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.

Proteomics and systems biology: current and future applications in the nutritional sciences

In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences.

Human urinary peptide database for multiple disease biomarker discovery

PURPOSE

Human urine is an ideal candidate for use in clinical diagnostics. It is easily available, as untrained personnel can collect it. It correlates well with the pathophysiology of a number of diseases, making it a useful source for clinical proteomics.

EXPERIMENTAL DESIGN

In this article, we give an update of the human urinary peptide database derived from over 13,000 data sets of CE-MS by now.

RESULTS

Urine samples from both patients and healthy subjects were analyzed by CE-MS; these included 47 different pathophysiological conditions. Besides defining biomarkers by their experimental parameters, information on their sequences provides fundamental data into the pathological pathways of diseases. Therefore, we have sequenced 953 urinary peptides by using state-of-the-art top-down MS/MS. Identified biomarkers of all clinical proteomic CE-MS studies including their regulation are also listed in this work.

CONCLUSIONS AND CLINICAL RELEVANCE

Biomarker discovery can be used in the management of a wide range of diseases, by combining these data sets of the database. Taking this approach, we can reveal details, at a molecular level, on the pathogenesis of a number of diseases, in particular those associated with urine production and excretion.

Urine proteomics in kidney and urogenital diseases: Moving towards clinical applications

To date, multiple biomarker discovery studies in urine have been conducted. Nevertheless, the rate of progression of these biomarkers to qualification and even more clinical application is extremely low. The scope of this article is to provide an overview of main clinically relevant proteomic findings from urine focusing on kidney diseases, bladder and prostate cancers. In addition, approaches for promoting the use of urine in clinical proteomics including potential means to facilitate the validation of existing promising findings (biomarker candidates identified from previous studies) and to increase the chances for success for the identification of new biomarkers are discussed.

Searching for the noninvasive biomarker holy grail: are urine proteomics the answer?

Recently, biobehavioral nursing scientists have focused their attention on the search for biomarkers or biological signatures to identify patients at risk for various health problems and poor disease outcomes. In response to the national impetus for biomarker discovery, the measurement of biological fluids and tissues has become increasingly sophisticated. Urine proteomics, in particular, may hold great promise for biobehavioral focused nursing scientists for examination of symptom-and syndrome-related research questions. Urine proteins are easily accessible secreted proteins that provide direct and indirect windows into bodily functions. Advances in proteomics and biomarker discovery provide new opportunities to conduct research studies with banked and fresh urine to benefit diagnosis, prognosis, and evaluation of outcomes in various disease populations. This article provides a review of proteomics and a rationale for utilizing urine proteomics in biobehavioral research. It addresses as well some of the challenges involved in data collection and sample preparation.

Urinary proteomics in cardiovascular disease: Achievements, limits and hopes

Cardiovascular disease (CVD) is the major cause of mortality and morbidity worldwide. Diagnosis of CVD and risk stratification of patients with CVD remains challenging despite the availability of a wealth of non-invasive and invasive tests. Clinical proteomics analyses a large number of peptides and proteins in biofluids. For clinical applications, the urinary proteome appears particularly attractive due to the relative low complexity compared with the plasma proteome and the noninvasive collection of urine. In this article, we review the results from pilot studies into urinary proteomics of coronary artery disease and discuss the potential of urinary proteomics in the context of pathogenesis of CVD.

Bile proteomic profiles differentiate cholangiocarcinoma from primary sclerosing cholangitis and choledocholithiasis

Early detection of malignant biliary tract diseases, especially cholangiocarcinoma (CC) in patients with primary sclerosing cholangitis (PSC), is very difficult and often comes too late to give the patient a therapeutic benefit. We hypothesize that bile proteomic analysis distinguishes CC from nonmalignant lesions. We used capillary electrophoresis mass spectrometry (CE-MS) to identify disease-specific peptide patterns in patients with choledocholithiasis (n = 16), PSC (n = 18), and CC (n = 16) in a training set. A model for differentiation of choledocholithiasis from PSC and CC (PSC/CC model) and another model distinguishing CC from PSC (CC model) were subsequently validated in independent cohorts (choledocholithiasis [n = 14], PSC [n = 18] and CC [n = 25]). Peptides were characterized by sequencing. Application of the PSC/CC model in the independent test cohort resulted in correct exclusion of 12/14 bile samples from patients with choledocholithiasis and identification of 40/43 patients with PSC or CC (86% specificity, 93% sensitivity). The corresponding receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.93 (95% confidence interval [CI]: 0.82-0.98, P = 0.0001). The CC model succeeded in an accurate detection of 14/18 bile samples from patients with PSC and 21/25 samples with CC (78% specificity, 84% sensitivity) in the independent cohort, resulting in an AUC value of 0.87 (95% CI: 0.73-0.95, P = 0.0001) in ROC analysis. Eight out of 10 samples of patients with CC complicating PSC were identified.

CONCLUSION

Bile proteomic analysis discriminates benign conditions from CC accurately. This method may become a diagnostic tool in future as it offers a new possibility to diagnose malignant bile duct disease and thus enables efficient therapy particularly in patients with PSC.

Urinary Proteomics for Prediction of Preeclampsia

Preeclampsia is a major determinant of fetal and maternal morbidity and mortality. We used a proteomic strategy to identify urinary biomarkers that predict preeclampsia before the onset of disease. We prospectively collected urine samples from women throughout pregnancy. Samples from gestational weeks 12 to 16 (n=45), 20 (n=50), and 28 (n=18) from women who subsequently had preeclampsia develop were matched to controls (n=86, n=49, and n=17, respectively). We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Disease-specific peptide patterns were generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. From comparison with nonpregnant controls, we defined a panel of 284 pregnancy-specific proteomic biomarkers. Subsequently, we developed a model of 50 biomarkers from specimens obtained at week 28 that was associated with future preeclampsia (classification factor in cases, 1.032±0.411 vs controls, −1.038±0.432; P <0.001). Classification factor increased markedly from week 12 to 16 to 28 in women who subsequently had preeclampsia develop (n=16; from −0.392±0.383 to 1.070±0.383; P <0.001) and decreased slightly in controls (n=16; from −0.647±0.437 to −1.024±0.433; P =0.043). Among the biomarkers are fibrinogen alpha chain, collagen alpha chain, and uromodulin fragments. The markers appear to predict preeclampsia at gestational week 28 with good confidence but not reliably at earlier time points (weeks 12–16 and 20). After prospective validation in other cohorts, these markers may contribute to better prediction, monitoring, and accurate diagnosis of preeclampsia.

A computational method for prediction of excretory proteins and application to identification of gastric cancer markers in urine

A novel computational method for prediction of proteins excreted into urine is presented. The method is based on the identification of a list of distinguishing features between proteins found in the urine of healthy people and proteins deemed not to be urine excretory. These features are used to train a classifier to distinguish the two classes of proteins. When used in conjunction with information of which proteins are differentially expressed in diseased tissues of a specific type versus control tissues, this method can be used to predict potential urine markers for the disease. Here we report the detailed algorithm of this method and an application to identification of urine markers for gastric cancer. The performance of the trained classifier on 163 proteins was experimentally validated using antibody arrays, achieving >80% true positive rate. By applying the classifier on differentially expressed genes in gastric cancer vs normal gastric tissues, it was found that endothelial lipase (EL) was substantially suppressed in the urine samples of 21 gastric cancer patients versus 21 healthy individuals. Overall, we have demonstrated that our predictor for urine excretory proteins is highly effective and could potentially serve as a powerful tool in searches for disease biomarkers in urine in general.

Challenges and opportunities for discovery of disease biomarkers using urine proteomics

Modern medicine has experienced a tremendous explosion in knowledge about disease pathophysiology, gained largely from understanding the molecular biology of human disease. Recent advances in mass spectrometry and proteomics now allow for simultaneous identification and quantification of thousands of unique proteins and peptides in complex biological tissues and fluids. In particular, proteomic studies of urine benefit from urine's less complex composition as compared to serum and tissues, and have been used successfully to discover novel markers of a variety of infectious, autoimmune, oncological, and surgical conditions. This perspective discusses the challenges of such studies that stem from the compositional variability and complexity of human urine, as well as instrumental sampling limitations and the effects of noise and selection bias. Strategies for the design of observational clinical trials, physical and chemical fractionation of urine specimens, mass spectrometry analysis, and functional data annotation are outlined. Rigorous translational investigations using urine proteomics are likely to discover novel and accurate markers of both rare and common diseases. This should aid the diagnosis, improve stratification of therapy, and identify novel therapeutic targets for a variety of childhood and adult diseases, all of which will be essential for the development of personalized and predictive medicine of the future.

Highlights from the 2010 BAS/BSCR spring meeting: New Frontiers in Cardiovascular Research

The British Atherosclerosis Society (BAS)/British Society for Cardiovascular Research (BSCR) spring meeting was held in Manchester, UK, on 7-8 June 2010. Experts in the field of systems biology, proteomics, metabolomics and miRNAs presented how these techniques can be used to discover 'New Frontiers in Cardiovascular Research'. The conference was attended by over 150 participants, mainly from the UK. A total of 2 days of presentations and a poster session with 55 posters provided the possibility to discuss the latest research results and showed the opportunities that new techniques can offer in cardiovascular research.

Capillary electrophoresis-mass spectrometry for the analysis of intact proteins 2007-2010

CE coupled to MS has proven to be a powerful analytical tool for the characterization of intact proteins, as it combines the high separation efficiency of CE with the selectivity of MS. This review provides an overview of the development and application of CE-MS methods within the field of intact protein analysis as published between January 2007 and June 2010. Ongoing technological developments with respect to CE-MS interfacing, capillary coatings for CE-MS, coupling of CIEF with MS and chip-based CE-MS are treated. Furthermore, CE-MS of intact proteins involving ESI, MALDI and ICP ionization is outlined and overviews of the use of the various CE-MS methods are provided by tables. Representative examples illustrate the applicability of CE-MS for the characterization of proteins, including glycoproteins, biopharmaceuticals, protein-ligand complexes, biomarkers and dietary proteins. It is concluded that CE-MS is a valuable technique with high potential for intact protein analysis, providing useful information on protein identity and purity, including modifications and degradation products.

Urine peptidomics for clinical biomarker discovery

Urine-based proteomic profiling is a novel approach that may result in the discovery of noninvasive biomarkers for diagnosing patients with different diseases, with the aim to ultimately improve clinical outcomes. Given new and emerging analytical technologies and data mining algorithms, the urine peptidome has become a rich resource to uncover naturally occurring peptide biomarkers for both systemic and renal diseases. However, significant analytical hurdles remain in sample collection and storage, experimental design, data analysis, and statistical inference. This study summarizes, focusing on our experiences and perspectives, the progress in addressing these challenges to enable high-throughput urine peptidomics-based biomarker discovery.

Urinary collagen fragments are significantly altered in diabetes: a link to pathophysiology

Background

The pathogenesis of diabetes mellitus (DM) is variable, comprising different inflammatory and immune responses. Proteome analysis holds the promise of delivering insight into the pathophysiological changes associated with diabetes. Recently, we identified and validated urinary proteomics biomarkers for diabetes. Based on these initial findings, we aimed to further validate urinary proteomics biomarkers specific for diabetes in general, and particularity associated with either type 1 (T1D) or type 2 diabetes (T2D).

Methodology/Principal Findings

Therefore, the low-molecular-weight urinary proteome of 902 subjects from 10 different centers, 315 controls and 587 patients with T1D (n = 299) or T2D (n = 288), was analyzed using capillary-electrophoresis mass-spectrometry. The 261 urinary biomarkers (100 were sequenced) previously discovered in 205 subjects were validated in an additional 697 subjects to distinguish DM subjects (n = 382) from control subjects (n = 315) with 94% (95% CI: 92–95) accuracy in this study. To identify biomarkers that differentiate T1D from T2D, a subset of normoalbuminuric patients with T1D (n = 68) and T2D (n = 42) was employed, enabling identification of 131 biomarker candidates (40 were sequenced) differentially regulated between T1D and T2D. These biomarkers distinguished T1D from T2D in an independent validation set of normoalbuminuric patients (n = 108) with 88% (95% CI: 81–94%) accuracy, and in patients with impaired renal function (n = 369) with 85% (95% CI: 81–88%) accuracy. Specific collagen fragments were associated with diabetes and type of diabetes indicating changes in collagen turnover and extracellular matrix as one hallmark of the molecular pathophysiology of diabetes. Additional biomarkers including inflammatory processes and pro-thrombotic alterations were observed.

Conclusions/Significance

These findings, based on the largest proteomic study performed to date on subjects with DM, validate the previously described biomarkers for DM, and pinpoint differences in the urinary proteome of T1D and T2D, indicating significant differences in extracellular matrix remodeling.

Gene expression profiling in whole blood of patients with coronary artery disease

Owing to the dynamic nature of the transcriptome, gene expression profiling is a promising tool for discovery of disease-related genes and biological pathways. In the present study, we examined gene expression in whole blood of 12 patients with CAD (coronary artery disease) and 12 healthy control subjects. Furthermore, ten patients with CAD underwent whole-blood gene expression analysis before and after the completion of a cardiac rehabilitation programme following surgical coronary revascularization. mRNA and miRNA (microRNA) were isolated for expression profiling. Gene expression analysis identified 365 differentially expressed genes in patients with CAD compared with healthy controls (175 up- and 190 down-regulated in CAD), and 645 in CAD rehabilitation patients (196 up- and 449 down-regulated post-rehabilitation). Biological pathway analysis identified a number of canonical pathways, including oxidative phosphorylation and mitochondrial function, as being significantly and consistently modulated across the groups. Analysis of miRNA expression revealed a number of differentially expressed miRNAs, including hsa-miR-140-3p (control compared with CAD, P=0.017), hsa-miR-182 (control compared with CAD, P=0.093), hsa-miR-92a and hsa-miR-92b (post- compared with pre-exercise, P<0.01). Global analysis of predicted miRNA targets found significantly reduced expression of genes with target regions compared with those without: hsa-miR-140-3p (P=0.002), hsa-miR-182 (P=0.001), hsa-miR-92a and hsa-miR-92b (P=2.2×10⁻¹⁶). In conclusion, using whole blood as a ‘surrogate tissue’ in patients with CAD, we have identified differentially expressed miRNAs, differentially regulated genes and modulated pathways which warrant further investigation in the setting of cardiovascular function. This approach may represent a novel non-invasive strategy to unravel potentially modifiable pathways and possible therapeutic targets in cardiovascular disease.

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.

Urinary proteomics--a tool for biomarker discovery

The strong need for the discovery of novel disease markers together with the development of high-throughput techniques that provide highly sensitive analysis of protein content in tissues and bodily fluids, using proteomics, has opened the completely new chapter in biomarker discovery. The detection of biomarkers based on urinary proteome analysis is rapidly advancing and may provide new tools to improve non-invasive diagnostics, prognostics, and therapy enhancement. As a tool for biomarker discovery, urinary proteomics is especially fruitful in the area of early diagnostics and differentiation of renal damage, and it possesses enormous potential for improving and expanding non-invasive cancer diagnostics. An abundance of urinary proteins could provide a wide variety of biomarkers for the diagnosis and follow-up of many systemic diseases as well. This article reviews the utility of urinary proteomics for biomarker discovery from the perspective of clinical application. Despite huge potential and prompt development of urinary proteomics, many challenges are still in front of us. Research effort and financial investment have to be oriented on providing strategies for exceeding current methodological and technical obstacles in a way to ensure the successful validation and implementation of newly discovered urinary biomarkers. The result is expected to be the development of new non-invasive tests and procedures able to guarantee higher efficiency of patient care and provide needed personalized medical approach.

The feasibility of MS and advanced data processing for monitoring Schistosoma mansoni infection

PURPOSE

Sensitive diagnosis, monitoring of disease progression and the evaluation of chemotherapeutic interventions are of prime importance for the improvement of control and prevention strategies for Schistosomiasis. The aim of the present study was to identify novel markers of Schistosoma mansoni infection and disease using urine samples from a large cohort from an area endemic for S. mansoni.

EXPERIMENTAL DESIGN

Urine samples were collected and processed on an automated sample clean-up and fractionation system combining strong cation exchange and reversed phase, and analyzed by MS (MALDI ToF MS). The ClinPro Tools(™) (CPT) software and the Discrete Wavelet Transformation-Support Vector Machine (DWT-SVM) procedure were used for classification and statistical analysis.

RESULTS

We observed a large difference in urinary peptide profiles between children and adults but classification based on infection was possible only for children. Here, in the external validation data set, 93% of the infected children were classified correctly with DWT-SVM (versus 76% for CPT). In addition 91% of low-infected children were classified correctly using DWT-SVM (versus 85% for CPT). The discriminating peptides were identified as fragments of collagen 1A1 and 1A3, and uromodulin.

CONCLUSIONS AND CLINICAL RELEVANCE

In conclusion, we provide the usefulness of a peptidomics profiling approach combined with DWT-SVM in the monitoring of S. mansoni infection.

Comprehensive human urine standards for comparability and standardization in clinical proteome analysis

PURPOSE

Urine proteomics is emerging as a powerful tool for biomarker discovery. The purpose of this study is the development of a well-characterized "real life" sample that can be used as reference standard in urine clinical proteomics studies.

EXPERIMENTAL DESIGN

We report on the generation of male and female urine samples that are extensively characterized by different platforms and methods (CE-MS, LC-MS, LC-MS/MS, 1-D gel analysis in combination with nano-LC MS/MS (using LTQ-FT ultra), and 2-DE-MS) for their proteome and peptidome. In several cases analysis involved a definition of the actual biochemical entities, i.e. proteins/peptides associated with molecular mass and detected PTMs and the relative abundance of these compounds.

RESULTS

The combination of different technologies allowed coverage of a wide mass range revealing the advantages and complementarities of the different technologies. Application of these samples in "inter-laboratory" and "inter-platform" data comparison is also demonstrated.

CONCLUSIONS AND CLINICAL RELEVANCE

These well-characterized urine samples are freely available upon request to enable data comparison especially in the context of biomarker discovery and validation studies. It is also expected that they will provide the basis for the comprehensive characterization of the urinary proteome.

Analysis of peptides in biological fluids by LC-MS/MS

Urine contains large amounts of small peptides, which may represent a rich, yet largely unexplored, source of novel biomarkers for disease monitoring. This chapter describes detailed procedures for the analysis of urinary polypeptides by LC-MS/MS. Hundreds to thousands of small peptides (∼700 to ∼7000 Da) can be detected in urine with the described techniques. Extraction procedures, based on commercially available reagents, effectively remove interfering urinary organic and inorganic salts and neutral compounds, making this a robust and simple assay with the power to detect hundreds to thousands of polypeptides in urine. Analysis time is relatively short, making this protocol a valuable alternative to conventional proteomic techniques based on multidimensional separations. The methodology is therefore particularly useful when the aim is to analyse samples with sufficient depth and throughput so as to make it useful to compare large numbers of specimens. Procedures for enhancing quantitative and qualitative analysis of LC-MS/MS data are also detailed.

Advances in urinary proteome analysis and biomarker discovery in pediatric renal disease

Recent progress in proteomic analysis and strategies for the identification of clinically useful biomarkers in biofluids has led to the identification of urine as an excellent non-invasive reservoir for biomarkers of disease. Urinary biomarkers have been identified and validated on independent cohorts in different high-incidence adult renal diseases, including diabetic nephropathy, chronic kidney disease and immunoglobulin A-nephropathy, but also in extrarenal disease, such as coronary artery disease. Unfortunately, this type of research is underrepresented in the pediatric population. Here, we present the rare studies in the pediatric population that identified potential clinically useful urinary biomarkers in ureteropelvic junction (UPJ) obstruction and renal Fanconi syndrome. These studies, although limited in number, clearly show the potential of urinary proteomics, especially in the pediatric population. It is anticipated that the advances made in the adult population, the lessons learned on the use of appropriate statistics and the inclusion of independent blinded validation cohorts in these types of studies will rapidly lead to clinical useful urinary biomarkers for other pediatric (renal) disease in a population where non-invasive analysis is particularly appreciated.

Standardized preprocessing of urine for proteome analysis

Proteome/peptidome profiling of human urine is a promising tool for the discovery of novel disease-associated biomarkers. However, a wide range of preanalytic variables influence the results of proteome/peptidome analysis regardless of the method used. We present a validated pretreatment protocol, which allows standardization of preanalytic modalities and facilitates reproducible peptidome profiling of human urine by means of magnetic bead (MB) separation in combination with MALDI-TOF MS. Such a procedure is necessary for generating consistent and reliable data from which meaningful results may be obtained for biomarker discovery and general proteomic experiments.

Urinary Proteome Analysis using Capillary Electrophoresis Coupled to Mass Spectrometry: A Powerful Tool in Clinical Diagnosis, Prognosis and Therapy Evaluation

Proteome analysis has emerged as a powerful tool to decipher (patho) physiological processes, resulting in the establishment of the field of clinical proteomics. One of the main goals is to discover biomarkers for diseases from tissues and body fluids. Due to the enormous complexity of the proteome, a separation step is required for mass spectrometry (MS)-based proteome analysis. In this review, the advantages and limitations of protein separation by two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization and capillary electrophoresis (CE) for proteomic analysis are described, focusing on CE-MS. CE-MS enables separation and detection of the small molecular weight proteome in biological fluids with high reproducibility and accuracy in one single processing step and in a short time. As sensitive and specific single biomarkers generally may not exist, a strategy to overcome this diagnostic void is shifting from single analyte detection to simultaneous analysis of multiple analytes that together form a disease-specific pattern. Such approaches, however, are accompanied with additional challenges, which we will outline in this review. Besides the choice of adequate technological platforms, a high level of standardization of proteomic measurements and data processing is also necessary to establish proteomic profiling. In this regard, demands concerning study design, choice of specimens, sample preparation, proteomic data mining, and clinical evaluation should be considered before performing a proteomic study.

Urine cytokines suggest an inflammatory response in the overactive bladder: a pilot study

PURPOSE

To study the hypothesis of detecting bladder inflammation associated with overactive bladder (OAB) through altered urine levels of cytokines, chemokines, and growth factors.

METHODS

Midstream urine specimens were collected from a prospective study done on eight asymptomatic control subjects and 17 idiopathic OAB patients. The urine was analyzed by a multiplex panel screen for 12 chemokines, cytokines, growth factors, and soluble receptors using Luminex™ xMAP(®) technology. Protein concentration values were normalized to the levels of creatinine.

RESULTS

This analysis revealed a significant elevation of seven key proteins in the urine of OAB patients relative to controls (*P < 0.05). A greater than tenfold elevation was measured in OAB, relative to controls, in the levels of monocyte chemotactic protein-1 (MCP-1), soluble fraction of the CD40 ligand (sCD40L) in urine was obtained from OAB patients relative to controls. At least five fold elevations were detected in the levels of macrophage inflammatory protein (MIP-1β), IL-12p70/p40, IL-5, epidermal growth factor (EGF), and growth-related oncogene GRO-α compared to controls. Significant threefold elevation was also noticed in the urine levels of sIL-2Rα, and IL-10 in the OAB group. The levels of the remaining proteins tested were not statistically significantly different from control values.

CONCLUSIONS

The presence of elevated levels in urine of inflammatory biomarkers involved in inflammation and tissue repair suggests a role for inflammation in OAB, and may help in diagnosis and treatment of this disease.

Industrialized MS-based proteomics in the search for circulating biomarkers

Proteomics is the study of the expression, structure and function of proteins under a range of cellular conditions. A rapidly evolving component of this field is clinical proteomics, which focuses on proteins involved in human disease and how they are affected by therapeutic intervention. MS is the main analytical technology for identifying and quantifying proteins whose expression is modulated across the normal to disease continuum. Applying this technology to clinical samples, however, is particularly challenging due to high biological variability in the population, a variety of disease stages, nonuniform response to therapy, multiple concomitant treatments and special requirements for handling samples from clinical trials. Given these challenges, an ‘industrialized’ approach is best suited to clinical biomarker development, with its standard operating procedures, process control and ‘chain of custody’. This review will focus, therefore, on MS-based industrialized proteomics for the discovery and verification of circulating candidate clinical protein biomarkers.

Capillary electrophoresis-mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: an update of recent developments

Proteome analysis has emerged as a powerful technology to decipher biological processes. One of the main goals is to discover biomarkers for diseases from tissues and body fluids. However, the complexity and wide dynamic range of protein expression present an enormous challenge to separation technologies and mass spectrometry (MS). In this review, we examine the limitations of proteomics, and aim towards the definition of the current key prerequisites. We focus on capillary electrophoresis coupled to mass spectrometry (CE-MS), because this technique continues to show great promise. We discuss CE-MS from an application point of view, and evaluate its merits and vices for biomarker discovery and clinical applications. Finally, we present several examples on the use of CE-MS to determine urinary biomarkers and implications for disease diagnosis, prognosis, and therapy evaluation.

Sources of Urinary Proteins and their Analysis by Urinary Proteomics for the Detection of Biomarkers of Disease

Renal disorders account for a substantial fraction of the budget for health care in many countries. Proteinuria is a frequent manifestation in afflicted patients, but the origin of the proteins varies based on the nature of the disorder. The emerging field of urinary proteomics has the potential to replace kidney biopsy as the diagnostic procedure of choice for patients with some glomerular forms of renal disease. To fully realize this potential, it is vital to understand the basis for the urinary excretion of protein in physiological and pathological conditions. In this review, we discuss the structure of the nephron, the functional unit of the kidney, and the process by which proteins/peptides enter the urine. We discuss several aspects of proteinuria that impact the proteomic analysis of urine of patients with renal diseases.

Profiling of rat urinary proteomic patterns associated with drug-induced nephrotoxicity using CE coupled with MS as a potential model for detection of drug-induced adverse effects

We have investigated urine obtained from Sprague Dawley rats before and after administration of cis-Platin, aiming at the definition of biomarkers for drug-induced cytotoxicity. Rats were treated with 3 or 6 mg/kg cis-Platin (i.p., single injection) and urine samples were collected before and after drug or saline treatment. Analysis of the low molecular weight proteome (<20 kDa) using capillary-electrophoresis coupled mass spectrometry allowed us to tentatively identify 34 urinary peptides that show significant differences between control and treated animals, and hence may serve as a potential biomarker for cis-Platin-induced nephrotoxicity. These biomarkers were confirmed in a blinded assessment of additional samples. The blinded data also revealed time-dependency of induced changes. Some of the potential biomarkers could be sequenced. This information revealed great similarity between cis-Platin-induced changes and significant changes in the urinary proteome of patients suffering from tubular injury (Fanconi syndrome). Our study strongly suggests that (drug-induced) nephrotoxicity can be detected with high accuracy in laboratory rodents using urinary proteome analysis. The effects observed are very similar to those seen in corresponding human diseases and similar approaches may be very helpful in evaluating drug-induced organ damage in preclinical animal models. This study aiming at the definition of biomarkers for drug-induced cytotoxicity may serve as a proof-of-principle for the use of urinary proteomics in assessment of drug-induced nephrotoxicity.

The non-invasive biopsy--will urinary proteomics make the renal tissue biopsy redundant?

Proteomics is a rapidly advancing technique which gives functional insight into gene expression in living organisms. Urine is an ideal medium for study as it is readily available, easily obtained and less complex than other bodily fluids. Considerable progress has been made over the last 5 years in the study of urinary proteomics as a diagnostic tool for renal disease. Advantages over the traditional renal biopsy include accessibility, safety, the possibility of serial sampling and the potential for non-invasive prognostic and diagnostic monitoring of disease and an individual's response to treatment. Urinary proteomics is now moving from a discovery phase in small studies to a validation phase in much larger numbers of patients with renal disease. Whilst there are still some limitations in methodology, which are assessed in this review, the possibility of urinary proteomics replacing the invasive tissue biopsy for diagnosis of renal disease is becoming an increasingly realistic option.

Prediction of muscle-invasive bladder cancer using urinary proteomics

PURPOSE

Minimally invasive methods of predicting the risk of muscle-invasive urothelial bladder carcinoma may expedite appropriate therapy and reduce morbidity and cost.

EXPERIMENTAL DESIGN

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.

RESULTS

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.

CONCLUSIONS

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.

Identification and validation of urinary biomarkers for differential diagnosis and evaluation of therapeutic intervention in anti-neutrophil cytoplasmic antibody-associated vasculitis

Renal activity and smoldering disease is difficult to assess in anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) because of renal scarring. Even repeated biopsies suffer from sampling errors in this focal disease especially in patients with chronic renal insufficiency. We applied capillary electrophoresis coupled to mass spectrometry toward urine samples from patients with active renal AAV to identify and validate urinary biomarkers that enable differential diagnosis of disease and assessment of disease activity. The data were compared with healthy individuals, patients with other renal and non-renal diseases, and patients with AAV in remission. 113 potential biomarkers were identified that differed significantly between active renal AAV and healthy individuals and patients with other chronic renal diseases. Of these, 58 could be sequenced. Sensitivity and specificity of models based on 18 sequenced biomarkers were validated using blinded urine samples of 40 patients with different renal diseases. Discrimination of AAV from other renal diseases in blinded samples was possible with 90% sensitivity and 86.7-90% specificity depending on the model. 10 patients with active AAV were followed for 6 months after initiation of treatment. Immunosuppressive therapy led to a change of the proteome toward "remission." 47 biomarkers could be sequenced that underwent significant changes during therapy together with regression of clinical symptoms, normalization of C-reactive protein, and improvement of renal function. Proteomics analysis with capillary electrophoresis-MS represents a promising tool for fast identification of patients with active AAV, indication of renal relapses, and monitoring for ongoing active renal disease and remission without renal biopsy.

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.