Discovery of biomarkers in human urine and cerebrospinal fluid by capillary electrophoresis coupled to mass spectrometry: Towards new diagnostic and therapeutic approaches

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.

CE-MS for Proteomics and Intact Protein Analysis

This chapter aims to explore various parameters involved in achieving high-end capillary electrophoresis hyphenated to mass spectrometry (CE-MS) analysis of proteins, peptides, and their posttranslational modifications. The structure of the topics discussed in this book chapter is conveniently mapped on the scheme of the CE-MS system itself, starting from sample preconcentration and injection techniques and finishing with mass analyzer considerations. After going through the technical considerations, a variety of relevant applications for this analytical approach are presented, including posttranslational modifications analysis, clinical biomarker discovery, and its growing use in the biotechnological industry.

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.

Urinary Peptides as Potential Non-Invasive Biomarkers for Lupus Nephritis: Results of the Peptidu-LUP Study

Background: Lupus nephritis (LN) is a severe manifestation of Systemic Lupus Erythematosus (SLE). The therapeutic strategy relies on kidney biopsy (KB) results. We tested whether urinary peptidome analysis could non-invasively differentiate active from non-active LN. Design: Urinary samples were collected from 93 patients (55 with active LN and 38 with non-active LN), forming a discovery (n = 42) and an independent validation (n = 51) cohort. Clinical characteristics were collected at inclusion and prospectively for 24 months. The urinary peptidome was analyzed by capillary-electrophoresis coupled to mass-spectrometry, comparing active LN to non-active LN, and assessing chronic lesions and response to therapy. The value of previously validated prognostic (CKD273) and differential diagnostic (LN172) signatures was evaluated. Results: Urinary peptides could not discriminate between active and non-active LN or predict early response to therapy. Tubulo-interstitial fibrosis was correlated to the CKD273. The LN172 score identified 92.5% of samples as LN. Few patients developed new-onset CKD. Conclusions: We validated the CKD273 and LN172 classifiers but did not identify a robust signature that could predict active LN and replace KB. The value of urinary peptidome to predict long-term CKD, or renal flares in SLE, remains to be evaluated.

Proteomic Analysis of Cerebrospinal Fluid in Children with Acute Enterovirus-Associated Meningoencephalitis Identifies Dysregulated Host Processes and Potential Biomarkers

Enteroviruses (EVs) are major causes of viral meningoencephalitis in children. To better understand the pathogenesis and identify potential biomarkers, cerebrospinal fluid proteome in children (n = 52) suffering from EV meningoencephalitis was compared to that in EV-negative control subjects (n = 53) using the BoxCar acquisition technique. Among 1697 proteins identified, 1193 with robust assay readouts were used for quantitative analyses. Differential expression analyses identified 154 upregulated and 227 downregulated proteins in the EV-positive group. Functional analyses showed that the upregulated proteins are mainly related to activities of lymphocytes and cytokines, inflammation, and responses to stress and viral invasion, while the downregulated proteins are mainly related to neuronal integrity and activity as well as neurogenesis. According to receiver operating characteristic analysis results, Rho-GDP-dissociation inhibitor 2 exhibited the highest sensitivity (96.2%) and specificity (100%) for discriminating EV-positive from EV-negative patients. The chemokine CXCL10 was most upregulated (>300-fold) with also high sensitivity (92.3%) and specificity (94.3%) for indicating EV positivity. Thus, this study uncovered perturbations of multiple host processes due to EV meningoencephalitis, especially the general trend of enhanced immune responses but impaired neuronal functions. The identified dysregulated proteins may also prompt biomarker development.

The novel urinary proteomic classifier HF1 has similar diagnostic and prognostic utility to BNP in heart failure

AIMS

Measurement of B-type natriuretic peptide (BNP) or N-terminal pro-BNP is recommended as part of the diagnostic workup of patients with suspected heart failure (HF). We evaluated the diagnostic and prognostic utility of the novel urinary proteomic classifier HF1, compared with BNP, in HF. HF1 consists of 85 unique urinary peptide fragments thought, mainly, to reflect collagen turnover.

METHODS AND RESULTS

We performed urinary proteome analysis using capillary electrophoresis coupled with mass spectrometry in 829 participants. Of these, 622 had HF (504 had chronic HF and 118 acute HF) and 207 were controls (62 coronary heart disease patients without HF and 145 healthy controls). The area under the receiver operating characteristic (ROC) curve (AUC) using HF1 for the diagnosis of HF (cases vs. controls) was 0.94 (95% CI, 0.92-0.96). This compared with an AUC for BNP of 0.98 (95% CI, 0.97-0.99). Adding HF1 to BNP increased the AUC to 0.99 (0.98-0.99), P < 0.001, and led to a net reclassification improvement of 0.67 (95% CI, 0.54-0.77), P < 0.001. Among 433 HF patients followed up for a median of 989 days, we observed 186 deaths. HF1 had poorer predictive value to BNP for all-cause mortality and did not add prognostic information when combined with BNP.

CONCLUSIONS

The urinary proteomic classifier HF1 performed as well, diagnostically, as BNP and provided incremental diagnostic information when added to BNP. HF1 had less prognostic utility than BNP.

Carbon dots sensitized lanthanide infinite coordination polymer nanoparticles: Towards ratiometric fluorescent sensing of cerebrospinal Aβ monomer as a biomarker for Alzheimer's disease

Herein, a novel ratiometric fluorescent probe based on CDs@Eu/GMP ICP nanoparticles was developed for the detection of Aβ monomer in rat as a biomarker for Alzheimer's disease (AD) by fully exploring the competitive coordination interaction and by taking advantage of excellent optical property of carbon dots sensitized lanthanide infinite coordination polymer (ICP) nanoparticles. The carbon dots (CDs) with abundant functional groups were encapsulated into Eu/GMP ICPs through self-adaptive chemistry, which could not only sensitize the red fluorescence of Eu/GMP ICPs effectively, but also act as an internal reference for self-correction. In the absence of Cu²⁺, the as-formed CDs@Eu/GMP ICPs exhibited the characteristic emission of CDs at 400 nm and strong emission of Eu³⁺ at 592 nm, 615 nm, 650 nm and 694 nm. With the addition of Cu²⁺, the red fluorescence of Eu³⁺ decreased due to the coordination interaction between CDs and Cu²⁺, thus destroyed the antenna effect. After the subsequent addition of Aβ monomer, the specific binding occurred between Cu²⁺ and Aβ monomer, and then the red fluorescence of Eu³⁺ restored again. During this process, the fluorescence of CDs remained unchanged, thus could be used as an internal reference to cancel out the environmental fluctuation and was more adaptive for the detection of Aβ monomer in biological fluids. The method demonstrated here was highly sensitive, free from the interference of other species in rat brain, the in vivo analysis of Aβ monomer in CSF and different brain regions from normal rats and Alzheimer's rats could be realized, which was of great significance for better understanding the mechanism of AD and paving the way to understand the chemical essence involved in AD.

Proteomic analysis of cerebrospinal fluid extracellular vesicles reveals synaptic injury, inflammation, and stress response markers in HIV patients with cognitive impairment

BACKGROUND

Extracellular vesicles (EVs) are nano-sized particles present in most body fluids including cerebrospinal fluid (CSF). Little is known about CSF EV proteins in HIV+ individuals. Here, we characterize the CSF EV proteome in HIV+ subjects and its relationship to neuroinflammation, stress responses, and HIV-associated neurocognitive disorders (HAND).

METHODS

CSF EVs isolated from 20 HIV+ subjects with (n = 10) or without (n = 10) cognitive impairment were characterized by electron microscopy, nanoparticle tracking analysis, immunoblotting, and untargeted LC/MS/MS mass spectrometry. Functional annotation was performed by gene ontology (GO) mapping and expression annotation using Biobase Transfac and PANTHER software. Cultured astrocytic U87 cells were treated with hydrogen peroxide for 4 h to induce oxidative stress and EVs isolated by ultracentrifugation. Selected markers of astrocytes (GFAP, GLUL), inflammation (CRP), and stress responses (PRDX2, PARK7, HSP70) were evaluated in EVs released by U87 cells following induction of oxidative stress and in CSF EVs from HIV+ patients by immunoblotting.

RESULTS

Mass spectrometry identified 2727 and 1626 proteins in EV fractions and EV-depleted CSF samples, respectively. CSF EV fractions were enriched with exosomal markers including Alix, syntenin, tetraspanins, and heat-shock proteins and a subset of neuronal, astrocyte, oligodendrocyte, and choroid plexus markers, in comparison to EV-depleted CSF. Proteins related to synapses, immune/inflammatory responses, stress responses, metabolic processes, mitochondrial functions, and blood-brain barrier were also identified in CSF EV fractions by GO mapping. HAND subjects had higher abundance of CSF EVs and proteins mapping to GO terms for synapses, glial cells, inflammation, and stress responses compared to those without HAND. GFAP, GLUL, CRP, PRDX2, PARK7, and HSP70 were confirmed by immunoblotting of CSF EVs from subjects with HAND and were also detected in EVs released by U87 cells under oxidative stress.

CONCLUSIONS

These findings suggest that CSF EVs derived from neurons, glial cells, and choroid plexus carry synaptic, immune/inflammation-related, and stress response proteins in HIV+ individuals with cognitive impairment, representing a valuable source for biomarker discovery.

Optimization for Peptide Sample Preparation for Urine Peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insight into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for noninvasive monitoring of human diseases. The noninvasive nature of urine collection and the abundance of peptides in the urine make analysis by high-throughput "peptidomics" methods an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regard to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements, in that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS-based peptidome analysis. We report on a novel adaptation of the standard solid-phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS-based peptidomics, in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. The mSPE method provides significantly improved efficiencies for the preparation of samples from urine. The mSPE method is found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis, due to optimized sample cleanup that provides improved experimental inference from confidently identified peptides.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

Novel Urinary Peptidomic Classifier Predicts Incident Heart Failure

BACKGROUND

Detection of preclinical cardiac dysfunction and prognosis of left ventricular heart failure (HF) would allow targeted intervention, and appears to be the most promising approach in its management. Novel biomarker panels may support this approach and provide new insights into the pathophysiology.

METHODS AND RESULTS

A retrospective comparison of urinary proteomic profiles generated by mass spectrometric analysis from 49 HF patients, 36 patients who progressed to HF within 2.6±1.6 years, and 192 sex- and age-matched controls who did not progress to HF enabled identification of 96 potentially HF-specific peptide biomarkers. Based on these 96 peptides, the classifier called Heart Failure Predictor (HFP) was established by support vector machine modeling. The incremental prognostic value of HFP was subsequently evaluated in urine samples from 175 individuals with asymptomatic diastolic dysfunction from an independent population cohort. Within 4.8 years, 17 of these individuals progressed to overt HF. The area under receiver-operating characteristic curve was 0.70 (95% CI, 0.56-0.82); P=0.0047 for HFP and 0.57 (0.42-0.72; P=0.62) for N-terminal pro b-type natriuretic peptide. Hazard ratios were 1.63 (CI, 1.04-2.55; P=0.032) per 1-SD increment in HFP and 0.70 (CI, 0.35-1.41; P=0.32) for a doubling of the logarithmically transformed N-terminal pro b-type natriuretic peptide.

CONCLUSIONS

HFP is a novel biomarker derived from the urinary proteome and might serve as a sensitive tool to improve risk stratification, patient management, and understanding of the pathophysiology of HF.

Prediction of acute coronary syndromes by urinary proteome analysis

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease

Background

When combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects.

Results

Computing the discovery sub-cohorts comprising \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\scriptscriptstyle \raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$3$}\right.} $$\end{document}23 of the study subjects based on the Wilcoxon rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largely different numbers (ranging from 2 to 398) of potential peptide biomarkers. Moreover, these biomarker patterns showed very little overlap limited to fragments of type I and III collagens as the common denominator. However, these differences in biomarker patterns did mostly not translate into significant differently performing diagnostic or prognostic classifiers modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and random forest. This was even true when different biomarker patterns were combined into master-patterns.

Conclusion

In conclusion, our study revealed a very considerable dependence of peptide biomarker discovery on statistical computing of urinary peptide profiles while the observed diagnostic and/or prognostic reliability of classifiers was widely independent of the modelling approach. This may however be due to the limited statistical power in classifier testing. Nonetheless, our study showed that urinary proteome analysis has the potential to provide valuable biomarkers for coronary artery disease mirroring especially alterations in the extracellular matrix. It further showed that for a comprehensive discovery of biomarkers and thus of pathological information, the results of different statistical methods may best be combined into a master pattern that then can be used for classifier modelling.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1390-1) contains supplementary material, which is available to authorized users.

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

Background

Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF.

Methods and Results

Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin.

Conclusion

CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Urine proteomics in the diagnosis of stable angina

Background

We have previously described a panel of 238 urinary polypeptides specific for established severe coronary artery disease (CAD). Here we studied this polypeptide panel in patients with a wider range of CAD severity.

Methods

We recruited 60 patients who underwent elective coronary angiography for investigation of stable angina. Patients were selected for either having angiographic evidence of CAD or not (NCA) following coronary angiography (n = 30/30; age, 55 ± 6 vs. 56 ± 7 years, P = 0.539) to cover the extremes of the CAD spectrum. A further 66 patients with severe CAD (age, 64 ± 9 years) prior to surgical coronary revascularization were added for correlation studies. The Gensini score was calculated from coronary angiograms as a measure of CAD severity. Urinary proteomic analyses were performed using capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. The urinary polypeptide pattern was classified using a predefined algorithm and resulting in the CAD₂₃₈ score, which expresses the pattern quantitatively.

Results

In the whole cohort of patients with CAD (Gensini score 60 [40; 98]) we found a close correlation between Gensini scores and CAD₂₃₈ (ρ = 0.465, P < 0.001). After adjustment for age (β = 0.144; P = 0.135) the CAD₂₃₈ score remained a significant predictor of the Gensini score (β =0.418; P < 0.001). In those with less severe CAD (Gensini score 40 [25; 61]), however, we could not detect a difference in CAD₂₃₈ compared to patients with NCA (−0.487 ± 0.341 vs. −0.612 ± 0.269, P = 0.119).

Conclusions

In conclusion the urinary polypeptide CAD₂₃₈ score is associated with CAD burden and has potential as a new cardiovascular biomarker.

Electronic supplementary material

The online version of this article (doi:10.1186/s12872-016-0246-y) contains supplementary material, which is available to authorized users.

Proteolytic processing of human serum albumin generates EPI-X4, an endogenous antagonist of CXCR4

The chemokine receptor CXCR4 is an important G protein-coupled receptor. Signaling via CXCL12 regulates a number of important biologic processes, including immune responses, organogenesis, or hematopoiesis. Dysregulation of CXCR4 signaling is associated with a variety of diseases, such as cancer development and metastasis, immunodeficiencies, or chronic inflammation. Here, we review our findings on endogenous peptide inhibitor of CXCR4 as a novel antagonist of CXCR4. This peptide is a 16-residue fragment of human serum albumin and was isolated as an inhibitor of CXCR4-tropic human immunodeficiency virus type 1 from a blood-derived peptide library. Endogenous peptide inhibitor of CXCR4 binds the second extracellular loop of CXCR4, thereby preventing engagement of CXCL12 and antagonizing the receptor. Consequently, endogenous peptide inhibitor of CXCR4 inhibits CXCL12-mediated migration of CXCR4-expressing cells in vitro, mobilizes hematopoietic stem cells, and suppresses inflammatory responses in vivo. We discuss the generation of endogenous peptide inhibitor of CXCR4, its relevance as biomarker for disease, and its role in human immunodeficiency virus/acquired immunodeficiency syndrome pathogenesis and cancer. Furthermore, we discuss why optimized endogenous peptide inhibitor of CXCR4 derivatives might have advantages over other CXCR4 antagonists.

An integrated microfluidic chip for immunocapture, preconcentration and separation of β-amyloid peptides

We present an integrated microfluidic chip for detection of β-amyloid (Aβ) peptides. Aβ peptides are major biomarkers for the diagnosis of Alzheimer's disease (AD) in its early stages. This microfluidic device consists of three main parts: (1) An immunocapture microcolumn based on self-assembled magnetic beads coated with antibodies specific to Aβ peptides, (2) a nano-porous membrane made of photopolymerized hydrogel for preconcentration, and (3) a microchip electrophoresis (MCE) channel with fluorescent detection. Sub-milliliter sample volume is either mixed off-chip with antibody coated magnetic beads and injected into the device or is injected into an already self-assembled column of magnetic beads in the microchannel. The captured peptides on the beads are then electrokinetically eluted and re-concentrated onto the nano-membrane in a few nano-liters. By integrating the nano-membrane, total assay time was reduced and also off-chip re-concentration or buffer exchange steps were not needed. Finally, the concentrated peptides in the chip are separated by electrophoresis in a polymer-based matrix. The device was applied to the capture and MCE analysis of differently truncated peptides Aβ (1-37, 1-39, 1-40, and 1-42) and was able to detect as low as 25 ng of synthetic Aβ peptides spiked in undiluted cerebrospinal fluid (CSF). The device was also tested with CSF samples from healthy donors. CSF samples were fluorescently labelled and pre-mixed with the magnetic beads and injected into the device. The results indicated that Aβ1-40, an important biomarker for distinguishing patients with frontotemporal lobe dementia from controls and AD patients, was detectable. Although the sensitivity of this device is not yet enough to detect all Aβ subtypes in CSF, this is the first report on an integrated or semi-integrated device for capturing and analyzing of differently truncated Aβ peptides. The method is less demanding and faster than the conventional Western blotting method currently used for research.

Discovery and characterization of an endogenous CXCR4 antagonist

CXCL12-CXCR4 signaling controls multiple physiological processes and its dysregulation is associated with cancers and inflammatory diseases. To discover as-yet-unknown endogenous ligands of CXCR4, we screened a blood-derived peptide library for inhibitors of CXCR4-tropic HIV-1 strains. This approach identified a 16 amino acid fragment of serum albumin as an effective and highly specific CXCR4 antagonist. The endogenous peptide, termed EPI-X4, is evolutionarily conserved and generated from the highly abundant albumin precursor by pH-regulated proteases. EPI-X4 forms an unusual lasso-like structure and antagonizes CXCL12-induced tumor cell migration, mobilizes stem cells, and suppresses inflammatory responses in mice. Furthermore, the peptide is abundant in the urine of patients with inflammatory kidney diseases and may serve as a biomarker. Our results identify EPI-X4 as a key regulator of CXCR4 signaling and introduce proteolysis of an abundant precursor protein as an alternative concept for chemokine receptor regulation.

Sandwich enzyme-linked immunosorbent assay for the quantification of human serum albumin fragment 408-423 in bodily fluids

Urinary levels of human serum albumin (hSA) fragment 408-423 have been proposed to represent an early marker for graft-versus-host disease (GvHD) and chronic kidney diseases. Here, we developed an enzyme-linked immunosorbent assay (ELISA) for the quantification of hSA(408-423). The sandwich ELISA has a detection limit of 0.5ng/ml and is highly specific for hSA(408-423) because it does not cross-react with other albumin fragments or the full-length precursor. This ELISA allows rapid and convenient quantification of hSA(408-423) in bodily fluids, further clarifying the prognostic and diagnostic value of this peptide in GvHD, kidney disease, and other disorders.

Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273).

METHODS

In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier.

RESULTS

We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found.

CONCLUSION

We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.

Optimization for peptide sample preparation for urine peptidomics

Analysis of native or endogenous peptides in biofluids can provide valuable insights into disease mechanisms. Furthermore, the detected peptides may also have utility as potential biomarkers for non-invasive monitoring of human diseases. The non-invasive nature of urine collection and the abundance of peptides in the urine makes analysis by high-throughput ‘peptidomics’ methods , an attractive approach for investigating the pathogenesis of renal disease. However, urine peptidomics methodologies can be problematic with regards to difficulties associated with sample preparation. The urine matrix can provide significant background interference in making the analytical measurements that it hampers both the identification of peptides and the depth of the peptidomics read when utilizing LC-MS based peptidome analysis. We report on a novel adaptation of the standard solid phase extraction (SPE) method to a modified SPE (mSPE) approach for improved peptide yield and analysis sensitivity with LC-MS based peptidomics in terms of time, cost, clogging of the LC-MS column, peptide yield, peptide quality, and number of peptides identified by each method. Expense and time requirements were comparable for both SPE and mSPE, but more interfering contaminants from the urine matrix were evident in the SPE preparations (e.g., clogging of the LC-MS columns, yellowish background coloration of prepared samples due to retained urobilin, lower peptide yields) when compared to the mSPE method. When we compared data from technical replicates of 4 runs, the mSPE method provided significantly improved efficiencies for the preparation of samples from urine (e.g., mSPE peptide identification 82% versus 18% with SPE; p = 8.92E-05). Additionally, peptide identifications, when applying the mSPE method, highlighted the biology of differential activation of urine peptidases during acute renal transplant rejection with distinct laddering of specific peptides, which was obscured for most proteins when utilizing the conventional SPE method. In conclusion, the mSPE method was found to be superior to the conventional, standard SPE method for urine peptide sample preparation when applying LC-MS peptidomics analysis due to the optimized sample clean up that provided improved experimental inference from the confidently identified peptides.

2D gel blood serum biomarkers reveal differential clinical proteomics of the neurodegenerative diseases

This review addresses the challenges of neuroproteomics and recent progress in biomarkers and tests for neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. The review will discuss how the application of quantitative 2D gel electrophoresis, combined with appropriate single-variable and multivariate biostatistics, allows for selection of disease-specific serum biomarkers. It will also address how the use of large cohorts of specifically targeted patient blood serum samples and complimentary age-matched controls, in parallel with the use of selected panels of these biomarkers, are being applied to the development of blood tests to specifically address unmet pressing needs in the differential diagnosis of these diseases, and to provide potential avenues for mechanism-based drug targeting and treatment monitoring. While exploring recent findings in this area, the review discusses differences in critical pathways of immune/inflammation and amyloid formation between Parkinson's disease and amyotrophic lateral sclerosis, as well as discernable synergistic relationships between these pathways that are revealed by this approach. The potential for pathway measurement in blood tests for differential diagnosis, disease burden and therapeutic monitoring is also outlined.

Type 1 diabetes: entering the proteomic era

During the last decade, a major breakthrough in the field of proteomics has been achieved. This review describes available techniques for proteomic analyses, both gel and non-gel based, particularly concentrating on relative quantification techniques. The principle of the different techniques is discussed, highlighting the advantages and drawbacks of recently available visualization methods in gel-based assays. In addition, recent developments for quantitative analysis in non-gel-based approaches are summarized. This review focuses on applications in Type 1 diabetes. These mainly include proteomic studies on pancreatic islets in animal models and in the human situation. Also discussed are mass spectrometry-based studies on T-cells, and studies on the development of diagnostic markers for diabetic nephropathology by capillary electrophoresis coupled to mass spectrometry.

Discovery and validation of urinary biomarkers for detection of renal cell carcinoma

INTRODUCTION

Renal cell carcinoma (RCC) is often accompanied by non-specific symptoms. The increase of incidentally discovered small renal masses also presents a diagnostic dilemma. This study investigates whether RCC-specific peptides with diagnostic potential can be detected in urine and whether a combination of such peptides could form a urinary screening tool.

MATERIALS AND METHODS

For the discovery of RCC-specific biomarkers, we have employed CE-MS to analyze urine samples from patients with RCC (N=40) compared to non-diseased controls (N=68).

RESULTS AND DISCUSSION

86 peptides were found to be specifically associated to RCC, of which sequence could be obtained for 40. A classifier based on these peptides was evaluated in an independent set of 76 samples, resulting in 80% sensitivity and 87% specificity. The specificity of the marker panel was further validated in a historical dataset of 1077 samples including age-matched controls (N=218), patients with related cancer types and renal diseases (N=859). In silico protease prediction based on the cleavage sites of differentially excreted peptides, suggested modified activity of certain proteases including cathepsins, ADAMTS and kallikreins some of which were previously found to be associated to RCC.

CONCLUSIONS

RCC can be detected with high accuracy based on specific urinary peptides.

BIOLOGICAL SIGNIFICANCE

Clear cell renal cell carcinoma (RCC) has the highest incidence among the renal malignancies, often presenting non-specific or no symptoms at all. Moreover, with no diagnostic marker being available so far, almost 30% of the patients are diagnosed with metastatic disease and 30-40% of the patients initially diagnosed with localized tumor relapse. These facts introduce the clinical need of early diagnosis. This study is focused on the investigation of a marker model based on urinary peptides, as a tool for the detection of RCC in selected patients at risk. Upon evaluation of the marker model in an independent blinded set of 76 samples, 80% sensitivity and 87% specificity were reported. An additional dataset of 1077 samples was subsequently employed for further evaluation of the specificity of the classifier.

CE-MS for proteomics: Advances in interface development and application

Capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a powerful technique for the analysis of proteins and peptides. Over the past few years, significant progress has been made in the development of novel and more effective interfaces for hyphenating CE to MS. This review provides an overview of these new interfacing techniques for coupling CE to MS, covering the scientific literature from January 2007 to December 2011. The potential of these new CE-MS interfacing techniques is demonstrated within the field of (clinical) proteomics, more specifically "bottom-up" proteomics, by showing examples of the analysis of various biological samples. The relevant papers on CE-MS for proteomics are comprehensively summarized in tables, including, e.g. information on sample type and pretreatment, interfacing and MS detection mode. Finally, general conclusions and future perspectives are provided.

Capillary electrophoresis and small molecule drug discovery: a perfect match?

Capillary electrophoresis (CE) is an analytical technique based on the separation of the analytes within a capillary owing to their different electrophoretic mobilities. It is widely used in pharmaceutical analyses owing to its versatility and high separation power. However, its penetration into the drug discovery scene has been relatively limited until recent years. Several factors have contributed to this low implementation, including the maturity of liquid chromatography, the scarcity of experienced CE practitioners, and certain limitations intrinsic to the technique. Recently, instrumental improvements and the growing demand for analytical information have lead to a continuously expanding range of routine electrophoretic applications throughout pharmaceutical discovery and development. In this article we review CE fundamentals, review well-established CE methodologies in drug discovery of small molecules and discuss trends that, in our opinion, might emerge in the coming years.

A pilot study on the effect of short-term consumption of a polyphenol rich drink on biomarkers of coronary artery disease defined by urinary proteomics

Polyphenol rich diets have been associated with a reduced risk of cardiovascular disease. We examined the effect of a polyphenol rich (P-R) drink on biomarkers assessed by urinary proteomics. Thirty nine middle aged and overweight subjects were randomized to P-R drink (n = 20) or placebo (n = 19) in addition to their normal diet. After two weeks urine samples were obtained for assessment of the urinary proteome using capillary electrophoresis coupled to a mass spectrometer. A total of 93 polypeptides were found to be candidates for differential distribution with a nominal p-value <0.05, though these differences did not reach significance when multiple testing was accounted for. Sequences were determined in 19 of these demonstrating that they originate from alpha-1 antitrypsin, collagens, fibrinogen alpha and IgG kappa. Levels of 27 polypeptides were greater than 4-fold different between the two groups. Of these, 7 were previously found to be part of a coronary artery disease (CAD) specific urinary biomarker pattern. Their direction of expression was closer to the healthy state in the P-R drink group and closer to CAD state in the placebo group. Our data suggest that the P-R drink may have beneficial effects on urinary biomarkers of CAD. The data encourage the planning of future prospective studies, aimed at investigating significant effects of polyphenol rich dietary products.

A distinct urinary biomarker pattern characteristic of female Fabry patients that mirrors response to enzyme replacement therapy

Female patients affected by Fabry disease, an X-linked lysosomal storage disorder, exhibit a wide spectrum of symptoms, which renders diagnosis, and treatment decisions challenging. No diagnostic test, other than sequencing of the alpha-galactosidase A gene, is available and no biomarker has been proven useful to screen for the disease, predict disease course and monitor response to enzyme replacement therapy. Here, we used urine proteomic analysis based on capillary electrophoresis coupled to mass spectrometry and identified a biomarker profile in adult female Fabry patients. Urine samples were taken from 35 treatment-naïve female Fabry patients and were compared to 89 age-matched healthy controls. We found a diagnostic biomarker pattern that exhibited 88.2% sensitivity and 97.8% specificity when tested in an independent validation cohort consisting of 17 treatment-naïve Fabry patients and 45 controls. The model remained highly specific when applied to additional control patients with a variety of other renal, metabolic and cardiovascular diseases. Several of the 64 identified diagnostic biomarkers showed correlations with measures of disease severity. Notably, most biomarkers responded to enzyme replacement therapy, and 8 of 11 treated patients scored negative for Fabry disease in the diagnostic model. In conclusion, we defined a urinary biomarker model that seems to be of diagnostic use for Fabry disease in female patients and may be used to monitor response to enzyme replacement therapy.

Current strategies in the discovery of small-molecule biomarkers for Alzheimer’s disease

With the number of patients suffering from Alzheimer’s disease rapidly increasing, there is a major requirement for an accurate biomarker capable of diagnosing the disease early. Much of the research is focused on protein and genetic approaches; however, small molecules may provide viable marker molecules. Examples that support this approach include known abnormalities in lipid metabolism, glucose utilization and oxidative stress, which have been demonstrated in patients suffering from the disease. Therefore, by-products of this irregular metabolism may provide accurate biomarkers. In this review we present the current approaches previously published in the literature used to investigate potential small-molecule and metabolite markers, and report their findings. A wide range of techniques are discussed, including separation approaches (LC, GC and CE), magnetic resonance technologies (NMR and magnetic resonance spectroscopy), and immunoassays.

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.

Addressing the challenge of defining valid proteomic biomarkers and classifiers

Background

The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.

Results

We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential.

Conclusions

Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

Identifying and validating biomarkers for Alzheimer's disease

The identification and validation of biomarkers for diagnosing Alzheimer's disease (AD) and other forms of dementia are increasingly important. To date, ELISA measurement of β-amyloid(1–42), total tau and phospho-tau-181 in cerebrospinal fluid (CSF) is the most advanced and accepted method to diagnose probable AD with high specificity and sensitivity. However, it is a great challenge to search for novel biomarkers in CSF and blood by using modern potent methods, such as microarrays and mass spectrometry, and to optimize the handling of samples (e.g. collection, transport, processing, and storage), as well as the interpretation using bioinformatics. It seems likely that only a combined analysis of several biomarkers will define a patient-specific signature to diagnose AD in the future.

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.

Urine proteomic analysis: use of two-dimensional gel electrophoresis, isotope coded affinity tags, and capillary electrophoresis

The identities and abundance levels of proteins excreted in urine are not only key indicators of diseases associated with renal function but are also indicators of the overall health of individuals. Urine specimens are readily available and provide a noninvasive means to assess and diagnose many disease states. Proteins in urine originate from two sources: the ultrafiltrate of plasma, and those that are shed from the urinary tract. The protein concentration in urine excreted from a normal adult is approximately 150 mg/day, and is typically not greater than 10 mg/100 mL in any single specimen. Following precipitation, concentration, and fractionation methods, proteins of interest from urine samples can be separated, identified, and quantified. One of the most commonly used techniques in the field of urine proteomics is gel electrophoresis followed by identification with mass spectrometry and protein database search algorithms. In this chapter, two-dimensional gel electrophoresis (2-DE) will be discussed, along with less frequently applied techniques, such as isotope coded affinity tags (ICAT) and capillary electrophoresis (CE). Publications discussing the application of these techniques to urine proteomic analyses of healthy individuals and urinary disease biomarker discovery will also be summarized.

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.