Proteomics Analysis Of Clinical Almost Tolerance Of Plasma And Urine In Renal Transplant Recipients

PartⅠPlasma proteomics analysis of clinical almost tolerance in renaltransplant recipientsObjective To screen plasma differential proteins and establish diagnostic model forearly diagnosis and prediction of clinical almost tolerance by analyzing plasma proteome oftolerant recipients, rejecting patient of kidney allografts and health populations usingsurface enhanced laser desorption/ionization time of flight mass spectrometry.Methods A total of43cases of plasma samples of clinical almost tolerance (group1),rejection (group2) and healthy controls (group0) were detected by PBSIIC massspectrometer and CM10protein chips (Bio-Rad). Mass spectrometry data acquisition andpreprocession were performed by Ciphergen ProteinChip3.2software, and the screening ofprotein peak and the establishment of diagnostic model were executed by Biomarker Wizardand Biomarker Pattern software respectively.Results152identifiable differential proteins peaks were obtained, including21proteinpeaks when P <0.05and11when P <0.01. Five mass spectrum peaks such as2565.15,1966.28,6674.78,1103.27,1716.69and1966.28were used to found the diagnostic model.The sensitivity, specificity and area under the ROC curve of the model were83.3%,92.0%and0.951respectively for the diagnosis of clinical almost tolerance, and91.7%,96.8%,0.989for rejection.Conclusions There are many kinds of small molecular weight proteins and peptidesinvolved in clinical almost tolerance from M/Z1000to5000in plasma. The diagnosticmodel built by Mass spectrum peak of2565.15,1966.28,6674.78,1103.27,1716.69and 1966.28can be used to diagnose clinical almost tolerance in kidney transplant with highsensitivity and specificity. PartⅡUrine proteomics analysis of clinical almost tolerance in renaltransplant recipientsObjective To screen urine differential proteins and establish diagnostic model for the newmethod for early diagnosis and prediction of clinical almost tolerance by analyzing urineproteome of operationally tolerant recipients, rejecting patient of kidney allografts andhealth populations using surface enhanced laser desorption/ionization time of flight massspectrometry.Methods A total of41cases of urine samples of clinical almost tolerance (group1),rejection (group2) and healthy controls (group0) were detected by PBSIIC massspectrometer and CM10protein chips (Bio-Rad). Mass spectrometry data acquisition andpreprocessing were performed by Ciphergen ProteinChip3.2software, and the screening ofprotein peak and the establishment of diagnostic model were executed utilizing BiomarkerWizard and Biomarker Pattern software respectively.Results166identifiable differential proteins peaks were obtained, including19proteinpeaks at P <0.05and11at P <0.01. Six mass spectrum peaks such as4241.63,6838.90,1589.70,7433.77,4331.12,3363.30were used to found the diagnostic model. Thesensitivity, specificity and area under the ROC curve of the model were88.9%,100.0%and0.990respectively for the diagnosis of clinical almost tolerance, and100.0%,86.7%,0.988 for rejection.Conclusions There exist many kinds of small molecular weight proteins and peptidesassociated with clinical almost tolerance from M/Z1000to5000in urine. The diagnosticmodel built by Mass spectrum peak of4241.63,6838.90,1589.70,7433.77,4331.12and3363.30can be utilized to diagnose clinical almost tolerance in kidney transplant with highsensitivity and specificity. PartⅢBioinformatics analysis of differential proteins in plasma andurine in clinically almost tolerant kidney recipientsObjective To investigate the biological mechanisms of clinical almost tolerance in renaltransplant through bioinformatics identification and functional annotation of differentialproteins mass spectrometry peaks in plasma and urine, and to screen of core proteins by theanalysis of protein-protein interaction network.Methods Bioinformatics identification of mass spectrometry peaks of differential proteinand Nodes in the two diagnostic models was carried out by searching SWISS-PROT andthe TrEMBL database using ExPASy’s tagident Tool. We applied "tissue expression" inDAVID network software to conduct tissue expression analysis, and used GO-BP andGO-CC to perform functional annotation. The MiMI plug-in for cytoscape was utilized toscreen core proteins by protein-protein interaction network(PPIN) analysis.Results Identification result showed that there are6putative uncharacterized proteins indifferential proteins, two in plasma and four in urine. The tissues or organs that express differential proteins are very extensive, among which kidney is the first source. Differentialproteins were involved in complicated and diversified biotic processes such as Cellproliferation and differentiation, signal transduction, secretion regulation, cell motility,protein transportation and degeneration, nucleic acid and energy metabolism and so on.Five core proteins in plasma were obtained by PPIN analysis such as PSME3, SHBG,NDUFV2, KNG1, TFF1; five core proteins in urine such as AGT, GRM7, ATP6V1H,RINT1, PTPRK.Conclusions Our analysis results show that renal transplantation can causes functionchange of various system in organism, indicating the complexity of biological mechanismsof clinical almost tolerance in kidney grafts. Proteins come from kidney may play animportant role in the study of clinical almost tolerance in renal transplant, which is animportant source of diagnostic markers. Protein transportation and degeneration, nucleicacid and energy metabolism are essential participants in clinical almost tolerance orrejection. Biological mechanisms of core proteins in renal transplantation requires in-depthresearch.