Study Of Transcriptomics, Proteomics And Metabolomics In Renal Transplantation

IntroductionAcute allograft renal rejection is one of the most important complications after renal transplantation, which directly leads to allograft loss and is detrimental to allograft long-term survival. Subclinical rejection, which is defined as histologically diagnosed acute rejection without clinical functional deterioration, has been reported to be associated with chronic allograft nephropathy and reduced graft survival. But all the diagnosis depends on renal biopsy, the current gold standard, which is invasive and with sample error drawbacks. Various approaches are studied to help to diagnose acute rejection and allograft dysfunction from cell function, mRNA and protein level. Graft rejection due to the process of alloimmunity is a complex pathophysiological biologic response that will likely require systems-level analyses to fully comprehend. Recent advances in technologies in transcriptomics, proteomics and metabolomics have shown the great promise to unravel the complex machinery of cell biology that drives allograft rejection. The gradually maturing microarray technology and evolving proteomics technology, coupled with developing bioimformatics have identifiedcombinatorial markers of graft rejection process. Furthermore, metabolome is 'downstream' of the transcripome and proteome, and the changes of metabolome are the ultimate answer of an organism to genetic alterations, disease or environment influences. To some extent, changes of mRNA and protein levels tell us what to be happened, while changes of metabolite levels tell us what have happened. So metabolomics is one of important parts in systems biology.In this study, we aimed to diagnose acute rejection and evaluate renal allograft function in view of systems biology by analyzing mRNA, protein and metabolite levels in renal transplant recipients.Part IGene expression profiling of peripheral blood mononuclear cell and biopsies in renal transplant recipientsAims To establish microarray system and analyze the gene expression profiling of peripheral blood mononuclear cell (PBMC) and biopsies in renal transplant recipients by prepared microarrays.Methods 449 immune related genes were selected based on transplant immunology and reported references. Pre-synthesized oligonucleotides were spotted on nylon membrane. Gene expression of PBMC of 15 recipients with stable renal function (TX), 5 with acute tubular necrosis (ATN), 15 with acute rejection (AR) including 6 with C4d+ acute humoral rejection/AHR and 9 with C4d- acute cellular rejection/ACR), 11 with borderline changes (BL), 9 with presumed rejection (PR) and 7 with non-rejection (NR) were analyzed. Gene profiling of biopsies of 10 recipients with stable function, 16 with acute rejection, 5 with acute tubular necrosis and 21 donor biopsies were also analyzed by prepared microarrays. Cyber-T test was used to detect the differences of gene expression between the groups and cluster analysis was performed by EPCLUST software. Real-time quantitive PCR was performed on 10 genes selected for relatively large fold-changes by microarray analysis. Results The reproducibility of microarray experiment is satisfactory. Significant differences of gene expression were detected in PBMC and biopsies between AR and TX recipients. 7 genes were higher expressed in C4d+AHR compared to C4d-ACR. BL, ATN can be differentiated from TX by hierarchy clustering analysis. PR and NR recipients were differentiated from AR recipients. 49 genes were detected with differential expression in donor renal biopsies. Expression level of these 49 genes can predict renal allograft function early posttransplantation. The results of geneexpression verified by PCR were identical to the results detected by microarrayanalysis.Conclusions The established microarray system can be applied to analyze geneexpression in renal transplantation. Gene expression can reflect the status of renalfunction and can be used to diagnose acute rejection. The higher expressed genesscreened out in AR recipients will help to apprehend the mechanism of acute rejectionand may be applied in diagnosis of acute rejection in the future.Part IIApplication of urine protein fingerprint analysis in diagnosis of renalallograft functionAims To establish urine protein fingerprint models for diagnosis of renal allograft function.Methods A total of 138 urine samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) combined with bioinformatics tools. Firstly, 22 urine samples from recipients of stable graft function proved by protocol biopsies, 10 from acute tubular necrosis (ATN) and 35 from acute rejection (20 from C4d- acute cellular rejection/ACR and 15 C4d+ AHR), 27 from subclinical rejection (SCR) were analyzed by SELDI-TOF-MS and Zhejiang University Cancer Institute - ProteinChip Data Analysis System (ZUCI-PDAS). The remaining 44 samples were blinded tested to evaluate the accuracy of diagnostic models.Results Pattern 1 comprised of 3 biomarkers could differentiate ATN from stable group with sensitivity and specificity of both 100%. Pattern 2 comprised of 3 biomarkers could differentiate stable group from acute rejection with specificity of 86.4% and sensitivity of 85.7%. Pattern 3 comprised of 5 biomarkers could distinguish ACR from C4d+ AHR with specificity and sensitivity of 95% and 80% respectively. The remaining 14 samples from stable group, 20 from acute rejection (10 from ACR and 10 from C4d+ AHR) and 10 samples from SCR were analyzed on the second day as an independent test set. The independent tests yielded a specificity of 78.6% and sensitivity of 100% for the pattern 2 and specificity of 90% and sensitivity of 80% for the pattern 3 respectively. Pattern 4 comprised of 4 biomarkers could differentiate SCR group from stable group with sensitivity of 81.5% and specificity of81.8%. And independent tests yielded a specificity of 71.4% and sensitivity of 90%. Conclusions Urine protein fingerprint analysis by SELDI-TOF MS combined with bioinfonnatics can help to discover new biomarkers and provide a non-invasive tool to diagnosis of acute rejection, C4d+ AHR and subclinical rejection.Part III GC/MS-based serum metabolic profiling of acute rejection in renaltransplantation—a pilot studyAims To analyze serum metabolic profiling in recipients with acute rejection andstable function, and to explore the significance of metabolomics analysis in renaltransplantation.Methods 22 recipients with acute rejection and 15 recipients with stable function wereenrolled and serum samples were analyzed by gas chromatograph-mass spectrometry(GC-MS). Metabolic profiling between the two group was analyzed by principalcomponent analysis and clustering analysis.Results 46 endogenous metabolites were identified. Principal component analysisbased on these metabolites discriminated acute rejection group from stable recipients.Among these metabolites, the levels of 17 metabolites were significant higher in ARgroup than those in stable group. These included amino acid (phenylalanine, serine,glycine, threonine, valine), carbohydrate (galactose oxime, glycose, fructose),carboxylic acid, lipids and other metabolite such as lactate, urea and myo-inositol. Thelevels of 5 metabolites of alanine, lysine, leucine, aminomalonic acid andtetradecanoic acid were lower in AR group compared to stable group. The predictionaccuracy of AR was 77.3% and stable function was 100% by supervised clusteringbased on these 22 metabolites.Conclusions This study demonstrated that metabolic profile was changed in responseto rejection process and renal function can be reflected by serum metabolite level.This study showed potential capability to diagnose acute rejection by metabolomeanalysis.