Urinary Polypeptides Research Articles

Levels of inter-alpha-trypsin inhibitor heavy chain H4 urinary polypeptide in gestational diabetes mellitus

ABSTRACT Gestational diabetes mellitus (GDM) can cause a variety of adverse maternal and fetal complications. The purpose of this study was to screen and identify the urinary polypeptides related to the severity of GDM and to analyze the correlation between urinary peptide levels and neonatal metabolic indices. A total of 31 normal pregnant women (N group) and 74 patients with GDM (GDM group) were randomly selected between February 2018 and August 2019. Patients with GDM were divided into two groups according to their fasting plasma glucose (FPG) levels. The urine samples were enriched using weak cation-exchange magnetic beads (MB-WCX), and eight different urine polypeptides were screened and analyzed. The peptide spectra were obtained using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The urinary peptide signatures of the two groups were compared using the BioExplorer software. The difference analysis of the eight urinary polypeptides between the normal pregnant (N) group and GDM group showed that two polypeptides with mass-to- charge ratios (m/z) of 2175.7 and 2318.8, respectively, were significantly different between the two groups (P < 0.01). The m/z 2175.7 polypeptide was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS), and the corresponding name of the molecule was inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4). The changes in ITIH4 levels correlated with those in the neonatal metabolic indices. By establishing the Fisher discriminant function equation for the GDM group, the difference in sample distribution and mean value of the two groups could be observed directly. Abbreviations: GDM: gestational diabetes mellitus; FPG: fasting plasma glucose; MB-WCX: weak cation exchange magnetic beads; MALDI-TOF MS: matrix-assisted laser desorption ionization time-of-flight mass spectrometry; m/z: mass charge ratio; LC-MS: liquid chromatography-tandem mass spectrometry; glycosylated hemoglobin (HbA1c); PPG: postprandial plasma glucose; ITIH4: inter-alpha-trypsin inhibitor heavy chain H4; IR: insulin resistance; NFPG: neonatal fasting plasma glucose; NH: neonatal height; NW: neonatal weight; BMI: body mass index; RPL: recurrent pregnancy loss; OGTT: oral glucose tolerance test; ADA: American Diabetes Association; LIS: Laboratory Information System

Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes mellitus.

BackgroundGestational diabetes mellitus (GDM) has many adverse outcomes that seriously threaten the short‐term and long‐term health of mothers and infants. This study comprehensively analyzed the clinical diagnostic value of GDM‐related clinical indexes and urine polypeptide research results, and established comprehensive index diagnostic models.MethodsIn this study, diagnostic values from the clinical indexes of serum triglyceride (TRIG), high‐density lipoprotein cholesterol (HDL‐C), fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), and 7 GDM‐related urinary polypeptides were analyzed retrospectively. The multiple logistic regression equation, multilayer perceptron neural network model, radial basis function, and discriminant analysis function models of GDM‐related indexes were established using machine language.ResultsThe results showed that HbA1c had the highest diagnostic value for GDM, with an area under the curve (AUC) of 0.769. When the cut‐off value was 4.95, the diagnostic sensitivity and specificity were 70.5% and 70.0%, respectively. Among the seven GDM‐related urinary polypeptides, human hemopexin (HEMO) had the highest diagnostic value, with an AUC of 0.690. When the cut‐off value was 368.5, the sensitivity and specificity were 79.5% and 43.3%, respectively. The AUC of the multilayer perceptron neural network model was 0.942, followed by binary logistic regression (0.938), radial basis function model (0.909), and the discriminant analysis function model (0.908).ConclusionThe establishment of a GDM diagnostic model combining blood glucose, blood lipid, and urine polypeptide indexes can lay a foundation for exploring machine language and artificial intelligence in diagnostic systems.

Relationship between cytokeratins CK8/18&amp;19 and KIM-1 level in urine with apoptosis and necrosis of nephrotheliocytes in rats with toxic nephropathy

The aim of the article. The aim of this work was to elucidate the role of apoptosis and necrosis in kidney tissue in the development of acute renal damage in poisoning rats with uranyl acetate. The research objectives included modeling acute poisoning in rats, collecting urine and kidney tissue with identifying markers of programmed cell death, tissue polypeptide antigen (TPA, fragments of cytokeratin CK8/18 19), and KIM-1 level in urine. An analysis of the relationship between an early increase in urinary excretion of the TPA and apoptosis level, a kidney injury molecule KIM-1, and necrosis of the tubular epithelial cells during rat poisoning with nephrotoxin uranyl acetate dihydrate.&#x0D; Materials and methods. Uranyl acetate dihydrate (CAS # 6159-44-0) was administered to 18-week old female Sprague-Dawley rats weighing 175199 g by intragastrically at a dose of 30 mg / 100 g body weight once through an atraumatic probe. Rats were divided into 2 groups: group 1 intact animals (12 individuals), group 2 animals with induced AKI (36 individuals). Daily urine was collected before, on the 1st, 3rd, and 7th day after poisoning in metabolic cages. The concentration of creatinine, KIM-1, tissue polypeptide antigen was measured in urine. In the kidney tissue samples, the fraction of dead cells and nephrothelial cells with apoptotic signs of nuclear changes by fluorescence microscopy with AMD Hoechst 33342 staining was determined. Data processing was performed using GraphPad Prism 6.0.&#x0D; Results. Acute kidney injury in rats with uranyl acetate dihydrate leads to a rapid increase in urinary excretion of cytokeratin fragments CK8/18 19 due to subtotal damage to nephrothelial cells due to activation of apoptosis, and then an increase in KIM-1 as a marker of necrotic cell death. Fluorescence microscopy of nuclear chromatin stained renal tubule cells showed a significant increase in the proportion of cells with apoptotic bodies, chromatin condensation, and a change in the shape of the nuclei.&#x0D; Conclusion. Examination of the curves of risk function showed that only creatinine in blood (p = 0.0002) and urine KIM-1 (p = 0.0005) had a significant level of association with rat mortality and necrosis of the nephrothelial cells. A comparative analysis of the relationship between apoptosis biomarker levels TPA (cytokeratin fragments CK8/18 19) and urinary nephrotoxicity marker KIM-1 with the proportion of kidney cells dying by the mechanism of necrosis and apoptosis revealed positive correlations of Spearman in pairs of cytokeratin CK8/18 19 apoptosis (r = 0.73, 95% CI 0.450.88, p 0.0001), KIM-1 necrosis (r = 0.98, 95% CI 0.960.99, p 0.0001). The revealed relationship indicated the possibility of determining urinary tissue polypeptide antigen TPA as a marker of the early stage of acute kidney damage as a surrogate marker of tubular cell apoptosis, and KIM-1 as a marker for necrosis of nephrothelial cells.

Urinary Peptides Associated Closely with Gestational Diabetes Mellitus.

Gestational diabetes mellitus (GDM) is a common disease of pregnant women, which has a higher incidence in recent years. The purpose of this study is to explore urinary biomarkers that could predict and monitor gestational diabetes mellitus (GDM). Urine samples from 30 normal pregnant women and 78 GDM patients were collected and purified by weak cationic exchange magnetic beads (MB-WCX), then analyzed by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). The urinary peptide signatures of the two groups were compared by BioExplorer software. The potential ability of the differently expressed peptides to distinguish GDM patients from normal pregnant women was evaluated by receiver operating characteristic (ROC) analysis. At last, the differently expressed peptides were identified by liquid chromatography tandem mass spectrometry (LC-MS). There were four differently expressed peptides (m/z 1000.5, 1117.5, 1142.9, and 2022.9) between two groups, which were identified as fragments of urinary albumin, α2-macroglobulin, human hemopexin, and α1-microglobulin, respectively. The diagnostic efficacy of m/z 1142.9 was better than the other peptides. The area under the curve (AUC) of the m/z 1142.9 was 0.690 (95% CI: 0.583-0.796). The discovery of urinary polypeptides provides the possibility for the early prediction of GDM and the monitoring of glucose metabolism in GDM patients by a noninvasive method.

Identification of urine biomarkers associated with lung adenocarcinoma.

Lung adenocarcinoma (LAC) progression is accompanied by changes in protein levels that may be reflected in body fluids, such as urine. Urine collected from LAC patients (n=34) and healthy controls (n=36) was analyzed via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) combined with weak cationic exchange magnetic beads. The results revealed 76 urinary polypeptides significantly different between LAC patients and normal controls (P<0.05). Twenty-two of these peptides were up-regulated and 54 were down-regulated. Thirteen peptides had average peak intensities >600. Twelve of these 13 peptides were successfully identified using nano-liquid chromatography-tandem MS. Receiver operating characteristic analyses identified seven peptides with superior LAC diagnostic performances. Immunohistochemical staining in 20 paired LAC and adjacent normal tissues showed that IGKC, AAT, SH3BGRL3, osteopontin and gelsolin levels were higher in LAC tissues than in adjacent tissuesand were closely associated with LAC. Urinary peptides assessments may thus provide a novel, noninvasive, repeatable method for detecting and monitoring LAC. New, low-cost detection methods and bioinformatics tools are therefore urgently needed for the analysis of low abundance proteins and peptides in body fluids.

Urine proteomic analysis in cystinuric children with renal stones

The gene mutations responsible for cystinuria do not fully explain kidney stone activity, suggesting that specific proteins may serve as promoters of cystine precipitation, aggregation or epithelial adherence. In this study we assessed (1) the differences in the urinary proteins between children with cystinuria and kidney stones (CYS) and healthy controls (HC), with particular attention to the fibrosis-related proteins, and (2) the presence of diagnostic biomarkers for CYS. We conducted a pilot study comparing individual urinary proteomes of 2 newly diagnosed children with CYS and 2 age- and gender-matched HC, using liquid chromatography-mass spectrometry. Relative protein abundance was estimated using spectral counting. Proteins of interest in both CYS and HC were selected using the following criteria: i) ≥5 spectral counts; ii) ≥2-fold difference in spectral counts; and iii) ≤0.05 p-value for the Fisher's Exact Test. This study demonstrates a different urinary polypeptide profile in two children with CYS compared to two HC. Of the 623 proteins identified by proteomic analysis, 180 exhibited at least a 2-fold increased relative abundance in CYS compared to HC. Of these, 39 were involved in response to stress, 26 in response to wounding, 21 in inflammatory response, 18 in immune response, and 4 in cellular response to oxidative stress. 133 proteins were found only in children with CYS, 33 of which met the selection criteria. Of these 33 unique proteins, six are known to be associated with fibrosis pathways (Table). The major limitation of this study is the small number of samples that were analyzed. Validation using highly specific methods such as ELISA is needed. We provide proteomic evidence of oxidative injury, inflammation, wound healing and fibrosis in two children with CYS. We speculate that oxidative stress and inflammation may cause remodeling via actin and vimentin pathways, leading to fibrosis. Additionally, we identified ITIH and MMP-9 as potential diagnostic biomarkers and novel therapeutic targets in CYS. These proteins merit further investigation.

MP80-19 URINE PROTEOMIC ANALYSIS IN CYSTINURIC PATIENTS WITH RENAL STONES

You have accessJournal of UrologyStone Disease: Evaluation II1 Apr 2015MP80-19 URINE PROTEOMIC ANALYSIS IN CYSTINURIC PATIENTS WITH RENAL STONES Larisa Kovacevic, Hong Lu, Yegappan Lakshmanan, Joseph A. Caruso, and David Goldfarb Larisa KovacevicLarisa Kovacevic More articles by this author , Hong LuHong Lu More articles by this author , Yegappan LakshmananYegappan Lakshmanan More articles by this author , Joseph A. CarusoJoseph A. Caruso More articles by this author , and David GoldfarbDavid Goldfarb More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2855AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The pathogenesis of cystinuric renal stones is not well understood. Using a proteomic approach, we aimed to assess (1) the differences in the function of urinary proteins between patients with cystinuria and renal stone (CYS), patients with renal stone and no cystinuria (RS), and healthy controls (HC); (2) the gender differences in the proteomic profile within CYS group;(3) the presence of diagnostic biomarkers for CYS. METHODS We compared urinary proteomes of 10 patients with CYS (35.7±10.6 years, 5 males), 10 age- and gender-matched patients with RS, and 10 age- and gender-matched HC, using liquid chromatography-mass spectrometry (LC-MS/MS). Proteins of interest were selected based on relative fold-differences in abundance compared to controls, and were expressed as ratios. The pathway analysis using Gene Ontology (GO) annotation was done on the top 10% of proteins that exhibited the more pronounced differences in abundance between the groups. These significantly expressed proteins were used to analyze the group differences for biological processes, cellular components, and molecular function. RESULTS Of the 2219 proteins identified by proteomic analysis, 146 proteins were up-regulated in CYS and 80 proteins were up-regulated in HC. GO analysis in CYS showed a significant number of peptides involved in signal transduction (68 in males and 80 in females), and cell-cell signaling (17 in males and 21 in females). However, the signaling pathway was different in CYS males (Peroxisome Proliferator-Activated Receptor, PPAR) than in CYS females (NOTCH3). Proteins involved in fibrosis were found predominantly in CYS males (Table). Inter-alpha-trypsin inhibitor (ITIH), paraoxonase (PON1) and ceruloplasmin (CP) were more abundant in male CYS than female CYS and their matched HC. CONCLUSIONS Patients with cystinuria and renal stones have a different urinary polypeptide profile compared to healthy controls. Fibrosis is more common in male CYS, and an aggressive approach may be warranted. Additionally, we identified ITIH and CP as potential diagnostic biomarkers and novel therapeutic targets in CYS. These unique proteins merit further investigation. Accession Number∗ Protein MW (kDa) No. of assigned peptides∗∗ No. of unique peptides∗∗ Sequence coverage CYS/HC CYS/HC (%)∗∗∗ ITIH2 Inter-alpha-trypsin inhibitor 106 100 100 43.5 PON1 Paraoxonase 40 41.22 40.67 58.9 CP Ceruloplasmin 122 26.8 26.5 80.6 VIME Vimentin 54 8.35 8.28 23.8 IGFBP-6 Insulin-like growth factor-binding protein 6 25 3.35 3.3 4.17 ACTG Actin 42 3.28 4.11 66.1 MMP9 Matrix metalloproteinase 9 78 3.13 3.09 15.9 VCAM Vascular cell adhesion protein 81 2.12 2.1 35.3 ∗ Human. ∗∗ Values presented as CYS/HC ratios in males. ∗∗∗ % of sequence that has been identified for each protein. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e1026-e1027 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Larisa Kovacevic More articles by this author Hong Lu More articles by this author Yegappan Lakshmanan More articles by this author Joseph A. Caruso More articles by this author David Goldfarb More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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Objectives Urine as a biofluid is commonly used in clinical diagnostics, including during pregnancy. Urine is a rich source of polypeptides and protein degradation products. Electron transfer dissociation (ETD) techniques have recently been applied to peptides, and improved fragmentation of long polypeptides has been demonstrated; ETD further facilitates analysis of labile post-translational modifications. We therefore applied this advanced technique for peptide dissociation to analyse and identify urinary polypeptides for the first time. Methods We examined the urinary peptidome from normal pregnant women during pregnancy, to demonstrate that peptides are readily observed. We utilised conventional MS/MS techniques and then used ETD (LC–MS/MS performed on an orbitrap equipped with ETD source, coupled to a nanoAcquity HPLC) to increase the identification rate of the peptides within these samples, as the polypeptide species observed are large and highly charged. Results An increase in the number of peptides whose identities could be ascribed using routine database searching methods was enabled via use of ETD. These peptides, as anticipated, were large and highly charged (typically giving charge states of 4–8+ over a mass-to-charge range of 400–2000 m/z). Conclusions Urinary proteomic profiling has not been able to discriminate between normal and pre-eclampsia pregnancies at gestations early enough to enable preventative strategies. Enhanced polypeptide identification achieved through ETD may facilitate clinically useful predictive tests. Disclosures S. Hart: None. L. Kenny: Commercial Interest: Metabolomic Diagnostics. J. Myers: None. P. Baker: Commercial Interest: Metabolomic Diagnostics.

Abstract 9992: Urine Proteome Analysis Allows Establishment of Polypeptide Patterns non-Invasively Reflecting Drug-Mediated Regression of Atherosclerosis in ApoE-/--Mice

Background: Recently, non-invasive urine proteome analysis established a multimarker proteome approach towards non-invasive detection of atherosclerosis in an ApoE-/--mouse model, and relevance of this proteome pattern was also confirmed in human atherosclerosis. Urine was used since it lacks peptidase activity, which is found in blood and could falsify proteome analysis. Additionally, non-invasive monitoring of anti-atherosclerotic therapy with statins is of high clinical interest, and could be an important tool for guiding medical therapy. We therefore aimed to identify urinary polypeptides reflecting statin therapy in the ApoE-/--mouse model. Methods and Results: Urine of ApoE-/--mice either on high fat diet (HFD) or chow diet (CD) was collected over 15 weeks; each group was divided into subgroups with or without statin therapy applied by oral gavaging (HFD+S and HFD-S). Capillary electrophoresis mass spectrometry (CE-MS) of urine samples identified 5 polypeptides specifically reflecting statin treatment, resulting in a shift towards a more “healthy” polypeptide pattern until week 12. Sequencing of the discovered polypeptides identified fragments of α1-antitrypsin, EGF, kidney androgen regulated protein (KAP) and napsin. In histology, plaque size was significantly reduced by statin treatment, and presence of marker proteins in atherosclerotic plaques was confirmed by immunohistochemistry. Furthermore, urinary excretion levels of α1-antitrypsin fragments significantly correlated with intraplaque α1-antitrypsin content, mirroring plaque protein expression in the urine proteome. Conclusions: Urine proteome analysis allows discovery of novel biomarkers for the non-invasive monitoring of an anti-atherosclerotic therapy with statins in an ApoE-/--mouse model. The established multimarker proteome pattern reflects regression of plaque size in histology, and marker excretion levels also correlate with their expression in immunohistochemistry. This approach allows monitoring of atherosclerosis progression and statin treatment in mice, and thus promises to be an important and clinically relevant tool for monitoring and guiding of established but even more so of novel developments of anti-atherosclerotic drugs.

Urinary proteome analysis in hypertensive patients with left ventricular diastolic dysfunction.

Despite the significant heart failure (HF) burden on society, easily applicable screening techniques, particularly for the early detection of asymptomatic left ventricular (LV) dysfunction, are lacking. The present study aimed to identify and test a set of urinary polypeptides that may indicate early LV diastolic dysfunction as defined by echocardiography in hypertensive patients in a cross-sectional case-control study nested within the FLEMish study on ENvironment, Genes and Health Outcome (FLEMENGHO). To identify potentially discriminating urinary biomarkers for LV diastolic dysfunction, we applied capillary electrophoresis coupled to mass spectrometry. In the discovery set, we compared 19 hypertensive patients with asymptomatic LV diastolic dysfunction with 19 healthy controls. In the absence of adjustment for multiple testing, 85 urinary peptides were different between cases and controls at a P-value of <0.033. With adjustment for multiple testing, three potential biomarkers remained significantly different between cases and controls (P ≤ 0.02). We combined the 85 potential biomarkers in a high-dimensional model (classifier), which we applied in a blinded manner to an independent test set of 16 hypertensive patients with symptomatic HF and 16 healthy controls. Upon unblinding, the area under the receiver operating characteristic curve (AUC) of the HF classification was 0.84 (95% CI: 0.70-0.98; P= 0.001). In asymptomatic hypertensive patients with LV diastolic dysfunction, we identified a set of urinary polypeptides specific for essential hypertension with LV diastolic dysfunction that subsequently distinguished hypertensive patients with overt HF from healthy controls.

Urine Proteome Analysis Reflects Atherosclerotic Disease in an ApoE−/− Mouse Model and Allows the Discovery of New Candidate Biomarkers in Mouse and Human Atherosclerosis

Noninvasive diagnosis of atherosclerosis via single biomarkers has been attempted but remains elusive. However, a previous polymarker or pattern approach of urine polypeptides in humans reflected coronary artery disease with high accuracy. The aim of the current study is to use urine proteomics in ApoE(-/-) mice to discover proteins with pathophysiological roles in atherogenesis and to identify urinary polypeptide patterns reflecting early stages of atherosclerosis. Urine of ApoE(-/-) mice either on high fat diet (HFD) or chow diet was collected over 12 weeks; urine of wild type mice on HFD was used to exclude diet-related proteome changes. Capillary electrophoresis coupled to mass spectrometry (CE-MS) of samples identified 16 polypeptides specific for ApoE(-/-) mice on HFD. In a blinded test set, these polypeptides allowed identification of atherosclerosis at a sensitivity of 90% and specificity of 100%, as well as monitoring of disease progression. Sequencing of the discovered polypeptides identified fragments of α(1)-antitrypsin, epidermal growth factor (EGF), kidney androgen-regulated protein, and collagen. Using immunohistochemistry, α(1)-antitrypsin, EGF, and collagen type I were shown to be highly expressed in atherosclerotic plaques of ApoE(-/-) mice on HFD. Urinary excretion levels of collagen and α(1)-antitrypsin fragments also significantly correlated with intraplaque collagen and α(1)-antitrypsin content, mirroring plaque protein expression in the urine proteome. To provide further confirmation that the newly identified proteins are relevant in humans, the presence of collagen type I, α(1)-antitrypsin, and EGF was also confirmed in human atherosclerotic disease. Urine proteome analysis in mice exemplifies the potential of a novel multimarker approach for the noninvasive detection of atherosclerosis and monitoring of disease progression. Furthermore, this approach represents a novel discovery tool for the identification of proteins relevant in murine and human atherosclerosis and thus also defines potential novel therapeutic targets.

Proteomic Pattern-Based Risk Stratification of Outcome Shows Significant Higher Accuracy Compared to HCT-CI in Patients with AML and MDS,

Abstract 4128 Objectives:Allogeneic stem cell transplantation (SCT) is an established treatment for many severe disorders of hematopoiesis. Although SCT has considerable curative potential, its application is limited by transplant-related complications such as infections and graft-versus host disease (GvHD) which could lead to high mortality rates especially in older or less fit patients. Therefore, a careful pre-SCT assessment of risk and benefit is mandatory and different scores have recently emerged as helpful tools. We have previously applied proteomics to identify a specific urinary polypeptide patterns (PP) predictive for developing acute GvHD (aGvHD) (Weissinger EM et al, Blood 2007;109:5511–5519). The aim of this study was to investigate whether the PPs can predict overall outcome after allo-SCT and to compare these findings to those of the hematopoietic cell transplantation comorbidity index (HCT-CI) (Sorror M et al, Blood 2005;106:2912–2919). Methods:In this retrospective analysis from Hannover Medical School, the datasets from all patients (pts) with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), who were allo-transplanted from a fully matched donor (matched related/unrelated donor (MRD/MUD)) between 2003–2008 and for whom relevant PP data were available, were included. Pts with a pt-donor HLA-mismatch constellation were excluded from this study. PP data from urine samples which were prospectively collected by day ≥ +7 after allo-SCT were correlated with overall survival (OS), aGvHD, non-relapse mortality (NRM), relapse rate and mortality (RM), and compared to the predictive value of the HCT-CI. Results:PP data were available from 111 pts (97 pts with AML, 14 with MDS; median age 52y; median EBMT score 4; 59 male/52 female; 69 MUD/42 MRD). They were grouped in high (PP-HRG), low (PP-LRG) or intermediate risk groups (PP-IRG). Forty-three pts (39%) belonged to the PP-LRG for aGvHD compared to 47 pts (42%) who were classified PP-HRG. Patient characteristics of PP-LRG and PP-HRG were similar in terms of age, sex and EBMT score (median 4 in both groups).OS compared favorably for the PP-LRG with an OS of 72% vs. 49% for the PP-HRG (p=0.03), also if only reduced intensity conditioning (RIC) was considered (73% vs 42%; p=0.01), respectively. There was a trend for higher incidence of NRM in the PP-HRG compared to PP-LRG (30% vs 14%, p=0.07) for the whole cohort, and a significant higher NRM rate, if only RIC was evaluated (35% vs 11%, p=0.01).However, if risk stratification was based on the HCT-CI, there was no significant difference between high risk (S-HRG) and low risk group (S-LRG) in terms of OS and NRM regardless of intensity of conditioning (OS for whole cohort: 57% vs 45%, p=0.4; OS for RIC: 56% vs 36%, p=0.2; NRM for whole cohort: 20% vs 23%, p=0.8; NRM for RIC: 18% vs 29%, p=0.4).Concerning the PP-IRG, there was a difference in OS between PP-IRG and PP-LRG (38% vs 73%, p=0.02). However, there was no significant difference in OS of the PP-IRG compared to the other PP-based risk groups nor between the HCT-CI based risk groups. Further, NRM did not show a significant difference neither for PP-based nor HCT-CI-based intermediate risk group compared to the other risk groups.Thirty vs 15 pts developed aGvHD in PP-HRG and PP-LRG (64% vs 35%, p<0.01) compared to 48% vs 64% (p=0.2) for S-HRG and S-LRG of the whole cohort, respectively. Incidence of aGvHD differed also significantly in the RIC cohort for PP-HRG and PP-LRG (65% vs 32%, p=0.01), but not for HCT-CI-based risk groups (47% vs 64%, p=0.1). Relapse rates and RM were not significantly different between high and low risk groups, neither for PP-based nor HCT-CI based (whole cohort and RIC subgroup), respectively. Conclusion:Risk stratification according to GvHD-match based PP, which has previously been shown to predict aGvHD, now also allows the identification of patient groups with significantly different OS and NRM. In comparison to the HCT-CI, PP-based prediction shows significantly higher accuracy in this rather homogeneous cohort of patients. Since proteomics is a new method which has been available only at a few centers, further multicenter analyses are essential to determinate the value of PP-based prediction of complications and outcome in SCT. Disclosures:Metzger:Mosaiques Diagnostics GmbH: Employment.

Naturally Occurring Human Urinary Peptides for Use in Diagnosis of Chronic Kidney Disease

Because of its availability, ease of collection, and correlation with physiology and pathology, urine is an attractive source for clinical proteomics/peptidomics. However, the lack of comparable data sets from large cohorts has greatly hindered the development of clinical proteomics. Here, we report the establishment of a reproducible, high resolution method for peptidome analysis of naturally occurring human urinary peptides and proteins, ranging from 800 to 17,000 Da, using samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases. As an example, by using this source of information, we were able to define urinary peptide biomarkers for chronic kidney diseases, allowing diagnosis of these diseases with high accuracy. Application of the chronic kidney disease-specific biomarker set to an independent test cohort in the subsequent replication phase resulted in 85.5% sensitivity and 100% specificity. These results indicate the potential usefulness of capillary electrophoresis coupled to MS for clinical applications in the analysis of naturally occurring urinary peptides.

Prediction of Muscle-invasive Bladder Cancer Using Urinary Proteomics

Minimally invasive methods of predicting the risk of muscle-invasive urothelial bladder carcinoma may expedite appropriate therapy and reduce morbidity and cost. Here, capillary electrophoresis coupled mass spectrometry was used to identify urinary polypeptide bladder cancer biomarkers in 127 patients. These markers were used to construct a panel discriminating muscle-invasive from noninvasive disease, which was refined in 297 additional samples from healthy volunteers, patients with malignant and nonmalignant genitourinary conditions. Sequencing of panel polypeptides was then done. Finally, the ability of the panel to predict muscle-invasive disease was evaluated prospectively in 130 bladder carcinoma patients. Four sequenced polypeptides formed a panel predictive of muscle-invasive disease. Prospective evaluation of this panel revealed a sensitivity of 81% [95% confidence interval (CI), 69-90] and specificity of 57% (95% CI, 45-69) for muscle-invasive disease. Multivariate analysis revealed the panel (P < 0.0001) and tumor grade (P = 0.0001), but not urine cytology, predict muscle invasion. A model including grade and panel polypeptide levels improved sensitivity [92% (95% CI, 82-97)] and specificity [68% (95% CI, 55-79)] for muscle-invasive disease. A model score of >0.88 provided a negative predictive value of 77% and positive predictive value of 90% for muscle invasion. Use of urinary peptides seems promising in estimating the probability a patient harbors muscle-invasive urothelial bladder cancer. These peptides may also shed novel insights into the biology of bladder tumor progression not obtainable by other methods. Clinical trials seem warranted to evaluate the effect of this approach on practice.

Application of Label-free Quantitative Peptidomics for the Identification of Urinary Biomarkers of Kidney Chronic Allograft Dysfunction

The advent of quantitative proteomics opens new opportunities in biomedical and clinical research. Although quantitative proteomics methods based on stable isotope labeling are in general preferred for biomolecular research, biomarker discovery is a case example of a biomedical problem that may be better addressed by using label-free MS techniques. As a proof of concept of this paradigm, we report the use of label-free quantitative LC-MS to profile the urinary peptidome of kidney chronic allograft dysfunction (CAD). The aim was to identify predictive biomarkers that could be used to personalize immunosuppressive therapies for kidney transplant patients. We detected (by LC-M/MS) and quantified (by LC-MS) 6000 polypeptide ions in undigested urine specimens across 39 CAD patients and 32 control individuals. Although unsupervised hierarchical clustering differentiated between the groups when including all the identified peptides, specific peptides derived from uromodulin and kininogen were found to be significantly more abundant in control than in CAD patients and correctly identified the two groups. These peptides are therefore potential biomarkers that might be used for the diagnosis of CAD. In addition, ions at m/z 645.59 and m/z 642.61 were able to differentiate between patients with different forms of CAD with specificities and sensitivities of 90% in a training set and, significantly, of approximately 70% in an independent validation set of samples. Interestingly low expression of uromodulin at m/z 638.03 coupled with high expression of m/z 642.61 diagnosed CAD in virtually all cases. Multiple reaction monitoring experiments further validated the results, illustrating the power of our label-free quantitative LC-MS approach for obtaining quantitative profiles of urinary polypeptides in a rapid, comprehensive, and precise fashion and for biomarker discovery.

Evaluation of Urinary Biomarkers for Coronary Artery Disease, Diabetes, and Diabetic Kidney Disease

In this study we sought to validate urinary biomarkers for diabetes and two common complications, coronary artery disease (CAD) and diabetic nephropathy (DN). A CAD score calculated by summing the product of a classification coefficient and signal amplitude of 15 urinary polypeptides was previously developed. Five sequences of biomarkers in the panel were identified as fragments of collagen alpha-1(I) and alpha-1(III). Prospectively collected urine samples available for analysis from 19 out of 20 individuals with CAD (15 with type 1 diabetes [T1D] and four without diabetes) and age-, sex-, and diabetes-matched controls enrolled in the Coronary Artery Calcification in Type 1 Diabetes study were analyzed for the CAD score using capillary electrophoresis and electrospray ionization mass spectrometry. Two panels of biomarkers that were previously defined to distinguish diabetes status were analyzed to determine their relationship to T1D. Three biomarker panels developed to distinguish DN (DNS) and two biomarker panels developed to distinguish renal disease (RDS) were examined to determine their relationship with renal function. The CAD score was associated with CAD (odds ratio with 95% confidence interval, 2.2 [1.3-5.2]; P = 0.0016) and remained significant when adjusted individually for age, albumin excretion rate (AER), blood pressure, waist circumference, intraabdominal fat, glycosylated hemoglobin, and lipids. DNS and RDS were significantly correlated with AER, cystatin C, and serum creatinine. The biomarker panels for diabetes were both significantly associated with T1D status (P < 0.05 for both). We validated a urinary proteome pattern associated with CAD and urinary proteome patterns associated with T1D and DN.

Urine Proteomics to Detect Biomarkers for Chronic Allograft Dysfunction

Despite optimal immunosuppressive therapy, more than 50% of kidney transplants fail because of chronic allograft dysfunction. A noninvasive means to diagnose chronic allograft dysfunction may allow earlier interventions that could improve graft half-life. In this proof-of-concept study, we used mass spectrometry to analyze differences in the urinary polypeptide patterns of 32 patients with chronic allograft dysfunction (14 with pure interstitial fibrosis and tubular atrophy and 18 with chronic active antibody-mediated rejection) and 18 control subjects (eight stable recipients and 10 healthy control subjects). Unsupervised hierarchical clustering showed good segregation of samples in groups corresponding mainly to the four biomedical conditions. Moreover, the composition of the proteome of the pure interstitial fibrosis and tubular atrophy group differed from that of the chronic active antibody-mediated rejection group, and an independent validation set confirmed these results. The 14 protein ions that best discriminated between these two groups correctly identified 100% of the patients with pure interstitial fibrosis and tubular atrophy and 100% of the patients with chronic active antibody-mediated rejection. In summary, this study establishes a pattern for two histologic lesions associated with distinct graft outcomes and constitutes a first step to designing a specific, noninvasive diagnostic tool for chronic allograft dysfunction.

Evaluation of Urine Proteome Pattern Analysis for Its Potential To Reflect Coronary Artery Atherosclerosis in Symptomatic Patients

Coronary artery disease (CAD) is a major cause of mortality and morbidity. Noninvasive proteome analysis could guide clinical evaluation and early/preventive treatment. Under routine clinical conditions, urine of 67 patients presenting with symptoms suspicious for CAD were analyzed by capillary electrophoresis directly coupled with mass spectrometry (CE-MS). All patients were subjected to coronary angiography and either assigned to a CAD or non-CAD group. A training set of 29 patients was used to establish CAD and non-CAD-associated proteome patterns of plasma as well as urine. Significant discriminatory power was achieved in urine but not in plasma. Therefore, urine proteomic analysis of further 38 patients was performed in a blinded study. A combination of 17 urinary polypeptides allowed separation of both groups in the test set with a sensitivity of 81%, a specificity of 92%, and an accuracy of 84%. Sequencing of urinary marker peptides identified fragments of collagen alpha1 (I and III), which we furthermore demonstrated to be expressed in atherosclerotic plaques of human aorta. In conclusion, specific CE-MS polypeptide patterns in urine were associated with significant CAD in patients with angina-typical symptoms. These promising findings need to be further evaluated in regard to reliability of a urine-based screening method with the potential of improving the diagnostic approaches for CAD.

Quantitative Urinary Proteome Analysis for Biomarker Evaluation in Chronic Kidney Disease

A limitation of proteomic methods with respect to their clinical applicability is the lack of possibilities to directly deduce the amount of a protein or peptide from a particular mass spectrometry (MS) spectrum. For quantification of chronic kidney disease (CKD)-specific urinary polypeptides in capillary electrophoresis coupled with mass spectrometry (CE-MS), we compared signal intensity calibration methods based on either urinary creatinine or stable isotope labeled synthetic marker analogues (absolute quantification) with those based on ion counting using highly abundant collagen fragments as nonmarker references (relative quantification). Our results indicate that relative quantification of biomarker excretion based on ion counts in reference to endogenous "housekeeping" peptides is sufficient for the determination of urinary polypeptide levels. The calculation of absolute concentrations via exogenous stable isotope-labeled peptide standards is of no additional benefit.

Urinary Proteomic Biomarkers in Coronary Artery Disease

Urinary proteomics is emerging as a powerful non-invasive tool for diagnosis and monitoring of variety of human diseases. We tested whether signatures of urinary polypeptides can contribute to the existing biomarkers for coronary artery disease (CAD). We examined a total of 359 urine samples from 88 patients with severe CAD and 282 controls. Spot urine was analyzed using capillary electrophoresis on-line coupled to ESI-TOF-MS enabling characterization of more than 1000 polypeptides per sample. In a first step a "training set" for biomarker definition was created. Multiple biomarker patterns clearly distinguished healthy controls from CAD patients, and we extracted 15 peptides that define a characteristic CAD signature panel. In a second step, the ability of the CAD-specific panel to predict the presence of CAD was evaluated in a blinded study using a "test set." The signature panel showed sensitivity of 98% (95% confidence interval, 88.7-99.6) and 83% specificity (95% confidence interval, 51.6-97.4). Furthermore the peptide pattern significantly changed toward the healthy signature correlating with the level of physical activity after therapeutic intervention. Our results show that urinary proteomics can identify CAD patients with high confidence and might also play a role in monitoring the effects of therapeutic interventions. The workflow is amenable to clinical routine testing suggesting that non-invasive proteomics analysis can become a valuable addition to other biomarkers used in cardiovascular risk assessment.