Capillary electrophoresis applied to proteomic analysis

In the postgenomic era, proteomics has become a dominant field for identifying and quantifying the complex protein machinery of the cell. The expression levels, posttranslational modifications, and specific interactions of proteins control the biology of such processes as development, differentiation, and signal transduction. Studies of the proteins involved in these processes often lead to a better understanding of biology and of human disease. Powerful separation techniques and sensitive detection methods enable researchers to untangle these complicated networks of processes. CE coupled with either MS or LIF are two of the techniques that make this possible. This review will cover proven CE-based methods for proteomics on the cell and tissue level and their application in biological and clinical studies, relevant new developments in enabling technology such as microfluidic CE-MS demonstrated on model systems, and comment on the future of CE in proteomics.

Evaluation of urine proteome pattern analysis for its potential to reflect coronary artery atherosclerosis in symptomatic patients

Coronary artery disease (CAD) is a major cause of mortality and morbidity. Noninvasive proteome analysis could guide clinical evaluation and early/preventive treatment. Under routine clinical conditions, urine of 67 patients presenting with symptoms suspicious for CAD were analyzed by capillary electrophoresis directly coupled with mass spectrometry (CE-MS). All patients were subjected to coronary angiography and either assigned to a CAD or non-CAD group. A training set of 29 patients was used to establish CAD and non-CAD-associated proteome patterns of plasma as well as urine. Significant discriminatory power was achieved in urine but not in plasma. Therefore, urine proteomic analysis of further 38 patients was performed in a blinded study. A combination of 17 urinary polypeptides allowed separation of both groups in the test set with a sensitivity of 81%, a specificity of 92%, and an accuracy of 84%. Sequencing of urinary marker peptides identified fragments of collagen alpha1 (I and III), which we furthermore demonstrated to be expressed in atherosclerotic plaques of human aorta. In conclusion, specific CE-MS polypeptide patterns in urine were associated with significant CAD in patients with angina-typical symptoms. These promising findings need to be further evaluated in regard to reliability of a urine-based screening method with the potential of improving the diagnostic approaches for CAD.

Recent advances in atherosclerosis-based proteomics: new biomarkers and a future perspective

Vascular proteomics is providing two main types of data: proteins that actively participate in vascular pathophysiological processes and novel protein candidates that can potentially serve as useful clinical biomarkers. Although both types of proteins can be identified by similar proteomic strategies and methods, it is important to clearly distinguish biomarkers from mediators of disease. A particular protein, or group of proteins, may participate in a pathogenic process but not serve as an effective biomarker. Alternatively, a useful biomarker may not mediate pathogenic pathways associated with disease (i.e., C-reactive protein). To date, there are no clear successful examples in which discovery proteomics has led to a novel useful clinical biomarker in cardiovascular diseases. Nevertheless, new sources of biomarkers are being explored (i.e., secretomes, circulating cells, exosomes and microparticles), an increasing number of novel proteins involved in atherogenesis are constantly described, and new technologies and analytical strategies (i.e., quantitative proteomics) are being developed to access low abundant proteins. Therefore, this presages a new era of discovery and a further step in the practical application to diagnosis, prognosis and early action by medical treatment of cardiovascular diseases.

Adapting mass spectrometry-based platforms for clinical proteomics applications: The capillary electrophoresis coupled mass spectrometry paradigm

Single biomarker detection is common in clinical laboratories due to the currently available method spectrum. For various diseases, however, no specific single biomarker could be identified. A strategy to overcome this diagnostic void is to shift from single analyte detection to multiplexed biomarker profiling. Mass spectrometric methods were employed for biomarker discovery in body fluids. The enormous complexity of biofluidic proteome compartments implies upstream fractionation. For this reason, mass spectrometry (MS) was coupled to two-dimensional gel electrophoresis, liquid chromatography, surface-enhanced laser desorption/ionization, or capillary electrophoresis (CE). Differences in performance and operating characteristics make them differentially suited for routine laboratory applications. Progress in the field of clinical proteomics relies not only on the use of an adequate technological platform, but also on a fast and efficient proteomic workflow including standardized sample preparation, proteomic data processing, statistical validation of biomarker selection, and sample classification. Based on CE-MS analysis, we describe how proteomic technology can be implemented in a clinical laboratory environment. In the last part of this review, we give an overview of CE-MS-based clinical studies and present information on identity and biological significance of the identified peptide biomarkers providing evidence of disease-induced changes in proteolytic processing and posttranslational modification.

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

BACKGROUND

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

METHODS

A CAD score calculated by summing the product of a classification coefficient and signal amplitude of 15 urinary polypeptides was previously developed. Five sequences of biomarkers in the panel were identified as fragments of collagen alpha-1(I) and alpha-1(III). Prospectively collected urine samples available for analysis from 19 out of 20 individuals with CAD (15 with type 1 diabetes [T1D] and four without diabetes) and age-, sex-, and diabetes-matched controls enrolled in the Coronary Artery Calcification in Type 1 Diabetes study were analyzed for the CAD score using capillary electrophoresis and electrospray ionization mass spectrometry. Two panels of biomarkers that were previously defined to distinguish diabetes status were analyzed to determine their relationship to T1D. Three biomarker panels developed to distinguish DN (DNS) and two biomarker panels developed to distinguish renal disease (RDS) were examined to determine their relationship with renal function.

RESULTS

The CAD score was associated with CAD (odds ratio with 95% confidence interval, 2.2 [1.3-5.2]; P = 0.0016) and remained significant when adjusted individually for age, albumin excretion rate (AER), blood pressure, waist circumference, intraabdominal fat, glycosylated hemoglobin, and lipids. DNS and RDS were significantly correlated with AER, cystatin C, and serum creatinine. The biomarker panels for diabetes were both significantly associated with T1D status (P < 0.05 for both).

CONCLUSIONS

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

Novel biomarkers for predicting preeclampsia

Preeclampsia is a major cause of maternal morbidity and mortality worldwide. Despite decades of research into the condition, the ability of clinicians to predict preeclampsia prior to the onset of symptoms has not improved significantly. In this review, we will examine the pathophysiology underlying preeclampsia and will look at potential biomarkers for early prediction and diagnosis. In addition, we will explore potential future areas of research into the condition.

Novel automated biomarker discovery work flow for urinary peptidomics

BACKGROUND

Urine is potentially a rich source of peptide biomarkers, but reproducible, high-throughput peptidomic analysis is often hampered by the inherent variability in factors such as pH and salt concentration. Our goal was to develop a generally applicable, rapid, and robust method for screening large numbers of urine samples, resulting in a broad spectrum of native peptides, as a tool to be used for biomarker discovery.

METHODS

Peptide samples were trapped, desalted, pH-normalized, and fractionated on a miniaturized automatic reverse-phase strong cation exchange (RP-SCX) cartridge system. We analyzed eluted peptides using MALDI-TOF, Fourier transform ion cyclotron resonance, and liquid chromatography-iontrap mass spectrometry. We determined qualitative and quantitative reproducibility of the system and robustness of the method using BSA digests and urine samples, and we used a selected set of urine samples from Schistosoma haematobium-infected individuals to evaluate clinical applicability.

RESULTS

The automated RP-SCX sample cleanup and fractionation system exhibits a high qualitative and quantitative reproducibility, with both BSA standards and urine samples. Because of the relatively high cartridge binding capacity (1-2 mL urine), eluted peptides can be measured with high sensitivity using multiple mass spectrometric techniques. As proof of principle, hemoglobin-derived peptides were identified in urine samples from S. haematobium-infected individuals, even when the microhematuria test was negative.

CONCLUSIONS

We present a practical, step-by-step method for screening and identification of urinary peptides. Alongside the analytical method evaluation on standard samples, we demonstrate its feasibility with actual clinical material.

The genetics of cardiovascular disease

Recent advances in genotyping technology and insights into disease mechanisms have increased interest in the genetics of cardiovascular disease. Several candidate genes involved in cardiovascular diseases were identified from studies using animal models, and the translation of these findings to human disease is an exciting challenge. There is a trend towards large-scale genome-wide association studies that are subject to strict quality criteria with regard to both genotyping and phenotyping. Here, we review some of the strategies that have been developed to translate findings from experimental models to human disease and outline the need for optimizing global approaches to analyze such results. Findings from ongoing studies are interpreted in the context of disease pathways instead of the more traditional focus on single genetic variants.

Urine in clinical proteomics

Urine has become one of the most attractive biofluids in clinical proteomics as it can be obtained non-invasively in large quantities and is stable compared with other biofluids. The urinary proteome has been studied by almost any proteomics technology, but mass spectrometry-based urinary protein and peptide profiling has emerged as most suitable for clinical application. After a period of descriptive urinary proteomics the field is moving out of the discovery phase into an era of validation of urinary biomarkers in larger prospective studies. Although mainly due to the site of production of urine, the majority of these studies apply to the kidney and the urinary tract, but recent data show that analysis of the urinary proteome can also be highly informative on non-urogenital diseases and used in their classification. Despite this progress in urinary biomarker discovery, the contribution of urinary proteomics to the understanding of the pathophysiology of disease upon analysis of the urinary proteome is still modest mainly because of problems associated to sequence identification of the biomarkers. Until now, research has focused on the highly abundant urinary proteins and peptides, but analysis of the less abundant and naturally existing urinary proteins and peptides still remains a challenge. In conclusion, urine has evolved as one of the most attractive body fluids in clinical proteomics with potentially a rapid application in the clinic.

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

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

Urinary proteomics in diabetes and CKD

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