atrophy (IF/TA).25.Mas V. Maluf D. Archer K. et al.Establishing the molecular pathways involved in chronic allograft nephropathy for testing new noninvasive diagnostic markers.Transplantation. 2007; 83: 448-457Crossref PubMed Scopus (66) Google Scholar,26.Mas V.R. Mas L.A. Archer K.J. et al.Evaluation of gene panel mRNAs in urine samples of kidney transplant recipients as a non-invasive tool of graft function.Mol Med. 2007; 13: 315-324Crossref PubMed Scopus (25) Google Scholar However, so far, there have been only few studies reported evaluating global miRNA expression changes associated with ACR or CAD with IF/TA in kidney allografts.27.Sui W. Dai Y. Huang Y. et al.Microarray analysis of microRNA expression in acute rejection after renal transplantation.Transpl Immunol. 2008; 19: 81-85Crossref PubMed Scopus (130) Google Scholar, 28.Anglicheau D. Sharma V.K. Ding R. et al.MicroRNA expression profiles predictive of human renal allograft status.Proc Natl Acad Sci USA. 2009; 106: 5330-5335Crossref PubMed Scopus (285) Google Scholar, 29.Lorenzen J.M. Volkmann I. Fiedler J. et al.Urinary miR-210 as a mediator of acute T-cell mediated rejection in renal allograft recipients.Am J Transplant. 2011; 11: 2221-2227Crossref PubMed Scopus (169) Google Scholar, 30.Scian M.J. Maluf D.G. David K.G. et al.MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA.Am J Transplant. 2011; 11: 2110-2122Crossref PubMed Scopus (145) Google Scholar Thus, the use of miRNA profiles as noninvasive biomarkers for monitoring graft function might have the potential for noninvasively monitoring graft function and deserves further exploration. We recently reported an miRNA profile of allograft tissue using microarrays, where miR-142-3p, miR-204, and miR-211 were differentially expressed between patients with histologically diagnosed CAD with IF/TA when compared with patients with normal histology and functioning allografts (normal function allograft: NFA), in both allograft tissue biopsies and paired urinary cell pellet samples.30.Scian M.J. Maluf D.G. David K.G. et al.MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA.Am J Transplant. 2011; 11: 2110-2122Crossref PubMed Scopus (145) Google Scholar Similar IF/TA-like expression changes were also detected in urinary cell pellets of patients with stable graft function, but that later developed CAD. This preliminary report suggested that miRNAs could be used to noninvasively monitor graft function. Detection of individual miRNAs (first identified in tissue samples) in urinary cell pellets using reverse transcriptase quantitative-polymerase chain reaction (RT-QPCR) also suggested the feasibility of generating miRNA signatures from urinary cell pellet samples. On the basis of our initial encouraging results, we now expand our CAD with IF/TA tissue miRNA signature by establishing an miRNA signature in urinary cell pellets using microarrays, and prospectively evaluating a combined panel of tissue/urine differentially expressed miRNAs. To validate the initially identified biomarkers and to establish a global miRNA signature in urine samples, we used a well-designed methodological approach to integrate the transcriptional profiles of tissue biopsies and urinary cell pellet samples from patients with and without biopsy-proven CAD with IF/TA. A selected panel of 12 combined (tissue and urinary cell pellet) differentially expressed miRNA markers were tested in an independent cohort of kidney transplant recipients at two time points after KT to assess their utility for the monitoring of graft function. As a first step in our study, we aimed to evaluate the utility of urinary cells pellets versus urinary exosomes as targets for evaluating kidney allograft using mRNA/miRNA measurements. After evaluating the expression of mRNAs representing specific regions of the kidney, such as the nephron and the collecting duct in both urinary cell pellets and urinary exosomes, we observed expression of all the evaluated mRNA in both sample types. Even when the level of expression of the studied genes was lower in urinary exosomes, they were comparable between sample types (Supplementary Information online; mRNA/miRNA detection in urine samples: exosomes versus sediments). Similar findings were observed for the tested miRNAs. Thus, these preliminary data were used as a proof of principle to support our hypothesis that urinary cell pellets represent an appropriate source of mRNAs/miRNAs for evaluating kidney function, warranting cross-sectional and prospective miRNA studies in our patient cohorts. Moreover, technical issues associated with isolation of urinary exosomes (e.g., ultracentrifugation, RNA concentration) limit the utility of potential new biomarkers to be readily adaptable in the clinical setting. Download .doc (.36 MB) Help with doc files Supplementary Information The overall study design is shown in Figure 1. Demographic and clinical patient data can be found in Table 1. Urinary cell pellets from patients with histologically diagnosed CAD with IF/TA and patients with NFA were selected for the initial discovery phase. These patients included the same cohort of enrolled cases for the evaluation and establishment of the global miRNA signature in allograft tissue recently reported30.Scian M.J. Maluf D.G. David K.G. et al.MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA.Am J Transplant. 2011; 11: 2110-2122Crossref PubMed Scopus (145) Google Scholar and an additional set to increase the sample size. From this analysis, 22 miRNAs were identified as significantly differentially expressed (false discovery rate=15%, and ≥2 fold change) between CAD with IF/TA and NFA samples (Figure 2 and Table 2). Core analysis was performed to interpret the data set in the context of biological processes, pathways, and molecular networks. The top scored network (score=33) showed connective tissue disorders, inflammatory disease, and inflammatory response as the associated network functions. Moreover, inflammatory response was identified as one of the top functions associated with these differentially expressed miRNAs (P=7.03E-18–1.59E-11).Table 1Patient demographics and clinical characteristics by study groupTraining setaCross-sectional evaluation.Validation setaCross-sectional evaluation.IF/TA, avg±stdNFA, avg±stdIF/TA, avg±stdNFA, avg±stdTesting groupbProspective evaluation (longitudinal study)., avg±stdRecipient demographics Age (years)42.3±17.643.4±14.146.1±16.341.0±11.252.6±12.6 Race (AA/Ca/O)7/2/110/1/15/1/18/0/259/6/1 Gender (M/F)5/56/64/36/434/32Donor demographics Age (years)44.0±21.741.2±17.254.0±12.048.7±17.540.1±16.8 Race (AA/Ca/O)5/5/05/7/04/3/02/7/121/44/1 Gender (M/F)5/55/72/53/741/25Transplant Donor type (SCD/ECD/DCD)5/4/110/2/03/3/19/0/138/10/18 CIT (min)875±3751047±348884±3771045±4201205±387 WIT (min)29.8±6.632.8±10.029.7±5.933.6±9.830.0±6.5 PPP time (min)640±556715±402845±279900±356828±359 Last donor Cr. (mg/dl)1.09±0.70.96±0.41.12±0.50.97±.51.1±0.8 DGF101023 Acute rejection20105 HLA-A mismatch0.8±0.91.4±0.81.1±0.91.2±0.91.4±0.7 HLA-B mismatch1.3±1.01.8±0.41.7±0.81.7±0.51.7±0.5 HLA-DR mismatch1.1±0.91.3±0.71.3±1.00.9±0.71.2±0.7 HLA total mismatch3.3±2.64.5±1.14.1±2.03.8±1.04.4±1.2 PRA at Tx (T cell)35.0±37.964.8±38.331.5±35.862.1±35.445.6±38.3 PRA at Tx (B cell)4.7±10.923.2±29.24.28±10.010.4±12.517.5±27.8 eGFR at 1 mo57.1±39.168.8±20.953.4±31.859.8±25.652.9±22.3 eGFR at 3 mo53.5±37.873.7±13.443.6±25.159.4±27.456.7±19.2 eGFR at 6 mo55.4±50.175.5±10.1954.1±44.760.8±28.657.3±20.9 eGFR at 9 mo45.4±34.976.9±15.737.3±16.063.3±29.057.6±21.4 eGFR at 12 mo41.2±21.775.6±12.828.5±12.769.3±16.656.6±20.8 eGFR at 18 mo55.2±47.777.9±13.318.5±16.271.0±21.756.4±22.7 eGFR at 24 mo (or last known)17.0±13.879.5±31.117.9±14.471.6±25.055.1±25.9Abbreviations: CIT, cold ischemia time; Cr, creatinine; DGF, delayed graft function; eGFR, estimated glomerular filtration rate; IF, interstitial fibrosis; mo, months; NFA, normal function allograft; PPP, pump perfusion preservation; PRA, panel reactive antibody; SCD/ECD/DCD, standard criteria donor/extended criteria donor/donation after cardiac death; TA, tubular atrophy; Tx, transplant; WIT, warm ischemia time.a Cross-sectional evaluation.b Prospective evaluation (longitudinal study). Open table in a new tab Figure 2Differentially expressed microRNAs. (a) Volcano plot of microRNA (miRNA) microarray data for normal function allograft (NFA) and interstitial fibrosis/tubular atrophy (IF/TA) samples. The y axis values show the negative logarithm base 10 of the P-value. The dotted horizontal line on the plot represents the α-level used for this analysis (0.005). The x axis is shown as the log2 difference in estimated relative expression values. Vertical dotted lines represent the threshold for the log2 fold change (equivalent to a 2 fold change). Thus, the red dots correspond to miRNAs that show a significant (P≤0.005) 2 fold or greater change in expression between NFA and IF/TA samples. (b) Principal component analysis of the miRNA results using microarrays showing separation of chronic allograft dysfunction (CAD) with IF/TA samples from NFA samples using the expression values of the differentially expressed miRNAs.View Large Image Figure ViewerDownload (PPT)Table 2List of significantly altered (P<0.01, FDR <15%) miRNAs identified as differentially expressed in urinary cell pellets between subjects with histologically diagnosed CAD with IF/TA and NFAmiRNAIF/TA mean (log2)NFA mean (log2)Fold changeP-valueq-valueHsa-mir-140-3p9.085.6211.04.63E-051.83E-02Hsa-mir-106bamiRNAs in bold were further validated in the longitudinal study.6.482.8512.47.13E-051.83E-02Hsa-mir-125b2.866.86-15.97.97E-051.83E-02Hsa-mir-200b1.945.03-8.51.24E-042.14E-02Hsa-mir-200bamiRNAs in bold were further validated in the longitudinal study.1.774.41-6.32.52E-042.93E-02Hsa-mir-486-5p11.594.72116.92.56E-042.93E-02Hsa-mir-99a3.036.34-9.94.31E-044.23E-02Hsa-mir-18510.136.6810.95.05E-044.33E-02Hsa-mir-4258.526.484.11.04E-037.51E-02Hsa-mir-92a10.658.683.91.09E-037.51E-02Hsa-mir-513a-5p0.761.12-1.31.61E-039.27E-02Hsa-mir-423-5p6.594.284.91.62E-039.27E-02Hsa-mir-23b7.229.17-3.91.75E-039.27E-02Hsa-mir-30aamiRNAs in bold were further validated in the longitudinal study.1.033.23-4.62.56E-031.11E-01Hsa-mir-193b3.215.89-6.42.66E-031.11E-01Hsa-mir-1841.123.28-4.52.84E-031.11E-01Hsa-mir-5751.293.09-3.52.93E-031.11E-01Hsa-mir-3751.584.51-7.63.11E-031.11E-01Hsa-mir-4518.383.5428.73.20E-031.11E-01Hsa-mir-2034.259.49-37.73.22E-031.11E-01Hsa-let-7f-2amiRNAs in bold were further validated in the longitudinal study.1.070.801.24.12E-031.35E-01Hsa-mir-3454.602.504.34.64E-031.45E-01Abbreviations: CAD, chronic allograft dysfunction; FDR, false discovery rate; IF, interstitial fibrosis; miRNA, microRNA; NFA, normal function allograft; TA, tubular atrophy.a miRNAs in bold were further validated in the longitudinal study. Open table in a new tab Abbreviations: CIT, cold ischemia time; Cr, creatinine; DGF, delayed graft function; eGFR, estimated glomerular filtration rate; IF, interstitial fibrosis; mo, months; NFA, normal function allograft; PPP, pump perfusion preservation; PRA, panel reactive antibody; SCD/ECD/DCD, standard criteria donor/extended criteria donor/donation after cardiac death; TA, tubular atrophy; Tx, transplant; WIT, warm ischemia time. Abbreviations: CAD, chronic allograft dysfunction; FDR, false discovery rate; IF, interstitial fibrosis; miRNA, microRNA; NFA, normal function allograft; TA, tubular atrophy. A set of five miRNAs were initially selected for independent validation using RT-QPCR, including two miRNAs differentially expressed in urinary cell pellets (miR-125b, miR-203) and three miRNAs that were previously identified in tissue samples and correlated with paired urine samples (miR-142-3p, miR-204, miR-211).30.Scian M.J. Maluf D.G. David K.G. et al.MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA.Am J Transplant. 2011; 11: 2110-2122Crossref PubMed Scopus (145) Google Scholar Additional criteria for selection of the panel included the following: array fold change, statistical significance, and in silico mRNA target prediction. The initial validation was performed using an independent set of urinary cell pellets (IF/TA=7 and NFA=10). Differential expression of all five miRNAs was confirmed between NFA and CAD with IF/TA patients (Figure 3). The ΔΔCt method was used to calculate the relative expression (fold change) between sample groups. This signature was then expanded (based on the criteria described in Materials and Methods) and further validated in a larger (N=66), longitudinally independent study, to evaluate the utility of the markers for monitoring graft function and progression to CAD. We performed an integrative analysis of mRNA and miRNA expression profiles and miRNA target predictions from three different algorithms (PITA, TargetScan, and miRanda) through MAGIA (MiRNA and Gene Integrative Analysis).31.Sales G. Coppe A. Bisognin A. et al.MAGIA, a web-based tool for miRNA and Genes Integrated Analysis.Am J Transplant. 2010; 38: W352-W359Google Scholar,32.Bisognin A. Sales G. Coppe A. et al.MAGIA2: from miRNA and genes expression data integrative analysis to microRNA-transcription factor mixed regulatory circuits (2012 update).Nucleic Acids Res. 2012; 40: W13-W21Crossref PubMed Scopus (96) Google Scholar The evaluation of mRNA in urinary cells has been a common approach during the past years for evaluating native and allograft kidneys.22.Afaneh C. Muthukumar T. Lubetzky M. et al.Urinary cell levels of mRNA for OX40,OX40L, PD-1, PD-L1, or PD-L2 and acute rejection of human renal allografts.Transplantation. 2010; 90: 1381-1387Crossref PubMed Scopus (48) Google Scholar, 23.Muthukumar T. Dadhania D. Ding R. et al.Messenger RNA for FOXP3 in the urine of renal-allograft recipients.N Engl J Med. 2005; 353: 2342-2351Crossref PubMed Scopus (490) Google Scholar, 24.Dadhania D. Snopkowski C. Ding R. et al.Validation of noninvasive diagnosis of BK virus nephropathy and identification of prognostic biomarkers.Transplantation. 2010; 90: 189-197Crossref PubMed Scopus (51) Google Scholar, 25.Mas V. Maluf D. Archer K. et al.Establishing the molecular pathways involved in chronic allograft nephropathy for testing new noninvasive diagnostic markers.Transplantation. 2007; 83: 448-457Crossref PubMed Scopus (66) Google Scholar, 26.Mas V.R. Mas L.A. Archer K.J. et al.Evaluation of gene panel mRNAs in urine samples of kidney transplant recipients as a non-invasive tool of graft function.Mol Med. 2007; 13: 315-324Crossref PubMed Scopus (25) Google Scholar, 33.He W. Tan R.J. Li Y. et al.Matrix metalloproteinase-7 as a surrogate marker predicts renal Wnt/β-catenin activity in CKD.J Am Soc Nephrol. 2012; 23: 294-304Crossref PubMed Scopus (116) Google Scholar, 34.Szeto C.C. Chow K.M. Lai K.B. et al.mRNA expression of target genes in the urinary sediment as a noninvasive prognostic indicator of CKD.Am J Kidney Dis. 2006; 47: 578-586Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 35.Wang G. Lai F.M. Lai K.B. et al.Messenger RNA expression of podocyte-associated molecules in the urinary sediment of patients with diabetic nephropathy.Nephron Clin Pract. 2007; 106: c169-c179Crossref PubMed Scopus (64) Google Scholar However, most of the urine samples have total RNA without the required concentration and/or quality and integrity for microarray analysis. As our previously published data showed that miRNAs identified in tissues could also be detected in urinary cell pellets,30.Scian M.J. Maluf D.G. David K.G. et al.MicroRNA profiles in allograft tissues and paired urines associate with chronic allograft dysfunction with IF/TA.Am J Transplant. 2011; 11: 2110-2122Crossref PubMed Scopus (145) Google Scholar these findings supported an integrative analysis using the new miRNA expression data together with our previously published data.25.Mas V. Maluf D. Archer K. et al.Establishing the molecular pathways involved in chronic allograft nephropathy for testing new noninvasive diagnostic markers.Transplantation. 2007; 83: 448-457Crossref PubMed Scopus (66) Google Scholar,36.Scian M.J. Maluf D.G. Archer K.J. et al.Gene expression changes are associated with loss of kidney graft function and interstitial fibrosis and tubular atrophy: diagnosis versus prediction.Transplantation. 2011; 91: 657-665Crossref PubMed Scopus (27) Google Scholar Using MAGIA, we identified a large network of correlated mRNA–miRNA pairs. Results were mapped using Cytoscape.37.Shannon P. Markiel A. Ozier O. et al.Cytoscape: a software environment for integrated models of biomolecular interaction networks.Genome Res. 2003; 13: 2498-2504Crossref PubMed Scopus (25580) Google Scholar A filtered network corresponding to the five miRNAs selected for initial independent validation was extracted from the results (Figure 4a). To identify annotated protein interactions, genes identified within this network were queried using STRING (http://string-db.org/) and mapped using Cytoscape (Figure 4b). Eighty-three genes identified in the mRNA–miRNA network were found to have documented protein–protein interactions with at least one other gene from the network. A merging of the two networks can be found in Supplementary Figure S1 online. ToppGene (http://toppgene.cchmc.org/) was used to identify biological processes over-represented by the 83 genes identified above. Top biological processes included regulation of apoptosis (P=1.87E-07), cell activation (P=1.86E-04), immune system process (P=4.66E-04), protein phosphorylation (P=1.01E-03), and activation of JAK2 kinase activity (P=3.15E-03). As a preliminary analysis and to justify the prospective evaluation of the selected markers in a larger cohort of samples, we tested differences in urinary cell miRNA profiles using microarrays at 3 months after KT. Total RNA from urinary cells from a set of 20 patients (N=10, stable good function at 24 months after KT; N=10, poor function at 24 months after KT) were evaluated. From this analysis, a total of 48 miRNAs were differentially expressed between groups (P<0.001, and ≥2 fold change) (Figure 5a), justifying further validation in the independent patient set with longitudinal samples using only selected markers. Moreover, from the analysis of differentially expressed miRNAs early after KT and the 22 miRNAs identifie
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