Dynamic changes of urinary proteins in a rat model of acute hypercoagulable state induced by tranexamic acid

The hypercoagulable state leads to the development of thrombotic diseases, but it is difficult to diagnose due to the lack of available biomarkers. This study aimed to investigate systematic changes of the urinary proteome in the acute hypercoagulable state. A rat model of the acute hypercoagulable state was induced by an antifibrinolytic agent tranexamic acid and urine samples were collected for proteomic analysis by liquid chromatography-tandem mass spectrometry. A total of 28 differential proteins were detected in the urinary proteome of the model rats, of which 12 had been previously considered as candidate biomarkers such as myoglobin, and 10 had been considered stable in healthy human urine. Of the 28 differentially expressed proteins 18 had counterparts in humans. Of these 18 proteins, 10 were members of the human core urinary proteome distributed in a variety of human tissues but concentrated in the urinary and digestive systems. Fumarylacetoacetase was verified as a potential marker of the acute hypercoagulable state by Western blot analysis. In conclusion, urine proteome analysis is a powerful approach to identify potential biomarkers of acute hypercoagulable state.     

Bladder cancer associated glycoprotein signatures revealed by urinary proteomic profiling

Current methods in the noninvasive detection and surveillance of bladder cancer via urine analysis include voided urine cytology (VUC) and some diagnostic urinary protein biomarkers; however, due to the poor sensitivity of VUC and high false-positive rates of currently available protein assays, detection of bladder cancer via urinalysis remains a challenge. In the study presented here, a rapid, high-sensitivity technique was developed to profile the N-linked glycoprotein component in naturally micturated human urine specimens. Concanavalin A (Con A) affinity chromatography coupled to nanoflow liquid chromatography was utilized to separate the complex peptide mixture prior to a linear ion trap MS analysis. Of 186 proteins identified with high confidence by multiple analyses, 40% were secreted proteins, 18% membrane proteins, and 14% extracellular proteins. In this study, the presence of several proteins appeared to be associated with the presence of bladder cancer, including alpha-1B-glycoprotein that was detected in all tumor-bearing patient samples but in none of the samples obtained from non-tumor-bearing individuals. The combination of Con A affinity chromatography and nano-LC/MS/MS provides an initial investigation of N-glycoproteins in complex biological samples and facilitates the identification of potential biomarkers of bladder cancer in noninvasively obtained human urine.     

Applications of urinary proteomics in biomarker discovery

Urine is an important source of biomarkers. This article reviews current advances, major challenges, and future prospects in the field of urinary proteomics. Because the practical clinical problem is to distinguish diseases with similar symptoms, merely comparing samples from patients of a particular disease to those of healthy individuals is inadequate for finding biomarkers with sufficient diagnostic power. In addition, the variation of expression levels of urinary proteins among healthy individuals and individuals under different physiological conditions adds to the difficulty in identifying biomarkers. We propose that establishing the natural variation in urinary protein expression among a healthy population can serve as a reference to help identify protein abundance changes that are caused by disease, not by individual variations or physiological changes. We also discuss that comparing protein expression levels between urine and plasma may reveal the physiological function of the kidney and that may facilitate biomarker discovery. Finally, we propose that establishing a data-sharing platform for data collection and integrating results from all urinary biomarker studies will help promote the development of urinary proteomics.     

应用蛋白质组学筛选宫颈癌患者尿液中的肿瘤标志物

通过比较健康女性和宫颈癌患者的尿蛋白质组,发现并分析差异表达蛋白,从中筛选潜在的宫颈癌的标志物。研究对象由43名宫颈癌患者（CC）和47名健康女性（HW）组成。用超速离心法沉淀尿蛋白,再用一维凝胶电泳（SDS-PAGE）与液相色谱-质谱联用技术（LC-MS/MS）鉴定尿液中的蛋白质,蛋白质定量采用无标定量。比较患者尿蛋白质组、健康对照的尿蛋白质组和宫颈癌组织蛋白质组,有1910个蛋白质是患者和健康对照共有的尿蛋白,这其中有746个蛋白质也存在于宫颈癌组织蛋白质组。在这746个蛋白质中找到84个上调蛋白和82下调蛋白。通过生物信息学分析发现牛皮癣素（S100A7）和癌胚抗原相关细胞黏附分子8（CEACAM8）是宫颈癌尿液样本独有蛋白质。在验证组的70例样本中,双盲法测试S100A7、CEACAM8以及两者联合诊断宫颈癌的敏感性能达到73%、87%、93%。结果提示,宫颈癌患者的尿蛋白质组与健康女性的尿蛋白质组不同,并且S100A7和CEACAM8可以作为宫颈癌潜在的肿瘤标志物。     

Multiplex single molecule counting technology used to generate interleukin 4, interleukin 6, and interleukin 10 reference limits

Detecting biomarkers at pg/ml concentrations or below is, in many situations, critical for quantifying levels in healthy individuals as well as the changes that can occur in the progression of disease states. The ability to detect multiple biomarkers from the same sample allows for better diagnoses, more efficient testing, and lower volumes of sample required. Based on single molecule counting technology, a multiplex instrument was designed and built that is capable of detecting cytokines and other low-abundance proteins at sub-pg/ml quantities in human plasma samples. The multiplex single molecule counting instrument was used to generate 95% reference limits for interleukin 4 (IL-4, <0.61 pg/ml), interleukin 6 (IL-6, <6.53 pg/ml), and interleukin 10 (IL-10, <1.08 pg/ml) from 100 healthy human donor plasma samples, with more than 90% of IL-4 concentrations and 100% of IL-6 and IL-10 concentrations above the limit of detection.     

LG3 fragment of endorepellin is a possible biomarker of severity in IgA nephropathy

IgA nephropathy (IgAN), the most common primary glomerulonephritis, is characterized by deposition of IgA in the glomerular mesangium. The diagnosis of IgAN still requires a kidney biopsy that cannot easily be repeated in the same patient during follow‐up. Therefore, identification of noninvasive urinary biomarkers would be very useful for monitoring patients with IgAN. We first used bidimensional electrophoresis (2DE) coupled to MALDI‐TOF‐TOF and Western blot to identify some urinary biomarkers associated with IgAN. Urine of IgAN patients showed an increase of albumin fragments, α‐1‐antitrypsin and α‐1‐β‐glycoprotein, along with a decrease of a single spot that was identified as the laminin G‐like 3 (LG3) fragment of endorepellin. The urinary proteomes of 43 IgAN patients were compared to those of 30 healthy individuals by ELISA. Quantification of LG3 confirmed a significant decrease in the urine of IgAN patients compared to healthy controls, except in ten patients in whom LG3 was increased. These ten patients had a more severe disease with lower glomerular filtration rate values. We found a significant inverse correlation between LG3 levels and glomerular filtration rate in the 43 patients with IgAN, which was not observed in 65 patients with other glomerular diseases including membranous nephropathy (23), lupus nephropathy (13), focal segmental glomerulosclerosis (15), diabetic nephropathy (14), and six patients with nonglomerular diseases. Therefore, we suggest that the LG3 fragment of endorepellin could be associated with IgAN severity and might be related to pathogenesis of IgAN.     

A comprehensive map of the human urinary proteome

The study of the human urinary proteome has the potential to offer significant insights into normal physiology as well as disease pathology. The information obtained from such studies could be applied to the diagnosis of various diseases. The high sensitivity, resolution, and mass accuracy of the latest generation of mass spectrometers provides an opportunity to accurately catalog the proteins present in human urine, including those present at low levels. To this end, we carried out a comprehensive analysis of human urinary proteome from healthy individuals using high-resolution Fourier transform mass spectrometry. Importantly, we used the Orbitrap for detecting ions in both MS (resolution 60 000) and MS/MS (resolution 15 000) modes. To increase the depth of our analysis, we characterized both unfractionated as well as lectin-enriched proteins in our experiments. In all, we identified 1,823 proteins with less than 1% false discovery rate, of which 671 proteins have not previously been reported as constituents of human urine. This data set should serve as a comprehensive reference list for future studies aimed at identification and characterization of urinary biomarkers for various diseases.     

Establishment of a near-standard two-dimensional human urine proteomic map

A proteomic map for human urine on two-dimensional (2-D) gels has been developed. Initial studies demonstrated that the urine proteins prepared by conventional methods showed interference and poor reproducibility in 2-D electrophoresis (2-DE). To address this issue, urine samples were dialyzed to remove any interfering molecules. The dialysis of urine proteins and the concentration by lyophilization without fractionation significantly improved the reproducibility and resolution and likely represents the total urine proteins on a 2-D gel. In addition, removing albumin from urine using Affi-Gel Blue helped to identify the low-abundant proteins. Using the developed method, we prepared proteins from urine collected from healthy females and males. The large inter- and intra-subject variation in protein profiles on 2-D gels made it difficult to establish a normal human urine proteomic 2-D map. To resolve this problem, urinary proteins were prepared from the pooled urine collected from 20 healthy females and males, respectively. The established male and female urine proteomes separated on 2-D gels were almost identical except for some potential sex-dependent protein spots. We have annotated 113 different proteins on the 2-D gel by peptide mass fingerprinting (PMF). We propose that the established total urine proteome can be used for 2-DE analysis, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and identification of novel disease-specific biomarkers.     

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.     

Discovery of lung cancer biomarkers by profiling the plasma proteome with monoclonal antibody libraries

A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC.     

Multiplexed quantification of 63 proteins in human urine by multiple reaction monitoring-based mass spectrometry for discovery of potential bladder cancer biomarkers

Three common urological diseases are bladder cancer, urinary tract infection, and hematuria. Seventeen bladder cancer biomarkers were previously discovered using iTRAQ - these findings were verified by MRM-MS in this current study. Urine samples from 156 patients with hernia (n=57, control), bladder cancer (n=76), or urinary tract infection/hematuria (n=23) were collected and subjected to multiplexed LC-MRM/MS to determine the concentrations of 63 proteins that are normally considered to be plasma proteins, but which include proteins found in our earlier iTRAQ study. Sixty-five stable isotope-labeled standard proteotypic peptides were used as internal standards for 63 targeted proteins. Twelve proteins showed higher concentrations in the bladder cancer group than in the hernia and the urinary tract infection/hematuria groups, and thus represent potential urinary biomarkers for detection of bladder cancer. Prothrombin had the highest AUC (0.796), with 71.1% sensitivity and 75.0% specificity for differentiating bladder cancer (n=76) from non-cancerous (n=80) patients. The multiplexed MRM-MS data was used to generate a six-peptide marker panel. This six-peptide panel (afamin, adiponectin, complement C4 gamma chain, apolipoprotein A-II precursor, ceruloplasmin, and prothrombin) can discriminate bladder cancer subjects from non-cancerous subjects with an AUC of 0.814, with a 76.3% positive predictive value, and a 77.5% negative predictive value. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.     

Concanavalin A-captured glycoproteins in healthy human urine

Both the urinary proteome and its glycoproteome can reflect human health status, and more directly, functions of kidney and urinary tracts. Because the high abundance protein albumin is not N-glycosylated, the urine N-glycoprotein enrichment procedure could deplete it, and urine proteome could thus provide a more detailed protein profile in addition to glycosylation information especially when albuminuria occurs in some kidney diseases. In terms of describing the details of urinary proteins, the urine glycoproteome is even a better choice than the proteome itself. Pooled urine samples from healthy volunteers were collected and acetone-precipitated for proteins. N-Linked glycoproteins enriched with concanavalin A affinity purification were separated and analyzed by SDS-PAGE-reverse phase LC/MS/MS or two-dimensional LC/MS/MS. A total of 225 urinary proteins were identified based on two-hit criteria with reliability over 97% for each peptide. Among these proteins, 94 were identified in previous urine proteome works, 150 were annotated as glycoproteins in Swiss-Prot, and 43 were predicted as glycoproteins by NetNGlyc 1.0. A number of known biomarkers and disease-related glycoproteins were identified. Because changes in protein quantity or the glycosylation status can lead to changes in the concanavalin A-captured glycoprotein profile, specific urine glycoproteome patterns might be observed for specific pathological conditions as multiplex urinary biomarkers. Knowledge of the urine glycoproteome is important in understanding kidney and body function.     

Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome

Urine is a readily and noninvasively obtainable body fluid. Mass spectrometry (MS)-based proteomics has shown that urine contains thousands of proteins. Urine is a potential source of biomarkers for diseases of proximal and distal tissues but it is thought to be more variable than the more commonly used plasma. By LC-MS/MS analysis on an LTQ-Orbitrap without prefractionation we characterized the urinary proteome of seven normal human donors over three consecutive days. Label-free quantification of triplicate single runs covered the urinary proteome to a depth of more than 600 proteins. The median coefficient of variation (cv) of technical replicates was 0.18. Interday variability was markedly higher with a cv of 0.48 and the overall variation of the urinary proteome between individuals was 0.66. Thus technical variability in our data was 7.5%, whereas intrapersonal variability contributed 45.5% and interpersonal variability contributed 47.1% to total variability. Determination of the normal fluctuation of individual urinary proteins should be useful in establishing significance thresholds in biomarker studies. Our data also allowed definition of a common and abundant set of 500 proteins that were readily detectable in all studied individuals. This core urinary proteome has a high proportion of secreted, membrane, and relatively high-molecular weight proteins.     

The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease

Aging induces morphological changes of the kidney and reduces renal function. We analyzed the low molecular weight urinary proteome of 324 healthy individuals from 2–73 years of age to gain insight on human renal aging. We observed age‐related modification of secretion of 325 out of over 5000 urinary peptides. The majority of these changes were associated with renal development before and during puberty, while 49 peptides were related to aging in adults. We therefore focussed the remainder of the study on these 49 peptides. The majority of these 49 peptides were also markers of chronic kidney disease, suggesting high similarity between aging and chronic kidney disease. Blinded evaluation of samples from healthy volunteers and diabetic nephropathy patients confirmed both the correlation of biomarkers with aging and with renal disease. Identification of a number of these aging‐related peptides led us to hypothesize that reduced proteolytic activity is involved in human renal aging. Finally, among the 324 supposedly healthy individuals, some had urinary aging‐related peptide excretion patterns typical of an individual significantly older than their actual age. In conclusion, these aging‐related biomarkers may allow noninvasive detection of renal lesions in healthy persons and show high resemblance between human aging and chronic kidney disease. This similarity has to be taken into account when searching for biomarkers of renal disease.     

Effects of cigarette smoking on the human urinary proteome

In this pilot study we used a proteomic approach to compare the urinary protein patterns of healthy smokers and non-smokers. Proteins were resolved by two-dimensional gel electrophoresis and identified by mass spectrometry. The relative abundance of three inflammatory proteins (S100A8, inter-alpha-trypsin inhibitor heavy chain 4, CD59) and that of two isoforms of pancreatic alpha amylase was significantly higher in smokers. Zinc-alpha-2-glycoprotein was the only protein down-regulated in smokers. Its abundance was significantly correlated with urinary glucocorticoids. Most of the proteins identified may be non-specific biomarkers of tobacco effects, since they are involved in inflammatory responses associated with several diseases. Of greater interest are the changes in abundance of pancreatic alpha amylase and zinc-alpha-2-glycoprotein, which after proper validation, might be candidate biomarkers of diseases resulting from exposure to tobacco smoke. The data also show for the first time that smoking can affect the expression profile of urinary proteins.     

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.     

Effects of Three Commonly-used Diuretics on the Urinary Proteome

bstract Biomarker is the measurable change associated with a physiological or pathophysiological process. Unlike blood which has mechanisms to keep the internal environment homeostatic,rine is more likely to reflect changes of the body. As a result, urine is likely to be a better biomarker ource than blood. However, since the urinary proteome is affected by many factors, including iuretics, careful evaluation of those effects is necessary if urinary proteomics is used for biomarker iscovery. Here, we evaluated the effects of three commonly-used diuretics(furosemide, F; hydrochlorothiazide,H; and spirolactone, S) on the urinary proteome in rats. Urine samples were collected before and after intragastric administration of diuretics at therapeutic doses and the roteomes were analyzed using label-free liquid chromatography–tandem mass spectrometry LC–MS/MS). Based on the criteria of P 6 0.05, a fold change P2, a spectral count P5, and false ositive rate(FDR) 61%, 14 proteins(seven for F, five for H, and two for S) were identified by rogenesis LC–MS. The human orthologs of most of these 14 proteins are stable in the healthy uman urinary proteome, and ten of them are reported as disease biomarkers. Thus, our results uggest that the effects of diuretics deserve more attention in future urinary protein biomarker tudies. Moreover, the distinct effects of diuretics on the urinary proteome may provide clues to     

Proteomic analysis of gingival crevicular fluid for discovery of novel periodontal disease markers

The protein composition of gingival crevicular fluid (GCF) may reflect the pathophysiology of periodontal diseases. A standard GCF proteomic pattern of healthy individuals would serve as a reference to identify biomarkers of periodontal diseases by proteome analyses. However, protein profiles of GCF obtained from apparently healthy individuals have not been well explored. As a step toward detection of proteomic biomarkers for periodontal diseases, we applied both gel-based and gel-free methods to analyze GCF obtained from healthy subjects as compared with supragingival saliva. To ensure optimized protein extraction from GCF, a novel protocol was developed. The proteins in GCF were extracted with high yield by urea buffer combined with ultrafiltration and the intensity of spots with supragingival saliva and GCF was compared using agarose two-dimensional electrophoresis. Eight protein spots were found to be significantly more intense in GCF. They included superoxide dismutase 1 (SOD1), apolipoprotein A-I (ApoA-I), and dermcidin (DCD). Moreover, GCF proteins from healthy subjects were broken down into small peptide fragments and then analyzed directly by LC-MS/MS analysis. A total of 327 proteins including ApoA-I, SOD1, and DCD were identified in GCF. These results may serve as reference for future proteomic studies searching for GCF biomarkers of periodontal diseases.     

Human urine proteomics: building a list of human urine cancer biomarkers

In the last decade, several reports have focused on the identification and characterization of proteins present in urine. In an effort to build a list of proteins of interest as biomarkers, we reviewed the largest urine proteomes and built two updated lists of proteins of interest (available as supplementary tables). The first table includes a consensus list of 443 proteins found in urine by independent laboratories and reported on the top three largest urine proteomes currently published. This consensus list of proteins could serve as biomarkers to diagnose, monitor and manage a number of diseases. Here, we focus on a reduced list of 35 proteins with potential interest as cancer biomarkers in urine following two criteria: first, proteins previously detected in urine using bottom-up proteomic experiments, and second, those suggested as cancer protein biomarkers in human plasma. In an effort to standardize the information presented and its use in future studies, here we include the updated International Protein Index (v. 3.80) and primary Swiss-Prot accession numbers, official gene symbols and recommended full names. The main variables that influence urine proteomic experiments are also discussed.     

Serum protein profiling to identify biomarkers for small renal cell carcinoma

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.