Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

BACKGROUND

Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.

METHODS

Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.

RESULTS

A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.

CONCLUSIONS

Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.

METHODS AND RESULTS

We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.

CONCLUSIONS

This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.

The clinical potential of prm-PASEF mass spectrometry

INTRODUCTION

The continuous technical improvement in sensitivity and specificity placed mass spectrometry as an alternative method for analyzing clinical samples. In parallel to the rapid development of discovery proteomics, targeted acquisition has been implemented as a complementary option for measuring a small set of proteins with high sensitivity and robustness in a large sample cohort. The combination of trapped ion mobility with a rapid time-of-flight (TOF) mass spectrometer improves the sensitivity even further and triggers the development of prm-PASEF.

AREAS COVERED

This article discusses the development of prm-PASEF and its advantages over the existing targeted and discovery methods for analyzing clinical samples. We are also highlighting the different requirements for the use of prm-PASEF on clinical samples.

EXPERT OPINION

prm-PASEF takes advantage of a dual ion-mobility trap enabling highly multiplexed targeted acquisition. It allows the implementation of a short chromatographic separation setup without sacrificing the number of targeted peptides. Analyzing clinical samples by prm-PASEF holds the promise to significantly improve throughput while maintaining sensitivity to detect the selected target proteins.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Possible Susceptibility Genes for Intervention against Chemotherapy-Induced Cardiotoxicity

Recent therapeutic advances have significantly improved the short- and long-term survival rates in patients with heart disease and cancer. Survival in cancer patients may, however, be accompanied by disadvantages, namely, increased rates of cardiovascular events. Chemotherapy-related cardiac dysfunction is an important side effect of anticancer therapy. While advances in cancer treatment have increased patient survival, treatments are associated with cardiovascular complications, including heart failure (HF), arrhythmias, cardiac ischemia, valve disease, pericarditis, and fibrosis of the pericardium and myocardium. The molecular mechanisms of cardiotoxicity caused by cancer treatment have not yet been elucidated, and they may be both varied and complex. By identifying the functional genetic variations responsible for this toxicity, we may be able to improve our understanding of the potential mechanisms and pathways of treatment, paving the way for the development of new therapies to target these toxicities. Data from studies on genetic defects and pharmacological interventions have suggested that many molecules, primarily those regulating oxidative stress, inflammation, autophagy, apoptosis, and metabolism, contribute to the pathogenesis of cardiotoxicity induced by cancer treatment. Here, we review the progress of genetic research in illuminating the molecular mechanisms of cancer treatment-mediated cardiotoxicity and provide insights for the research and development of new therapies to treat or even prevent cardiotoxicity in patients undergoing cancer treatment. The current evidence is not clear about the role of pharmacogenomic screening of susceptible genes. Further studies need to done in chemotherapy-induced cardiotoxicity.

High-throughput metabolite profiling: identification of plasma taurine as a potential biomarker of functional outcome after aneurysmal subarachnoid hemorrhage

OBJECTIVE

Metabolite profiling (or metabolomics) can identify candidate biomarkers for disease and potentially uncover new pathways for intervention. The goal of this study was to identify potential biomarkers of functional outcome after subarachnoid hemorrhage (SAH).

METHODS

The authors performed high-throughput metabolite profiling across a broad spectrum of chemical classes (163 metabolites) on plasma samples taken from 191 patients with SAH who presented to Massachusetts General Hospital between May 2011 and October 2016. Samples were drawn at 3 time points following ictus: 0-5, 6-10, and 11-14 days. Elastic net (EN) and LASSO (least absolute shrinkage and selection operator) machine learning analyses were performed to identify metabolites associated with 90-day functional outcomes as assessed by the modified Rankin Scale (mRS). Additional univariate and multivariate analyses were then conducted to further examine the relationship between metabolites and clinical variables and 90-day functional outcomes.

RESULTS

One hundred thirty-seven (71.7%) patients with aneurysmal SAH met the criteria for inclusion. A good functional outcome (mRS score 0-2) at 90 days was found in 79 (57.7%) patients. Patients with good outcomes were younger (p = 0.002), had lower admission Hunt and Hess grades (p < 0.0001) and modified Fisher grades (p < 0.0001), and did not develop hydrocephalus (p < 0.0001) or delayed cerebral ischemia (DCI) (p = 0.049). EN and LASSO machine learning methods identified taurine as the leading metabolite associated with 90-day functional outcome (p < 0.0001). Plasma concentrations of the amino acid taurine from samples collected between days 0 and 5 after aneurysmal SAH were 21.9% (p = 0.002) higher in patients with good versus poor outcomes. Logistic regression demonstrated that taurine remained a significant predictor of functional outcome (p = 0.013; OR 3.41, 95% CI 1.28-11.4), after adjusting for age, Hunt and Hess grade, modified Fisher grade, hydrocephalus, and DCI.

CONCLUSIONS

Elevated plasma taurine levels following aneurysmal SAH predict a good 90-day functional outcome. While experimental evidence in animals suggests that this effect may be mediated through downregulation of pro-inflammatory cytokines, additional studies are required to validate this hypothesis in humans.

The cardiovascular robustness hypothesis: Unmasking young adults' hidden risk for premature cardiovascular death

An undetected high risk for premature death of cardiovascular disease (CVD) among individuals with low-to-moderate risk factor levels is an acknowledged obstacle to CVD prevention. In this paper, we present the hypothesis that the vasculature's robustness against risk factor load will complement conventional risk factor models as a novel stratifier of risk. Figuratively speaking, mortality risk prediction without robustness scoring is akin to predicting the breaking risk of a lake's ice sheet considering load only while disregarding the sheet's bearing strength. Taking the cue from systems biology, which defines robustness as the ability to maintain function against internal and external challenges, we develop a robustness score from the physical parameters that comprehensively quantitate cardiovascular function. We derive the functional parameters using a recently introduced novel system, VascAssist 2 (iSYMED GmbH, Butzbach, Germany). VascAssist 2 (VA) applies the electronic-hydraulic analogy to a digital model of the arterial tree, replicating non-invasively acquired pule pressure waves by modulating the electronic equivalents of the physical parameters that describe in vivo arterial hemodynamics. As the latter is also subject to aging-associated degeneration which (a) progresses at inter-individually different rates, and which (b) affects the biomarker-mortality association, we express the robustness score as a correction factor to calendar age (CA), the dominant risk factor in all CVD risk factor models. We then propose a method for the validation of the score against known time-to-event data in reference populations. Our conceptualization of robustness implies that risk factor-challenged individuals with low robustness scores will face preferential elimination from the population resulting in a significant robustness-CA correlation in this strata absent in the unchallenged stratum. Hence, we also present an outline of a cross-sectional study design suitable to test this hypothesis. We finally discuss the objections that may validly be raised against our robustness hypothesis, and how available evidence encourages us to refute these objections.

Proteomic analysis of the cardiac extracellular matrix: clinical research applications

INTRODUCTION

The cardiac extracellular matrix (ECM) provides anatomical, biochemical, and physiological support to the left ventricle. ECM proteins are difficult to detect using unbiased proteomic approaches due to solubility issues and a relatively low abundance compared to cytoplasmic and mitochondrial proteins present in highly prevalent cardiomyocytes. Areas covered: Proteomic capabilities have dramatically improved over the past 20 years, due to enhanced sample preparation protocols and increased capabilities in mass spectrometry (MS), database searching, and bioinformatics analysis. This review summarizes technological advancements made in proteomic applications that make ECM proteomics highly feasible. Expert commentary: Proteomic analysis of the ECM provides an important contribution to our understanding of the molecular and cellular processes associated with cardiovascular disease. Using results generated from proteomics approaches in basic science applications and integrating proteomics templates into clinical research protocols will aid in efforts to personalize medicine.

Involvement of oxidative modification of proteins related to ATP synthesis in the left ventricles of hamsters with cardiomyopathy

Inflammation enhanced by accumulation of reactive oxygen species plays an essential role in the progression of cardiovascular diseases. Using the 2D-oxyblot analysis and 2D-difference image gel electrophoresis (2D-DIGE), we compared the levels of ROS-induced carbonyl modification of myocardial proteins in the whole left ventricles between 6-week-old hamsters with dilated (TO-2) and hypertrophic cardiomyopathy (Bio14.6) and control hamsters (F1B). Then, 2D electrophoresis combined with MALDI-TOF/TOF tandem mass spectrometry detected 18 proteins with increased carbonyl level in cardiomyopathy hamsters compared with control hamster. Carbonyl modification of proteins related to ATP synthesis, including citric acid cycle and electron transport system, was observed in the hearts of hamsters with both types of cardiomyopathy. Further analysis indicated that left ventricular carbonyl production correlated negatively with succinyl-CoA:3-ketoacid-coenzyme A transferase 1 activity (r 2 = 0.60, P = 0.0007) and ATP concentration (r 2 = 0.29, P = 0.037), suggesting that protein carbonylation has negative effects on the levels of these biomolecules. Furthermore, carbonyl production significantly correlated with plasma Troponin T level (r 2 = 0.33, P = 0.026). Reduction of energy metabolism by oxidative damage may contribute to the development of left ventricular impairment in cardiomyopathy.

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

Ischemia/Reperfusion

Ischemic disorders, such as myocardial infarction, stroke, and peripheral vascular disease, are the most common causes of debilitating disease and death in westernized cultures. The extent of tissue injury relates directly to the extent of blood flow reduction and to the length of the ischemic period, which influence the levels to which cellular ATP and intracellular pH are reduced. By impairing ATPase-dependent ion transport, ischemia causes intracellular and mitochondrial calcium levels to increase (calcium overload). Cell volume regulatory mechanisms are also disrupted by the lack of ATP, which can induce lysis of organelle and plasma membranes. Reperfusion, although required to salvage oxygen-starved tissues, produces paradoxical tissue responses that fuel the production of reactive oxygen species (oxygen paradox), sequestration of proinflammatory immunocytes in ischemic tissues, endoplasmic reticulum stress, and development of postischemic capillary no-reflow, which amplify tissue injury. These pathologic events culminate in opening of mitochondrial permeability transition pores as a common end-effector of ischemia/reperfusion (I/R)-induced cell lysis and death. Emerging concepts include the influence of the intestinal microbiome, fetal programming, epigenetic changes, and microparticles in the pathogenesis of I/R. The overall goal of this review is to describe these and other mechanisms that contribute to I/R injury. Because so many different deleterious events participate in I/R, it is clear that therapeutic approaches will be effective only when multiple pathologic processes are targeted. In addition, the translational significance of I/R research will be enhanced by much wider use of animal models that incorporate the complicating effects of risk factors for cardiovascular disease. © 2017 American Physiological Society. Compr Physiol 7:113-170, 2017.

Urinary Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy

The last decade has seen a surge in publications describing novel biomarkers for early detection of diabetic nephropathy (DN), but as yet none have outperformed albuminuria in well-designed prospective studies. This is partially attributable to our incomplete understanding of the many complex interrelated mechanisms underlying DN development, a heterogeneous process unlikely to be captured by a single biomarker. Proteomics offers the advantage of simultaneously analysing the entire protein content of a biological sample, and the technique has gained attention as a potential tool for a more accurate diagnosis of disease at an earlier stage as well as a means by which to unravel the pathogenesis of complex diseases such as DN using an untargeted approach. This review will discuss the potential of proteomics as both a clinical and research tool, evaluating exploratory work in animal models as well as diagnostic potential in human subjects.

Current Trends and Future Perspectives of State-of-the-Art Proteomics Technologies Applied to Cardiovascular Disease Research

The use of mass spectrometry (MS)-dependent protein research is increasing in the cardiovascular sciences. A major reason for this is the versatility of and ability for MS technologies to accommodate a variety of biological questions such as those pertaining to basic research and clinical applications. In addition, mass spectrometers are becoming easier to operate, and require less expertise to run standard proteomics experiments. Nonetheless, despite the increasing interest in proteomics, many non-expert end users may not be as familiar with the variety of mass spectrometric tools and workflows available to them. We therefore review the major strategies used in unbiased and targeted MS, while providing specific applications in cardiovascular research. Because MS technologies are developing rapidly, it is important to understand the core concepts, strengths and weaknesses. Most importantly, we hope to inspire the further integration of this exciting technology into everyday research in the cardiovascular sciences. (Circ J 2016; 80: 1674-1683).

Soluble Glycoprotein 130 and Heat Shock Protein 27 as Novel Candidate Biomarkers of Chronic Heart Failure with Preserved Ejection Fraction

BACKGROUND

Despite their importance, the current clinical biomarkers of chronic heart failure have limitations. In this study, soluble glycoprotein 130 (sgp130), heat shock protein 27 (hsp27), dipeptidyl peptidase IV (dpp4) and cathepsin S (CTSS) were tested for their potential as novel biomarkers for diagnosing chronic heart failure (CHF) with preserved ejection fraction.

METHODS

We compared the circulating levels of sgp130, hsp27, dpp4, and cathepsin S in patients with CHF with preserved ejection fraction (n=50) and in controls (n=50), determined how well these candidate biomarkers distinguish patients with CHF from controls, and assessed whether these candidates are superior to N-terminal pro brain natriuretic peptide (NT-pro-BNP) as diagnostic tools.

RESULTS

After adjusting for clinical covariates, patients with CHF showed significantly higher mean concentrations of sgp130 (317.38pg/ml vs. 215.90 pg/ml), hsp27 (2601.02 pg/ml vs. 923.61 pg/ml) and NT-pro-BNP (982.35 pg/ml vs. 331.99 pg/ml), but not dpp4 (6930.9 4pg/ml vs. 7081.37 pg/ml) or CTSS (1050.46 pg/ml vs. 984.96 pg/ml), than did controls. In the receiver operating characteristic curve analysis, hsp27 showed the most notable difference between CHF patients and controls, with the largest area under the curve (AUC) (0.920); the AUC values for sgp130 and NT-pro-BNP were 0.877 and 0.882, respectively.

CONCLUSIONS

Soluble glycoprotein 130 and hsp27 are novel candidate biomarkers for diagnosing CHF with preserved ejection fraction and thus warrant further investigation; neither dpp4 nor CTSS showed promise as biomarkers of CHF.

Proteomic Profile of Unstable Atheroma Plaque: Increased Neutrophil Defensin 1, Clusterin, and Apolipoprotein E Levels in Carotid Secretome

Because of the clinical significance of carotid atherosclerosis, the search for novel biomarkers has become a priority. The aim of the present study was to compare the protein secretion profile of the carotid atherosclerotic plaque (CAP, n = 12) and nonatherosclerotic mammary artery (MA, n = 10) secretomes. We used a nontargeted proteomic approach that incorporated tandem immunoaffinity depletion, iTRAQ labeling, and nanoflow liquid chromatography coupled to high-resolution mass spectrometry. In total, 162 proteins were quantified, of which 25 showed statistically significant differences in secretome levels between carotid atherosclerotic plaque and nondiseased mammary artery. We found increased levels of neutrophil defensin 1, apolipoprotein E, clusterin, and zinc-alpha-2-glycoprotein in CAP secretomes. Results were validated by ELISA assays. Also, differentially secreted proteins are involved in pathways such as focal adhesion and leukocyte transendothelial migration. In conclusion, this study provides a subset of identified proteins that are differently expressed in secretomes of clinical significance.

Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis

The data explosion in the last decade is revolutionizing diagnostics research and the healthcare industry, offering both opportunities and challenges. These high-throughput “omics” techniques have generated more scientific data in the last few years than in the entire history of mankind. Here we present a brief summary of how “big data” have influenced early diagnosis of complex diseases. We will also review some of the most commonly used “omics” techniques and their applications in diagnostics. Finally, we will discuss the issues brought by these new techniques when translating laboratory discoveries to clinical practice.

A standardized kit for automated quantitative assessment of candidate protein biomarkers in human plasma

Background: An increasingly popular mass spectrometry-based quantitative approach for health-related research in the biomedical field involves the use of stable isotope-labeled standards (SIS) and multiple/selected reaction monitoring (MRM/SRM). To improve inter-laboratory precision and enable more widespread use of this ‘absolute’ quantitative technique in disease-biomarker assessment studies, methods must be standardized. Results/methodology: Using this MRM-with-SIS-peptide approach, we developed an automated method (encompassing sample preparation, processing and analysis) for quantifying 76 candidate protein markers (spanning >4 orders of magnitude in concentration) in neat human plasma. Discussion/conclusion: The assembled biomarker assessment kit – the ‘BAK-76’ – contains the essential materials (SIS mixes), methods (for acquisition and analysis), and tools (Qualis-SIS software) for performing biomarker discovery or verification studies in a rapid and standardized manner.

Invariant Natural Killer T Cells Play a Role in Chemotaxis, Complement Activation and Mucus Production in a Mouse Model of Airway Hyperreactivity and Inflammation

CD1d-restricted invariant natural killer T (iNKT) cells play a critical role in the induction of airway hyperreactivity (AHR). After intranasal alpha-galactosylceramide (α-GalCer) administration, bronchoalveolar lavage fluid (BALF) proteins from mouse lung were resolved by two-dimensional differential gel electrophoresis (2D-DIGE), and identified by tandem mass spectroscopy. A lack of iNKT cells prevented the development of airway responses including AHR, neutrophilia and the production of the proinflammatory cytokines in lungs. Differentially abundant proteins in the BALF proteome of α-GalCer-treated wild type mice included lungkine (CXCL15), pulmonary surfactant-associated protein D (SFTPD), calcium-activated chloride channel regulator 1 (CLCA1), fragments of complement 3, chitinase 3-like proteins 1 (CH3LI) and 3 (CH3L3) and neutrophil gelatinase-associated lipocalin (NGAL). These proteins may contribute to iNKT regulated AHR via several mechanisms: altering leukocyte chemotaxis, increasing airway mucus production and possibly via complement activation.

Serum proteomes distinguish children developing type 1 diabetes in a cohort with HLA-conferred susceptibility

We determined longitudinal serum proteomics profiles from children with HLA-conferred diabetes susceptibility to identify changes that could be detected before seroconversion and positivity for disease-associated autoantibodies. Comparisons were made between children who seroconverted and progressed to type 1 diabetes (progressors) and those who remained autoantibody negative, matched by age, sex, sample periodicity, and risk group. The samples represented the prediabetic period and ranged from the age of 3 months to 12 years. After immunoaffinity depletion of the most abundant serum proteins, isobaric tags for relative and absolute quantification were used for sample labeling. Quantitative proteomic profiles were then measured for 13 case-control pairs by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, a label-free LC-MS/MS approach was used to analyze depleted sera from six case-control pairs. Importantly, differences in abundance of a set of proteins were consistently detected before the appearance of autoantibodies in the progressors. Based on top-scoring pairs analysis, classification of such progressors was observed with a high success rate. Overall, the data provide a reference of temporal changes in the serum proteome in healthy children and children progressing to type 1 diabetes, including new protein candidates, the levels of which change before clinical diagnosis.

Current trends in magnetic particle enrichment for mass spectrometry-based analysis of cardiovascular protein biomarkers

Magnetic particles have traditionally been utilized to isolate and enrich various cardiovascular protein biomarkers for mass spectrometry-based proteomic analysis. The application of functionalized magnetic particles for immunocapture is attractive due to their easy manipulation, large surface area-to-volume ratios for maximal antibody binding, good recovery and high magnetic saturation. Magnetic particle enrichment coupled with mass spectrometry can act as a complementary tool for clinical sandwich-immunoassay development since it can provide improved target specificity and true metrological traceability. The purpose of this review is to summarize current separation methods and technologies that use magnetic particles to enrich protein biomarkers from complex matrices, specifically focusing on cardiovascular disease-related proteins and the advantages of magnetic particles over existing techniques.

Evaluation of Rapigest Efficacy for the Digestion of Proteins from Cell Cultures and Heart Tissue

INTRODUCTION

Rapigest is an acid-labile detergent used in proteomics for the improvement of protein digestion.

MATERIALS AND METHOD

To test the efficacy of Rapigest for proteomics analysis of different sample types we used protein extracts from S9 cell line and mouse heart tissue and performed protein isolation, digestion and mass spectrometry analysis.

RESULTS

For the S9 cell line, there was no significant difference concerning the number of identifications (peptides, proteins) between Rapigest and No Rapigest samples, though slightly more peptides and proteins were identified in the Rapigest samples. For the mouse heart tissue samples, Rapigest use resulted in the identification of a higher number of proteins. Rapigest did not modify the protein profile with respect to the biological compartments covered by the identified proteins in S9 cell line samples, but produced a small increase in the representation of cytoplasm proteins and a small decrease in the representation of membrane proteins in the mouse heart tissue samples.

DISCUSSIONS

Results are comparable to other studies that evaluated the efficacy of Rapigest for the analysis of tissue samples, recommending Rapigest for the improvement of protein digestion and implicitly identification, without the modification of the protein profile in the samples.

CONCLUSION

Rapigest may be successfully used for the improvement of protein identification from heart tissue samples using mass spectrometry.

BiomarCaRE: rationale and design of the European BiomarCaRE project including 300,000 participants from 13 European countries

Biomarkers are considered as tools to enhance cardiovascular risk estimation. However, the value of biomarkers on risk estimation beyond European risk scores, their comparative impact among different European regions and their role towards personalised medicine remains uncertain. Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) is an European collaborative research project with the primary objective to assess the value of established and emerging biomarkers for cardiovascular risk prediction. BiomarCaRE integrates clinical and epidemiological biomarker research and commercial enterprises throughout Europe to combine innovation in biomarker discovery for cardiovascular disease prediction with consecutive validation of biomarker effectiveness in large, well-defined primary and secondary prevention cohorts including over 300,000 participants from 13 European countries. Results from this study will contribute to improved cardiovascular risk prediction across different European populations. The present publication describes the rationale and design of the BiomarCaRE project.

Plasma levels of microRNA-499 provide an early indication of perioperative myocardial infarction in coronary artery bypass graft patients

BACKGROUND

Recent studies indicated that microRNAs (miRNAs, miRs) were important for many biological and pathological processes, and they might be potential biomarkers for cardiovascular diseases. The present study aims to determine the release patterns of miRNAs in cardiac surgery and to analyze the ability of miRs to provide early prediction of perioperative myocardial infarction (PMI) in patients undergoing coronary artery bypass graft (CABG) surgery.

METHODOLOGY/PRINCIPAL FINDINGS

Thirty on-pump CABG patients were recruited in this study; and miR-499, miR-133a and miR-133b, cardiac troponin I (cTnI) were selected for measurement. Serial plasma samples were collected at seven perioperative time points (preoperatively, and 1, 3, 6, 12, 24, and 48 hours after declamping) and were tested for cTnI and miRs levels. Importantly, miR levels peaked as early as 1-3 hours, whereas cTnI levels peaked at 6 hours after declamping. Peak plasma concentrations of miRs correlated significantly with cTnI (miR-499, r = 0.583, P = 0.001; miR-133a, r = 0.514, P = 0.006; miR-133b, r = 0.437, P = 0.05), indicating the degree of myocardial damage. In addition, 30 off-pump CABG patients were recruited; miR-499 and miR-133a levels were tested, which were significantly lower in off-pump group than in on-pump group. A prospective cohort of CABG patients (n = 120) was recruited to study the predictive power of miRs for PMI. The diagnosis of PMI strictly adhered to the principles of universal definition of myocardial infarction. The data analysis revealed that miR-499 had higher sensitivity and specificity than cTnI, and indicated that miR-499 could be an independent risk factor for PMI.

CONCLUSION

Our results demonstrate that circulating miR-499 is a novel, early biomarker for identifying perioperative myocardial infarction in cardiac surgery.

Cardiac complications of chemotherapy: role of biomarkers

OPINION STATEMENT

Both conventional and novel antineoplastic drugs may cause damage to the heart, ultimately affecting patients' survival and quality of life. In fact, the most frequent and typical clinical manifestation of cardiotoxicity, asymptomatic or symptomatic left ventricular dysfunction, may be induced not only by conventional cancer therapy, like anthracyclines, but also by new antitumoral targeted therapy such as trastuzumab. At present, left ventricular ejection fraction assessment represents the main standard practice for cardiac monitoring during cancer therapy, but it detects myocardial damage only when a functional impairment has already occurred, not allowing for early preventive strategies. In the last decade, a newer approach based on the measurement of cardiospecific biomarkers has been proposed, proving to have higher prognostic value than imaging modalities. In particular, cardiac troponin elevation during chemotherapy allows us to identify patients who are more prone to develop myocardial dysfunction and cardiac events during follow up. In these patients, the use of an angiotensin-converting enzyme inhibitor, such as enalapril, has shown to be effective in improving clinical outcome, giving the chance for a cardioprotective strategy in a selected population.

Analysis of pericardial effusion from idiopathic pericarditis patients by two-dimensional gel electrophoresis

Pericardial fluid (PF) is often considered to be reflection of the serum by which information regarding the physiological status of the heart can be obtained. Some local and systemic disorders may perturb the balance between synthesis and discharge of PF and may cause its aberrant accumulation in the pericardial cavity as pericardial effusion (PE). PE may then lead to an increased intrapericardial pressure from which the heart function is undesirably affected. For some cases, the causes for the perturbance of fluid balance are well understood, but in some other cases, they are not apparent. It may, thus, be helpful to understand the molecular mechanisms behind this troublesome condition to elucidate a clinical approach for therapeutic uses. In this study, protein profiles of PEs from idiopathic pericarditis patients were analyzed. Control samples from patients undergoing elective cardiac surgery (ECS) were included for comparison. In addition to high abundant serum-originated proteins that may not hold significance for understanding the molecular mechanisms behind this disease, omentin-1 was identified and its level was higher for more than two-fold in PE of IP patients. Increased levels of omentin-1 in PE may open a way for understanding the molecular mechanisms behind idiopathic pericarditis (IP).

Bottom-Up Mass Spectrometry-Based Proteomics as an Investigative Analytical Tool for Discovery and Quantification of Proteins in Biological Samples

OBJECTIVE

The objective of this overview is to introduce bottom-up mass spectrometry (MS)-based proteomics approaches and strategies, widely used in other biomedical research fields, to the wound-healing research community.

APPROACHES

TWO MAJOR PROTEOMICS WORKFLOWS ARE DISCUSSED: gel-based and gel-free chromatographic separation to reduce the complexity of the sample at protein and peptide level, respectively, prior to nano-liquid chromatography-tandem mass spectrometry analysis. Other strategies to discover less abundant proteins present in the sample, are also briefly discussed along with label-free and label-incorporated methods for protein quantification. Overall, the experimental workflows are designed and continually improved to increase the number of proteins identifiable and quantifiable.

DISCUSSION

Recent advances and improvements in all areas of proteomics workflow from sample preparation, to acquisition of massive amounts of data, to bioinformatics analysis have made this technology an indispensable tool for in-depth large-scale characterization of complex proteomes. This technology has been successfully applied in studies focusing on biomarker discovery, differential protein expression, protein-protein interactions, and post-translational modifications in complex biological samples such as cerebrospinal fluid, serum and plasma, and urine from patients. The publications from these studies have reported greater number of identified proteins, novel biomarker candidates, and post-translational modifications previously unknown.

CONCLUSIONS

The qualitative and quantitative protein analysis of the protein population of wound tissues or fluids at different stages is important in wound healing research. Given the complexities and analytical challenges of these samples, MS-based proteomic workflows further improved with recent advances offer a powerful and attractive technology for this purpose.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Development of an MRM assay panel with application to biobank samples from patients with myocardial infarction

As part of a Swedish national cardiological research initiative, the development of a quantitative MRM assay is reported for the quantification of eleven putative cardiovascular disease markers. Within the study, patient samples from the LUNDHEARTGENE biobank were processed and nanoLC-MS/MS analysis was performed together with a stable isotope dilution strategy for absolute quantification of the target proteins. Excellent linear regressions were achieved for 9 of the 11 peptides with LOQ ranged in the attomolar range. We have utilized the assay for the screening of plasma samples from patients with chest pain, and performed a comparative analysis of patients with ST-segment elevation myocardial infarction and chest pain due to other causes. The assay demonstrates high reproducibility and correlate with clinical findings. Strong correlations were found for several of the apolipoproteins and their respective lipid subfractions (LDL, HDL or triglycerides). APOC1, APOC2 and APOE were elevated in patients with STEMI.

BIOLOGICAL SIGNIFICANCE

An MRM assay were developed for putative cardiovascular disease markers as target proteins, and applied to biobanking sample material. The comparative analysis of patients with ST-segment elevation myocardial infarction and chest pain due to other causes showed elevated levels of APOC1, APOC2 and APOE in patients with STEMI. These observations raise interesting novel hypotheses about the role of apolipoproteins C1, C2 and E in the pathophysiology of acute myocardial infarction, which merits further studies.

Matrix metalloproteinase (MMP)-9: a proximal biomarker for cardiac remodeling and a distal biomarker for inflammation

Adverse cardiac remodeling following myocardial infarction (MI) remains a significant cause of congestive heart failure. Additional and novel strategies that improve our ability to predict, diagnose, or treat remodeling are needed. Numerous groups have explored single and multiple biomarker strategies to identify diagnostic prognosticators of remodeling progression, which will improve our ability to promptly and accurately identify high-risk individuals. The identification of better clinical indicators should further lead to more effective prediction and timely treatment. Matrix metalloproteinase (MMP-9) is one potential biomarker for cardiac remodeling, as demonstrated by both animal models and clinical studies. In animal MI models, MMP-9 expression significantly increases and is linked with inflammation, diabetic microvascular complications, extracellular matrix degradation and synthesis, and cardiac dysfunction. Clinical studies have also established a relationship between MMP-9 and post-MI remodeling and mortality, making MMP-9 a viable candidate to add to the multiple biomarker list. By definition, a proximal biomarker shows a close relationship with its target disease, whereas a distal biomarker exhibits non-targeted disease modifying outcomes. In this review, we explore the ability of MMP-9 to serve as a proximal biomarker for cardiac remodeling and a distal biomarker for inflammation. We summarize the current molecular basis and clinical platform that allow us to include MMP-9 as a biomarker in both categories.

Biomarkers in native and transplant kidneys: opportunities to improve prediction of outcomes in chronic kidney disease

PURPOSE OF REVIEW

Predicting the outcomes of patients with chronic kidney disease (CKD) is important from both patient and healthcare system perspectives. This review examines the current state of conventional and nonconventional biomarkers as noninvasive tools to improve risk-stratification and outcome prediction in CKD.

RECENT FINDINGS

Conventional biomarkers (serum creatinine, urine albumin, and clinical variables such as sex, age, and diabetes) have been the cornerstone of most prediction models for CKD progression to end-stage renal disease (ESRD), and adverse cardiovascular outcomes including death. With better understanding of the pathophysiology of CKD and the evolution of molecular diagnostics, numerous novel or nonconventional markers have emerged. They have been examined individually and in combination to predict specific outcomes. We highlight these markers and studies, conducted primarily in patients with native kidneys. In those with transplant kidneys, markers of both acute and chronic kidney dysfunction have been examined, although to a lesser extent. Similarities and differences in knowledge derived from these two populations are highlighted.

SUMMARY

Improving prediction of outcomes in CKD patients with either native or transplant kidneys remains an important goal. Increasingly sophisticated biomarkers may potentially identify targets for clinical research, improve the nature and timing of therapeutic interventions, and guide resource allocation.

Proteomic analysis of right and left cardiac ventricles under aerobic conditions and after ischemia/reperfusion

Ischemia/reperfusion (I/R) injury is a major consequence of a cardiovascular intervention. The study of changes of the left and right ventricle proteomes from hearts subjected to I/R may be a key to revealing the pathological mechanisms underlying I/R-induced heart contractile dysfunction. Isolated rat hearts were perfused under aerobic conditions or subjected to 25 min global ischemia and 30 min reperfusion. At the end of perfusion, right and left ventricular homogenates were analyzed by 2DE. Contractile function and coronary flow were significantly reduced by I/R. 2DE followed by mass spectrometry identified ten protein spots whose levels were significantly different between aerobic left and right ventricles, eight protein spots whose levels were different between aerobic and I/R left ventricle, ten protein spots whose levels were different between aerobic and I/R right ventricle ten protein spots whose levels were different between the I/R groups. Among these protein spots were ATP synthase beta subunit, myosin light chain 2, myosin heavy chain fragments, peroxiredoxin-2, and heat shock proteins, previously associated with cardiovascular disease. These results reveal differences between proteomes of left and right ventricle both under aerobic conditions and in response to I/R that contribute to a better understanding of I/R injury.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

Metabolomics as a key integrator for "omic" advancement of personalized medicine and future therapies

Investigation into biological complexity, whether for a better understanding of disease or drug process, is a monumental task plaguing investigators. The lure of "omic" technologies for circumventing much of these challenges has led to widespread efforts and adoption. It is becoming clearer that a single "omic" approach (e.g., genomics) is often insufficient for completely defining the complexity in these biological systems. Hence, there is an increasing awareness that a "systems" approach will serve to increase resolution and confidence and provide a strong foundation for further hypothesis-driven investigation. Although certain metabolites are already considered clinically important, the profiling of metabolites via metabolomics (the profiling of metabolites to fully characterize metabolic pathways) is the most recent to mature of these "omic" technologies and has been only recently adopted as compared to genomic or proteomic approaches in systems inquiries. Recent reports suggest that this "omic" may well be a key data stream in systems investigations for endeavors in personalized medicine and biomarker identification, as it seems most closely relevant to the phenotype.

Human myocardial protein pattern reveals cardiac diseases

Proteomic profiles of myocardial tissue in two different etiologies of heart failure were investigated using high performance liquid chromatography (HPLC)/Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Right atrial appendages from 10 patients with hemodynamically significant isolated aortic valve disease and from 10 patients with isolated symptomatic coronary heart disease were collected during elective cardiac surgery. As presented in an earlier study by our group (Baykut et al., 2006), both disease forms showed clearly different pattern distribution characteristics. Interesting enough, the classification patterns could be used for correctly sorting unknown test samples in their correct categories. However, in order to fully exploit and also validate these findings there is a definite need for unambiguous identification of the differences between different etiologies at molecular level. In this study, samples representative for the aortic valve disease and coronary heart disease were prepared, tryptically digested, and analyzed using an FT-ICR MS that allowed collision-induced dissociation (CID) of selected classifier masses. By using the fragment spectra, proteins were identified by database searches. For comparison and further validation, classifier masses were also fragmented and analyzed using HPLC-/Matrix-assisted laser desorption ionization (MALDI) time-of-flight/time-of-flight (TOF/TOF) mass spectrometry. Desmin and lumican precursor were examples of proteins found in aortic samples at higher abundances than in coronary samples. Similarly, adenylate kinase isoenzyme was found in coronary samples at a higher abundance. The described methodology could also be feasible in search for specific biomarkers in plasma or serum for diagnostic purposes.

Interstitial Fluid and Lymph Formation and Transport: Physiological Regulation and Roles in Inflammation and Cancer

The interstitium describes the fluid, proteins, solutes, and the extracellular matrix (ECM) that comprise the cellular microenvironment in tissues. Its alterations are fundamental to changes in cell function in inflammation, pathogenesis, and cancer. Interstitial fluid (IF) is created by transcapillary filtration and cleared by lymphatic vessels. Herein we discuss the biophysical, biomechanical, and functional implications of IF in normal and pathological tissue states from both fluid balance and cell function perspectives. We also discuss analysis methods to access IF, which enables quantification of the cellular microenvironment; such methods have demonstrated, for example, that there can be dramatic gradients from tissue to plasma during inflammation and that tumor IF is hypoxic and acidic compared with subcutaneous IF and plasma. Accumulated recent data show that IF and its convection through the interstitium and delivery to the lymph nodes have many and diverse biological effects, including in ECM reorganization, cell migration, and capillary morphogenesis as well as in immunity and peripheral tolerance. This review integrates the biophysical, biomechanical, and biological aspects of interstitial and lymph fluid and its transport in tissue physiology, pathophysiology, and immune regulation.

Biomarkers intersect with the exposome

The exposome concept promotes use of omic tools for discovering biomarkers of exposure and biomarkers of disease in studies of diseased and healthy populations. A two-stage scheme is presented for profiling omic features in serum to discover molecular biomarkers and then for applying these biomarkers in follow-up studies. The initial component, referred to as an exposome-wide-association study (EWAS), employs metabolomics and proteomics to interrogate the serum exposome and, ultimately, to identify, validate and differentiate biomarkers of exposure and biomarkers of disease. Follow-up studies employ knowledge-driven designs to explore disease causality, prevention, diagnosis, prognosis and treatment.

Inhibition of the transcription factor c-Jun by the MAPK family, and not the NF-κB pathway, suggests that peanut extract has anti-inflammatory properties

BACKGROUND

Tumor necrosis factor-α (TNF-α) is involved in inflammatory responses in atherosclerosis. We propose an in vitro cellular assay to evaluate the anti-inflammatory mechanisms of potential modifiers such as food extracts. In the current model we assessed an anti-inflammatory effect of polyphenol-rich peanut extract in lipopolysaccharide (LPS)-induced THP-1 monocytes.

METHODS

THP-1 monocytes were incubated with peanut extract (5, 25, 50 and 100 μg/mL) consisting of 39% flavonols, 37% flavanols and 24% phenolic acid (or BAY 11-7082 (5 μM) as experiment control) for 1 h and then stimulated with LPS (500 ng/mL) for 4 h. Cytotoxicity was measured as lactate dehydrogenase (LDH) activity release. NF-κB and MAPK family were determined by TransAm kit while TNF-α mRNA levels and its mRNA stability by RT-PCR. Intra- and extracellular TNF-α protein was measured by ELISA, and TNF-α converting enzyme (TACE) activity by a fluorimetric assay.

RESULTS

Peanut extract inhibited the maximal LPS-induced extracellular TNF-α protein secretion by 18%, 29% and 47% at 25, 50 and 100 μg/mL, respectively (P<0.05). LPS stimulation revealed that 85% of TNF-α was released extracellularly while 15% remained intracellular. Peanut extract did not modify NF-κB but, instead, reduced c-Jun transcription factor activity (P<0.05), decreased TNF-α mRNA (albeit non-significantly) and had no effect on mRNA stability and TACE activity.

CONCLUSION

Polyphenol-rich peanut extract reduces extracellular TNF-α protein by inhibiting c-Jun transcription factor from MAPK family, suggesting an anti-inflammatory effect. The proposed THP-1 monocyte model could be used to assess food extract impact (site and size effects) on the inflammation pathway.

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.