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

Proteomics in Cardiovascular disease

This study focuses on recent advances in proteomics and provides an up-to-date use of this technology in identifying cardiovascular disease (CVD) biomarkers. A total of eight electronic databases (PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang, Vip, Sinomed, and CNKI) were searched and five were used for integrative analysis of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR) and 1 secondary indicator area under the curve (AUC). This systematic review and integrative analysis summarized potential biomarkers previously identified by proteomics. The integrative analysis suggested that proteomics technology had high clinical value in CVD diagnosis. The findings provided new possible directions for the prevention or diagnosis of CVD.

Prediction of coronary artery disease using urinary proteomics

AIMS

Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.

METHODS AND RESULTS

Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).

CONCLUSION

A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.

Extracellular vesicles as biomarkers and modulators of atherosclerosis pathogenesis

Extracellular vesicles (EVs) are small, lipid bilayer-enclosed structures released by various cell types that play a critical role in intercellular communication. In atherosclerosis, EVs have been implicated in multiple pathophysiological processes, including endothelial dysfunction, inflammation, and thrombosis. This review provides an up-to-date overview of our current understanding of the roles of EVs in atherosclerosis, emphasizing their potential as diagnostic biomarkers and their roles in disease pathogenesis. We discuss the different types of EVs involved in atherosclerosis, the diverse cargoes they carry, their mechanisms of action, and the various methods employed for their isolation and analysis. Moreover, we underscore the importance of using relevant animal models and human samples to elucidate the role of EVs in disease pathogenesis. Overall, this review consolidates our current knowledge of EVs in atherosclerosis and highlights their potential as promising targets for disease diagnosis and therapy.

Differential urinary proteomics analysis of myocardial infarction using iTRAQ quantification

Myocardial infarction (MI) is a disease characterized by high morbidity and mortality rates. MI biomarkers are frequently used in clinical diagnosis; however, their specificity and sensitivity remain unsatisfactory. Urinary proteome is an easy, efficient and noninvasive source to examine biomarkers associated with various diseases. The present study, to the best of the authors' knowledge, is the first to examine the urinary proteome using the isobaric tags for relative and absolute quantitation (iTRAQ) technology to identify potential diagnostic biomarkers of MI. The urinary proteome was analyzed within 12 h following the first symptoms of early‑onset MI and at day 7 following percutaneous coronary intervention via iTRAQ labeling and two‑dimensional liquid chromatography‑tandem mass spectrometry. Candidate biomarkers were validated by multiple reaction monitoring (MRM) analysis. A total of 233 urinary proteins were differentially expressed. Gene enrichment analysis identified that the urinary proteome in patients with MI was associated with atherosclerosis, abnormal coagulation and abnormal cell metabolism. In total, 12 differentially expressed urinary proteins were validated by MRM analysis, five of which were associated with MI for the first time in the present study. Binary logistic regression analysis suggested that the combination of five urinary proteins (antithrombin‑III, complement C3, α‑1‑acid glycoprotein 1, serotransferrin and cathepsin Z) may be used to diagnose MI with 94% sensitivity and 93% specificity. In addition, the protein expression levels of three proteins were significantly restored to normal levels following surgical treatment. The verified candidate biomarkers may be used for the diagnosis of MI, and for monitoring the disease status and the effects of treatments for MI. The present results may facilitate future clinical applications of the urinary proteome to diagnose MI.

Differential urinary proteins to diagnose coronary heart disease based on iTRAQ quantitative proteomics

Coronary artery disease (CAD) is a manifestation of systemic atherosclerotic disease. It is assessed by intervention or traditional scoring risk factors. Diagnosis is limited by inaccurate and invasive methods. Developing noninvasive methods to screen for the risk of CAD is a major challenge. We aimed to identify urinary proteins associated with CAD. We utilized iTRAQ labeling followed by 2D LC-MS/MS to compare the urinary proteome of CAD patients to healthy cohorts. The multiple reaction monitoring (MRM) was used to verify the differential proteins. ROC analysis based on MRM data was used to evaluate the diagnostic application. A total of 876 proteins were quantified, and 100 differential proteins were found. Functional analysis revealed that the differential proteins were mainly associated with Liver X Receptor/Retinoid X Receptor (LXR/RXR) pathway activation, atherosclerosis signaling, production of nitric oxide and reactive oxygen species, and the top upstream regulator of the differential proteins by IPA analysis indicated to the APOE. Nineteen differential proteins were verified by MRM analysis. ROC based on MRM data revealed that the combination of two proteins (APOD and TFF1) could diagnose CAD with 85% sensitivity and 99% specificity (AUC 0.95). The urinary proteome might reflect the pathophysiological changes in CAD and be used for the clinical study of CAD.

NMR-based metabolomics identifies patients at high risk of death within two years after acute myocardial infarction in the AMI-Florence II cohort

BACKGROUND

Risk stratification and management of acute myocardial infarction patients continue to be challenging despite considerable efforts made in the last decades by many clinicians and researchers. The aim of this study was to investigate the metabolomic fingerprint of acute myocardial infarction using nuclear magnetic resonance spectroscopy on patient serum samples and to evaluate the possible role of metabolomics in the prognostic stratification of acute myocardial infarction patients.

METHODS

In total, 978 acute myocardial infarction patients were enrolled in this study; of these, 146 died and 832 survived during 2 years of follow-up after the acute myocardial infarction. Serum samples were analyzed via high-resolution ¹H-nuclear magnetic resonance spectroscopy and the spectra were used to characterize the metabolic fingerprint of patients. Multivariate statistics were used to create a prognostic model for the prediction of death within 2 years after the cardiovascular event.

RESULTS

In the training set, metabolomics showed significant differential clustering of the two outcomes cohorts. A prognostic risk model predicted death with 76.9% sensitivity, 79.5% specificity, and 78.2% accuracy, and an area under the receiver operating characteristics curve of 0.859. These results were reproduced in the validation set, obtaining 72.6% sensitivity, 72.6% specificity, and 72.6% accuracy. Cox models were used to compare the known prognostic factors (for example, Global Registry of Acute Coronary Events score, age, sex, Killip class) with the metabolomic random forest risk score. In the univariate analysis, many prognostic factors were statistically associated with the outcomes; among them, the random forest score calculated from the nuclear magnetic resonance data showed a statistically relevant hazard ratio of 6.45 (p = 2.16×10^-16). Moreover, in the multivariate regression only age, dyslipidemia, previous cerebrovascular disease, Killip class, and random forest score remained statistically significant, demonstrating their independence from the other variables.

CONCLUSIONS

For the first time, metabolomic profiling technologies were used to discriminate between patients with different outcomes after an acute myocardial infarction. These technologies seem to be a valid and accurate addition to standard stratification based on clinical and biohumoral parameters.

Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome

In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), miRNA, ncRNA, and lincRNA biomarkers and proteomic and metabolomic biomarkers. Complicated pathomechanisms of CKD development and progression require not a single marker but their combination in order to mirror all types of alterations occurring in the course of this disease. It seems that in the not so distant future, conventional markers may be exchanged for new ones, however, confirmation of their efficacy, sensitivity and specificity as well as the reduction of analysis costs are required.

How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study

Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, we used a specific clinical problem - to discriminate pathophysiology and potential biomarkers between two similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) - to make a step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could find specific biological phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such as up-regulation of plasminogen activation. Moreover, through Cytoscape and DisGeNET we could pinpoint surrogate markers either for AVS (e.g. popeye domain containing protein 2 and 28S ribosomal protein S36, mitochondrial) or for CAD (e.g. ankyrin repeat and SOCS box protein 7) which deserve future validation. Data recycling and integration as well as research orientation are among the main advantages of resorting to bioinformatics analysis, hence these tutorials can be of great convenience for proteomics investigators.

BIOLOGICAL SIGNIFICANCE

As we saw for aortic valve stenosis and coronary artery disease, it can be of great relevance to perform preliminary bioinformatics analysis with already published proteomics data. It not only saves us time in the lab (avoiding work duplication) as it points out new hypothesis to explain the phenotypical presentation of the diseases as well as new surrogate markers with clinical relevance, deserving future scrutiny. These essential steps can be easily overcome if one follows the steps proposed in our tutorial for STRING, DisGeNET, Cytoscape and ClueGO utilization.

Prediction of acute coronary syndromes by urinary proteome analysis

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

Features of the urine peptidome under the condition of hypertensive pathologies of pregnancy

In order to find a peptide panel to differentiate close hypertensive conditions a case-control study was designed for 64 women from 4 groups: preeclampsia (PE), chronic hypertension superimposed with PE, chronic hypertension, and healthy individuals. Chromatography coupled with mass-spectrometry and subsequent bioinformatic analysis showed several patterns in the changes of the urine peptidome. There were 36 peptides common for four groups. Twenty two of them 22 belonged to alpha-1-chain of collagen I, nine peptides were from alpha-1-chain of collagen III, two from alpha-2-chain of collagen I, one from alpha-1/2-chain of collagen I, one from alpha-1-chain of collagen I/XVIII and one from uromodulin. Patients with hypertensive disorders had 34 common peptides: 12 from alpha-1-chain of collagen I, 10 from fibrinogen alpha-chain, eight from alpha-1-chain of collagen III, and 4 per other types of collagen. Comparative analysis revealed 12 peptides, which could be used as a diagnostic panel for confident discrimination of pregnant women with various hypertensive disorders.

Quantitative body fluid proteomics in medicine - A focus on minimal invasiveness

Identification of new biomarkers specific for various pathological conditions is an important field in medical sciences. Body fluids have emerging potential in biomarker studies especially those which are continuously available and can be collected by non-invasive means. Changes in the protein composition of body fluids such as tears, saliva, sweat, etc. may provide information on both local and systemic conditions of medical relevance. In this review, our aim is to discuss the quantitative proteomics techniques used in biomarker studies, and to present advances in quantitative body fluid proteomics of non-invasively collectable body fluids with relevance to biomarker identification. The advantages and limitations of the widely used quantitative proteomics techniques are also presented. Based on the reviewed literature, we suggest an ideal pipeline for body fluid analyses aiming at biomarkers discoveries: starting from identification of biomarker candidates by shotgun quantitative proteomics or protein arrays, through verification of potential biomarkers by targeted mass spectrometry, to the antibody-based validation of biomarkers. The importance of body fluids as a rich source of biomarkers is discussed.

SIGNIFICANCE

Quantitative proteomics is a challenging part of proteomics applications. The body fluids collected by non-invasive means have high relevance in medicine; they are good sources for biomarkers used in establishing the diagnosis, follow up of disease progression and predicting high risk groups. The review presents the most widely used quantitative proteomics techniques in body fluid analysis and lists the potential biomarkers identified in tears, saliva, sweat, nasal mucus and urine for local and systemic diseases.

Urine proteomics in the diagnosis of stable angina

Background

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

Methods

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

Results

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

Conclusions

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

Electronic supplementary material

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

Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism

PURPOSE

Early and accurate detection of deep vein thrombosis (DVT) is an important clinical need. Based on the hypothesis that urinary peptides may hold information on DVT in conjunction with pulmonary embolism (PE), the study was aimed at identifying such peptide biomarkers using capillary electrophoresis coupled mass spectrometry.

EXPERIMENTAL DESIGN

Patients with symptoms of unprovoked/idiopathic DVT and/or PE were examined by doppler-sonography or angio-computed tomography. Urinary proteome analysis allowed for identification of respective peptide biomarkers. To confirm their biological relevance, we induced PE in mice and assessed human ex vivo thrombi.

RESULTS

We identified 62 urinary peptides as DVT-specific biomarkers, i.e. fragments of collagen type I and a fragment of fibrinogen β-chain. The presence of fibrinogen α/β in the acute thrombus, and collagen type I and osteopontin in the older, organized thrombus was demonstrated. The classifier DVT62 established through support vector machine (SVM) modeling based on the 62 identified peptides was validated in an independent cohort of 47 subjects (six cases and 41 controls) with a sensitivity of 100% and specificity of 83%.

CONCLUSIONS AND CLINICAL RELEVANCE

Urine proteome analysis enabled the detection of DVT-specific peptides, which were validated in human and mouse tissue. Furthermore, it allowed for the establishment of an urinary-proteome based classifier that is relatively specific for DVT. The data provide the basis for assessment of these biomarkers in a prospective clinical study.

Implementation of CE-MS-identified proteome-based biomarker panels in drug development and patient management

The recent advancements in clinical proteomics enabled identification of biomarker panels for a large range of diseases. A number of CE-MS-identified biomarker panels were verified and implemented in clinical studies. Despite multiple challenges, accumulating evidence supports the value and the need for proteome-based biomarker panels. In this perspective, we provide an overview of clinical studies indicating the added value of CE-MS biomarker panels over traditional diagnostics and monitoring methods. We outline apparent advantages of applying novel proteomic biomarker panels for disease diagnosis, prognosis, staging, drug development and patient management. Facing the plethora of benefits associated with the use of CE-MS biomarker panels, we envision their implementation into the medical practice in the near future.

Pediatric Origins of Nephrolithiasis-Associated Atherosclerosis

OBJECTIVES

To determine if nephrolithiasis-associated atherosclerosis has pediatric origins and to consider possible association between kidney stones and atherosclerosis-related proteins.

STUDY DESIGN

We matched children aged 12-17 years with kidney stones and without kidney stones. Carotid artery intima-media thickness (cIMT) was measured by ultrasound. Participants' urine was investigated by enzyme-linked immunosorbent assay for the atherosclerosis-related proteins fibronectin 1, macrophage scavenger receptor 1, osteopontin, and vascular cell adhesion molecule 1 levels, and normalized to urine creatinine levels.

RESULTS

Subjects with nephrolithiasis had higher cIMT in the right common carotid artery and overall mean measurement. Urine osteopontin and fibronectin 1 were significant predictors of cIMT.

CONCLUSIONS

We have provided initial preliminary evidence that nephrolithiasis-associated atherosclerosis has pediatric origins and performed studies that begin to identify potential reasons for the association of nephrolithiasis and vascular disease.

Combined Plasma and Tissue Proteomic Study of Atherogenic Model Mouse: Approach To Elucidate Molecular Determinants in Atherosclerosis Development

Atherogenic cardiovascular diseases are the major cause of mortality. Prevention and prediction of incidents is important; however, biomarkers that directly reflect the disease progression remain poorly investigated. To elucidate molecular determinants of atherogenesis, proteomic approaches are advantageous by using model animals for comparing changes occurring systematically (bloodstream) and locally (lesion) in accordance with the disease progression stages. We conducted differential mass spectrometric analysis between apolipoprotein E deficient (apoED) and wild-type (wt) mice using the plasma and arterial tissue of both types of mice obtained at four pathognomonic time points of the disease. A total of 100 proteins in the plasma and 390 in the arterial tissues were continuously detected throughout the four time points; 29 were identified in common. Of those, 13 proteins in the plasma and 36 in the arterial tissues showed significant difference in abundance between the apoED and wt mice at certain time points. Importantly, we found that quantitative variation patterns regarding the pathognomonic time points did not always correspond between the plasma and arterial tissues, resulting in gaining insight into atherosclerotic plaque progression. These characteristic proteins were found to be components of inflammation, thrombus formation, and vascular remodeling, suggesting drastic and integrative alteration in accordance with atherosclerosis development.

New perspectives on bioactivity of olive oil: evidence from animal models, human interventions and the use of urinary proteomic biomarkers

Olive oil (OO) is the primary source of fat in the Mediterranean diet and has been associated with longevity and a lower incidence of chronic diseases, particularly CHD. Cardioprotective effects of OO consumption have been widely related with improved lipoprotein profile, endothelial function and inflammation, linked to health claims of oleic acid and phenolic content of OO. With CVD being a leading cause of death worldwide, a review of the potential mechanisms underpinning the impact of OO in the prevention of disease is warranted. The current body of evidence relies on mechanistic studies involving animal and cell-based models, epidemiological studies of OO intake and risk factor, small- and large-scale human interventions, and the emerging use of novel biomarker techniques associated with disease risk. Although model systems are important for mechanistic research nutrition, methodologies and experimental designs with strong translational value are still lacking. The present review critically appraises the available evidence to date, with particular focus on emerging novel biomarkers for disease risk assessment. New perspectives on OO research are outlined, especially those with scope to clarify key mechanisms by which OO consumption exerts health benefits. The use of urinary proteomic biomarkers, as highly specific disease biomarkers, is highlighted towards a higher translational approach involving OO in nutritional recommendations.

Proteomic urinary biomarker approach in renal disease: from discovery to implementation

Biomarkers hold the promise of significantly improving health care by enabling prognosis and diagnosis with improved accuracy, and at earlier points in time. Previous results have indicated that single biomarkers are not suitable to describe complex diseases such as kidney disease. Here we provide an update on the progress of urinary proteomics-based studies and strategies to develop biomarker-based classifiers that tolerate instability and inconsistency of individual biomarkers. The examples focus on two major fields in nephrology: chronic kidney disease in the adult population and obstructive nephropathies in the pediatric population. When employed adequately, urinary proteomics demonstrates a clear value in kidney disease, indicating that the current status quo ruling for decades now could be changed by applying modern "omics" approaches. However, while research is able to deliver these useful tools for patient management, the issues associated with implementation are not yet solved. Active engagement of the relevant clinical professional societies, as well as patient's organizations, might help to implement these omics approaches that have shown a clear benefit for the patient.

Advances in urinary protein biomarkers for urogenital and non-urogenital pathologies

The discovery of protein biomarkers that reflect the biological state of the body is of vital importance to disease management. Urine is an ideal source of biomarkers that provides a non-invasive approach to diagnosis, prognosis and prediction of diseases. Consequently, the study of the human urinary proteome has increased dramatically over the last 10 years, with many studies being published. This review focuses on urinary protein biomarkers that have shown potential, in initial studies, for diseases affecting the urogenital tract, specifically chronic kidney disease and prostate cancer, as well as other non-urogenital pathologies such as breast cancer, diabetes, atherosclerosis and osteoarthritis. PubMed was searched for peer-reviewed literature on the subject, published in the last 10 years. The keywords used were "urine, biomarker, protein, and/or prostate cancer/breast cancer/chronic kidney disease/diabetes/atherosclerosis/osteoarthritis". Original studies on the subject, as well as a small number of reviews, were analysed including the strengths and weaknesses, and we summarized the performance of biomarkers that demonstrated potential. One of the biggest challenges found is that biomarkers are often shared by several pathologies so are not specific to one disease. Therefore, the trend is shifting towards implementing a panel of biomarkers, which may increase specificity. Although there have been many advances in urinary proteomics, these have not resulted in similar advancements in clinical practice due to high costs and the lack of large data sets. In order to translate these potential biomarkers to clinical practice, vigorous validation is needed, with input from industry or large collaborative studies.

The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics

The purpose of clinical proteomics is to characterise protein profiles of a plethora of diseases with the aim of finding specific biomarkers. These are particularly valuable for early diagnosis, and represent key molecules suitable to elucidate pathogenic mechanisms. Samples deriving from patients (i.e. blood, urine, cerebrospinal fluid, biopsies) are the sources for clinical proteomics. Due to the complexity of the extracted samples their direct analysis is unachievable. Any analytical clinical proteomics study should start with the choice of the optimal combination of strategies with respect to both sample preparations and MS approaches. Protein or peptide fractionation (off-line or on-line) is essential to reduce complexity of biological samples and to achieve the most complete and reproducible analysis. The aim of this review is to introduce the readers to a functional range of strategies to help scientists in their proteomics set up. In particular, the separation approaches of proteins or peptides (both gel-based and gel-free) are reviewed with special attention paid to their advantages and limitations, and to the different liquid chromatography techniques used to peptide fractionation after protein enzymatic digestion and before their detection. Finally, the role of mass spectrometry (MS) for protein identification and quantification is discussed including emerging MS data acquisition strategies.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

Quantitative proteomics reveals novel therapeutic and diagnostic markers in hypertension

Hypertension is a prevalent disorder in the world representing one of the major risk factors for heart attack and stroke. These risks are increased in salt sensitive individuals. Hypertension and salt sensitivity are complex phenotypes whose pathophysiology remains poorly understood and, remarkably, salt sensitivity is still laborious to diagnose.

Here we present a urinary proteomic study specifically designed to identify urinary proteins relevant for the pathogenesis of hypertension and salt sensitivity. Despite previous studies that underlined the association of UMOD gene variants with hypertension, this work provides novel evidence showing different uromodulin protein level in the urine of hypertensive patients compared to healthy individuals. Notably, we also show that patients with higher level of uromodulin are homozygous for UMOD risk variant and display a decreased level of salt excretion, highlighting the essential role of UMOD in the regulation of salt reabsorption in hypertension. Additionally, we found that urinary nephrin 1, a marker of glomerular slit diaphragm, may predict a salt sensitive phenotype and positively correlate with increased albuminuria associated with this type of hypertension.

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We identified urinary proteins differently excreted in hypertensive patients.

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Nephrin 1 might predict salt sensitive phenotype and glomerular complications.

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Uromodulin impacts salt homeostasis in hypertension.

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We provide new insights into the pathogenesis of hypertension and salt sensitivity.

Biomarkers for AAA: Encouraging steps but clinical relevance still to be delivered

Potential biomarkers have been investigated using proteomic studies in a variety of diseases. Some biomarkers have central roles in both diagnosis and monitoring of various disorders in clinical medicine, such as troponins, brain natriuretic peptide, and C-reactive protein. Although several biomarkers have been suggested in human abdominal aortic aneurysm (AAA), reliable markers have been lacking. In this issue, Moxon et al. [Proteomics Clin Appl. 2014, 8, 762-772] undertook a broad and systematic proteomic approach toward identification of biomarkers in a commonly used AAA mouse model (AAA created by angiotensin-II infusion). In this mouse model, apolipoprotein C1 and matrix metalloproteinase-9 were identified as novel biomarkers of stable AAA. This finding represents an important step forward, toward a clinically relevant role of biomarkers in AAA. This also encourages for further studies toward the identification of biomarkers (or their combinations) that can predict AAA progression and rupture, which would represent a major progress in AAA management and would establish an AAA biomarker as a much anticipated clinical tool.

Early diabetic nephropathy in type 1 diabetes: new insights

PURPOSE OF REVIEW

Despite improvements in glycemic and blood pressure control in patients with type 1 diabetes, diabetic nephropathy remains the most common cause of chronic kidney disease worldwide. A major challenge in preventing diabetic nephropathy is the inability to identify high-risk patients at an early stage, emphasizing the importance of discovering new therapeutic targets and implementation of clinical trials to reduce diabetic nephropathy risk.

RECENT FINDINGS

Limitations of managing patients with diabetic nephropathy with renin-angiotensin-aldosterone system blockade have been identified in recent clinical trials, including the failure of primary prevention studies in T1D and the demonstration of harm with dual renin-angiotensin-aldosterone system blockade. Fortunately, several new targets, including serum uric acid, insulin sensitivity, vasopressin, and sodium-glucose cotransporter-2 inhibition, are promising in the prevention and treatment of diabetic nephropathy.

SUMMARY

Diabetic nephropathy is characterized by a long clinically silent period without signs or symptoms of disease. There is an urgent need for improved methods of detecting early mediators of renal injury, to ultimately prevent the initiation and progression of diabetic nephropathy. In this review, we will focus on early diabetic nephropathy and summarize potential new therapeutic targets.

Left ventricular diastolic function in relation to the urinary proteome: a proof-of-concept study in a general population

Background

In previous studies, we identified two urinary proteomic classifiers, termed HF1 and HF2, which discriminated subclinical diastolic left ventricular (LV) dysfunction from normal. HF1 and HF2 combine information from 85 and 671 urinary peptides, mainly up- or down-regulated collagen fragments. We sought to validate these classifiers in a population study.

Methods

In 745 people randomly recruited from a Flemish population (49.8 years; 51.3% women), we measured early and late diastolic peak velocities of mitral inflow (E and A) and mitral annular velocities (e' and a') by conventional and tissue Doppler echocardiography, and the urinary proteome by capillary electrophoresis coupled with mass spectrometry.

Results

In the analyses adjusted for sex, age, body mass index, blood pressure, heart rate, LV mass index and intake of medications, we expressed effect sizes per 1-SD increment in the classifiers. HF1 was associated with 0.204 cm/s lower e' peak velocity (95% confidence interval, 0.057–0.351; p = 0.007) and 0.145 higher E/e' ratio (0.023–0.268; p = 0.020), while HF2 was associated with a 0.174 higher E/e' ratio (0.046–0.302; p = 0.008). According to published definitions, 67 (9.0%) participants had impaired LV relaxation and 96 (12.9%) had elevated LV filling pressure. The odds of impaired relaxation associated with HF1 was 1.38 (1.01–1.88; p = 0.043) and that of increased LV filling pressure associated with HF2 was 1.38 (1.00–1.90; p = 0.052).

Conclusions

In a general population, the urinary proteome correlated with diastolic LV dysfunction, proving its utility for early diagnosis of this condition.

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Aim: to validate urinary proteomic markers for LV dysfunction in a population.

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Result: LV diastolic dysfunction was associated with the urinary proteome.

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Perspective: Screening for subclinical LV dysfunction becomes possible.

Unveiling the rat urinary proteome with three complementary proteomics approaches

Urine is a suitable biological fluid to look for markers of physiological and pathological processes, including renal and nonrenal diseases. In addition, it is an optimal body sample for diagnosis, because it is easily obtained without invasive procedures and can be sampled in large quantities at almost any time. Rats are frequently used as a model to study human diseases, and rat urine has been analyzed to search for disease biomarkers. The normal human urinary proteome has been studied extensively, but the normal rat urinary proteome has not been studied in such depth. In light of this, we were prompted to analyze the normal rat urinary proteome using three complementary proteomics platforms: SDS-PAGE separation, followed by LC-ESI-MS/MS; 2DE, followed by MALDI-TOF-TOF and 2D-liquid chromatography-chromatofocusing, followed by LC-ESI-Q-TOF. A total of 366 unique proteins were identified, of which only 5.2% of unique proteins were identified jointly by the three proteomics platforms used. This suggests that simultaneous proteomics techniques provide complementary and nonredundant information. Our analysis affords the most extensive rat urinary protein database currently available and this may be useful in the study of renal physiology and in the search for biomarkers related to renal and nonrenal diseases.

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'.

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

BACKGROUND

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

METHODOLOGY/PRINCIPAL FINDINGS

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

CONCLUSIONS/SIGNIFICANCE

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

Recent advances in proteomic technologies applied to cardiovascular disease

In recent years, the diagnosis of cardiovascular disease (CVD) has increased its potential, also thanks to mass spectrometry (MS) proteomics. Modern MS proteomics tools permit analyzing a variety of biological samples, ranging from single cells to tissues and body fluids, like plasma and urine. This approach enhances the search for informative biomarkers in biological samples from apparently healthy individuals or patients, thus allowing an earlier and more precise diagnosis and a deeper comprehension of pathogenesis, development and outcome of CVD to further reduce the enormous burden of this disease on public health. In fact, many differences in protein expression between CVD-affected and healthy subjects have been detected, but only a few of them have been useful to establish clinical biomarkers because they did not pass the verification and validation tests. For a concrete clinical support of MS proteomics to CVD, it is, therefore, necessary to: ameliorate the resolution, sensitivity, specificity, throughput, precision, and accuracy of MS platform components; standardize procedures for sample collection, preparation, and analysis; lower the costs of the analyses; reduce the time of biomarker verification and validation. At the same time, it will be fundamental, for the future perspectives of proteomics in clinical trials, to define the normal protein maps and the global patterns of normal protein levels, as well as those specific for the different expressions of CVD.

Recent advances in capillary electrophoresis-based proteomic techniques for biomarker discovery

Due to the inherent disadvantage of biomarker dilution in complex biological fluids such as serum/plasma, urine, and saliva, investigative studies directed at tissues obtained from the primary site of pathology probably afford the best opportunity for the discovery of disease biomarkers. Still, the large variation of protein relative abundances with clinical specimens often exceeds the dynamic range of currently available proteomic techniques. Furthermore, since the sizes of human tissue biopsies are becoming significantly smaller due to the advent of minimally invasive methods and early detection and treatment of lesions, a more effective discovery-based proteomic technology is critically needed to enable comprehensive and comparative studies of protein profiles that will have diagnostic and therapeutic relevance.This review therefore focuses on the most recent advances in capillary electrophoresis-based single and multidimensional separations coupled with mass spectrometry for performing comprehensive proteomic analysis of clinical specimens. In addition to protein identification, monitoring quantitative changes in protein expression is essential for the discovery of disease-associated biomarkers. Comparative proteomics involving measurements in changes of biological pathways or functional processes are further expected to provide relevant markers and networks, molecular relationships among different stages of disease, and molecular mechanisms that drive the progression of disease.

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.

Proteomic profiling of breast tissue collagens and site-specific characterization of hydroxyproline residues of collagen alpha-1-(I)

In a quantitative proteomics-based breast cancer study of complementary normal and tumor biopsies, 22 collagen isoforms were detected by LC-MALDI TOF/TOF MS. By applying proline oxidation, representing hydroxyproline, in database search parameters a substantial increase in assigned MS/MS was achieved, boosting the average (three experiments) number of peptides from 306 to 8126 for collagen alpha-1(I). The plethora of peptide identities for alpha-1(I) was disproportionate with full length protein sequence coverage which only increased from 28.3 to 64.4%. The peptides, in fact, constituted an extensive two-dimensional array of isomers exhibiting heterogeneity in degree and location of hydroxyproline residues. A total of 3433 peptides, scores>36 (p<0.01), constituting 94% of the triple helix region of collagen alpha-1(I) provided a census of proline hydroxylation levels defined as the rate of site occupancy for each peptide isomer (r) and the total site occupancy for each proline residue (t). MS/MS and MS/MS/MS analysis, by MALDI-QIT-TOF MS, was used to corroborate site-specific proline hydroxylation of the original data. In addition, iTRAQ data for each collagen isoform in each of 10 patients (grouped by disease) was determined and indicated an increase in fibrillar collagens in invasive carcinoma but little change in fibroadenoma or DCIS.

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.

Urine proteome analysis reflects atherosclerotic disease in an ApoE-/- mouse model and allows the discovery of new candidate biomarkers in mouse and human atherosclerosis

Noninvasive diagnosis of atherosclerosis via single biomarkers has been attempted but remains elusive. However, a previous polymarker or pattern approach of urine polypeptides in humans reflected coronary artery disease with high accuracy. The aim of the current study is to use urine proteomics in ApoE(-/-) mice to discover proteins with pathophysiological roles in atherogenesis and to identify urinary polypeptide patterns reflecting early stages of atherosclerosis. Urine of ApoE(-/-) mice either on high fat diet (HFD) or chow diet was collected over 12 weeks; urine of wild type mice on HFD was used to exclude diet-related proteome changes. Capillary electrophoresis coupled to mass spectrometry (CE-MS) of samples identified 16 polypeptides specific for ApoE(-/-) mice on HFD. In a blinded test set, these polypeptides allowed identification of atherosclerosis at a sensitivity of 90% and specificity of 100%, as well as monitoring of disease progression. Sequencing of the discovered polypeptides identified fragments of α(1)-antitrypsin, epidermal growth factor (EGF), kidney androgen-regulated protein, and collagen. Using immunohistochemistry, α(1)-antitrypsin, EGF, and collagen type I were shown to be highly expressed in atherosclerotic plaques of ApoE(-/-) mice on HFD. Urinary excretion levels of collagen and α(1)-antitrypsin fragments also significantly correlated with intraplaque collagen and α(1)-antitrypsin content, mirroring plaque protein expression in the urine proteome. To provide further confirmation that the newly identified proteins are relevant in humans, the presence of collagen type I, α(1)-antitrypsin, and EGF was also confirmed in human atherosclerotic disease. Urine proteome analysis in mice exemplifies the potential of a novel multimarker approach for the noninvasive detection of atherosclerosis and monitoring of disease progression. Furthermore, this approach represents a novel discovery tool for the identification of proteins relevant in murine and human atherosclerosis and thus also defines potential novel therapeutic targets.

MALDI mass spectrometric imaging of cardiac tissue following myocardial infarction in a rat coronary artery ligation model

Although acute myocardial infarction (MI) is consistently among the top causes of death in the United States, the spatial distribution of lipids and metabolites following MI remains to be elucidated. This work presents the investigation of an in vivo rat model of MI using mass spectrometric imaging (MSI) and multivariate data analysis. MSI was conducted on cardiac tissue following a 24-h left anterior descending coronary artery ligation to analyze multiple compound classes. First, the spatial distribution of a small metabolite, creatine, was used to identify areas of infarcted myocardium. Second, multivariate data analysis and tandem mass spectrometry were used to identify phospholipid (PL) markers of MI. A number of lysophospholipids demonstrated increased ion signal in areas of infarction. In contrast, select intact PLs demonstrated decreased ion signal in the area of infarction. The complementary nature of these two lipid classes suggests increased activity of phospholipase A(2), an enzyme that has been implicated in coronary heart disease and inflammation.