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

Contemporary proteomic strategies for clinical epigenetic research and potential impact for the clinic

Novel proteomic methods are revealing the intricacy of the epigenetic landscape affecting gene regulation and improving our knowledge of the pathogenesis of complex diseases. Despite the enormous amount of data regarding epigenetic modifications present in DNA and histones, deciphering their biological relevance in the context of the disease and health is currently still an ongoing process. Here, we consider the relationship between epigenetic research in tumorigenesis and the prospect of knowledge transfer to clinical use, focusing primarily on the epigenetic histone post-translational modifications, which could be used as biomarkers. We additionally focus on the use of proteomic techniques in research and evaluate their usefulness in clinical setting.

Overview of proteomics studies in obstructive sleep apnea

Obstructive sleep apnea (OSA) is an underdiagnosed common public health concern causing deleterious effects on metabolic and cardiovascular health. Although much has been learned regarding the pathophysiology and consequences of OSA in the past decades, the molecular mechanisms associated with such processes remain poorly defined. The advanced high-throughput proteomics-based technologies have become a fundamental approach for identifying novel disease mediators as potential diagnostic and therapeutic targets for many diseases, including OSA. Here, we briefly review OSA pathophysiology and the technological advances in proteomics and the first results of its application to address critical issues in the OSA field.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Pathogenesis of immunoglobulin A nephropathy

PURPOSE OF REVIEW

In this article, we review recent findings on the pathogenesis and genetics of immunoglobulin A (IgA) nephropathy.

RECENT FINDINGS

During the past 2 years, the understanding of the pathogenesis of IgA nephropathy has evolved as a result of progress in technology and new tools that have been developed. Since 1968, when IgA nephropathy was described as an IgA-IgG immune-complex disease, the knowledge base expanded to allow definition of IgA nephropathy as an autoimmune disease with a multihit pathogenetic process. Specifically, galactose-deficient immunoglobulin A1 (IgA1) is recognized by unique autoantibodies, resulting in the formation of pathogenic immune complexes that ultimately deposit in the glomerular mesangium and induce renal injury. New approaches using high-resolution mass spectrometry have provided unique insight at the molecular level into IgA1 O-glycosylation. Cutting-edge genome-wide association studies revealed multiple disease-associated risk loci and have mapped their geographic and racial distribution.

SUMMARY

Recent studies of molecular and genetic defects operating in IgA nephropathy can define new biomarkers specific for the disease that can be developed into clinical assays to aid in the diagnosis, assessment of prognosis, and monitoring of disease progression. Moreover, disease-specific targets are being discovered that may lead to development of new approaches for treatment.

Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist's point of view

Background

For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development. Progress towards solving any of these disparate problems depends upon overcoming the common challenge of interpreting the large data sets generated. Despite interim successes, many data interpretation problems in mass spectrometry are still challenging. Further, though these challenges are inherently interdisciplinary in nature, the significant domain-specific knowledge gap between disciplines makes interdisciplinary contributions difficult.

Results

This paper provides an introduction to the burgeoning field of computational mass spectrometry. We illustrate key concepts, vocabulary, and open problems in MS-omics, as well as provide invaluable resources such as open data sets and key search terms and references.

Conclusions

This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform novel algorithmic solutions to open problems.

Clinical proteomics in obstetrics and neonatology

Clinical proteomics has been applied to the identification of biomarkers of obstetric and neonatal disease. We will discuss a number of encouraging studies that have led to potentially valid biomarkers in the context of Down's syndrome, preterm birth, amniotic infections, preeclampsia, intrauterine growth restriction and obstructive uropathies. Obtaining noninvasive biomarkers (e.g., from the maternal circulation, urine or cervicovaginal fluid) may be more feasible for obstetric diseases than for diseases of the fetus, for which invasive methods are required (e.g., amniotic fluid, fetal urine). However, studies providing validated proteomics-identified biomarkers are limited. Efforts should be made to save well-characterized samples of these invasive body fluids so that many valid biomarkers of pregnancy-related diseases will be identified in the coming years using proteomics based analysis upon adoption of 'clinical proteomics guidelines'.

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.

Classical MALDI-MS versus CE-based ESI-MS proteomic profiling in urine for clinical applications

Human urine is an attractive and informative biofluid for medical diagnosis, which has been shown to reflect the (patho)-physiology of not only the urogenital system, but also others such as the cardiovascular system. For this reason, many studies have concentrated on the study of the urine proteome, aiming to find relevant biomarkers that could be applied in a clinical setting. However, this goal can only be achieved after reliable quantitative and qualitative analysis of the urinary proteome. In the last two decades, MS-based platforms have evolved to become indispensable tools for biomarker research. In this review, we will present and compare two of the most clinically relevant analytical platforms that have been used for the study of the urinary proteome, namely CE-based ESI-MS and classical MALDI-MS. These platforms, although not directly comparable, have been extensively used in proteomic profiling and therefore their comparison is fundamentally relevant to this field.

The Role of Mass Spectrometry in the "Omics" Era

Mass spectrometry (MS) is one of the key analytical technology on which the emerging ''-omics'' approaches are based. It may provide detection and quantization of thousands of proteins and biologically active metabolites from a tissue, body fluid or cell culture working in a ''global'' or ''targeted'' manner, down to ultra-trace levels. It can be expected that the high performance of MS technology, coupled to routine data handling, will soon bring fruit in the request for a better understanding of human diseases, leading to new molecular biomarkers, hence affecting drug targets and therapies. In this review, we focus on the main advances in the MS technologies, influencing genomics, transcriptomics, proteomics, lipidomics and metabolomics fields, up to the most recent MS applications to meta-omic studies.

Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function

BACKGROUND AND OBJECTIVES

Patients with CKD display altered plasma amino acid profiles. This study estimated the association between the estimated GFR and urinary and plasma amino acid profiles in CKD patients.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Urine and plasma samples were taken from 52 patients with different stages of CKD, and plasma samples only were taken from 25 patients on maintenance hemodialysis. Metabolic profiling was performed by liquid chromatography coupled with tandem mass spectrometry after phenylisothiocyanate derivatization.

RESULTS

Most plasma amino acid concentrations were decreased in hemodialysis patients, whereas proline, citrulline, asparagine, asymmetric dimethylarginine, and hydroxykynurenine levels were increased (P<0.05). Both plasma levels and urinary excretion of citrulline were higher in the group of patients with advanced CKD (CKD stages 2 and 3 versus CKD stages 4 and 5; in plasma: 35.9±16.3 versus 61.8±23.6 µmol/L, P<0.01; in urine: 1.0±1.2 versus 7.1±14.3 µmol/mol creatinine, P<0.001). Plasma asymmetric dimethylarginine levels were higher in advanced CKD (CKD stages 2 and 3, 0.57±0.29; CKD stages 4 and 5, 1.02±0.48, P<0.001), whereas urinary excretion was lower (2.37±0.93 versus 1.51±1.43, P<0.001). Multivariate analyses adjusting on estimated GFR, serum albumin, proteinuria, and other covariates revealed associations between diabetes and plasma citrulline (P=0.02) and between serum sodium and plasma asymmetric dimethylarginine (P=0.03). Plasma tyrosine to phenylalanine and valine to glycine ratios were lower in advanced CKD stages (P<0.01).

CONCLUSION

CKD patients have altered plasma and urinary amino acid profiles that are not corrected by dialysis. Depending on solutes, elevated plasma levels were associated with increased or decreased urinary excretion, depicting situations of uremic retention (asymmetric dimethylarginine) or systemic overproduction (citrulline). These results give some insight in the CKD-associated modifications of amino acid metabolism, which may help improve their handling.

Advanced hyphenated chromatographic-mass spectrometry in mycotoxin determination: current status and prospects

Mass spectrometric techniques are essential for advanced research in food safety and environmental monitoring. These fields are important for securing the health of humans and animals, and for ensuring environmental security. Mycotoxins, toxic secondary metabolites of filamentous fungi, are major contaminants of agricultural products, food and feed, biological samples, and the environment as a whole. Mycotoxins can cause cancers, nephritic and hepatic diseases, various hemorrhagic syndromes, and immune and neurological disorders. Mycotoxin-contaminated food and feed can provoke trade conflicts, resulting in massive economic losses. Risk assessment of mycotoxin contamination for humans and animals generally depends on clear identification and reliable quantitation in diversified matrices. Pioneering work on mycotoxin quantitation using mass spectrometry (MS) was performed in the early 1970s. Now, unambiguous confirmation and quantitation of mycotoxins can be readily achieved with a variety hyphenated techniques that combine chromatographic separation with MS, including liquid chromatography (LC) or gas chromatography (GC). With the advent of atmospheric pressure ionization, LC-MS has become a routine technique. Recently, the co-occurrence of multiple mycotoxins in the same sample has drawn an increasing amount of attention. Thus, modern analyses must be able to detect and quantitate multiple mycotoxins in a single run. Improvements in tandem MS techniques have been made to achieve this purpose. This review describes the advanced research that has been done regarding mycotoxin determination using hyphenated chromatographic-MS techniques, but is not a full-circle survey of all the literature published on this topic. The present work provides an overview of the various hyphenated chromatographic-MS-based strategies that have been applied to mycotoxin analysis, with a focus on recent developments. The use of chromatographic-MS to measure levels of mycotoxins, including aflatoxins, ochratoxins, patulin, trichothecenes, zearalenone, and fumonisins, is discussed in detail. Both free and masked mycotoxins are included in this review due to different methods of sample preparation. Techniques are described in terms of sample preparation, internal standards, LC/ultra performance LC (UPLC) optimization, and applications and survey. Several future hyphenated MS techniques are discussed as well, including multidimensional chromatography-MS, capillary electrophoresis-MS, and surface plasmon resonance array-MS.

Biomarkers in IgA nephropathy: relationship to pathogenetic hits

INTRODUCTION

IgA nephropathy, the most prevalent glomerular disease in the world, requires a renal biopsy for diagnosis. Reliable biomarkers are needed for the non-invasive diagnosis of this disease and to more fully delineate its natural history and risk for progression.

AREAS COVERED

In this review, the authors examine serum levels of galactose-deficient IgA1 (Gd-IgA1) and glycan-specific IgG and IgA autoantibodies that are integral to pathogenesis of IgA nephropathy. They also explore biomarkers related to alternative and lectin pathways of complement activation and serum and urinary peptide biomarkers detected by mass spectrometric methods. The literature search included review of all publications having IgA nephropathy in the title that were cited in PubMed and Scopus over the past 10 years and a non-systematic review of abstracts published for the annual meetings of the American Society of Nephrology and the International Symposia on IgA Nephropathy.

EXPERT OPINION

Serum Gd-IgA1 level and glycan-specific autoantibody levels are prime candidates to become diagnostic biomarkers for IgA nephropathy because of their central role in the earliest stages of disease pathogenesis. Assays for serum levels of complement proteins C3 and factor H are readily available in clinical practice and deserve continued study, either alone or in tandem with total serum IgA or serum Gd-IgA1 levels, as prognostic biomarkers for patients with IgA nephropathy. Urinary peptidomic data are also reviewed because this approach can successfully differentiate patients with IgA nephropathy from healthy controls and from patients with other forms of renal disease.

CKD273, a new proteomics classifier assessing CKD and its prognosis

National Kidney Foundation CKD staging has allowed uniformity in studies on CKD. However, early diagnosis and predicting progression to end stage renal disease are yet to be improved. Seventy six patients with different levels of CKD, including outpatients and dialysed patients were studied for transcriptome, metabolome and proteome description. High resolution urinary proteome analysis was blindly performed in the 53 non-anuric out of the 76 CKD patients. In addition to routine clinical parameters, CKD273, a urinary proteomics-based classifier and its peptides were quantified. The baseline values were analyzed with regard to the clinical parameters and the occurrence of death or renal death during follow-up (3.6 years) as the main outcome measurements. None of the patients with CKD273<0.55 required dialysis or died while all fifteen patients that reached an endpoint had a CKD273 score >0.55. Unsupervised clustering analysis of the CKD273 peptides separated the patients into two main groups differing in CKD associated parameters. Among the 273 biomarkers, peptides derived from serum proteins were relatively increased in patients with lower glomerular filtration rate, while collagen-derived peptides were relatively decreased (p<0.05; Spearman). CKD273 was different in the groups with different renal function (p<0.003). The CKD273 classifier separated CKD patients according to their renal function and informed on the likelihood of experiencing adverse outcome. Recently defined in a large population, CKD273 is the first proteomic-based classifier successfully tested for prognosis of CKD progression in an independent cohort.

Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study

Treatment options for autosomal dominant polycystic kidney disease (ADPKD) will likely become available in the near future, hence reliable diagnostic and prognostic biomarkers for the disease are strongly needed. Here, we aimed to define urinary proteomic patterns in ADPKD patients, which aid diagnosis and risk stratification. By capillary electrophoresis online coupled to mass spectrometry (CE-MS), we compared the urinary peptidome of 41 ADPKD patients to 189 healthy controls and identified 657 peptides with significantly altered excretion, of which 209 could be sequenced using tandem mass spectrometry. A support-vector-machine based diagnostic biomarker model based on the 142 most consistent peptide markers achieved a diagnostic sensitivity of 84.5% and specificity of 94.2% in an independent validation cohort, consisting of 251 ADPKD patients from five different centers and 86 healthy controls. The proteomic alterations in ADPKD included, but were not limited to markers previously associated with acute kidney injury (AKI). The diagnostic biomarker model was highly specific for ADPKD when tested in a cohort consisting of 481 patients with a variety of renal and extrarenal diseases, including AKI. Similar to ultrasound, sensitivity and specificity of the diagnostic score depended on patient age and genotype. We were furthermore able to identify biomarkers for disease severity and progression. A proteomic severity score was developed to predict height adjusted total kidney volume (htTKV) based on proteomic analysis of 134 ADPKD patients and showed a correlation of r = 0.415 (p<0.0001) with htTKV in an independent validation cohort consisting of 158 ADPKD patients. In conclusion, the performance of peptidomic biomarker scores is superior to any other biochemical markers of ADPKD and the proteomic biomarker patterns are a promising tool for prognostic evaluation of ADPKD.

Vascular proteomics

Cardiovascular diseases constitute the largest of death in developed countries, being atherosclerosis the major contributor. Atherosclerosis is a process of chronic inflammation, characterized by the accumulation of lipids, cells, and fibrous elements in medium and large arteries. There is a continuum in atherosclerotic cardiovascular pathology that extends from the initial endothelial damage to diseases such as angina, myocardial infarction, and stroke. The extent of inflammation, proteolysis, calcification, and neovascularization influences the development of advanced lesions (atheroma plaques) on the arteries. Plaque rupture and the ensuing thrombosis cause the acute complications of atherosclerosis, i.e., myocardial infarction and cerebral ischemia. Thus, identification of early biomarkers of plaque unstability and susceptibility to rupture is of capital importance in preventing acute events. In recent years proteomics has been successfully applied to study proteins involved in these pathological processes. Thus, proteomic studies have been carried out focusing on different elements such as vascular tissues (arteries), artery layers, cells looking at proteomes and secretomes, plasma/serum, exosomes, lipoproteins, and metabolites. This chapter will provide an overview of latest advances in proteomic studies of atherosclerosis and related vascular diseases.

Urinary proteomics based on capillary electrophoresis coupled to mass spectrometry in kidney disease

Urine is an excellent sample source in the proteomic study of diseases. It is available in large quantities, is relatively stable, is not contaminated by cells or lipids, and has shown to provide information not only on the organs in contact with the urinary tract but also of more remote organs and tissues. In addition to these qualities, it can be collected by untrained personnel. For these reasons, urinary proteomic studies have escalated in recent years with the aim of identifying biomarkers that could be use for diagnosis or to predict the outcome of renal pathologies. In this chapter, we present one of the analytical platforms that has been successfully used in a number of studies for the identification and validation of biomarkers in kidney diseases. This technique is capillary electrophoresis coupled online to an electrospray ionization time-of-flight mass spectrometer (CE-MS). This technology has proven to be highly reproducible, sensitive with a quick analysis time, important features when analytical platforms have to be used in a clinical setting.

Evaluation of the Zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level.

Urinary proteomics for early diagnosis in diabetic nephropathy

Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis-coupled mass spectrometry was used to profile the low-molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3-5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.

Capillary electrophoresis/mass spectrometry relevant to pharmaceutical and biotechnological applications

Advanced analytical techniques play a crucial role in the pharmaceutical and biotechnological field. In this context, capillary electrophoresis/mass spectrometry (CE/MS) has attracted attention due to efficient and selective separation in combination with powerful detection allowing identification and detailed characterization. Method developments and applications of CE/MS have been focused on questions not easily accessible by liquid chromatography/mass spectrometry (LC/MS) as the analysis of intact proteins, carbohydrates, and various small molecules, including peptides. Here, recent approaches and applications of CE/MS relevant to (bio)pharmaceuticals are reviewed and discussed to show actual developments and future prospects. Based on other reviews on related subjects covering large parts of previous works, the paper is focused on general ideas and contributions of the last 2 years; for the analysis of glycans, the period is extended back to 2006.

Untargeted screening of urinary peptides with liquid chromatography coupled to hybrid linear-ion trap tandem mass spectrometry

We formerly developed and applied a liquid chromatography coupled with hybrid triple quadrupole-linear ion trap mass spectrometry technique for the detection and identification of exogenous compounds in clinical and forensic toxicology. In this study, we aimed to adapt this technique to the detection and identification of the constituents of the urinary peptidome. After solid-phase extraction, separation was performed using gradient reversed-phase liquid chromatography. The mass spectrometer was operated in the information-dependent acquisition mode, switching between: a survey scan acquired in the enhanced multi-charged scan mode with dynamic subtraction of background noise; and two dependent scans obtained in the enhanced product ion scan mode. The results obtained show that: (i) the present procedure is able to detect and identify peptides which, together with their inferred parent proteins, are similar to those referenced in the related literature; (ii) the structure of some peptides can generally be resolved from their enhanced product ion spectra; and (iii) confirmation of the sequences proposed through library search by in silico verification of the fragments observed in the enhanced product ion spectra seems to be indispensable to avoid misinterpretations.

How to get proteomics to the clinic? Issues in clinical proteomics, exemplified by CE-MS

Clinical proteomics is defined as application of proteome analysis aiming at improving the current clinical situation. As such, the success of clinical proteomics should be assessed based on the clinical impact following implementation of the findings. While we have experienced significant technological advancements in mass spectrometry in the last years, based on the above measure, this has not at all resulted in similar advancements in clinical proteomics. Although a large number of proteomic biomarkers have been described, most of them were not subsequently validated, and certainly have had no impact in clinical decision making as yet. Under the current conditions, it appears likely that the situation will not change significantly: we will be flooded by reports on biomarkers, but not see any implementation. In this article, some key issues in proteomic biomarker research are pinpointed, based on the experience with CE-MS, likely also holding true for biomarkers resulting from other analysis domains.

On-line CE/ESI/MS interfacing: recent developments and applications in proteomics

After shining as the ultimate separation - sequencing technique used for the successful completion of the Human Genome Project, in the early 2000s CE experienced lowered popularity among separation scientists. The renewed interest in recent years relates to the separation needs, especially in proteomics, metabolomics, and glycomics, where CE complements liquid chromatography techniques. This interest is further boosted by the regulators requiring additional separation techniques for characterization of newly developed pharmaceuticals. This paper gives a short overview of recent developments in the on-line interfacing of CE separation techniques with electrospray ionization/mass spectrometric analysis. Both the instrumentation and selected CE/ESI/MS applications including analyses of peptides, proteins, and glycans are discussed with the stress on research published in the past 3 years. Techniques related to the proteomic and glycomic analyses such as sample preconcentration, on-line protein digestion, and analyte derivatization prior CE/ESI/MS analysis are also included.

Analysis of plasma nucleotides in rat by micellar electrokinetic capillary chromatography/electrospray ionization mass spectrometry

RATIONALE

The aim of this study was to establish a simultaneous quantitative analysis method of nine endogenous nucleotides in rat plasma using micellar electrokinetic capillary chromatography/electrospray ionization mass spectrometry (MEKC/ESI-MS).

METHODS

To select the optimum conditions for separation of the nucleotides, various pH, concentrations of running buffers and surfactants were tested. Ammonium acetate (20 mM) containing the surfactant dodecyltrimethylammonium bromide (2 mM, pH 3.5) was selected as the micellar running buffer. The plasma samples were prepared by precipitating the proteins with 2 mM EDTA in 60% ethanol. The samples were analyzed using capillary electrophoresis (CE)/MS and selected ion monitoring (SIM) mode with positive ionization. CE was performed using a silica capillary column in reversed polarity mode.

RESULTS

The limits of detection (LODs) and limits of quantification (LOQs) of the nucleotides ranged from 0.05-5 and 2.0-20 μM, respectively. The calibration curves were linear (R(2) >0.99) for all analytes, and the accuracy and precision were within ±15%. The developed method was applied to the analysis of nucleotides in rat plasma that was collected after oral administration of acetaminophen (1000 mg/kg/day) to evaluate the changes in plasma nucleotide levels under hepatotoxic conditions.

CONCLUSIONS

Decreased level of GTP and increased level of cytosine nucleotides were found to be associated with liver toxicity, which led to the conclusion that liver toxicity is closely related to changes in nucleotide levels in plasma.

Proteomics in hypertension and other cardiovascular diseases

Hypertension is a major cardiovascular risk factor with a multifactorial pathogenesis, including genetic and environmental factors. In addition to hypothesis-driven strategies, unbiased approaches such as genomics, proteomics, and metabolomics are useful tools to help unravel the pathophysiology of hypertension and associated organ damage. During development of cardiovascular disease the key organs and tissues undergo extensive functional and structural changes that are characterized by alterations in the amount and type of proteins that are expressed. Proteomic approaches study the expression of large numbers of proteins in organs, tissues, cells, and body fluids. A number of different proteomic platforms are available, many of which combine two methods to separate proteins and peptides after an initial digestion step. Identification of these peptides and changes in their expression in parallel with disease processes or medical treatment will help to identify as yet unknown pathophysiological pathways. There is also potential to use proteomic signatures as biomarkers of cardiovascular disease that will contribute to population screening, diagnosis of diseases and their severity, and monitoring of therapeutic interventions.

Urinary proteomics to support diagnosis of stroke

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (−0.30 to 1.25) in cases compared to −1.04 (IQR −1.86 to −0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials.

Proteomic candidate biomarkers of drug-induced nephrotoxicity in the rat

Improved biomarkers of acute nephrotoxicity are coveted by the drug development industry, regulatory agencies, and clinicians. In an effort to identify such biomarkers, urinary peptide profiles of rats treated with two different nephrotoxins were investigated. 493 marker candidates were defined that showed a significant response to cis-platin comparing a cis-platin treated cohort to controls. Next, urine samples from rats that received three consecutive daily doses of 150 or 300 mg/kg gentamicin were examined. 557 potential biomarkers were initially identified; 108 of these gentamicin-response markers showed a clear temporal response to treatment. 39 of the cisplatin-response markers also displayed a clear response to gentamicin. Of the combined 147 peptides, 101 were similarly regulated by gentamicin or cis-platin and 54 could be identified by tandem mass spectrometry. Most were collagen type I and type III fragments up-regulated in response to gentamicin treatment. Based on these peptides, classification models were generated and validated in a longitudinal study. In agreement with histopathology, the observed changes in classification scores were transient, initiated after the first dose, and generally persistent over a period of 10–20 days before returning to control levels. The data support the hypothesis that gentamicin-induced renal toxicity up-regulates protease activity, resulting in an increase in several specific urinary collagen fragments. Urinary proteomic biomarkers identified here, especially those common to both nephrotoxins, may serve as a valuable tool to investigate potential new drug candidates for the risk of nephrotoxicity.

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.

Urinary proteomics in the assessment of chronic kidney disease

PURPOSE OF REVIEW

Urinary proteomics has emerged as an approach that could deliver relevant clinical information. In this review, we aim at highlighting the recent developments, especially with respect to clinical implementation. We review several of the recent publications reporting on larger cohorts, focusing on those that aim at qualification and/or validation of urinary proteomics biomarkers.

RECENT FINDINGS

Several components of the urinary proteome, especially its low molecular weight fraction (sometimes referred to as the 'peptidome'), have been significantly associated with chronic kidney disease (CKD). Independent studies, encompassing sometimes close to 1000 independent samples, indicate that specific peptides from extracellular matrix (ECM) proteins encompass a major component of the urinary proteome. Highly significant changes in the abundance of some of these peptides are associated with CKD indicating that alterations in ECM, reflected via the urinary proteome, may represent an early stage in CKD pathology. These peptides may serve as specific early biomarkers, and interference with pathological ECM accumulation may be a valuable new therapeutic approach in CKD.

SUMMARY

Urinary proteomic biomarkers have emerged as clinically relevant variables. First studies involving several hundred individuals indicate a potential added benefit of urinary proteomic biomarkers. First large clinical trials are being initiated to employ urinary proteomics in clinical decision making.

Biomarker analysis for prostate cancer diagnosis using LC-MS and CE-MS

Prostate cancer is one of the most common cancer types in men. In addition, it is the second leading cause of cancer death in the USA and Canada. Prostate cancer diagnosis is not a precise science yet. Discovery of potential biomarkers for early prostate cancer diagnosis and monitoring is crucially important. LC-MS and CE-MS have been widely used analytical techniques in the biomarker discovery. This review will describe the applications of LC-MS with different ionization techniques, such as ESI, atmospheric-pressure photoionization and atmospheric-pressure chemical ionization, and CE-MS techniques used in prostate cancer biomarker analysis.

A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

BACKGROUND

Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue.

RESULTS

The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations.

CONCLUSIONS

We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific can be interpreted as a sub-mode and retained for further analysis to identify potential biomarkers. As opposed to standard matrix factorization methods this can be achieved on a sample (experiment)-by-sample basis. Postulating one or more components with indifferent features enables their removal from disease and control specific components on a sample-by-sample basis. This yields selected components with reduced complexity and generally, it increases prediction accuracy.