High-resolution proteome/peptidome analysis of peptides and low-molecular-weight proteins in urine

Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer

Liquid biopsy, as a novel noninvasive tool for biomarker discovery, has gained a lot of attention and represents a significant innovation in precision medicine. Due to its minimally invasive nature, liquid biopsy has fewer complications and can be scheduled more frequently to provide individualized snapshots of the disease at successive time points. This is particularly valuable in providing simultaneous measurements of tumor burden during treatment and early detection of tumor recurrence or drug resistance. Blood-based liquid biopsy is an attractive, minimally invasive alternative, which has shown promise in diagnosis, risk stratification, disease monitoring, and more. Urine has gained popularity due to its less invasive sampling, the ability to easily repeat samples, and the ability to track tumor evolution in real time, making it a powerful tool for diagnosis and treatment monitoring, especially in urologic cancers. In this review, we provide a detailed discussion on the potential clinical applications of prostate cancer (PCa) and bladder cancer (BCa), with cell-free DNA (cfDNA), microRNAs (miRNAs), proteins, and peptides as liquid biopsy biomarkers.

Urinary matrix Gla protein is associated with mortality risk in Flemish population: A prospective study

Background

Vascular calcification is strongly related to the risk of mortality and cardiovascular (CV) diseases. In vascular calcification, matrix Gla protein (MGP), a small vitamin K-dependent protein, is an important mineralization inhibitor. Recent studies showed that circulating MGP is associated with mortality risk. However, the longitudinal association between urinary excretion of MGP and all-cause mortality was not established.

Materials and methods

Urinary MGP was measured in 776 randomly recruited Flemish population (mean age: 51.2 years; 50.9% women) at baseline (during 2005–2010) using capillary electrophoresis coupled with mass spectrometry. Plasma inactive MGP [desphospho-uncarboxylated MGP (dp-ucMGP)] levels were quantified in 646 individuals by ELISA kits. Mortality status was ascertained through the Belgian Population Registry until 2016. The longitudinal association with mortality was determined by the multivariate-adjusted Cox proportional hazards regression models. The multivariate linear regression models were used to identify determinants of urinary MGP level.

Results

Over the 9.2 years, 47 (6.06%) participants died, including 15 CV deaths. For a doubling of urinary MGP, the hazard ratios (HRs) were 1.31 (95% CI: 1.01–1.69, P = 0.040) for all-cause mortality and 2.05 (95% CI: 1.11–3.79, P = 0.023) for CV mortality with adjustment for covariates, including estimated glomerular filtration rate and urine microalbumin. The addition of urinary MGP to the basic models improved the reclassification as suggested by the increased net reclassification improvement [64.01% (95% CI: 32.64–98.63)] and integrated discrimination improvement [2.33% (95% CI: 0.24–4.71)]. Circulating inactive MGP, total cholesterol, urine microalbumin, and smoking were significantly associated with urinary MGP levels (P ≤ 0.041), independent of sex and age.

Conclusion

Elevated urinary MGP was associated with an increased risk of all-cause mortality and CV mortality and improved the risk reclassification for all-cause mortality. These findings suggested that urinary MGP might be useful in mortality risk assessment in the general population. However, these observations need to be replicated in larger studies with a longer follow-up time.

Urinary Proteomic Profile of Arterial Stiffness Is Associated With Mortality and Cardiovascular Outcomes

Background

The underlying mechanisms of arterial stiffness remain not fully understood. This study aimed to identify a urinary proteomic profile to illuminate its pathogenesis and to determine the prognostic value of the profile for adverse outcomes.

Methods and Results

We measured aortic stiffness using pulse wave velocity (PWV) and analyzed urinary proteome using capillary electrophoresis coupled with mass spectrometry in 669 randomly recruited Flemish patients (mean age, 50.2 years; 51.1% women). We developed a PWV‐derived urinary proteomic score (PWV‐UP) by modeling PWV with proteomics data at baseline through orthogonal projections to latent structures. PWV‐UP that consisted of 2336 peptides explained the 65% variance of PWV, higher than 36% explained by clinical risk factors. PWV‐UP was significantly associated with PWV (adjusted β=0.73 [95% CI, 0.67–0.79]; P<0.0001). Over 9.2 years (median), 36 participants died, and 75 experienced cardiovascular events. The adjusted hazard ratios (+1 SD) were 1.46 (95% CI, 1.08–1.97) for all‐cause mortality, 2.04 (95% CI, 1.07–3.87) for cardiovascular mortality, and 1.39 (95% CI, 1.11–1.74) for cardiovascular events (P≤0.031). For PWV, the corresponding estimates were 1.25 (95% CI, 0.97–1.60), 1.35 (95% CI, 0.85–2.15), and 1.22 (95% CI, 1.02–1.47), respectively (P≥0.033). Pathway analysis revealed that the peptides in PWV‐UP mostly involved multiple pathways, including collagen turnover, cell adhesion, inflammation, and lipid metabolism.

Conclusions

PWV‐UP was highly associated with PWV and could be used as a biomarker of arterial stiffness. PWV‐UP, but not PWV, was associated with all‐cause mortality and cardiovascular mortality, implying that PWV‐UP–associated peptides may be multifaceted and involved in diverse pathological processes beyond arterial stiffness.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Gastrointestinal Digestion Model Assessment of Peptide Diversity and Microbial Fermentation Products of Collagen Hydrolysates

Osteoarthritis (OA), the most common form of arthritis, is associated with metabolic diseases and gut microbiome dysbiosis. OA patients often take supplements of collagen hydrolysates (CHs) with a high peptide content. Following digestion, some peptides escape absorption to induce prebiotic effects via their colonic fermentation to generate short-chain fatty acids (SCFAs), branched-chain fatty acids (BCFAs) and colonic gases (NH₄ and H₂S). The capacity of CHs to generate microbial metabolites is unknown. Proteomic analysis of two CHs (CH-GL and CH-OPT) demonstrated different native peptide profiles with increased peptide diversity after in vitro gastric and small intestinal digestion. Subsequent 24 h fermentation of the CH digests in a dynamic gastrointestinal (GI) digestion model containing human fecal matter showed that CH-OPT increased (p < 0.05) H₂S, SCFAs (propionic, butyric and valeric acids), BCFAs, and decreased NH₄ in the ascending colon reactor with no major changes seen with CH-GL. No major effects were observed in the transverse and descending vessels for either CH. These findings signify that CHs can induce prebiotic effects in the ascending colon that are CH dependent. More studies are needed to determine the physiological significance of CH-derived colonic metabolites, in view of emerging evidence connecting the gut to OA and metabolic diseases.

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

PURPOSE

Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail.

EXPERIMENTAL DESIGN

We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples.

RESULTS

BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins. Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

Candidate Urine Peptide Biomarkers for IgA Nephropathy: Where Are We Now?

Early detection, prognosis, and management of IgA nephropathy (IgAN) remain a challenge. Histological examination of renal tissue still comprises the only way to confirm an IgAN diagnosis. It is of great importance to establish noninvasive diagnostic, prognostic, and predictive biomarkers that would improve the clinical care and outcome of patients suffering from IgAN. This review summarises the findings from previous mass spectrometry- (MS-) based studies dedicated to the discovery of urinary peptide profiles specific to IgAN. There is a substantial number of urinary peptides that have been discovered to date, which show promise as biomarkers of IgAN; however, all of them require further, rigorous validation in well-planned studies, involving a large number of subjects who represent diverse and numerous populations.

Urine peptidomic biomarkers for diagnosis of patients with systematic lupus erythematosus

Background Systematic lupus erythematosus (SLE) is characterized with various complications which can cause serious organ damage in the human body. Despite the significant improvements in disease management of SLE patients, the non-invasive diagnosis is entirely missing. In this study, we used urinary peptidomic biomarkers for early diagnosis of disease onset to improve patient risk stratification, vital for effective drug treatment. Methods Urine samples from patients with SLE, lupus nephritis (LN) and healthy controls (HCs) were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS) for state-of-the-art biomarker discovery. Results A biomarker panel made up of 65 urinary peptides was developed that accurately discriminated SLE without renal involvement from HC patients. The performance of the SLE-specific panel was validated in a multicentric independent cohort consisting of patients without SLE but with different renal disease and LN. This resulted in an area under the receiver operating characteristic (ROC) curve (AUC) of 0.80 ( p < 0.0001, 95% confidence interval (CI) 0.65-0.90) corresponding to a sensitivity and a specificity of 83% and 73%, respectively. Based on the end terminal amino acid sequences of the biomarker peptides, an in silico methodology was used to identify the proteases that were up or down-regulated. This identified matrix metalloproteinases (MMPs) as being mainly responsible for the peptides fragmentation. Conclusions A laboratory-based urine test was successfully established for early diagnosis of SLE patients. Our approach determined the activity of several proteases and provided novel molecular information that could potentially influence treatment efficacy.

An integrated proteomic and peptidomic assessment of the normal human urinome

BACKGROUND

Urine represents an ideal source of clinically relevant biomarkers as it contains a large number of proteins and low molecular weight peptides. The comprehensive characterization of the normal urinary proteome and peptidome can serve as a reference for future biomarker discovery. Proteomic and peptidomic analysis of urine can also provide insight into normal physiology and disease pathology, especially for urogenital diseases.

METHODS

We developed an integrated proteomic and peptidomic analytical protocol in normal urine. We employed ultrafiltration to separate protein and peptide fractions, which were analyzed separately using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on the Q-Exactive mass spectrometer.

RESULTS

By analyzing six urines from healthy individuals with advanced age, we identified 1754 proteins by proteomic analysis and 4543 endogenous peptides, arising from 566 proteins by peptidomic analysis. Overall, we identified 2091 non-redundant proteins by this integrated approach. In silico protease activity analysis indicated that metalloproteases are predominantly involved in the generation of the endogenous peptide signature. In addition, a number of proteins that were detected in normal urine have previously been implicated in various urological malignancies, including bladder cancer and renal cell carcinoma (RCC).

CONCLUSIONS

We utilized a highly sensitive proteomics approach that enabled us to identify one of the largest sets of protein identifications documented in normal human urine. The raw proteomics and peptidomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD003595.

Proteomic Analysis of Vitreous Humor in Retinal Vein Occlusion

Purpose

To analyze the protein profile of human vitreous of untreated patients with retinal vein occlusion (RVO).

Methods

Sixty-eight vitreous humor (VH) samples (44 from patients with treatment naïve RVO, 24 controls with idiopathic floaters) were analyzed in this clinical-experimental study using capillary electrophoresis coupled to mass spectrometer and tandem mass spectrometry. To define potential candidate protein markers of RVO, proteomic analysis was performed on RVO patients (n = 30) and compared with controls (n = 16). To determine validity of potential biomarker candidates in RVO, receiver operating characteristic (ROC) was performed by using proteome data of independent RVO (n = 14) and control samples (n = 8).

Results

Ninety-four different proteins (736 tryptic peptides) could be identified. Sixteen proteins were found to be significant when comparing RVO and control samples (P = 1.43E-05 to 4.48E-02). Five proteins (Clusterin, Complement C3, Ig lambda-like polypeptide 5 (IGLL5), Opticin and Vitronectin), remained significant after using correction for multiple testing. These five proteins were also detected significant when comparing subgroups of RVO (central RVO, hemi-central RVO, branch RVO) to controls. Using independent samples ROC-Area under the curve was determined proving the validity of the results: Clusterin 0.884, Complement C3 0.955, IGLL5 1.000, Opticin 0.741, Vitronectin 0.786. In addition, validation through ELISA measurements was performed.

Conclusion

The results of the study reveal that the proteomic composition of VH differed significantly between the patients with RVO and the controls. The proteins identified may serve as potential biomarkers for pathogenesis induced by RVO.

Identification of small peptides arising from hydrolysis of meat proteins in dry fermented sausages

In this study, proteolysis and low molecular weight (LMW) peptides (<3kDa) from commercial Argentinean fermented sausages were characterized by applying a peptidomic approach. Protein profiles and peptides obtained by Tricine-SDS-PAGE and RP-HPLC-MS, respectively, allowed distinguishing two different types of fermented sausages, although no specific biomarkers relating to commercial brands or quality were recognized. From electrophoresis, α-actin, myoglobin, creatine kinase M-type and L-lactate dehydrogenase were degraded at different intensities. In addition, a partial characterization of fermented sausage peptidome through the identification of 36 peptides, in the range of 1000-2100 Da, arising from sarcoplasmic (28) and myofibrillar (8) proteins was achieved. These peptides had been originated from α-actin, myoglobin, and creatine kinase M-type, but also from the hydrolysis of other proteins not previously reported. Although muscle enzymes exerted a major role on peptidogenesis, microbial contribution cannot be excluded as it was postulated herein. This work represents a first peptidomic approach for fermented sausages, thereby providing a baseline to define key peptides acting as potential biomarkers.

Proteomics of vitreous humor of patients with exudative age-related macular degeneration

BACKGROUND

There is absence of specific biomarkers and an incomplete understanding of the pathophysiology of exudative age-related macular degeneration (AMD).

METHODS AND FINDINGS

Eighty-eight vitreous samples (73 from patients with treatment naïve AMD and 15 control samples from patients with idiopathic floaters) were analyzed with capillary electrophoresis coupled to mass spectrometry in this retrospective case series to define potential candidate protein markers of AMD. Nineteen proteins were found to be upregulated in vitreous of AMD patients. Most of the proteins were plasma derived and involved in biological (ion) transport, acute phase inflammatory reaction, and blood coagulation. A number of proteins have not been previously associated to AMD including alpha-1-antitrypsin, fibrinogen alpha chain and prostaglandin H2-D isomerase. Alpha-1-antitrypsin was validated in vitreous of an independent set of AMD patients using Western blot analysis. Further systems biology analysis of the data indicated that the observed proteomic changes may reflect upregulation of immune response and complement activity.

CONCLUSIONS

Proteome analysis of vitreous samples from patients with AMD, which underwent an intravitreal combination therapy including a core vitrectomy, steroids and bevacizumab, revealed apparent AMD-specific proteomic changes. The identified AMD-associated proteins provide some insight into the pathophysiological changes associated with AMD.

Proteomics for breast cancer urine biomarkers

Although the survival of breast cancer (BC) patients has increased over the last two decades due to improved screening programs and postoperative adjuvant systemic therapies, many patients die from metastatic relapse. Current biomarkers used in the clinic are not useful for the early detection of BC, or monitoring its progression, and have limited value in predicting response to treatment. The development of proteomic techniques has sparked new searches for novel protein markers for many diseases including BC. Proteomic techniques allow for a high-throughput analysis of samples with the visualization and quantification of thousands of potential protein and peptide markers. Human urine is one of the most interesting and useful biofluids for routine testing and provides an excellent resource for the discovery of novel biomarkers, with the advantage over tissue biopsy samples due to the ease and less invasive nature of collection. In this review, we summarize the results from studies where urine was used as a source for BC biomarker research and discuss urine sample preparation, its advantage, challenges, and limitation. We focus on the gel-based proteomic approaches as well as the recent development of quantitative techniques in BC urine biomarker detection. Finally, the future use of modern proteomic techniques in BC biomarker identification will be discussed.

Collagen peptides in urine: a new promising biomarker for the detection of colorectal liver metastases

Introduction

For both patients and the outpatient clinic the frequent follow-up visits after a resection of colorectal cancer (CRC) are time consuming and due to large patient numbers expensive. Therefore it is important to develop an effective non-invasive test for the detection of colorectal liver metastasis (CRLM) which could be used outside the hospital. The urine proteome is known to provide detailed information for monitoring changes in the physiology of humans. Urine collection is non-invasive and urine naturally occurring peptides (NOPs) have the advantage of being easily accessible without labour-intensive sample preparation. These advantages make it potentially useful for a quick and reliable application in clinical settings. In this study, we will focus on the identification and validation of urine NOPs to discriminate patients with CRLM from healthy controls.

Materials and Methods

Urine samples were collected from 24 patients with CRLM and 25 healthy controls. In the first part of the study, samples were measured with a nano liquid chromatography (LC) system (Thermo Fisher Scientific, Germaring, Germany) coupled on-line to a hybrid linear ion trap/Orbitrap mass spectrometer (LTQ-Orbitrap-XL, Thermo Fisher Scientific, Bremen, Germany). A discovery set was used to construct the model and consecutively the validation set, being independent from the discovery set, to check the acquired model. From the peptides which were selected, multiple reaction monitoring (MRM's) were developed on a UPLC-MS/MS system.

Results

Seven peptides were selected and applied in a discriminant analysis a sensitivity of 84.6% and a specificity of 92.3% were established (Canonical correlation:0.797, Eigenvalue:1.744, F:4.49, p:0.005). The peptides AGPP(-OH)GEAGKP(-OH)GEQGVP(-OH)GDLGA P(-OH)GP and KGNSGEP(-OH)GAPGSKGDTGAKGEP(-OH)GPVG were selected for further quantitative analysis which showed a sensitivity of 88% and a specificity of 88%.

Conclusion

Urine proteomic analysis revealed two very promising peptides, both part from collagen type 1, AGPP(-OH)GEAGKP(-OH)GEQGVP(-OH)GDLGAP(-OH)GP and KGNSGEP(-OH)GAPGSKGDTGAKGEP(-OH)GPVG which could detect CRLM in a non-invasive manner.

Recent technological developments in proteomics shed new light on translational research on diabetic microangiopathy

Diabetic microangiopathy has become a heavy social burden worldwide, but at present it is still difficult to predict and diagnose this ailment at an early stage. Various proteomics approaches have been applied to the pathophysiological study of diabetic microangiopathy. Conventional proteomics methods, including gel-based methods, exhibit limited sensitivity and robustness and have typically been used in high- or middle-abundance biomarker discovery. Clinical samples from patients with diabetic microangiopathy, such as biopsy samples, are minute in size. Therefore sample preparation, quantitative labelling and mass spectrometry technologies need to be optimized for low-abundance protein detection, multiple-sample processing and precision quantitation. In this review, we briefly introduce the recent technological developments in proteomics methods and summarize current proteomics-based, translational research on diabetic microangiopathy. Recent technological developments in proteomics tools may shed new light on the pathogenesis of diabetic microangiopathy and biomarkers and therapeutic targets related to this condition.

Seminal plasma as a source of prostate cancer peptide biomarker candidates for detection of indolent and advanced disease

BACKGROUND

Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease.

METHODOLOGY/PRINCIPAL FINDINGS

In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.

CONCLUSIONS/SIGNIFICANCE

Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.

Urinary proteome analysis in hypertensive patients with left ventricular diastolic dysfunction

AIMS

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).

METHODS AND RESULTS

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).

CONCLUSION

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.

A method for isolation and identification of urinary biomarkers in patients with diabetic nephropathy

PURPOSE

The poor performance of current tests for predicting the onset, progression and treatment response of diabetic nephropathy has engendered a search for more sensitive and specific urinary biomarkers. Our goal was to develop a new method for protein biomarker discovery in urine from these patients.

EXPERIMENTAL DESIGN

We analyzed urine from normal subjects and patients with early and advanced nephropathy. Proteins were separated using a novel analysis process including immunodepletion of high-abundance proteins followed by two-stage LC fractionation of low-abundance proteins. The proteins in the fractions were sequenced using MS/MS.

RESULTS

Immunodepletion of selected high-abundance proteins followed by two-stage LC produced approximately 700 fractions, each less complex and more amenable to analysis than the mixture and requiring minimal processing for MS identification. Comparison of fractions between normal and diabetic nephropathy subjects revealed several low-abundance proteins that reproducibly distinguished low glomerular filtration rate (GFR) from both high GFR diabetic and normal subjects, including uteroglobin, a protein previously associated with renal scarring.

CONCLUSIONS AND CLINICAL RELEVANCE

We developed a novel method to identify low-abundance urinary proteins that enables the discovery of potential biomarkers to improve the diagnosis and management of patients with diabetic nephropathy.

Proteomics and systems biology for understanding diabetic nephropathy

Like many diseases, diabetic nephropathy is defined in a histopathological context and studied using reductionist approaches that attempt to ameliorate structural changes. Novel technologies in mass spectrometry-based proteomics have the ability to provide a deeper understanding of the disease beyond classical histopathology, redefine the characteristics of the disease state, and identify novel approaches to reduce renal failure. The goal is to translate these new definitions into improved patient outcomes through diagnostic, prognostic, and therapeutic tools. Here, we review progress made in studying the proteomics of diabetic nephropathy and provide an introduction to the informatics tools used in the analysis of systems biology data, while pointing out statistical issues for consideration. Novel bioinformatics methods may increase biomarker identification, and other tools, including selective reaction monitoring, may hasten clinical validation.

Peptide fingerprinting of Alzheimer's disease in cerebrospinal fluid: identification and prospective evaluation of new synaptic biomarkers

Background

Today, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer's disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.

Methods and Findings

Cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer's disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho₁₈₁-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.

Conclusions

The method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.

Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

Background

Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ≥5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82).

Methodology/Principal Findings

Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients.

Conclusions

These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.

Urinary proteomics as a novel tool for biomarker discovery in kidney diseases

Urine has become one of the most attractive biofluids in clinical proteomics, for its procurement is easy and noninvasive and it contains sufficient proteins and peptides. Urinary proteomics has thus rapidly developed and has been extensively applied to biomarker discovery in clinical diseases, especially kidney diseases. In this review, we discuss two important aspects of urinary proteomics in detail, namely, sample preparation and proteomic technologies. In addition, data mining in urinary proteomics is also briefly introduced. At last, we present several successful examples on the application of urinary proteomics for biomarker discovery in kidney diseases, including diabetic nephropathy, IgA nephropathy, lupus nephritis, renal Fanconi syndrome, acute kidney injury, and renal allograft rejection.

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 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.

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.

Pre-analytical factors in clinical proteomics investigations: impact of ex vivo protein modifications for multiple sclerosis biomarker discovery

Serum proteome investigations have raised an incredible interest in the research of novel molecular biomarker, nevertheless few of the proposed evidences have been translated to the clinical practice. One of the limiting factors has been the lack of generally accepted guidelines for clinical proteomics studies and the lack of a robust analytical and pre-analytical ground for the proposed classification models. Pre-analytical issues may results in a deep impact for biomarker discovery campaign. In this study we present a systematic evaluation of sample storage and sampling conditions for clinical proteomics investigations. We have developed and validated a linear MALDI-TOF-MS protein profiling method to explore the low protein molecular weight region (5-20 kDa) of serum samples. Data normalization and processing was performed using optimise peak detection routine (LIMPIC) able to describe each group under investigation. Data were acquired either from healthy volunteers and from multiple sclerosis patients in order to highlight ex vivo protein profile alteration related to different physio-pathological conditions. Our data showed critical conditions for serum protein profiles depending on storage times and temperatures: 23 degrees C, 4 degrees C, -20 degrees C and -80 degrees C. We demonstrated that upon a -20 degrees C short term storage, characteristic degradation profiles are associated with different clinical groups. Protein signals were further identified after preparative HPLC separation by peptide sequencing on a nanoLC-Q-TOF TANDEM mass spectrometer. Apolipoprotein A-IV and complement C3 protein fragments, transthyretin and the oxidized isoforms in different apolipoprotein species represent the major molecular features of such a degradation pattern.

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.

Evaluation of urinary biomarkers for coronary artery disease, diabetes, and diabetic kidney disease

BACKGROUND

In this study we sought to validate urinary biomarkers for diabetes and two common complications, coronary artery disease (CAD) and diabetic nephropathy (DN).

METHODS

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.

RESULTS

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).

CONCLUSIONS

We validated a urinary proteome pattern associated with CAD and urinary proteome patterns associated with T1D and DN.

Systematic internal standard selection for capillary liquid chromatography-mass spectrometry time normalization to facilitate serum proteomics

Because blood interacts with almost all tissues of the body, it is likely that changes in the overall health of an organism will be reflected in the quantities of specific serum peptides and proteins, making them biomarkers. Due to the complexity of serum, pre-analytical sample simplification and separation are needed prior to mass spectrometric analysis. Use of a reverse-phase capillary column coupled to a mass spectrometer allows for separation and analysis of serum as part of efforts to discover biomarkers. Even after sample simplification by organic solvent precipitation, data files for a single sample typically exceed one gigabyte, making it difficult to analyze complete serum mass spectrometry profiles with currently available software. However, with adequate safeguards, it appears possible to consider portions of mass spectra to find differences in peak intensities between clinical comparison groups visually. To facilitate this, the elution profile was divided into 2-min intervals in which mass spectrometry data were averaged. This required that molecular species had defined reproducible elution times. Given liquid chromatography coupled to mass spectrometry variation, misalignment of elution times of individual peaks occurred often. Hence, internal time controls were identified within each window and used for elution time normalization. This significantly reduced variability in data. This approach allowed for peak alignment across samples, improving biomarker discovery.

CE-MS analysis of the human urinary proteome for biomarker discovery and disease diagnostics

Owing to its availability, ease of collection, and correlation with pathophysiology of diseases, urine is an attractive source for clinical proteomics. However, many proteomic studies have had only limited clinical impact, due to factors such as modest numbers of subjects, absence of disease controls, small numbers of defined biomarkers, and diversity of analytical platforms. Therefore, it is difficult to merge biomarkers from different studies into a broadly applicable human urinary proteome database. Ideally, the methodology for defining the biomarkers should combine a reasonable analysis time with high resolution, thereby enabling the profiling of adequate samples and recognition of sufficient features to yield robust diagnostic panels. Capillary electrophoresis coupled to mass spectrometry (CE-MS), which was used to analyze urine samples from healthy subjects and patients with various diseases, is a suitable approach for this task. The database of these datasets compiled from the urinary peptides enabled the diagnosis, classification, and monitoring of a wide range of diseases. CE-MS exhibits excellent performance for biomarker discovery and allows subsequent biomarker sequencing independent of the separation platform. This approach may elucidate the pathogenesis of many diseases, and better define especially renal and urological disorders at the molecular level.

Urinary proteomics in diabetes and CKD

Urinary biomarkers for diabetes, diabetic nephropathy, and nondiabetic proteinuric renal diseases were sought. For 305 individuals, biomarkers were defined and validated in blinded data sets using high-resolution capillary electrophoresis coupled with electrospray-ionization mass spectrometry. A panel of 40 biomarkers distinguished patients with diabetes from healthy individuals with 89% sensitivity and 91% specificity. Among patients with diabetes, 102 urinary biomarkers differed significantly between patients with normoalbuminuria and nephropathy, and a model that included 65 of these correctly identified diabetic nephropathy with 97% sensitivity and specificity. Furthermore, this panel of biomarkers identified patients who had microalbuminuria and diabetes and progressed toward overt diabetic nephropathy over 3 yr. Differentiation between diabetic nephropathy and other chronic renal diseases reached 81% sensitivity and 91% specificity. Many of the biomarkers were fragments of collagen type I, and quantities were reduced in patients with diabetes or diabetic nephropathy. In conclusion, this study shows that analysis of the urinary proteome may allow early detection of diabetic nephropathy and may provide prognostic information.

Discovery and validation of urinary biomarkers for prostate cancer

Only 30% of patients with elevated serum prostate specific antigen (PSA) levels who undergo prostate biopsy are diagnosed with prostate cancer (PCa). Novel methods are needed to reduce the number of unnecessary biopsies. We report on the identification and validation of a panel of 12 novel biomarkers for prostate cancer (PCaP), using CE coupled MS. The biomarkers could be defined by comparing first void urine of 51 men with PCa and 35 with negative prostate biopsy. In contrast, midstream urine samples did not allow the identification of discriminatory molecules, suggesting that prostatic fluids may be the source of the defined biomarkers. Consequently, first void urine samples were tested for sufficient amounts of prostatic fluid, using a prostatic fluid indicative panel ("informative" polypeptide panel; IPP). A combination of IPP and PCaP to predict positive prostate biopsy was evaluated in a blinded prospective study. Two hundred thirteen of 264 samples matched the IPP criterion. PCa was detected with 89% sensitivity, 51% specificity. Including age and percent free PSA to the proteomic signatures resulted in 91% sensitivity, 69% specificity.

Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future

Sparked by the article from Lescuyer and colleagues in a recent issue, we aim here to further encourage interest in and discussion of clinically relevant biomarker research. We express our view on proteomics for biomarker discovery by addressing multiple relevant issues, including the inherent differences between biological fluids (and how these differences affect current analytical approaches) and experimental design to maximize the efficiency of moving from the bench to the bedside. Herein, we also include suggestions for definition of the term "biomarker", based on the use of a set of universal characterization/validation requirements, and illustrate several recent examples of successful transitions of benchtop proteomic studies work to clinical practice.