Prediction of acute coronary syndromes by urinary proteome analysis

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.

Advances in research on biomarkers associated with acute myocardial infarction: A review

Acute myocardial infarction (AMI), the most severe cardiovascular event in clinical settings, imposes a significant burden with its annual increase in morbidity and mortality rates. However, it is noteworthy that mortality due to AMI in developed countries has experienced a decline, largely attributable to the advancements in medical interventions such as percutaneous coronary intervention. This trend highlights the importance of accurate diagnosis and effective treatment to preserve the myocardium at risk and improve patient outcomes. Conventional biomarkers such as myoglobin, creatine kinase isoenzymes, and troponin have been instrumental in the diagnosis of AMI. However, recent years have witnessed the emergence of new biomarkers demonstrating the potential to further enhance the accuracy of AMI diagnosis. This literature review focuses on the recent advancements in biomarker research in the context of AMI diagnosis.

Integration of Urinary Peptidome and Fecal Microbiome to Explore Patient Clustering in Chronic Kidney Disease

Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice.

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 peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

BACKGROUND

There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides.

METHODS

Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated.

RESULTS

In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17-1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47-1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39-1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I).

CONCLUSION

The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death.

Prognosis and Personalized In Silico Prediction of Treatment Efficacy in Cardiovascular and Chronic Kidney Disease: A Proof-of-Concept Study

(1) Background: Kidney and cardiovascular diseases are responsible for a large fraction of population morbidity and mortality. Early, targeted, personalized intervention represents the ideal approach to cope with this challenge. Proteomic/peptidomic changes are largely responsible for the onset and progression of these diseases and should hold information about the optimal means of treatment and prevention. (2) Methods: We investigated the prediction of renal or cardiovascular events using previously defined urinary peptidomic classifiers CKD273, HF2, and CAD160 in a cohort of 5585 subjects, in a retrospective study. (3) Results: We have demonstrated a highly significant prediction of events, with an HR of 2.59, 1.71, and 4.12 for HF, CAD, and CKD, respectively. We applied in silico treatment, implementing on each patient's urinary profile changes to the classifiers corresponding to exactly defined peptide abundance changes, following commonly used interventions (MRA, SGLT2i, DPP4i, ARB, GLP1RA, olive oil, and exercise), as defined in previous studies. Applying the proteomic classifiers after the in silico treatment indicated the individual benefits of specific interventions on a personalized level. (4) Conclusions: The in silico evaluation may provide information on the future impact of specific drugs and interventions on endpoints, opening the door to a precision-based medicine approach. An investigation into the extent of the benefit of this approach in a prospective clinical trial is warranted.

Molecular Mapping of Urinary Complement Peptides in Kidney Diseases

Defective complement activation has been associated with various types of kidney disease. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors.

Urinary peptidome and diabetic retinopathy in the DIRECT-Protect 1 and 2 trials

BACKGROUND

Given the association of diabetic retinopathy (DR) and kidney disease, we investigated the urinary peptidome to presence and deterioration of DR in a post hoc analysis of trials investigating the effect of candesartan on progression of DR in type 1 and type 2 diabetes, respectively.

METHODS

Baseline urinary peptidomic analysis was performed on a random selection of 783 and 792 subjects in two randomized controlled trials, DIRECT-Protect 1 and 2, respectively. End points were two-step (RET2) and three-step (RET3) change in Early Treatment of Diabetic Retinopathy Study protocol (ETDRS) defined level. Peptide levels were correlated to baseline EDTRS level in a discovery set of 2/3 of the participants from DIRECT-Protect 1. The identified peptides were then validated cross-sectionally in the remaining 1/3 from DIRECT-Protect 1. Thereafter, peptides identified in the discovery set were assessed in the entire DIRECT-Protect 1 and 2 cohorts and significant peptides were tested longitudinally.

RESULTS

Follow-up ranged 4.0-4.7 years. 24 peptides were associated with baseline DR in the discovery set. COL3A1 (seq: NTG~) and COL4A1 (seq: DGA~) were associated with baseline DR in the validation set (Rho: -.223, p < 0.001 and Rho: -.141, p = 0.024). Neither was significantly associated with end points. Assessing the 24 identified peptides in the entire cohorts, several collagen peptides were associated with baseline DR and end points; however, there was no overlap across diabetes types.

CONCLUSIONS

We identified several urinary peptides (mainly collagen) associated with the presence of DR, however they could not be conclusively associated with worsening of DR.

Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma

Simple Summary

In this study, the application of capillary electrophoresis mass spectrometry enabled identification of 31 urinary peptides significantly associated with hepatocellular carcinoma diagnosis and prognosis. Further assessment of these peptides lead to prediction of cellular proteases involved in their development namely Meprin A subunit α and Kallikrein-6. Subsequent identification of the proteases was verified by immunohistochemistry in normal liver, cirrhosis and hepatocellular carcinoma. Histopathological assessment of the proteases revealed numerical gradient staining signifying their involvement in liver fibrosis and hepatocellular carcinoma formation. The discovered urinary peptides offered a potential noninvasive tool for diagnosis and prognosis of hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC) is known to be associated with protein alterations and extracellular fibrous deposition. We investigated the urinary proteomic profiles of HCC patients in this prospective cross sectional multicentre study. 195 patients were recruited from the UK (Coventry) and Germany (Hannover) between 1 January 2013 and 30 June 2019. Out of these, 57 were HCC patients with a background of liver cirrhosis (LC) and 138 were non-HCC controls; 72 patients with LC, 57 with non-cirrhotic liver disease and 9 with normal liver function. Analysis of the urine samples was performed by capillary electrophoresis (CE) coupled to mass spectrometry (MS). Peptide sequences were obtained and 31 specific peptide markers for HCC were identified and further integrated into a multivariate classification model. The peptide model demonstrated 79.5% sensitivity and 85.1% specificity (95% CI: 0.81–0.93, p < 0.0001) for HCC and 4.1-fold increased risk of death (95% CI: 1.7–9.8, p = 0.0005). Proteases potentially involved in HCC progression were mapped to the N- and C-terminal sequence motifs of the CE-MS peptide markers. In silico protease prediction revealed that kallikrein-6 (KLK6) elicits increased activity, whilst Meprin A subunit α (MEP1A) has reduced activity in HCC compared to the controls. Tissue expression of KLK6 and MEP1A was subsequently verified by immunohistochemistry.

Molecular Network Analysis of the Urinary Proteome of Alzheimer's Disease Patients

Background/Aims

The identification of predictive biomarkers for Alzheimer's disease (AD) from urine would aid in screening for the disease, but information about biological and pathophysiological changes in the urine of AD patients is limited. This study aimed to explore the comprehensive profile and molecular network relations of urinary proteins in AD patients.

Methods

Urine samples collected from 18 AD patients and 18 age- and sex-matched cognitively normal controls were analyzed by mass spectrometry and semiquantified with the normalized spectral index method. Bioinformatics analyses were performed on proteins which significantly increased by more than 2-fold or decreased by less than 0.5-fold compared to the control (p < 0.05) using DAVID bioinformatics resources and KeyMolnet software.

Results

The levels of 109 proteins significantly differed between AD patients and controls. Among these, annotation clusters related to lysosomes, complement activation, and gluconeogenesis were significantly enriched. The molecular relation networks derived from these proteins were mainly associated with pathways of lipoprotein metabolism, heat shock protein 90 signaling, matrix metalloproteinase signaling, and redox regulation by thioredoxin.

Conclusion

Our findings suggest that changes in the urinary proteome of AD patients reflect systemic changes related to AD pathophysiology.

Sample Treatment for Urine Proteomics

Urine is a biological fluid that can be collected noninvasively in relatively large quantities which can be used for the search of biomarkers of disease, both diseases of the urological tract and systemic diseases. One of the most important aspects in proteomic studies is sample treatment before further analysis. Methods of preparation of a urine sample depend on the techniques that will be used later for separation and identification of the proteins. Also, urine preparation should be as simple as possible to increase reproducibility. Normal urine has a much diluted protein concentration with a high-salt content, which interferes with proteomic analysis. Thus, an initial step in the handling of urine sample should be to concentrate and eliminate salts. As range of protein concentrations in urine spans several orders of magnitude, effective proteomic analyses require either removal of most abundant protein or enrichment of the less abundant ones. In this chapter, we discuss the aspects related to the collection and treatment of urine for proteomic studies.

Application of urinary proteomics as possible risk predictor of renal and cardiovascular complications in patients with type 2-diabetes and microalbuminuria

BACKGROUND

Analyses of the urinary proteome have been proposed as a novel approach for early assessment of increased risk of renal- or cardiovascular disease. Here we investigate the potentials of various classifiers derived from urinary proteomics for prediction of renal and cardiovascular comorbidities in patients with type 2-diabetes.

METHODS

The study was a post hoc analysis of the randomized controlled Steno-2 trial comparing intensified multifactorial intervention to conventional treatment of type 2-diabetes and microalbuminuria. 151 diabetic patients with persistent microalbuminuria were included in year 1995 and followed for up to 19 years. For renal outcomes, two classifiers (CKD273 and a novel, GFR-based classifier) and for cardiovascular outcomes, three classifiers (CAD238, ACSP and ACSP75) were applied. Renal endpoints were progression to macroalbuminuria, impaired renal function (GFR < 45 ml/min/1.73 m²) or progression to end stage renal disease (ESRD) or death. Cardiovascular endpoints were coronary artery disease and a composite endpoint of incident death of cardiovascular disease, myocardial infarction or revascularization, stroke, amputation or peripheral revascularization.

RESULTS

CKD273 was not consistently associated with renal outcomes. The GFR-based classifier was associated with impaired renal function, but lost significance in extensively adjusted models. Both the ACSP75 and ACSP-scores, but not the CAD238-score were inversely associated (opposing the hypothesis) with cardiovascular endpoints. None of the classifiers improved prediction of any outcome on top of standard risk factors.

CONCLUSIONS

Risk-scores based upon urinary proteomics did not improve prediction of renal and cardiovascular endpoints on top of standard risk factors such as age and GFR during long-term (19 years) follow up in patients with type 2-diabetes and microalbuminuria.

Urinary proteomic signatures associated with β-blockade and heart rate in heart transplant recipients

Objectives

Heart transplant (HTx) recipients have a high heart rate (HR), because of graft denervation and are frequently started on β-blockade (BB). We assessed whether BB and HR post HTx are associated with a specific urinary proteomic signature.

Methods

In 336 HTx patients (mean age, 56.8 years; 22.3% women), we analyzed cross-sectional data obtained 7.3 years (median) after HTx. We recorded medication use, measured HR during right heart catheterization, and applied capillary electrophoresis coupled with mass spectrometry to determine the multidimensional urinary classifiers HF1 and HF2 (known to be associated with left ventricular dysfunction), ACSP75 (acute coronary syndrome) and CKD273 (renal dysfunction) and 48 sequenced urinary peptides revealing the parental proteins.

Results

In adjusted analyses, HF1, HF2 and CKD273 (p ≤ 0.024) were higher in BB users than non-users with a similar trend for ACSP75 (p = 0.06). Patients started on BB within 1 year after HTx and non-users had similar HF1 and HF2 levels (p ≥ 0.098), whereas starting BB later was associated with higher HF1 and HF2 compared with non-users (p ≤ 0.014). There were no differences in the urinary biomarkers (p ≥ 0.27) according to HR. BB use was associated with higher urinary levels of collagen II and III fragments and non-use with higher levels of collagen I fragments.

Conclusions

BB use, but not HR, is associated with a urinary proteomic signature that is usually associated with worse outcome, because unhealthier conditions probably lead to initiation of BB. Starting BB early after HTx surgery might be beneficial.

Plasma proteomic analysis reveals altered protein abundances in cardiovascular disease

BACKGROUND

Cardiovascular disease (CVD) describes the pathological conditions of the heart and blood vessels. Despite the large number of studies on CVD and its etiology, its key modulators remain largely unknown. To this end, we performed a comprehensive proteomic analysis of blood plasma, with the scope to identify disease-associated changes after placing them in the context of existing knowledge, and generate a well characterized dataset for further use in CVD multi-omics integrative analysis.

METHODS

LC-MS/MS was employed to analyze plasma from 32 subjects (19 cases of various CVD phenotypes and 13 controls) in two steps: discovery (13 cases and 8 controls) and test (6 cases and 5 controls) set analysis. Following label-free quantification, the detected proteins were correlated to existing plasma proteomics datasets (plasma proteome database; PPD) and functionally annotated (Cytoscape, Ingenuity Pathway Analysis). Differential expression was defined based on identification confidence (≥ 2 peptides per protein), statistical significance (Mann-Whitney p value ≤ 0.05) and a minimum of twofold change.

RESULTS

Peptides detected in at least 50% of samples per group were considered, resulting in a total of 3796 identified proteins (838 proteins based on ≥ 2 peptides). Pathway annotation confirmed the functional relevance of the findings (representation of complement cascade, fibrin clot formation, platelet degranulation, etc.). Correlation of the relative abundance of the proteins identified in the discovery set with their reported concentrations in the PPD was significant, confirming the validity of the quantification method. The discovery set analysis revealed 100 differentially expressed proteins between cases and controls, 39 of which were verified (≥ twofold change) in the test set. These included proteins already studied in the context of CVD (such as apolipoprotein B, alpha-2-macroglobulin), as well as novel findings (such as low density lipoprotein receptor related protein 2 [LRP2], protein SZT2) for which a mechanism of action is suggested.

CONCLUSIONS

This proteomic study provides a comprehensive dataset to be used for integrative and functional studies in the field. The observed protein changes reflect known CVD-related processes (e.g. lipid uptake, inflammation) but also novel hypotheses for further investigation including a potential pleiotropic role of LPR2 but also links of SZT2 to CVD.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background

The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown.

Methods

Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years.

Results

CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model.

Conclusion

A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description

OBJECTIVES

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET; NCT03152422) aims: (i) to construct new multidimensional urinary proteomic (UP) classifiers that after heart transplantation (HTx) help in detecting graft vasculopathy, monitoring immune system activity and graft performance, and in adjusting immunosuppression; (ii) to sequence UP peptide fragments and to identify key proteins mediating HTx-related complications; (iii) to validate UP classifiers by demonstrating analogy between UP profiles and tissue proteomic signatures (TP) in diseased explanted hearts, to be compared with normal donor hearts; (iv) and to identify new drug targets. This article describes the uPROPHET database construction, follow-up strategies and baseline characteristics of the HTx patients.

METHODS

HTx patients enrolled at the University Hospital Gasthuisberg (Leuven) collected mid-morning urine samples. Cardiac biopsies were obtained at HTx. UP and TP methods and the statistical work flow in pursuit of the research objectives are described in detail in the Data supplement.

RESULTS

Of 352 participants in the UP study (24.4% women), 38.9%, 40.3%, 5.7% and 15.1% had ischemic, dilated, hypertrophic or other cardiomyopathy. The median interval between HTx and first UP assessment (baseline) was 7.8 years. At baseline, mean values were 56.5 years for age, 25.2 kg/m2 for body mass index, 142.3/84.8 mm Hg and 124.2/79.8 mm Hg for office and 24-h ambulatory systolic/diastolic pressure, and 58.6 mL/min/1.73 m2 for the estimated glomerular filtration rate. Of all patients, 37.2% and 6.5% had a history of mild (grade = 1B) or severe (grade ≥ 2) cellular rejection. Anti-body mediated rejection had occurred in 6.2% patients. The number of follow-up urine samples available for future analyses totals over 950. The TP study currently includes biopsies from 7 healthy donors and 15, 14, and 3 patients with ischemic, dilated, and hypertrophic cardiomyopathy.

CONCLUSIONS

uPROPHET constitutes a solid resources for UP and TP research in the field of HTx and has the ambition to lay the foundation for the clinical application of UP in risk stratification in HTx patients.