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.

A comprehensive proteomic profiling of urinary exosomes and the identification of early non-invasive biomarker in patients with coronary artery disease

Urinary small extracellular vesicles or exosomes (uEVs) source could be an emerging trove of biomarkers in coronary artery disease (CAD). It is a chronic inflammatory disease having a long asymptomatic phase of fatty-fibrous development in arteries leading to angina, myocardial infarction, and death. Our study was aimed at identifying differential protein expression profiling of uEVs in CAD. We collected urine samples of CAD patients (n = 41) age 18-65 years and gender matched healthy controls (n = 41). We isolated uEVs using differential ultracentrifugation. Further, uEV samples were characterized by western blotting exosome markers (Flotillin, TSG, CD63, and CD9), nano tracking analysis, and transmission and scanning electron microscopy. A total of 508 proteins were identified by iTRAQ-based mass spectrometry. We observed protein expression levels of AZGP1, SEMG1/2, ORM1, IGL, SERPINA5, HSPG2, prosaposin, gelsolin, and CD59 were upregulated, and UMOD, KNG1, AMBP, prothrombin, and TF were downregulated. Protein-protein interactions, gene ontology and pathway analysis were performed to functionally annotate identified uEVs proteins. A novel uEVs differential protein signature is shown. On validating UMOD protein by ELISA in two clinically different CAD, stable-CAD patients had lower levels than healthy controls whereas recent myocardial infarction patients had lowest. Our findings suggest UMOD importance as early diagnostic biomarker. SIGNIFICANCE: Coronary artery disease is a chronic inflammatory disease caused by gradual deposition of cholesterol and fat along with other proteins to develop plaque inside arteries. This further leads to blockage of artery, heart attack and death. There are no identifiable early biomarkers to diagnose this. For the first time, we have identified the differentially expressed proteins isolated from non-invasive uEV of CAD patients compared to healthy controls by using MS Orbitrap and iTRAQ labelling of peptides. We have identified decreased levels of UMOD protein in CAD. These findings have been confirmed by ELISA. Furthermore, the levels of UMOD were observed as more highly decreased in recent myocardial infarction CAD patients, indicating the importance of this protein as an early diagnostic biomarker. Conclusively, our study represents a non-invasive urinary EVs trove of differentially expressed proteins in CAD. This will form a groundwork for understanding the pathophysiology of CAD and will help in future translational research utilizing uEVs.

Differentiating primary and secondary FSGS using non-invasive urine biomarkers

Background

Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS.

Methods

Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n = 98) and patients with other chronic kidney diseases (CKDs, n = 100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC (n = 110) and CKD (n = 170).

Results

Proteomics data from patients with pFSGS were first compared to NC (n = 98). This resulted in 1179 biomarker (P < 0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies (n = 100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%).

Conclusion

A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%-99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application.

Cardioprotective and hypotensive mechanistic insights of hydroethanolic extract of Cucumis melo L. kernels in isoprenaline-induced cardiotoxicity based on metabolomics and in silico electrophysiological models

Background: Cardiovascular diseases (CVD) continue to threaten health worldwide, and account for a significant portion of deaths and illnesses. In both developing and industrialized nations, they challenge their health systems. There are several traditional uses of Cucurbitaceae seeds in Pakistan, India, Iran, and China, including treating cardiovascular, neurological, and urogenital diseases. Methods: In the present work, integrated techniques of metabolomics profiling and computational cardiomyocyte stimulation were used to investigate possible mechanisms of C. melo in isoprenaline (ISO)-induced myocardial infarction. In vitro, vasoconstrictions, paired atria, and in vivo invasive blood pressure measurement models were performed to explore the mechanism of action of C. melo hydroethanolic seed extract (Cm-EtOH). Results: Results showed that Cm-EtOH demonstrates NO-based endothelium-derived relaxing factor (EDRF) vasorelaxant response, negative chronotropic and inotropic response in the atrium, and hypotensive effects in normotensive rats. Results also revealed that Cm-EtOH decreases cardiomyocyte hypertrophy and reverts the altered gene expressions, biochemical, and metabolites in ISO-induced myocardial infarction (MI) rats. The extract additionally reversed ISO-induced MI-induced oxidative stress, energy consumption, and amino acid metabolism. Moreover, C. melo seeds increased EDRF function, energy production, and antioxidant capacity to treat myocardial and vascular disorders. In computational cardiomyocyte simulation, gallic acid reduced action potential duration, upstroke velocity (dV/dtmax), and effective refractory period. Conclusion: This study highlights the therapeutic potential of C. melo seeds to treat cardiovascular diseases and provides mechanistic insight into its antihypertensive and cardioprotective activities.

Urinary peptidome analysis in CKD and IgA nephropathy

Background

Chronic kidney disease (CKD) has emerged as a significant challenge to human health and economic stability in aging societies worldwide. Current clinical practice strategies remain insufficient for the early identification of kidney dysfunction, and the differential diagnosis of immunoglobulin A nephropathy (IgAN) predominantly relies on invasive kidney biopsy procedures.

Methods

First, we assessed a case-control cohort to obtain urine samples from healthy controls and biopsy-confirmed CKD patients. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) was applied to detect urinary peptide and then these urinary peptide profiles were used to construct diagnostic models to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. Furthermore, we assessed the robustness of the diagnostic models and their reproducibility by applying different algorithms.

Results

A rapid and accurate working platform for detecting CKD and its IgAN subtype based on urinary peptide pattern detected by MALDI-TOF MS was established. Naturally occurring urinary peptide profiles were used to construct a diagnostic model to distinguish CKD patients from controls and identify IgAN patients from other nephropathy patients. The performance of several algorithms was assessed and demonstrated that the robustness of the diagnostic models as well as their reproducibility were satisfactory.

Conclusions

The present findings suggest that the CKD-related and IgAN-specific urinary peptides discovered facilitate precise identification of CKD and its IgAN subtype, offering a dependable framework for screening conditions linked to renal dysfunction. This will aid in comprehending the pathogenesis of nephropathy and identifying potential protein targets for the clinical management of nephropathy.

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.

Urinary Peptidomics and Pulse Wave Velocity: The African-PREDICT Study

Increased arterial stiffness is related to early vascular aging and is an independent predictor for cardiovascular disease and mortality. Molecular mechanisms underlying increased arterial stiffness are largely unexplored, especially at the proteome level. We aimed to explore the relationship between pulse wave velocity and urinary proteomics. We included 919 apparently healthy (no chronic illnesses) Black and White men and women (equally distributed) between 20 and 30 years from the African-PREDICT study. Capillary electrophoresis time-of-flight mass spectrometry was used to analyze the urinary proteome. We measured the carotid-femoral pulse wave velocity to estimate arterial stiffness. In the total group, pulse wave velocity correlated positively with collagen-derived peptides including collagen types I, II, III, IV, V, and IX and inversely with collagen type XI (adjusted for mean arterial pressure). Regarding noncollagen-derived peptides, pulse wave velocity positively correlated with polymeric immunoglobulin receptor peptides (n = 2) (all q-value ≤0.05). In multivariable adjusted analyses, pulse wave velocity associated positively and independently with seven urinary peptides (collagen type I, n = 5) (all p-value ≤0.05). We found significant positive and independent associations between pulse wave velocity and the collagen type I-derived peptides, suggesting that dysregulation of collagen type I in the extracellular matrix scaffold could lead to early onset of increased arterial stiffness.

Potential roles of microRNAs and long noncoding RNAs as diagnostic, prognostic and therapeutic biomarkers in coronary artery disease

Coronary artery disease (CAD), which is mainly caused by atherosclerotic processes in coronary arteries, became a significant health issue. MicroRNAs (miRNAs), and long noncoding RNAs (lncRNAs), have been shown to be stable in plasma and could thereby be adopted as biomarkers for CAD diagnosis and treatment. MiRNAs can regulate CAD development through different pathways and mechanisms, including modulation of vascular smooth muscle cell (VSMC) activity, inflammatory responses, myocardial injury, angiogenesis, and leukocyte adhesion. Similarly, previously studies have indicated that the causal effects of lncRNAs in CAD pathogenesis and their utility in CAD diagnosis and treatment, has been found to lead to cell cycle transition, proliferation dysregulation, and migration in favour of CAD development. Differential expression of miRNAs and lncRNAs in CAD patients has been identified and served as diagnostic, prognostic and therapeutic biomarkers for the assessment of CAD patients. Thus, in the current review, we summarize the functions of miRNAs and lncRNAs, which aimed to identify novel targets for the CAD diagnosis, prognosis, and treatment.

A urinary peptidomics approach for early stages of cardiovascular disease risk: The African-PREDICT study

Cardiovascular disease (CVD) affects individuals across the lifespan, with multiple cardiovascular (CV) risk factors increasingly present in young populations. The underlying mechanisms in early cardiovascular disease development are complex and still poorly understood. We therefore employed urinary proteomics as a novel approach to gain better insight into early CVD-related molecular pathways based on a CVD risk stratification approach. This study included 964 apparently healthy (no self-reported chronic illnesses, free from clinical symptoms of CVD) black and white men and women (aged 20-30 years old) from the African Prospective study on the Early Detection and Identification of Cardiovascular disease and Hypertension (African-PREDICT) study. Cardiovascular risk factors used for stratification included obesity, physical inactivity, tobacco use, high alcohol intake, hyperglycemia, dyslipidemia and hypertension. Participants were divided into low (0 risk factors), medium (1-2 risk factors) and high (≥3 risk factors) CV risk groups. We analyzed urinary peptidomics by capillary electrophoresis time-of-flight mass spectrometry. After adjusting for ethnicity, sex and age, 65 sequenced urinary peptides were differentially expressed between the CV risk groups (all q-values ≤ 0.01). These peptides included a lower abundance of collagen type I- and III-derived peptides in the high compared to the low CV risk group. With regard to noncollagen peptides, we found a lower abundance of alpha-1-antitrypsin fragments in the high compared to the low CV risk group (all q-values ≤ 0.01). Our findings indicate lower abundances of collagen types I and III in the high compared to the low CV risk group, suggesting potential early alterations in the CV extracellular matrix.

A Coronary Artery Disease Monitoring Model Built from Clinical Data and Alpha-1-Antichymotrypsin

Coronary artery disease (CAD) is one of the most common subtypes of cardiovascular disease. The progression of CAD initiates from the plaque of atherosclerosis and coronary artery stenosis, and eventually turns into acute myocardial infarction (AMI) or stable CAD. Alpha-1-antichymotrypsin (AACT) has been highly associated with cardiac events. In this study, we proposed incorporating clinical data on AACT levels to establish a model for estimating the severity of CAD. Thirty-six healthy controls (HCs) and 162 CAD patients with stenosis rates of <30%, 30−70%, and >70% were included in this study. Plasma concentration of AACT was determined by enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve analysis and associations were conducted. Further, five machine learning models, including decision tree, random forest, support vector machine, XGBoost, and lightGBM were implemented. The lightGBM model obtained a sensitivity of 81.4%, a specificity of 67.3%, and an area under the curve (AUC) of 0.822 for identifying CAD patients with a stenosis rate of <30% versus >30%. In this study, we provided a demonstration of a monitoring model with clinical data and AACT.

Metabolomics based mechanistic insights to vasorelaxant and cardioprotective effect of ethanolic extract of Citrullus lanatus (Thunb.) Matsum. & Nakai. seeds in isoproterenol induced myocardial infraction

BACKGROUND

Cardiovascular diseases (CVDs) are a significant cause of morbidity and death in the current world, posing a challenge to both developing and industrialized nation's health systems. Citrullus lanatus (Thunb.) Matsum. & Nakai. seeds have long been utilized to supplement and enhance health and treat cardiovascular illnesses. However, its treatments for CVDs are still unknown. More research is required to fully comprehend the impact of C. lanatus seeds on vasorelaxation and myocardial infractions.

PURPOSE

Therefore, an integrated metabolomics profiling technique was used to investigate possible pathways of C. lanatus in isoproterenol (ISO)-induced myocardial infarction (MI). Isoproterenol causes long-term cardiac hypertrophy by causing cardiomyocyte compensatory loss, eventually leading to heart failure.

METHODS

In vitro models of vasoconstriction, atrium, and in vivo models of invasive blood pressure measurement and isoproterenol (ISO) induced cardiac hypertrophy in rats were used to understand underlying mechanistic by LC-MS/MS based dynamic metabolomics analysis of the serum and heart samples to be investigated the effect of ethanolic extract of C. lanatus (Cl.EtOH).

RESULTS

Cl.EtOH exhibited vasorelaxant, negative chronotropic, and inotropic effects in in-vitro models whereas, a potent hypotensive effect was observed in normotensive rats. The Cl.EtOH protected the animals from ISO-induced myocardial infarction (MI) with therapeutic interventions in left ventricular thickness, cardiomyocyte hypertrophy, mRNA gene expression, biochemical assays, and metabolomic profiling of serum and heart tissues.

CONCLUSIONS

For the first time, our study confirmed that C. lanatus seeds (Cl.EtOH) possess significant antihypertensive and prevent ISO-induced myocardial infarction. These findings comprehensively demonstrated mechanistic insights of Cl.EtOH in vasorelaxation and myocardial infarction. The current study provides evidence for further mechanistic studies and the development of C. lanatus seeds as a potential therapeutic intervention for patients with cardiovascular disorders.

Metabolomics analysis delineates the therapeutic effects of hydroethanolic extract of Cucumis sativus L. seeds on hypertension and isoproterenol-induced myocardial infarction

Cucumis sativus L., widely cultivated as an edible vegetable. Its seeds are well reputed for cardiovascular preventive properties. However, the mechanisms underlying for cardiovascular protection of C. sativus are still unidentified. Therefore, this study utilized a metabolomics approach to investigate putative mechanisms of C. sativus seeds in myocardial infarction (MI) and in vitro models of vasoconstriction, atrium, and invasive blood pressure measurement. Results showed that Cu.EtOH extract showed a vasorelaxant response with potent hypotensive effect in normotensive rats and L-NAME induced hypertension. Cu.EtOH caused a negative inotropic and positive chronotropic effect on the atrium. Cu.EtOH protected the animals from ISO-induced myocardial infarction (MI) interventions in left ventricular thickness, cardiomyocyte hypertrophy, mRNA gene expression, and biochemical assays. The metabolomics data suggested that Cu.EtOH mainly affected amino acid metabolism, BCAA degradation, ketone bodies degradation, and oxidative stress. Our study showed that Cu.EtOH suppressed inflammation with a strong anti-myocardial infarction impact. Additionally, our findings indicated Cu.EtOH reverted the amino acid metabolism, BCAA, and ketone bodies degradation. The findings show the antihypertensive mechanism of Cu.EtOH may include the modulation of endothelium-derived relaxing factor (EDRF) produced from nitric oxide (NO) and is connected with vascular endothelial function. C. sativus seeds, in particular, played a pivotal role in the treatment of myocardial and vascular disorders by enhancing the EDRF mechanism, energy generation, and antioxidant capacity. In summary, our findings showed the mechanistic insights on the therapeutic potential of C. sativus seeds for cardiovascular disorders.

Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms

Cardiovascular disease (CVD) is one of the most common causes of death that kills approximately 17 million people annually. The main reasons behind CVD are myocardial infarction and the failure of the heart to pump blood normally. Doctors could diagnose heart failure (HF) through electronic medical records on the basis of patient's symptoms and clinical laboratory investigations. However, accurate diagnosis of HF requires medical resources and expert practitioners that are not always available, thus making the diagnosing challengeable. Therefore, predicting the patients' condition by using machine learning algorithms is a necessity to save time and efforts. This paper proposed a machine-learning-based approach that distinguishes the most important correlated features amongst patients' electronic clinical records. The SelectKBest function was applied with chi-squared statistical method to determine the most important features, and then feature engineering method has been applied to create new features correlated strongly in order to train machine learning models and obtain promising results. Optimised hyperparameter classification algorithms SVM, KNN, Decision Tree, Random Forest, and Logistic Regression were used to train two different datasets. The first dataset, called Cleveland, consisted of 303 records. The second dataset, which was used for predicting HF, consisted of 299 records. Experimental results showed that the Random Forest algorithm achieved accuracy, precision, recall, and F1 scores of 95%, 97.62%, 95.35%, and 96.47%, respectively, during the test phase for the second dataset. The same algorithm achieved accuracy scores of 100% for the first dataset and 97.68% for the second dataset, while 100% precision, recall, and F1 scores were reached for both datasets.

Urine proteomics for prediction of disease progression in patients with IgA nephropathy

BACKGROUND

Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification.

METHODS

In this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models.

RESULTS

Of the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83-0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64-0.81).

CONCLUSIONS

A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone.

Urinary Protein and Peptide Markers in Chronic Kidney Disease

Chronic kidney disease (CKD) is a non-specific type of kidney disease that causes a gradual decline in kidney function (from months to years). CKD is a significant risk factor for death, cardiovascular disease, and end-stage renal disease. CKDs of different origins may have the same clinical and laboratory manifestations but different progression rates, which requires early diagnosis to determine. This review focuses on protein/peptide biomarkers of the leading causes of CKD: diabetic nephropathy, IgA nephropathy, lupus nephritis, focal segmental glomerulosclerosis, and membranous nephropathy. Mass spectrometry (MS) approaches provided the most information about urinary peptide and protein contents in different nephropathies. New analytical approaches allow urinary proteomic-peptide profiles to be used as early non-invasive diagnostic tools for specific morphological forms of kidney disease and may become a safe alternative to renal biopsy. MS studies of the key pathogenetic mechanisms of renal disease progression may also contribute to developing new approaches for targeted therapy.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Multi-omics approaches for revealing the complexity of cardiovascular disease

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Urinary Proteomic Characteristics of Hyperuricemia and Their Possible Links with the Occurrence of Its Concomitant Diseases

Hyperuricemia (HUA), a chronic disease caused by metabolic disorders of purine, is often accompanied by other diseases such as gout, type 2 diabetes mellitus (T2DM), and hyperlipidemia. However, little is known about the relationship between HUA and these diseases on the protein level. We performed label-free liquid chromatography MS/MS spectrometry analysis of urine samples from 26 HUA patients and 25 healthy controls, attempting to establish the possible protein links between HUA and these diseases by profiling urine proteome. A total of 2119 proteins were characterized in sample proteomes. Among them, 11 were found decreased and 2 were found increased in HUA samples. Plausible pathways found by enrichment analysis of these differentially expressed proteins (DEPs) include the processes for insulin receptor recycling and lipid metabolism, suggesting potential links between HUA and T2DM and hyperlipidemia. The abundance changes of three key proteins (VATB1, CFAD, and APOC3) involved in these processes were validated by enzyme-linked immunosorbent assay (ELISA). In conclusion, our result provides proteomic evidence, for the first time, that the aberrant pathways enriched by described key DEPs are closely related to the incidence of HUA and its concomitant diseases.

Gold nanoparticles-based assays for biodetection in urine

Urine is a biofluid easy to collect through a non-invasive technique that allows collecting a large volume of sample. The use of urine for disease diagnosis is not yet well explored. However, it has gained attention over the last three years. It has been applied in the diagnosis of several illnesses such as kidney disease, bladder cancer, prostate cancer and cardiovascular diseases. In the last decade, gold nanoparticles (Au NPs) have attracted attention in biosensors' development for the diagnosis of diseases due to their electrical and optical properties, ability to conjugate with biomolecules, high sensitivity, and selectivity. Therefore, this article aims to present a comprehensive view of state of the art on the advances made in the quantification of analytes in urinary samples using AuNPs based assays, with a focus on protein analysis. The type of diagnosis methods, the Au NPs synthesis approaches and the strategies for surface modification aiming at selectivity towards the different targets are highlighted.

Identification of Protein Changes in the Urine of Hypothyroid Patients Treated with Thyroxine Using Proteomics Approach

The thyroid gland and thyroid hormones control a multitude of homeostatic functions including maintenance of fluid and electrolyte balance and normal functioning of the kidneys. Thyroid dysfunction alters the sytemic hemodynamic and metabolic balance, thereby affecting the kidney. In this study, we aimed to identify and characterize the urinary proteome of the patients with hypothyroidism. An untargeted proteomic approach with network analysis was used to identify changes in total urinary proteome in patients with newly diagnosed overt hypothyroidism. Urine samples were collected from nine age-matched patients' before and after l-thyroxine treatment. Differences in the abundance of urinary proteins between hypothyroid and euthyroid states were determined using a two-dimensional difference in gel electrophoresis (2D-DIGE) coupled to matrix-assisted laser desorption and ionization time-of-flight (MALDI TOF) mass spectrometry. Alterations in the abundance of urinary proteins, analyzed by Progenesis software, revealed statistically significant differential abundance in a total of 49 spots corresponding to 42 proteins, 28 up and 14 down (≥1.5-fold change, analysis of variance (ANOVA), p ≤ 0.05). The proteins identified in the study are known to regulate processes related to transport, acute phase response, oxidative stress, generation of reactive oxygen species, cellular proliferation, and endocytosis. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) identified dysregulation of pathways related to amino acid metabolism, molecular transport, and small-molecule biochemistry and involved the MAPK kinase, vascular endothelial growth factor (VEGF), PI3 kinase/Akt, protein kinase C (PKC), signaling pathways. The identified proteins were involved in the regulation of thyroglobulin (Tg) and thyrotropin (TSH) metabolism. Alterations in their levels indicate the presence of a compensatory mechanism aimed at increasing the regulation of Tg in the hypothyroid state.

Urinary proteome profiling for stratifying patients with familial Parkinson's disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non‐invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non‐carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.

Review: Detection and quantification of proteins in human urine

Extensive medical research showed that patients, with high protein concentration in urine, have various kinds of kidney diseases, referred to as proteinuria. Urinary protein biomarkers are useful for diagnosis of many health conditions – kidney and cardio vascular diseases, cancers, diabetes, infections. This review focuses on the instrumental quantification (electrophoresis, chromatography, immunoassays, mass spectrometry, fluorescence spectroscopy, the infrared spectroscopy, and Raman spectroscopy) of proteins (the most of all albumin) in human urine matrix. Different techniques provide unique information on what constituents of the urine are. Due to complex nature of urine, a separation step by electrophoresis or chromatography are often used for proteomics study of urine. Mass spectrometry is a powerful tool for the discovery and the analysis of biomarkers in urine, however, costs of the analysis are high, especially for quantitative analysis. Immunoassays, which often come with fluorescence detection, are major qualitative and quantitative tools in clinical analysis. While Infrared and Raman spectroscopies do not give extensive information about urine, they could become important tools for the routine clinical diagnostics of kidney problems, due to rapidness and low-cost. Thus, it is important to review all the applicable techniques and methods related to urine analysis. In this review, a brief overview of each technique's principle is introduced. Where applicable, research papers about protein determination in urine are summarized with the main figures of merits, such as the limit of detection, the detectable range, recovery and accuracy, when available.

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Urinary protein biomarkers are useful for diagnosis of many conditions: kidney and cardio vascular diseases, cancers.

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Liquid chromatography – mass spectroscopy is a powerful tool for urine proteomics, but used mostly in science.

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Immunoassays are widely used in both clinical and bio-analytical laboratories.

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IR and Raman spectroscopies are promising tools for diagnostics of urine due to low-cost and rapidness.

Circulating microRNA as a Biomarker for Coronary Artery Disease

Coronary artery disease (CAD) is the leading cause of sudden cardiac death in adults, and new methods of predicting disease and risk-stratifying patients will help guide intervention in order to reduce this burden. Current CAD detection involves multiple modalities, but the consideration of other biomarkers will help improve reliability. The aim of this narrative review is to help researchers and clinicians appreciate the growing relevance of miRNA in CAD and its potential as a biomarker, and also to suggest useful miRNA that may be targets for future study. We sourced information from several databases, namely PubMed, Scopus, and Google Scholar, when collating evidentiary information. MicroRNAs (miRNA) are short, noncoding RNAs that are relevant in cardiovascular physiology and pathophysiology, playing roles in cardiac hypertrophy, maintenance of vascular tone, and responses to vascular injury. CAD is associated with changes in miRNA expression profiles, and so are its risk factors, such as abnormal lipid metabolism and inflammation. Thus, they may potentially be biomarkers of CAD. Nevertheless, there are limitations in using miRNA. These include cost and the presence of several confounding factors that may affect miRNA profiles. Furthermore, there is difficulty in the normalisation of miRNA values between published studies, due to pre-analytical variations in samples.

Urinary Proteome of Newborn Calves-New Potential in Non-Invasive Neonatal Diagnostic

Simple Summary

Urine is an underappreciated biological sample in the context of health monitoring of newborn calves. It can be collected noninvasively and used for diagnostic purposes, which is an attractive option in veterinary examination. The establishment of urinary protein maps of healthy newborn calves is important for diagnosing and monitoring the progression of various diseases. The aim of this study was to characterize the urinary proteome profile of healthy newborn calves. We applied 2-D gel electrophoresis combined with MALDI-TOF-TOF-MS/MS and 1-D gel electrophoresis coupled with LC-MS-MS analysis to separate and identify proteins present in the urine of newborn calves. Urinary proteomic analysis allowed us to detect proteins which are characteristic of embryonic and neonatal development and proteins involved in the cardiovascular and digestive system, and bone and kidney development. Moreover, we identified proteins that can be used to assess normal glomerular and tubular physiology. In calves, one of the main causes of mortality in the neonatal period is diarrhea, affecting fluid and electrolyte balance. We also identified proteins involved in the renal transport of water and electrolytes, which can be useful in the early diagnosis and prevention of these type of disorders.

Abstract

Urine is a biological diagnostic material suitable not only for the analysis of kidney and urinary tract functions but also the function of other tissues and organs. The urine proteome of adult mammals differs from the urine proteome of neonatal ones. The establishment of urinary protein maps of healthy newborn calves is important for diagnosing and monitoring the progression of various diseases. The experiment was carried out on a Polish-Friesian var. of Black-and-White male calves in the sixth day of postnatal life. The two proteomics approaches used for separation and identification of urinary proteins were: 2-DE with MALDI-TOF-TOF-MS/MS and 1-DE with LC-MS/MS. This resulted in the identification of 692 urinary proteins. The majority of them were classified as extracellular proteins (40.32%), as well as proteins involved in regulation of major cellular processes (31.07%). We have observed the presence of unique proteins associated with embryonic (ameloblastin, alpha-fetoprotein, Delta-like protein, embryo-specific fibronectin 1 transcript variant, Indian hedgehog homolog) and kidney development (angiotensin-converting enzyme, angiotensinogen, aquaporin-1, calbindin, glypican 3, nidogen 1, pro-cathepsin H). Additionally, proteins involved in the renal regulation of water and electrolyte balance (angiotensinogen, angiotensin-converting enzyme, aquaporin-1, ezrin, uromodulin) were detected. Presented in the current study 1-D and 2-D urinary proteomic maps are the basis for the identification and detection of prognostic biomarkers important for defining a calf’s health status.

The urinary proteomics classifier chronic kidney disease 273 predicts cardiovascular outcome in patients with chronic kidney disease

Background

The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1–G5.

Methods

We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1–G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs).

Results

In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1–G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a &gt;10-fold increased risk as compared with the lowest tertile.

Conclusions

The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management.

Urine proteomics study reveals potential biomarkers for the differential diagnosis of cholangiocarcinoma and periductal fibrosis

Cholangiocarcinoma (CCA) is a primary malignant tumor of the epithelial lining of biliary track associated with endemic Opisthorchis viverrini (Ov) infection in northeastern Thailand. Ov-associated periductal fibrosis (PDF) is the precancerous lesion for CCA, and can be detected by ultrasonography (US) to facilitate early detection. However, US cannot be used to distinguish PDF from cancer. Therefore, the objective of this study was to discover and qualify potential urine biomarkers for CCA detection in at-risk population. Biomarker discovery was conducted on pooled urine samples, 42 patients per group, with PDF or normal bile duct confirmed by ultrasound. After depletion of high abundance proteins, 338 urinary proteins were identified from the 3 samples (normal-US, PDF-US, CCA). Based on fold change and literature review, 70 candidate proteins were selected for qualification by multiple reaction monitoring mass spectrometry (MRM-MS) in 90 individual urine samples, 30 per group. An orthogonal signal correction projection to latent structures discriminant analysis (O-PLS-DA) multivariate model constructed from the 70 candidate biomarkers significantly discriminated CCA from normal and PDF groups (P = 0.003). As an independent validation, the expression of 3 candidate proteins was confirmed by immunohistochemistry in CCA tissues: Lysosome associated membrane glycoprotein 1 (LAMP1), lysosome associated membrane glycoprotein 2 (LAMP2) and cadherin-related family member 2 (CDHR2). Further evaluation of these candidate biomarkers in a larger cohort is needed to support their applicability in a clinical setting for screening and monitoring early CCA and for CCA surveillance.

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.

Urinary excretion of homocysteine thiolactone and the risk of acute myocardial infarction in coronary artery disease patients: the WENBIT trial

OBJECTIVES

No individual homocysteine (Hcy) metabolite has been studied as a risk marker for coronary artery disease (CAD). Our objective was to examine Hcy-thiolactone, a chemically reactive metabolite generated by methionyl-tRNA synthetase and cleared by the kidney, as a risk predictor of incident acute myocardial infarction (AMI) in the Western Norway B-Vitamin Intervention Trial.

DESIGN

Single centre, prospective double-blind clinical intervention study, randomized in a 2 × 2 factorial design.

SUBJECTS AND METHODS

Patients with suspected CAD (n = 2049, 69.8% men; 61.2-year-old) were randomized to groups receiving daily (i) folic acid (0.8 mg)/vitamin B₁₂ (0.4 mg)/vitamin B₆ (40 mg); (ii) folic acid/vitamin B₁₂ ; (iii) vitamin B₆ or (iv) placebo. Urinary Hcy-thiolactone was quantified at baseline, 12 and 38 months.

RESULTS

Baseline urinary Hcy-thiolactone/creatinine was significantly associated with plasma tHcy, ApoA1, glomerular filtration rate, potassium and pyridoxal 5'-phosphate (positively) and with age, hypertension, smoking, urinary creatinine, plasma bilirubin and kynurenine (negatively). During median 4.7-years, 183 patients (8.9%) suffered an AMI. In Cox regression analysis, Hcy-thiolactone/creatinine was associated with AMI risk (hazard ratio = 1.58, 95% confidence interval = 1.10-2.26, P = 0.012 for trend; adjusted for age, gender, tHcy). This association was confined to patients with pyridoxic acid below median (adjusted HR = 2.72, 95% CI = 1.47-5.03, P = 0.0001; P_interaction = 0.020). B-vitamin/folate treatments did not affect Hcy-thiolactone/creatinine and its AMI risk association.

CONCLUSIONS

Hcy-thiolactone/creatinine ratio is a novel AMI risk predictor in patients with suspected CAD, independent of traditional risk factors and tHcy, but modified by vitamin B₆ catabolism. These findings lend a support to the hypothesis that Hcy-thiolactone is mechanistically involved in cardiovascular disease.

Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer

Background

Urine has evolved as a promising body fluids in clinical proteomics because it can be easily and noninvasively obtained and can reflect physiological and pathological status of the human body. Many efforts have been made to characterize more urinary proteins in recent years, but few have focused on the analysis throughput and detection reproducibility. Increasing the urine proteomic profiling throughput and reproducibility is urgently needed for discovering potential biomarker in large cohorts.

Methods

In this study, we developed a fast and robust workflow for streamlined urinary proteome analysis. The workflow integrate highly efficient sample preparation technique and urinary specific data-independent acquisition (DIA) approach. The performance of the workflow was systematically evaluated and the workflow was subsequently applied in a proof-of-concept urine proteome study of 21 kidney cancer (KC) patients and 22 healthy controls.

Results

With this workflow, the entire sample preparation process takes less than 3 h and allows multiplexing on standard centrifuges. Without pre-fractionation, our newly developed DIA method allows quantitative analysis of ~ 1000 proteins within 80 min of MS time (~ 15 samples/day). The quantitation accuracy of the whole workflow was excellent with median CV of 9.1%. The preliminary study on KC identified 125 significantly changed proteins.

Conclusions

The result suggested the feasibility of applying the high throughput workflow in extensive urinary proteome profiling and clinical relevant biomarker discovery.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9220-2) contains supplementary material, which is available to authorized users.

The effect of sodium/glucose cotransporter 2 (SGLT2) inhibition on the urinary proteome

Treatment with empagliflozin, an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), is associated with slower progression of diabetic kidney disease. In this analysis, we explored the hypothesis that empagliflozin may have an impact on urinary peptides associated with chronic kidney disease (CKD). In this post-hoc, exploratory analysis, we investigated urine samples obtained from 40 patients with uncomplicated type 1 diabetes (T1D) before and after treatment with empagliflozin for 8 weeks to for significant post-therapy changes in urinary peptides. We further assessed the association of these changes with CKD in an independent cohort, and with a previously established urinary proteomic panel, termed CKD273. 107 individual peptides significantly changed after treatment. The majority of the empagliflozin-induced changes were in the direction of “CKD absent” when compare to patients with CKD and controls. A classifier consisting of these 107 peptides scored significantly different in controls, in comparison to CKD patients. However, empagliflozin did not impact the CKD273 classifier. Our data indicate that empagliflozin induces multiple significant changes in the urinary proteomic markers such as mucin and clusterin. The relationship between empagliflozin-induced proteomic changes and clinical outcomes merits further investigation.

Multiplexed Temporal Quantification of the Exercise-regulated Plasma Peptidome

Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimized 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyze endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodeling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease/substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits.

Novel Urinary Peptidomic Classifier Predicts Incident Heart Failure

BACKGROUND

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

METHODS AND RESULTS

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

CONCLUSIONS

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

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.

Development of a Novel Enzyme-Linked Immunosorbent Assay Targeting a Neo-Epitope Generated by Cathepsin-Mediated Turnover of Type III Collagen and Its Application in Chronic Obstructive Pulmonary Disease

A high level of extracellular matrix (ECM) turnover characterizes several lung diseases with fibrotic features. Type III collagen is one of the most abundant collagens in lung parenchyma, and cathepsins play a role in lung pathology, being responsible for tissue remodeling. In this study, we explore the diagnostic features of neo-epitope fragments of type III collagen generated by cathepsins that could reflect the pathological tissue turnover in patients with different diseases. A novel enzyme-linked immunosorbent assay (ELISA) measuring cathepsins B, L, S and K -generated type III collagen fragments (C3C) was developed for assessment in serum and plasma. The assay was biologically validated in serum from patients with chronic obstructive pulmonary disease (COPD). Serological levels of C3C were significantly elevated in patients with COPD compared to healthy controls (p = 0.0006). Levels of C3C in serum and heparin plasma of COPD patients had a highly significant correlation (R² = 0.86, p<0.0001). The data suggests that the C3C fragment is elevated in patients with COPD compared to healthy controls.

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.

Identifying new Risk Markers and Potential Targets: The Value of the Proteome

Several protein biomarkers, including cardiac troponin T, cardiac troponin I, B-type natriuretic peptide, C-reactive protein and apolipoprotein A-I, are widely employed in the evaluation of cardiovascular disease. Several of such potential biomarkers, or their multiscores, have been assessed over the last years for the prediction of cardiovascular risk but only a few of them have been validated for clinical use. Substantial improvement in the cardiovascular risk prediction and reclassification relative to traditional models therefore remains a difficult task presently unresolved. Hence, a potential importance of alternative approaches which may rely on novel proteomic biomarkers among others. Plasma or serum concentrations of numerous proteins were measured using proteomic approaches to establish their relationships with cardiovascular disease; none of them was however evaluated for cardiovascular risk prediction and subject stratification in rigorous large-scale studies. Thus, further research is needed to identify novel candidates that can improve cardiovascular risk prediction, subject stratification and standard care. Proteomics will undoubtedly remain a key approach to address this major clinical and scientific challenge.

Urinary biomarkers for renal tract malformations

INTRODUCTION

Renal tract malformations (RTMs) are congenital anomalies of the kidneys and urinary tract, which are the major cause of end-stage renal disease in children. Using immunoassay-based approaches (ELISA, western blot), individual urinary proteins including transforming growth factor β, tumor necrosis factor and monocyte attractant proteins 1 were found to be associated to RTMs. However, only mass spectrometry (MS) based methods leading to the identification of panels of protein-based markers composed of fragments of the extracellular matrix allowed the prediction of progression of RTMs and its complications. Areas covered: In this review, we summarized relevant studies identified in "Pubmed" using the keywords "urinary biomarkers" and "proteomics" and "renal tract malformations" or "hydronephrosis" or "ureteropelvic junction obstruction" or "posterior urethral valves" or "vesicoureteral reflux". These publications represent studies on potential protein-based biomarkers, either individually or combined in panels, of RTMs in human and animal models. Expert commentary: Successful use in the clinic of these protein-based biomarkers will need to involve larger scale studies to reach sufficient power. Improved performance will potentially come from combining immunoassay- and MS-based markers.

Excessive amount of lactose in the diet of two-week-old calves induces urinary protein changes

Abstract. The present paper was undertaken to analyse and identify urinary proteins that were significantly altered in urine of calves in response to short-term administration of milk replacer with lactose addition. We used 2-D electrophoresis combined with matrix-assisted laser desorption/ionisation and time-of-flight (MALDI-TOF) mass spectrometry. Of all spots analysed, four showed significantly decreased abundance: alpha-1-antiproteinase (A1AT), serotransferrin (TF), sex hormone-binding globulin (SHBG) and cytochrome P450 2E1 (CYP2E1). One displayed an increased abundance: adenosine triphosphate (ATP)-citrate synthase. The changes in abundance of SHBG and CYP2E1 proteins were caused by the direct effect of an oversupply of sugar, while A1AT, TF and ATP-citrate synthase showed altered abundance probably due to indirect effects. The results of this study confirmed that calves' urine is a very precious biological material to evaluate the renal function, and it may be valuable in veterinary and zootechnical diagnostics.

A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.

Plasma proteomic analysis of stable coronary artery disease indicates impairment of reverse cholesterol pathway

Coronary artery disease (CAD) is one of the largest causes of death worldwide yet the traditional risk factors, although useful in identifying people at high risk, lack the desired predictive accuracy. Techniques like quantitative plasma proteomics holds immense potential to identify newer markers and this study (conducted in three phases) was aimed to identify differentially expressed proteins in stable CAD patients. In the first (discovery) phase, plasma from CAD cases (angiographically proven) and controls were subjected to iTRAQ based proteomic analysis. Proteins found to be differentially expressed were then validated in the second and third (verification and validation) phases in larger number of (n = 546) samples. After multivariate logistic regression adjusting for confounding factors (age, diet, etc.), four proteins involved in the reverse cholesterol pathway (Apo A1, ApoA4, Apo C1 and albumin) along with diabetes and hypertension were found to be significantly associated with CAD and could account for approximately 88% of the cases as revealed by ROC analysis. The maximum odds ratio was found to be 6.70 for albumin (p < 0.0001), followed by Apo AI (5.07, p < 0.0001), Apo CI (4.03, p = 0.001), and Apo AIV (2.63, p = 0.003). Down-regulation of apolipoproteins and albumin implicates the impairment of reverse cholesterol pathway in CAD.

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.