Urinary Peptidomic Biomarkers Of Metabolic Syndrome With Early Renal Injury

Objective To optimize the peptidome analysis of magnetic bead-based weak cation exchange chromatography (MB-WCX) based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) method and to generate urine peptidome profiling of metabolic syndrome with early renal injury. Methods (1) Six preanalytical variables (urine collection methods, urine thawing temperature, pH values, urinary protein concentration, MALDI-TOF targets and spectra acquisition modes) were investigated on the influence of urine peptidome profiling, and the precision of this experiment method was evaluated. (2) Urine samples were collected from epidemiologic study of MS and renal involvement in Pinggu district, Beijing during the period of 2008 and 2009. MS was diagnosed by ATPⅢcriteria, while MS patients with early renal injury were defined as 20μg/min≤urinary albumin excretion(UAE)<200μg/min and estimated glomerμLar filtration rate (eGFR)≥60ml/min.1.73m2.Participants were divided into three group:group I (healthy subjects), group II (MS patients with normoalbuminuria) and group III (MS patients with microalbuminuria). ResμLts (1) An optimized method for urine peptidome profiling by MB-MALDI-TOF-MS included:overnight urine collection, thawing at room temperature, applying 30μL urine per sample, using polished steel target and acquire spectra data by manual mode. Within-day and between-day coefficient of variation (CV) ranged from 7.7% to 14.2% and from 7.9% to 23.0% respectively. (2) One hundred and sixty-five subjects were enrolled into our study(sixty five subjects in groupⅠ, fifty-four subjects in group II and forty-six subjects in groupⅢ) and their urinary peptidome spectra were generated separately by the optimized MB-MALDI-TOF-MS method. Conclusion We have established a high accurate and reproducible analytic platform for urine peptidome profiling, it is suitable for high-throughput clinical proteomics research technology. Appling this approach, we established urinary peptidome patterns of healthy subjects, MS with normoalbuminuria and MS with microalbuminuria. Objective To explore potential urine biomarkers and to generate diagnostic models by bioinformatics tools, to identify urinary peptide biomarkers of MS with early renal injury. Methods Two bioinformatics software had been applied to analyze urinary peptidome profiling mass spectrometry data:(1) Subjects in groupⅠ, groupⅡand groupⅢwere divided into training set and testing set. Statistical tests in ClinProTools 2.1 software were adopted to screen differential peptide peaks of urinary peptidome in training set of groupⅠversus groupⅢ, groupⅡversus groupⅢand comparison of three groups separately. Genetic algorithm (GA) was used to establish diagnostic models of groupⅠversus groupⅢ, groupⅡversus groupⅢ.10-fold cross validation in training set was used to evaluate recognizability and external validation in testing set was used to assess prediction ability of diagnostic models. (2) Random forests (RF) algorithm in Matlab7.10.0 software was used to screen differential peptide peaks, then combined support vector machine (S VM) algorithm to generate diagnostic models of group I versus group III, groupⅡversus groupⅢand three groups comparison separately.10-fold cross validation and receiver operating characteristic curve (ROC) were used to evaluate recognizability of diagnostic models. Differential peptide peaks were identified by linear ion trap-orbitrap mass spectrometry (LTQ Orbitrap Velos) and biologic function of these identified peptide biomarkers were analyzed. ResμLts (1)GroupⅠversus Group III:GA based model showed 100% sensitivity,92.1% specificity and 95.9% accuracy by 10-fold cross-validation in training set in identifying MS patients with early renal injury, and it revealed 76.2% sensitivity, 80% specificity and 78.4% accuracy in testing set; Correspondingly, SVM algorithm based model reported 82.0% sensitivity,90.9% specificity and 87.3% accuracy. Area under curve(AUC) value of receiver operating characteristic curve (ROC curve) was 0.924. Four peptide peaks were included in two diagnostic models with m/z 2755.97,3016.72.9076.41 and 11728.45;(2)GroupⅡversus GroupⅢ:GA based model showed 100% sensitivity,87.5% specificity and 93.4% accuracy by 10-fold cross-validation in training set, and it revealed 71.4% sensitivity,73.1% specificity and 72.3% accuracy in testing set; Correspondingly, SVM algorithm based model reported 89.2% sensitivity,81.1% specificity and 85.5% accuracy, AUC value was 0.911. Four peptide peaks were included in two diagnostic models with m/z 2755.97, 3016.72,9076.41,10052.09;(3)Three groups comparison:SVM algorithm based model showed 64.5% overall accuracy, the model included eight peptide peak:m/z 2048.72,2562.67,2755.97,8779.30,9076.41,10052.09,10530.43 and 11728.45.(4) Three differential peaks of m/z 1884.33,2562.67 and 2661.41 were identified as peptide derived from fibrinogen alpha chain. Fragment of m/z 2562.67 was up-regμLated in urine in patients of MS and MS with early renal injury, while fragments of m/z 1884.33 and 2661.41 were up-regμLated in MS patients without renal injury. Conclusion Diagnostic models based on GA and SVM showed highly accuracy and class prediction in discriminating MS patients with early renal injury. Three peptide peaks identified based on MS/MS spectra were all peptide fragments of fibrinogen alpha chain, suggesting fibrinogen alpha chain might be urinary peptide biomarkers of MS with early renal injury and participated in pathogenesis of MS and MS with renal injury...