Small peptide CSF fingerprint of amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by abnormal protein aggregation in the motor neurons. Present and earlier proteomic studies to characterize peptides in cerebrospinal fluid (CSF) associated with motoneuron pathology did not target low molecular weight proteins and peptides. We hypothesized that specific changes in CSF peptides or low molecular weight proteins are significantly altered in ALS, and that these changes may support deciphering molecular pathophysiology and even guide approaches towards therapeutic interventions.

METHODS

Cerebrospinal fluid (CSF) from 50 ALS patients and 50 non-ALS controls was collected, centrifuged immediately after collection, aliquoted into polypropylene test tubes, frozen within 30-40 min after the puncture, and stored at -80°C until use. Peptides were sequenced using capillary electrophoresis or liquid chromatography/mass spectrometry (CE-MS/MS or LC-MS/MS).

FINDINGS

In the CSF of 50 patients and 50 non-ALS controls 33 peptides were found, of which 14 could be sequenced using a non-lytic single-pot proteomic detection method, CE/MS. ALS deregulated peptides vs. controls included Integral membrane protein 2B, Neurosecretory protein VGF, Osteopontin, Neuroendocrine protein 7B2 (Secretogranin-V), EGF-containing fibulin-like extracellular matrix protein 1, Xylosyltransferase 1 XT-1, Chromogranin-A, Superoxide dismutase SOD-1, Secretogranin-1 (Chromogranin B), NR2F2 Nuclear Receptor Subfamily 2 Group F Member 2 and Collagen alpha-1(VII) chain.

INTERPRETATION

Most striking deregulations in CSF from ALS patients were found in VGF, Osteopontin, SOD-1 and EFEMP1 peptides. No associations of disease severity, duration and region of onset with sequenced peptides were found.

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.

Multiple urinary peptides are associated with hypertension: a link to molecular pathophysiology

OBJECTIVES

Hypertension is a common condition worldwide; however, its underlying mechanisms remain largely unknown. This study aimed to identify urinary peptides associated with hypertension to further explore the relevant molecular pathophysiology.

METHODS

Peptidome data from 2876 individuals without end-organ damage were retrieved from the Human Urinary Proteome Database: general population (discovery) or type 2 diabetic (validation) cohorts. Participants were divided based on systolic blood pressure (SBP) and diastolic BP (DBP) into hypertensive (SBP ≥140 mmHg and/or DBP ≥90 mmHg) and normotensive (SBP <120 mmHg and DBP <80 mmHg, without antihypertensive treatment) groups. Differences in peptide abundance between the two groups were confirmed using an external cohort (n = 420) of participants without end-organ damage, matched for age, BMI, eGFR, sex, and the presence of diabetes. Furthermore, the association of the peptides with BP as a continuous variable was investigated. The findings were compared with peptide biomarkers of chronic diseases and bioinformatic analyses were conducted to highlight the underlying molecular mechanisms.

RESULTS

Between hypertensive and normotensive individuals, 96 (mostly COL1A1 and COL3A1) peptides were found to be significantly different in both the discovery (adjusted) and validation (nominal significance) cohorts, with consistent regulation. Of these, 83 were consistently regulated in the matched cohort. A weak, yet significant, association between their abundance and standardized BP was also observed.

CONCLUSION

Hypertension is associated with an altered urinary peptide profile with evident differential regulation of collagen-derived peptides. Peptides related to vascular calcification and sodium regulation were also affected. Whether these modifications reflect the pathophysiology of hypertension and/or early subclinical organ damage requires further investigation.

Assessment and Risk Prediction of Chronic Kidney Disease and Kidney Fibrosis Using Non-Invasive Biomarkers

Effective management of chronic kidney disease (CKD), a major health problem worldwide, requires accurate and timely diagnosis, prognosis of progression, assessment of therapeutic efficacy, and, ideally, prediction of drug response. Multiple biomarkers and algorithms for evaluating specific aspects of CKD have been proposed in the literature, many of which are based on a small number of samples. Based on the evidence presented in relevant studies, a comprehensive overview of the different biomarkers applicable for clinical implementation is lacking. This review aims to compile information on the non-invasive diagnostic, prognostic, and predictive biomarkers currently available for the management of CKD and provide guidance on the application of these biomarkers. We specifically focus on biomarkers that have demonstrated added value in prospective studies or those based on prospectively collected samples including at least 100 subjects. Published data demonstrate that several valid non-invasive biomarkers of potential value in the management of CKD are currently available.

Multiple urinary peptides display distinct sex-specific distribution

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry (CE-MS) datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic, or predictive classifiers for each individual sex.

Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease

BACKGROUND AND HYPOTHESIS

Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies.

METHODS

The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models.

RESULTS

In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state.

CONCLUSION

Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.

OSTEO18, a novel urinary proteomic signature, associated with osteoporosis in heart transplant recipients

Background

Immunosuppressive treatment in heart transplant (HTx) recipient causes osteoporosis. The urinary proteomic profile (UPP) includes peptide fragments derived from the bone extracellular matrix. Study aims were to develop and validate a multidimensional UPP biomarker for osteoporosis in HTx patients from single sequenced urinary peptides identifying the parent proteins.

Methods

A single-center HTx cohort was analyzed. Urine samples were measured by capillary electrophoresis coupled with mass spectrometry. Cases with osteoporosis and matching controls were randomly selected from all available 389 patients. In derivation case-control dataset, 1576 sequenced peptides detectable in ≥30 % of patients. Applying statistical analysis on these, an 18-peptide multidimensional osteoporosis UPP biomarker (OSTEO18) was generated by support vector modeling. The 2 replication datasets included 118 and 94 patients. For further validation, the whole cohort was analyzed. Statistical methods included logistic regression and receiver operating characteristic curve (ROC) analysis.

Results

In derivation dataset, the AUC, sensitivity and specificity of OSTEO18 were 0.83 (95 % CI: 0.76-0.90), 74.3 % and 87.1 %, respectively. In replication datasets, results were confirmatory. In the whole cohort (154 osteoporotic patients [39.6 %]), the ORs for osteoporosis increased (p < 0.0001) across OSTEO18 quartiles from 0.39 (95 % CI: 0.25-0.61) to 3.14 (2.08-4.75). With full adjustment for known osteoporosis risk factors, OSTEO18 improved AUC from 0.708 to 0.786 (p = 0.0003) for OSTEO18 categorized (optimized threshold: 0.095) and to 0.784 (p = 0.0004) for OSTEO18 as continuously distributed classifier.

Conclusion

OSTEO18 is a clinically meaningful novel biomarker indicative of osteoporosis in HTx recipients and is being certified as in-vitro diagnostic.

Association of Air Pollution with a Urinary Biomarker of Biological Aging and Effect Modification by Vitamin K in the FLEMENGHO Prospective Population Study

BACKGROUND

A recently developed urinary peptidomics biological aging clock can be used to study accelerated human aging. From 1990 to 2019, exposure to airborne particulate matter (PM) became the leading environmental risk factor worldwide.

OBJECTIVES

This study investigated whether air pollution exposure is associated with accelerated urinary peptidomic aging, independent of calendar age, and whether this association is modified by other risk factors.

METHODS

In a Flemish population, the urinary peptidomic profile (UPP) age (UPP-age) was derived from the urinary peptidomic profile measured by capillary electrophoresis coupled with mass spectrometry. UPP-age-R was calculated as the residual of the regression of UPP-age on chronological age, which reflects accelerated aging predicted by UPP-age, independent of chronological age. A high-resolution spatial-temporal interpolation method was used to assess each individual's exposure to PM 10 , PM 2.5 , black carbon (BC), and nitrogen dioxide (NO 2 ). Associations of UPP-age-R with these pollutants were investigated by mixed models, accounting for clustering by residential address and confounders. Effect modifiers of the associations between UPP-age-R and air pollutants that included 18 factors reflecting vascular function, renal function, insulin resistance, lipid metabolism, or inflammation were evaluated. Direct and indirect (via UPP-age-R) effects of air pollution on mortality were evaluated by multivariable-adjusted Cox models.

RESULTS

Among 660 participants (50.2% women; mean age: 50.7 y), higher exposure to PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R. Studying effect modifiers showed that higher plasma levels of desphospho-uncarboxylated matrix Gla protein (dpucMGP), signifying poorer vitamin K status, steepened the slopes of UPP-age-R on the air pollutants. In further analyses among participants with dpucMGP ≥ 4.26 μ g / L (median), an interquartile range (IQR) higher level in PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R of 2.03 [95% confidence interval (CI): 0.60, 3.46], 2.22 (95% CI: 0.71, 3.74), 2.00 (95% CI: 0.56, 3.43), and 2.09 (95% CI: 0.77, 3.41) y, respectively. UPP-age-R was an indirect mediator of the associations of mortality with the air pollutants [multivariable-adjusted hazard ratios from 1.094 (95% CI: 1.000, 1.196) to 1.110 (95% CI: 1.007, 1.224)] in participants with a high dpucMGP, whereas no direct associations were observed.

DISCUSSION

Ambient air pollution was associated with accelerated urinary peptidomics aging, and high vitamin K status showed a potential protective effect in this population. Current guidelines are insufficient to decrease the adverse health effects of airborne pollutants, including healthy aging trajectories. https://doi.org/10.1289/EHP13414.

Urinary peptide analysis to predict the response to blood pressure medication

BACKGROUND AND HYPOTHESIS

The risk of Diabetic Kidney Disease (DKD) progression is significant despite renin-angiotensin system (RAS) blocking agents treatment. Current clinical tools cannot predict whether or not patients will respond to the treatment with RAS-inhibitors (RASi). We aimed to investigate if proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor (ACEi) and angiotensin receptor blockers (ARBs) treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR-equations, for DKD progression.

METHODS

We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 year) based on the eGFR values of each GFR-equation. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi.

RESULTS

The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR-equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen, and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts.

CONCLUSIONS

The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.

Subclinical and clinical acute kidney injury share similar urinary peptide signatures and prognosis

PURPOSE

Acute kidney injury (AKI) is a frequent and severe condition in intensive care units (ICUs). In 2020, the Acute Dialysis Quality Initiative (ADQI) group proposed a new stage of AKI, referred to as stage 1S, which represents subclinical disease (sAKI) defined as a positive biomarker but no increase in serum creatinine (sCr). This study aimed to determine and compare the urinary peptide signature of sAKI as defined by biomarkers.

METHODS

This is an ancillary analysis of the prospective, observational, multinational FROG-ICU cohort study. AKI was defined according to the Kidney Disease Improving Global Outcome definition (AKIKDIGO). sAKI was defined based on the levels of the following biomarkers, which exceeded the median value: neutrophil gelatinase-associated lipocalin (pNGAL, uNGAL), cystatin C (pCysC, uCysC), proenkephalin A 119-159 (pPENKID) and liver fatty acid binding protein (uLFABP). Urinary peptidomics analysis was performed using capillary electrophoresis-mass spectrometry. Samples were collected at the time of study inclusion.

RESULTS

One thousand eight hundred eighty-five patients had all biomarkers measured at inclusion, which included 1154 patients without AKI (non-AKIKDIGO subgroup). The non-AKIKDIGO subgroup consisted of individuals at a median age of 60 years [48, 71], among whom 321 (27.8%) died. The urinary peptide signatures of sAKI, regardless of the biomarkers used for its definition, were similar to the urinary peptide signatures of AKIKDIGO (inflammation, haemolysis, and endothelial dysfunction). These signatures were also associated with 1-year mortality.

CONCLUSION

Biomarker-defined sAKI is a common and severe condition observed in patients within intensive care units with a urinary peptide signature that is similar to that of AKI, along with a comparable prognosis.

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.

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

INTRODUCTION

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

METHODS

Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

RESULTS

Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

DISCUSSION

This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Peptides as "better biomarkers"? Value, challenges, and potential solutions to facilitate implementation

Peptides carry important functions in normal physiological and pathophysiological processes and can serve as clinically useful biomarkers. Given the ability to diffuse passively across endothelial barriers, endogenous peptides can be examined in several body fluids, including among others urine, blood, and cerebrospinal fluid. This review article provides an update on the recently published literature that reports on investigating native peptides in body fluids using mass spectrometry-based platforms, specifically those studies that focus on the application of peptides as biomarkers to improve clinical management. We emphasize on the critical evaluation of their clinical value, how close they are to implementation, and the associated challenges and potential solutions to facilitate clinical implementation. During the last 5 years, numerous studies have been published, demonstrating the increased interest in mass spectrometry for the assessment of endogenous peptides as potential biomarkers. Importantly, the presence of few successful examples of implementation in patients' management and/or in the context of clinical trials indicates that the peptide biomarker field is evolving. Nevertheless, most studies still report evidence based on small sample size, while validation phases are frequently missing. Therefore, a gap between discovery and implementation still exists.

Reviewing the Regulators of COL1A1

The collagen family contains 28 proteins, predominantly expressed in the extracellular matrix (ECM) and characterized by a triple-helix structure. Collagens undergo several maturation steps, including post-translational modifications (PTMs) and cross-linking. These proteins are associated with multiple diseases, the most pronounced of which are fibrosis and bone diseases. This review focuses on the most abundant ECM protein highly implicated in disease, type I collagen (collagen I), in particular on its predominant chain collagen type I alpha 1 (COLα1 (I)). An overview of the regulators of COLα1 (I) and COLα1 (I) interactors is presented. Manuscripts were retrieved searching PubMed, using specific keywords related to COLα1 (I). COL1A1 regulators at the epigenetic, transcriptional, post-transcriptional and post-translational levels include DNA Methyl Transferases (DNMTs), Tumour Growth Factor β (TGFβ), Terminal Nucleotidyltransferase 5A (TENT5A) and Bone Morphogenic Protein 1 (BMP1), respectively. COLα1 (I) interacts with a variety of cell receptors including integrinβ, Endo180 and Discoidin Domain Receptors (DDRs). Collectively, even though multiple factors have been identified in association to COLα1 (I) function, the implicated pathways frequently remain unclear, underscoring the need for a more spherical analysis considering all molecular levels simultaneously.

Identifying a urinary peptidomics profile for hypertension in young adults: The African-PREDICT study: Urinary peptidomics and hypertension: Urinary peptidomics and hypertension

Hypertension is one of the most important and complex risk factors for cardiovascular diseases (CVDs). By using urinary peptidomics analyses, we aimed to identify peptides associated with hypertension, building a framework for future research towards improved prediction and prevention of premature development of CVD. We included 78 hypertensive and 79 normotensive participants from the African-PREDICT study (aged 20-30 years), matched for sex (51% male) and ethnicity (49% black and 51% white). Urinary peptidomics data were acquired using capillary-electrophoresis-time-of-flight-mass-spectrometry. Hypertension-associated peptides were identified and combined into a support vector machine-based multidimensional classifier. When comparing the peptide data between the normotensive and hypertensive groups, 129 peptides were nominally differentially abundant (Wilcoxon p < 0.05). Nonetheless, only three peptides, all derived from collagen alpha-1(III), remained significantly different after rigorous adjustments for multiple comparisons. The 37 most significant peptides (all p ≤ 0.001) served as basis for the development of a classifier, with 20 peptides being combined into a unifying score, resulting in an AUC of 0.85 in the ROC analysis (p < 0.001), with 83% sensitivity at 80% specificity. Our study suggests potential value of urinary peptides in the classification of hypertension, which could enable earlier diagnosis and better understanding of the pathophysiology of hypertension and premature cardiovascular disease development.

Plasma and Urinary Biomarkers Improve Prediction of Mortality through 1 Year in Intensive Care Patients: An Analysis from FROG-ICU

BACKGROUND

This study aimed to assess the value of blood and urine biomarkers in addition to routine clinical variables in risk stratification of patients admitted to ICU.

METHODS

Multivariable prognostic models were developed in this post hoc analysis of the French and EuRopean Outcome ReGistry in Intensive Care Units study, a prospective observational study of patients admitted to ICUs. The study included 2087 patients consecutively admitted to the ICU who required invasive mechanical ventilation or a vasoactive agent for more than 24 h. The main outcome measures were in-ICU, in-hospital, and 1 year mortality.

RESULTS

Models including only SAPS II or APACHE II scores had c-indexes for in-hospital and 1 year mortality of 0.64 and 0.65, and 0.63 and 0.61, respectively. The c-indexes for a model including age and estimated glomerular filtration rate were higher at 0.69 and 0.67, respectively. Models utilizing available clinical variables increased the c-index for in-hospital and 1 year mortality to 0.80 and 0.76, respectively. The addition of biomarkers and urine proteomic markers increased c-indexes to 0.83 and 0.78.

CONCLUSIONS

The commonly used scores for risk stratification in ICU patients did not perform well in this study. Models including clinical variables and biomarkers had significantly higher predictive values.

Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented.

Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD

Chronic kidney disease (CKD) is prevalent in 10% of world’s adult population. The role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. The aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Urine samples from eight CKD and two healthy subjects were analyzed by CE-MS/MS and glycopeptides were identified by a specific software followed by manual inspection of the spectra. Distribution of the identified glycopeptides and their correlation with Age, eGFR and Albuminuria were evaluated in 3810 existing datasets. In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Three glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) exhibited positive correlation with Age. The IGF2 glycopeptide (tPPTVLPDNFPRYP) showed a strong negative association with eGFR. These results suggest that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may reflect changes in mature IGF2 protein. Further experiments corroborated this hypothesis as IGF2 increased plasma levels were observed in CKD patients. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD, meriting further investigation.

Mass Spectrometry-Based Biomarkers to Detect Prostate Cancer: A Multicentric Study Based on Non-Invasive Urine Collection without Prior Digital Rectal Examination

(1) Background: Prostate cancer (PCa) is the most frequently diagnosed cancer in men. Wide application of prostate specific antigen test has historically led to over-treatment, starting from excessive biopsies. Risk calculators based on molecular and clinical variables can be of value to determine the risk of PCa and as such, reduce unnecessary and invasive biopsies. Urinary molecular studies have been mostly focusing on sampling after initial intervention (digital rectal examination and/or prostate massage). (2) Methods: Building on previous proteomics studies, in this manuscript, we aimed at developing a biomarker model for PCa detection based on urine sampling without prior intervention. Capillary electrophoresis coupled to mass spectrometry was applied to acquire proteomics profiles from 970 patients from two different clinical centers. (3) Results: A case-control comparison was performed in a training set of 413 patients and 181 significant peptides were subsequently combined by a support vector machine algorithm. Independent validation was initially performed in 272 negative for PCa and 138 biopsy-confirmed PCa, resulting in an AUC of 0.81, outperforming current standards, while a second validation phase included 147 PCa patients. (4) Conclusions: This multi-dimensional biomarker model holds promise to improve the current diagnosis of PCa, by guiding invasive biopsies.

Association of fatal and non-fatal adverse health outcomes with urinary peptides reflecting collagen I turnover

Background

Imbalance of collagen I (COL1) turnover, featured by increased synthesis and decreased degradation of collagen fibers, is a hallmark of fibrosis in the heart and blood vessels that associates with poor cardiovascular outcomes. Such as imbalance of COL1 turnover could be reflected in urine and serve as fingerprint for future adverse outcomes in general population, and high risk subjects.

Purpose

We hypothesize that imbalance of proteomic signatures of urinary peptides (UPs) reflecting COL1 turnover relate to adverse health outcomes in participants from a general population

Methods

We randomly recruited 776 participants (51.2% women; 50.5 years) from the Flemish Study on Environment, Genes and Health Outcomes cohort and measured UPs proteome by capillary electrophoresis coupled with mass spectrometry. Our analyses focused on 148 peptides of COL1 alpha-1 (COL1A1) chain that retained ≥70% signal in the whole sample. The primary endpoint included fatal and nonfatal cardiovascular endpoints. Secondary endpoints consisted of total mortality, fatal and nonfatal cardiac, coronary, and heart failure endpoints. Multivariate Cox proportional models, partial least squares analysis (PLS), log-likelihood test, and receiver operating characteristics (ROC) curve were applied.

Results

Over a median follow up of 12.4 years, 110 primary endpoints occurred, 61 participants died, 81, 41 and 24 experienced cardiac, coronary, and heart failure endpoints; respectively. In PLS analyses, upregulation of UPs signatures closer to C- and N-terminal locations of the COL1A1 chain whereas downregulation of mid-region UPs were associated with lower risk of adverse health outcomes. This pattern was inverted in subjects with cardiovascular disease, as upregulation of terminal and downregulation of mid region UPs increased risk. Adding UPs to a basic model including sex, age and usual cardiovascular risk factors significantly improved model performance between 2.54% to 4.93% (P≤0.001) for prediction of adverse health outcomes. In ROC plots, adding UPs to the basic model increased the area under the curve up to 4.00% (P&lt;0.012).

Conclusions

UPs reflecting COL1 turnover predicted adverse health outcomes. The inverted up- and down regulations of UPs in between participants with and without previous cardiovascular diseases might be explained by a shift in the UPs signatures of COL1 fragments linked to distinct fibrotic processes. Urinary proteomic might have clinical importance in documenting the extent of collagen accumulation that relates to adverse health outcomes. In patients at high cardiovascular risk, modification of collagen I fibers turnover might be a potential treatment target

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): The European Union the European Research Council and the European Research Area Net for Cardiovascular Diseases.

Cardioprotection by selective SGLT2 inhibitors in a non-diabetic mouse model of myocardial ischemia/reperfusion injury: a class or a drug effect?

Background

Empagliflozin (EMPA), Dapagliflozin (DAPA) and Ertugliflozin (ERTU) are selective sodium glucose co-transporter 2 inhibitors (SGLT2i) acting against type 2 diabetes mellitus.

Purpose

Due to differences in clinical trial outcomes, we aimed to 1) compare the cardioprotective effects of selective SGLT2i in terms of infarct size (IS) reduction and 2) reveal the mechanism of cardioprotection in non-diabetic mice.

Methods

C57BL/6 mice were randomized and orally received EMPA (10mg/kg/day), DAPA (9.0mg/kg/day), ERTU (9.7mg/kg/day) or vehicle for 7 days. IS was measured after 30' ischemia (I), and 120' reperfusion (R). EMPA, DAPA and ERTU were given at equivalent stoichiometrically doses (ESD). Body weight and fasting blood glucose (FBG) levels were determined at baseline and at the end of the treatment. On the 7th day, mice were housed in metabolic cages for 24 hours. Urine volume (UV), food and water uptake and 24h-glucose levels were determined to examine the extend of SGLT-2 inhibition by the drugs. In a second series, the ischemic myocardium was taken (10'R), shotgun proteomics were performed and several cardioprotective pathways were evaluated. In a third series, the dominant pathways were evaluated through molecular analyses and mitochondrial functionality. The causal relationships in the mechanism of protection, was established by inhibiting the concomitant cardioprotective pathways. Static, the specific STAT-3 inhibitor and wortmannin (a PI3K inhibitor) were administered and IS was measured upon 30'I/120' R.

Results

EMPA and DAPA but not ERTU reduced IS at this dose. Body weight and FBG levels were not affected by the treatments. EMPA, DAPA and ERTU lead to significant increase in UV and urinary glucose levels compared to the control group independently of the water and food intake. There was no significant difference in the parameters among the different SGLT-2i indicating that the chosen doses are sufficient to produce the same pharmacological SGLT-2 inhibition in mice. Proteomics revealed mitochondrial metabolism and NF-kB signaling as significant. Only EMPA preserved mitochondrial functionality in complex I & II linked oxidative phosphorylation. NF-kB, RISK and STAT-3 activation and the downstream reduction in apoptosis were evident in EMPA and DAPA groups coinciding with IS reduction. Static and wortmannin significantly attenuated IS reduction both in EMPA and DAPA groups indicating that STAT-3 and PI3K activation are the leading mechanisms of cardioprotection. Among several upstream mediators, fibroblast growth factor 2 (FGF-2) and caveolin-3 were increased in EMPA and DAPA groups.

Conclusions

Short term EMPA, DAPA and ERTU at the chosen ESD inhibit SGLT-2i in a similar extent but only EMPA and DAPA reduce IS. Our study reveals drug specific effects on cardioprotection against I/R injury. Cardioprotection afforded by EMPA and DAPA are STAT-3 and PI3K dependent and associated with increased FGF-2 and Cav-3 expression.

Funding Acknowledgement

Type of funding sources: None.

Cardioprotection by selective SGLT-2 inhibitors in a non-diabetic mouse model of myocardial ischemia/reperfusion injury: a class or a drug effect?

Major clinical trials with sodium glucose co-transporter-2 inhibitors (SGLT-2i) exhibit protective effects against heart failure events, whereas inconsistencies regarding the cardiovascular death outcomes are observed. Therefore, we aimed to compare the selective SGLT-2i empagliflozin (EMPA), dapagliflozin (DAPA) and ertugliflozin (ERTU) in terms of infarct size (IS) reduction and to reveal the cardioprotective mechanism in healthy non-diabetic mice. C57BL/6 mice randomly received vehicle, EMPA (10 mg/kg/day) and DAPA or ERTU orally at the stoichiometrically equivalent dose (SED) for 7 days. 24 h-glucose urinary excretion was determined to verify SGLT-2 inhibition. IS of the region at risk was measured after 30 min ischemia (I), and 120 min reperfusion (R). In a second series, the ischemic myocardium was collected (10th min of R) for shotgun proteomics and evaluation of the cardioprotective signaling. In a third series, we evaluated the oxidative phosphorylation capacity (OXPHOS) and the mitochondrial fatty acid oxidation capacity by measuring the respiratory rates. Finally, Stattic, the STAT-3 inhibitor and wortmannin were administered in both EMPA and DAPA groups to establish causal relationships in the mechanism of protection. EMPA, DAPA and ERTU at the SED led to similar SGLT-2 inhibition as inferred by the significant increase in glucose excretion. EMPA and DAPA but not ERTU reduced IS. EMPA preserved mitochondrial functionality in complex I&II linked oxidative phosphorylation. EMPA and DAPA treatment led to NF-kB, RISK, STAT-3 activation and the downstream apoptosis reduction coinciding with IS reduction. Stattic and wortmannin attenuated the cardioprotection afforded by EMPA and DAPA. Among several upstream mediators, fibroblast growth factor-2 (FGF-2) and caveolin-3 were increased by EMPA and DAPA treatment. ERTU reduced IS only when given at the double dose of the SED (20 mg/kg/day). Short-term EMPA and DAPA, but not ERTU administration at the SED reduce IS in healthy non-diabetic mice. Cardioprotection is not correlated to SGLT-2 inhibition, is STAT-3 and PI3K dependent and associated with increased FGF-2 and Cav-3 expression.

MO379: Collagen-Derived Peptides in CKD: A Link to Fibrosis

BACKGROUND AND AIMS

Collagen is a major component of the extracellular matrix (ECM) and has an essential role in the onset and progression of fibrosis and chronic kidney disease (CKD). Collagen alpha-1(I) (col1a1) is the most abundant collagen type in humans and the involvement of col1a1in CKD onset and progression is well established. We aimed to assess in detail the association of urinary col1a1 fragments with the progression of CKD and ageing.

METHOD

About 5000 urinary peptidomic datasets from healthy participants and CKD patients were retrieved from the ‘Human Urinary Proteome Database’. Inclusion criteria were age &gt;18 years and eGFR being available. Urinary col1a1 fragments were identified and their abundance and correlation with eGFR and age were investigated.

RESULTS

Of 707 specific col1a1 peptides, 63 were significantly and highly positively associated with eGFR, while only six peptides showed a significant and strong negative association. A similar tendency was observed for ageing, where the abundance of most of the 244 peptides significantly associated with age, decreased with increasing age. The ten most significantly correlated peptides associated with eGFR corrected for age or age in a cohort matched for eGFR are listed in the table.

CONCLUSION

The results show a strong association between the reduced abundance of urinary collagen peptides and loss of kidney function as well as ageing. The data suggest that the col1a1 peptides indicate reduced degradation of collagen in CKD and aging. The results further suggest that fibrosis, potentially also of other organs, may be the consequence of attenuation of collagen degradation, and not increased synthesis.

No evidence of differential impact of sunflower and rapeseed oil on biomarkers of coronary artery disease or chronic kidney disease in healthy adults with overweight and obesity: result from a randomised control trial

Purpose

The perceived benefits and risks associated with seed oil intake remain controversial, with a limited number of studies investigating the impact of intake on a range of compounds used as cardiometabolic markers. This study aimed to explore the proteomic and cardiometabolic effects of commonly consumed seed oils in the UK, with different fatty acid profiles.

Methods

In a parallel randomised control design, healthy adults (n = 84), aged 25–72 with overweight or obesity were randomised to one of three groups: control (habitual diet, CON); 20 mL rapeseed oil per day (RO), or 20 mL sunflower oil per day (SO). Blood, spot urine and anthropometric measures were obtained at 0, 6 and 12 weeks. Proteomic biomarkers analysis was conducted for coronary arterial disease (CAD) and chronic kidney disease (CKD) using capillary electrophoresis coupled to mass spectrometry (CE-MS). Blood lipids, fasting blood glucose, glycative/oxidative stress and inflammatory markers were also analysed.

Results

No differences in change between time points were observed between groups for CAD or CKD peptide fingerprint scores. No change was detected within groups for CAD or CKD scores. No detectable differences were observed between groups at week 6 or 12 for the secondary outcomes, except median 8-isoprostane, ~ 50% higher in the SO group after 12-weeks compared to RO and CON groups (p = 0.03).

Conclusion

The replacement of habitual fat with either RO or SO for 12 weeks does not lead to an improvement or worsening in cardiovascular health markers in people with overweight or obesity.

Trial registration

Trial registration clinicaltrials.gov NCT04867629, retrospectively registered 30/04/2021.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-022-02810-5.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

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

METHODS AND RESULTS

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

CONCLUSIONS

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

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.

Increased Collagen Turnover Is a Feature of Fibromuscular Dysplasia and Associated With Hypertrophic Radial Remodeling: A Pilot, Urine Proteomic Study

Fibromuscular dysplasia (FMD), a nonatherosclerotic, noninflammatory disease of medium-sized arteries, is an underdiagnosed disease. We investigated the urinary proteome and developed a classifier for discrimination of FMD from healthy controls and other diseases. We further hypothesized that urinary proteomics biomarkers may be associated with alterations in medium-sized, but not large artery geometry and mechanics. The study included 33 patients with mostly multifocal, renal FMD who underwent in depth arterial exploration using ultra-high frequency ultrasound. The cohort was separated in a training set of 23 patients with FMD from Belgium and an independent test set of 10 patients with FMD from Italy. For each set, controls matched 2:1 were selected from the Human Urinary Proteome Database. The specificity of the classifier was tested in 700 additional controls from general population studies, patients with chronic kidney disease (n=66) and coronary artery disease (n=31). Three hundred thirty-five urinary peptides, mostly related to collagen turnover, were identified in the training cohort and combined into a classifier. When applying in the test cohort, the area under the receiver operating characteristic curve was 1.00, 100% specificity at 100% sensitivity. The classifier maintained a high specificity in additional controls (98.3%), patients with chronic kidney (90.9%) and coronary artery (96.8%) diseases. Furthermore, in patients with FMD, the proteomic score was positively associated with radial wall thickness and wall cross-sectional area. In conclusion, a proteomic score has the potential to discriminate between patients with FMD and controls. If confirmed in a wider and more diverse cohort, these findings may pave the way for a noninvasive diagnostic test of FMD.

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

Background

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.

Methods

In this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005-10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985-2004). All participants (2005-10) were also invited to an additional follow-up examination in 2009-13. Participants who took part in both the 2005-10 follow-up examination and in the additional 2009-13 follow-up visit constituted the derivation dataset, which included their 2005-10 data, and the time-shifted internal validation dataset, which included their 2009-13 data. The remaining participants who only had 2005-10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.

Findings

778 individuals (395 [51%] women and 383 [49%] men; aged 16·2-82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005-10) and time-shifted internal validation (2009-13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005-10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8-13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22-1·95) for total mortality, 1·72 (1·20-2·47) for cardiovascular mortality, and 1·40 (1·06-1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.

Interpretation

The UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.

Funding

European Research Council, Ministry of the Flemish Community, OMRON Healthcare.

The novel proteomic signature for the detection of cardiac allograft vasculopathy

Background

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

Methods

Urinary proteome was measured by capillary electrophoresis coupled to mass spectrometry in 217 heart transplantation recipients. Participants were further randomly and evenly divided into the derivation cohort and validation cohort. The proteomic signature for CAV was identified by decision tree-based machine learning in the derivation cohort and further tested in the validation cohort. The pathway analysis was investigated with Reactome Pathway Database.

Results

We identified a proteomic signature with 27 urinary peptides, which yielded areas under the curve (AUC) of 0.83 and 0.71 in the derivation and validation cohort, respectively. In the validation cohort, it had a sensitivity of 68.4%, specificity of 73.2%, accuracy of 71.6%, negative predictive value of 81.3%. Including the proteomic signature into the basic model further improved the diagnostic accuracy with an relative integrated discrimination improvement of 25.9% and the continuous net reclassification improvement of 83.3% (p≤0.023). The pathways analysis on revealed that collagen turnover, platelet aggregation and coagulation, cell adhesion and motility might involve in the pathogenesis of CAV.

Conclusions

The proteomic signature might be valuable for the surveillance of CAV thereby reduce the frequency of invasive procedures after HTx. Moreover, the highlighted pathways might provide insights in the potential novel treatment targets for CAV.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Research Council Advanced Researcher Grant and Proof-of-Concept Grant ROC curves of the urinary proteomicThe 25 highlighted enrichment pathways

SGLT2-Inhibition reverts urinary peptide changes associated with severe COVID-19: An in-silico proof-of-principle of proteomics-based drug repurposing

Severe COVID‐19 is reflected by significant changes in urine peptides. Based on this observation, a clinical test predicting COVID‐19 severity, CoV50, was developed and registered as in vitro diagnostic in Germany. We have hypothesized that molecular changes displayed by CoV50, likely reflective of endothelial damage, may be reversed by specific drugs. Such an impact by a drug could indicate potential benefits in the context of COVID‐19. To test this hypothesis, urinary peptide data from patients without COVID‐19 prior to and after drug treatment were collected from the human urinary proteome database. The drugs chosen were selected based on availability of sufficient number of participants in the dataset (n > 20) and potential value of drug therapies in the treatment of COVID‐19 based on reports in the literature. In these participants without COVID‐19, spironolactone did not demonstrate a significant impact on CoV50 scoring. Empagliflozin treatment resulted in a significant change in CoV50 scoring, indicative of a potential therapeutic benefit. The study serves as a proof‐of‐principle for a drug repurposing approach based on human urinary peptide signatures. The results support the initiation of a randomized control trial testing a potential positive effect of empagliflozin for severe COVID‐19, possibly via endothelial protective mechanisms.

CD99 and polymeric immunoglobulin receptor peptides deregulation in critical COVID-19: A potential link to molecular pathophysiology?

Identification of significant changes in urinary peptides may enable improved understanding of molecular disease mechanisms. We aimed towards identifying urinary peptides associated with critical course of COVID-19 to yield hypotheses on molecular pathophysiological mechanisms in disease development. In this multicentre prospective study urine samples of PCR-confirmed COVID-19 patients were collected in different centres across Europe. The urinary peptidome of 53 patients at WHO stages 6-8 and 66 at WHO stages 1-3 COVID-19 disease was analysed using capillary electrophoresis coupled to mass spectrometry. 593 peptides were identified significantly affected by disease severity. These peptides were compared with changes associated with kidney disease or heart failure. Similarities with kidney disease were observed, indicating comparable molecular mechanisms. In contrast, convincing similarity to heart failure could not be detected. The data for the first time showed deregulation of CD99 and polymeric immunoglobulin receptor peptides and of known peptides associated with kidney disease, including collagen and alpha-1-antitrypsin. Peptidomic findings were in line with the pathophysiology of COVID-19. The clinical corollary is that COVID-19 induces specific inflammation of numerous tissues including endothelial lining. Restoring these changes, especially in CD99, PIGR and alpha-1-antitripsin, may represent a valid and effective therapeutic approach in COVID-19, targeting improvement of endothelial integrity. This article is protected by copyright. All rights reserved.

Proteomic Biomarkers in the Cardiorenal Syndrome: Toward Deciphering Molecular Pathophysiology

Cardiorenal syndrome (CRS) is defined by coexisting heart and renal dysfunctions. Malfunction of 1 organ may cause dysfunction of the other with variable causative disease that defines the type of CRS (1-5). Numerous studies showed that the prevalence of cardiovascular disease is increased in patients with chronic kidney disease (CKD). Similarly, CKD affects a large proportion of patients with heart failure. This overlap between primary heart or primary kidney disease blurs cause-effect inferences of the initiator/target organ. The classical subdivision of CRS in 5 categories does not provide pathophysiological suggestions for targeted intervention. It seems timely to revisit the value of CRS biomarkers in a pathophysiology-centered approach. We systematically reviewed the literature in CRS, which revealed 53 clinical studies describing the use of 44 biomarkers and 4 proteomic panels. All biomarkers are involved in at least one of the CRS comorbidities. Among the pathways affected, inflammation, aberrant glucose metabolism, neurohormonal activation, and oxidative stress are well described. There is growing evidence that fibrosis may be the "cornerstone" that unifies most of the pathways leading to CRS. Formation of excess fibrous connective tissue antedates CRS in many cases. This review highlights that biomarkers reflecting fibrosis may be of substantial clinical value in the early detection, prognostication, and guiding treatment of CRS. Biomarkers detecting changes in collagen turnover in the extracellular matrix of heart and kidney appear able to depict subclinical changes in the fibrotic remodeling of tissues and constitute a promising approach toward personalized intervention in CRS.

Developing novel drug candidates and repurposed drugs for prostate cancer based on molecular profiles

Prostate cancer (PCa) carries a growing burden on society. Lack of curative treatment and poor prognosis among patients with advanced PCa implies an urgent need for novel and improved drug identification. This is hampered by the disease's high molecular heterogeneity and complex molecular pathophysiology, resulting in drugs being efficient in few patients and cancer developing resistance to treatment. De novo drug discovery has proven to be complex and challenging. Along with technological advancements (mainly linked to -omics approaches) that allow for comprehensive characterization of the molecular changes underlying disease, and considering respective developments in bioinformatics, computational drug repurposing has emerged as a promising approach to shorten the way from discovery to clinical application and address the disease molecular complexity. With this article, we aimed at reviewing recent studies in which drugs/ compounds for PCa were defined through the investigation of molecular profiling (-omics) data and application of drug repurposing strategies. A brief overview of the technical requirements and associated challenges with the latter are also provided. For that purpose, a literature search was conducted using the PubMed database. Numerous drugs/ compounds have been proposed as potential PCa therapeutics, mostly based on the investigation of genomics and transcriptomics data. In most cases, further assessment in disease models is required. Since ultimately proteins are targeted by drugs, expanding on the use of proteomics profiling data (alone or in combination with other -omics) is expected to advance further defining new/repurposed drugs for PCa.

Urinary peptides in heart failure: a link to molecular pathophysiology

Aims

Heart failure (HF) is a major public health concern worldwide. The diversity of HF makes it challenging to decipher the underlying complex pathological processes using single biomarkers. We examined the association between urinary peptides and HF with reduced (HFrEF), mid‐range (HFmrEF) and preserved (HFpEF) ejection fraction, defined based on the European Society of Cardiology guidelines, and the links between these peptide biomarkers and molecular pathophysiology.

Methods and results

Analysable data from 5608 participants were available in the Human Urinary Proteome database. The urinary peptide profiles from participants diagnosed with HFrEF, HFmrEF, HFpEF and controls matched for sex, age, estimated glomerular filtration rate, systolic and diastolic blood pressure, diabetes and hypertension were compared applying the Mann–Whitney test, followed by correction for multiple testing. Unsupervised learning algorithms were applied to investigate groups of similar urinary profiles. A total of 577 urinary peptides significantly associated with HF were sequenced, 447 of which (77%) were collagen fragments. In silico analysis suggested that urinary biomarker abnormalities in HF principally reflect changes in collagen turnover and immune response, both associated with fibrosis. Unsupervised clustering separated study participants into two clusters, with 83% of non‐HF controls allocated to cluster 1, while 65% of patients with HF were allocated to cluster 2 (P < 0.0001). No separation based on HF subtype was detectable.

Conclusions

Heart failure, irrespective of ejection fraction subtype, was associated with differences in abundance of urinary peptides reflecting collagen turnover and inflammation. These peptides should be studied as tools in early detection, prognostication, and prediction of therapeutic response.

Understanding glomerular diseases through proteomics

INTRODUCTION

Chronic kidney disease is avery common and complex chronic disease. Uncovering the pathological patterns of CKD on the molecular level of bio-fluids and tissue appears to be both vital and promising for a more favorable outcome. We reviewed recently discovered proteomics biomarkers for CKD to provide new insight into disease pathology.

AREAS COVERED

We review the application of proteome analysis in the context of CKD with various etiologies within the last 5 years. Proteins and peptides associated with CKD as derived from multiple sources (urine, blood and tissue) are reported along with their various biological pathways.

EXPERT OPINION

A systematic and theoretical comprehension of the CKD pathology is essential for its successful management. The underlying complexity of the disease further requires specific conditions for reliable and interpretable results. In this context, clinical proteomics has resulted in first encouraging findings in CKD. A more complete understanding of the biological pathways related to the disease, based on the scope of a holistic proteomic approach, could improve substantially the management of CKD, especially when in conjunction with the current trend of personalized medicine.

A Novel Pipeline for Drug Repurposing for Bladder Cancer Based on Patients' Omics Signatures

Multi-omics signatures of patients with bladder cancer (BC) can guide the identification of known de-risked therapeutic compounds through drug repurposing, an approach not extensively explored yet. In this study, we target drug repurposing in the context of BC, driven by tissue omics signatures. To identify compounds that can reverse aggressive high-risk Non-Muscle Invasive BC (NMIBC) to less aggressive low-risk molecular subtypes, the next generation Connectivity Map (CMap) was employed using as input previously published proteomics and transcriptomics respective signatures. Among the identified compounds, the ATP-competitive inhibitor of mTOR, WYE-354, showed a consistently very high score for reversing the aggressive BC molecular signatures. WYE-354 impact was assessed in a panel of eight multi-origin BC cell lines and included impaired colony growth and proliferation rate without any impact on apoptosis. Overall, with this study we introduce a promising pipeline for the repurposing of drugs for BC treatment, based on patients' omics signatures.

Discovery, validation and sequencing of urinary peptides for diagnosis of liver fibrosis-A multicentre study

Background

Liver fibrosis is a consequence of chronic inflammation and is associated with protein changes within the hepatocytes structure. In this study, we aimed to investigate if this is reflected by the urinary proteome and can be explored to diagnose liver fibrosis in patients with chronic liver disease.

Methods

In a multicentre combined cross-sectional and prospective diagnostic test validation study, 129 patients with varying degrees of liver fibrosis and 223 controls without liver fibrosis were recruited. Additionally, 41 patients with no liver, but kidney fibrosis were included to evaluate interference with expressions of kidney fibrosis. Urinary low molecular weight proteome was analysed by capillary electrophoresis coupled to mass spectrometry (CE-MS) and a support vector machine marker model was established by integration of peptide markers for liver fibrosis.

Findings

CE-MS enabled identification of 50 urinary peptides associated with liver fibrosis. When combined into a classifier, LivFib-50, it separated patients with liver fibrosis (N = 31) from non-liver disease controls (N = 123) in cross-sectional diagnostic phase II evaluation with an area under the curve (AUC) of 0.94 (95% confidence intervals (CI): 0.89–0.97, p<0.0001). When adjusted for age, LivFib-50 demonstrated an AUC of 0.94 (95% CI: 0.89–0.97, p<0.0001) in chronic liver disease patients with (N = 19) or without (N = 17) liver fibrosis progression. In this prospective diagnostic phase III validation set, age-adjusted LivFib-50 showed 84.2% sensitivity (95% CI: 60.4–96.6) and 82.4% specificity (95% CI: 56.6–96.2) for detection of liver fibrosis. The sequence-identified peptides are mainly fragments of collagen chains, uromodulin and Na/K-transporting ATPase subunit γ. We also identified ten putative proteolytic cleavage sites, eight were specific for matrix metallopeptidases and two for cathepsins.

Interpretation

In liver fibrosis, urinary peptides profiling offers potential diagnostic markers and leads to discovery of proteolytic sites that could be targets for developing anti-fibrotic therapy.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.

Abstract B18: Molecular classification of NMIBC based on protein abundance

Recent investigations, mainly at the DNA/RNA levels, of muscle-invasive (MIBC) and non-muscle invasive bladder cancer (NMIBC) highlighted the existence of molecular subtypes exhibiting diverse levels of aggressiveness. Studies at the protein level are scarce but are needed to enhance present findings and fill existing gaps. The objective of our work is to subtype NMIBC at the proteomic level and place it in the context of the existing NMIBC classification schemes, targeting to ultimately define optimal patient management. One hundred seventeen fresh-frozen tissue specimens from primary untreated BC cases (98 NMIBC and 19 MIBC samples) were processed for high-resolution LC-MS/MS analysis. By utilizing only the high-confidence spectrometric data (FDR &lt; 0.01), as well as the mean abundance of the top 3 most abundant peptides per protein for protein quantification, more than 5,000 protein IDs in total were identified. The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA), and investigation of subtype-specific features, pathways, and genesets. Based on protein abundance, NMIBC patients were optimally stratified to 3 subtypes (classes), differing at size, clinicopathologic, and molecular backgrounds: Class 1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and expressed predominantly an immune/inflammatory phenotype, features involved in cell proliferation, unfolded protein response, and DNA damage response; class 2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile; and class 3 tumors were rich in luminal/differentiation markers, in line with their pathologic classification (mostly low stage/grade/risk samples). PCA revealed proximity of class 1 and conversely, remoteness of class 3 to the proteome of MIBC. Using GSEA and a Random Forest-based classifier on UROMOL's data (Hedegaard J et al., Cell 2016), we identified strong associations between our proteomics class 1 and the progressed UROMOL subtype and likewise between our proteomics class 3 and the two nonprogressed UROMOL subtypes. Several proteins distinguishing these two extreme proteomic subtypes (class 1 vs. 3) were found to also consistently differ at the mRNA levels between NMIBC “progressor” and “nonprogressor” groups of the UROMOL and LUND cohorts. Collectively, protein-based classification of NMIBC resulted in the definition of 3 classes with distinct pathologic and molecular profiles, sharing analogies to the existing transcriptomic NMIBC subtypes. The presented results facilitate shortlisting potential NMIBC prognosticators for further validation in clinical trials.

Citation Format: Rafael S. Stroggilos, Marika Mokou, Agnieszka Latosinska, Manousos Makridakis, Vasiliki Lygirou, Emmanouil Mavrogeorgis, Dimitris Drekolias, Maria Frantzi, William Mullen, Charalampos Fragkoulis, Konstantinos Stasinopoulos, Georgios Papadopoulos, Georgios Stathouros, Konstantinos Ntoumas, Harald Mischak, Jerome Zoidakis, Antonia Vlahou. Molecular classification of NMIBC based on protein abundance [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2019 May 18-21; Denver, CO. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(15_Suppl):Abstract nr B18.

P0652THYMOSIN BETA 4 AND KIDNEY FUNCTION: AN INVERSE RELATIONSHIP?

Background and Aims

Thymosin β4 (Tβ4) is an abundant peptide in urine and plasma, involved in multiple processes including cell morphology, wound healing, and inflammation. A major, yet not well defined role of Tβ4 was described in the context of kidney and cardiovascular disease. Knock out of Tβ4 resulted, among others, in increased albuminuria in a nephrotoxic nephritis model. In animal models it has been shown that Tβ4 can modify renal and cardiovascular disease, fibrosis and inflammation. However, most of the data are derived from animal models and the relevance of Tβ4 in human is less clear. As a first step towards investigating the potential relevance of Tβ4 in chronic kidney disease (CKD) in human, we investigated the abundance of Tβ4 in human urine.

Method

We investigated human urine in 2240 subjects with a wide spread of kidney function for the presence of Tβ4 using capillary-electrophoresis coupled mass spectrometry (CE-MS) and tandem mass spectrometry. In addition, we evaluated the presence of Tβ4 abundance in hemodialysis fluid of 10 subjects.

Results

The full length acetylated Tβ4 peptide could be identified in human urine in high abundance (molecular mass 4960.485 Da). In addition, an oxidized isoform could also be observed at high abundance. When investigating the distribution of both isoforms in control subjects and patients at different stages of CKD, a highly significant inverse association of the abundance of Tβ4 with eGFR could be observed for both, with rho -0.388 for the non-oxidized and -0.387 for the oxidized isoform, p&lt;0.0001 in each case. In hemodialysate, Tβ4 is found as the most abundant peptide in 2 of the 10 analyzed subjects, on average the second most abundant peptide, with β2 microglobuline being the most abundant one.

Conclusion

Tβ4 is highly significantly inversely associated with kidney function, suggesting a role of Tβ4 in kidney disease and/or its complications. Based on the experimental data published from animal models, the increase in Tβ4 may reflect a potential physiological mechanism to counteract CKD and its complications. Investigation of association of Tβ4 levels with progression in CKD, cardiovascular complications and death may inform on a potentially beneficial effect of Tβ4, is currently ongoing in longitudinal datasets and will be presented.

P0651PROTEOMIC CHARACTERIZATION OF OBESITY-RELATED NEPHROPATHY

Background and Aims

During the last years, with the increase in secondary diseases following obesity the focus on the context of abnormally high body weight and chronic kidney disease (CKD) has changed. While in individual care, CKD along with obesity and improving renal function along with weight loss can be seen frequently and trials on obesity reduction yielded promising renal results, systematic insight into the pathogenesis and epidemiology of nephropathy related to obesity without diabetes (Obesity-related nephropathy, ORN) is rather limited. This work reaches out to investigate impact of BMI on urine peptides and to characterize yet non-specified peptide patterns in urine that may be indicative of ORN and may indicate possible molecular mechanisms of ORN pathophysiology. We used urine samples from earlier projects which could be characterized for presence of diabetes, BMI, eGFR, and age. By defining such urinary peptide patterns, pathophysiology-enlightening hypotheses should be stimulated and pathways suggested to enable specific future characterization of ORN.

Method

In this study we investigated the association of urinary peptides with body mass index (BMI), and renal function in proteome datasets from 4015 individuals. For this study urinary proteome data stored in the Human Urine Proteome Database obtained by capillary electrophoresis coupled to mass spectrometry (CE-MS) were assessed.

Results

365 urinary peptides significantly associated with BMI could be identified. The majority of these peptides were collagen fragments. In addition, most of the peptides also demonstrated a significant concordant association with eGFR in the investigated cohort with diabetes exhibiting no significant association. A classifier based on 150 urine peptides enabled distinguishing non-obese subjects with preserved kidney function from obese, non-diabetic subjects with eGFR&gt;45 mL/min/1.73m in an independent cohort with an area under curve (AUC) of 0.93 (Fig.). The vast majority of significantly associated peptides are specific collagen fragments, most of them from collagen type I, and most of them (in fact all of the 10 most significant ones) are reduced with increasing BMI.

Conclusion

These analyses in a huge cohort of urine samples from patients with known eGFR, BMI and diabetes status showed a complex multi-level association between peptide fragments with renal risk in obesity. Of particular note, these relationships could be retrieved although the overall cohort inherited an almost negative eGFR – BMI gross correlation, presumably due to the origin of the dataset from a non-obesity-directed approach. Most peptides associated with GFR and BMI belonged to the collagen superfamily and were inversely associated with BMI but directly with eGFR.

This concordant observation indicates a reduction of collagen degradation with increasing BMI and/or reduced kidney function. It is tempting to speculate that collagen homeostasis along with body mass on one hand, and kidney function on the other, are substantially interconnected. The very strict association of BMI with collagen fragments was surprising, but also indicates specificity.

P0650URINE PEPTIDOME ANALYSIS IN HEART FAILURE, CHRONIC KIDNEY DISEASE AND CARDIORENAL SYNDROME TOWARDS THE DEFINITION OF DISEASE-SPECIFIC MARKERS

Background and Aims

The cardiorenal syndrome (CRS) reflects the complex interplay between kidney and heart diseases, but its molecular basis remains poorly understood. Multiple studies have demonstrated the association of urinary biomarkers with both heart and kidney diseases. However, their relevance and involvement in CRS have not been investigated yet. To address this gap, a study was designed with the aim to compare urinary biomarkers specific for heart failure (HF) and chronic kidney disease (CKD) with peptides representing CRS, with the ultimate target to connect these findings towards a better understanding of CRS pathophysiology.

Method

A total of 3.463 urinary peptidomic datasets from patients with HF, CKD, or with both HF and CKD (CRS) as well as patients with no apparent diseases (controls) were retrieved and analyzed from the urinary peptidomics database (Latosinska A et al., Electrophoresis 2019; 40: 2294-2308). Following the matching for age, gender, heart and kidney function, differences in the abundance of urinary peptides were investigated in a cohort comprised of 390 patients with HF, 257 patients with CKD, 392 patients with CRS and 356 controls. The non-parametric Mann-Whitney U test was applied, followed by correction for multiple testing using the Benjamini-Hochberg method. To map the peptides to the protein precursor, the alignment tool Geneious (www. geneious.com) was applied, while the PeptideRanker (http://distilldeep.ucd.ie/PeptideRanker/) was used to predict probability of peptide being bioactive.

Results

The multiple pair-wise comparisons resulted in the identification of numerous differentially abundant peptides (p&lt;0.05) between the studied conditions, including among others 176 HF-specific, 146 CKD-specific and 35 CRS-specific peptides. Among the HF-specific peptides, the majority (n=94, 53.4%) originated from collagen type I, II and III. In the case of CKD-specific peptides, 24 (16.43%) originated from alpha-1-antitrypsin, 19 (13.0%) from b2-microglobulin and 15 (10.27%) from collagen type I. For the CRS specific peptides, fragments of Ig lambda-2 chain C regions (n=4, 11.42%), collagen type III (n=4, 11.42%), secreted and transmembrane protein 1 (n=3, 8.57%) and gelsolin (n=1, 2.85%) were identified (figure: 1). Of the 176 HF-specific peptides, 94 (53.40%) were predicted as bioactive, including, among others, fragments of collagen types I (n=43, 45.74%) and III (n=21, 22.34%). In the former, peptides with the higher bioactivity scores were aligned close to the N terminus of the precursor protein, whereas in the latter, peptides were in close proximity to both N and C termini. Along the same lines, 32 (21.91%) of the 146 CKD-specific peptides were predicted as bioactive, including peptides from collagen types I and III with the highest score, as well as fragments from collagen type V and the C terminus of the b2-microglobulin and alpha-1-antitrypsin proteins. No CRS-specific peptides could be predicted as bioactive.

Conclusion

Specific urinary peptides significantly associated with CRS, but not with HF or CKD, could be identified. These data indicate that on a molecular level, CRS is not merely the result of a combination of HF and CKD, but may represent a distinct pathology, defined via specific proteomic changes. It is expected that interpretation of these findings in the context of existing literature as well as in vitro activity assays will help to understand their biological relevance in CRS.

The novel urinary proteomic classifier HF1 has similar diagnostic and prognostic utility to BNP in heart failure

AIMS

Measurement of B-type natriuretic peptide (BNP) or N-terminal pro-BNP is recommended as part of the diagnostic workup of patients with suspected heart failure (HF). We evaluated the diagnostic and prognostic utility of the novel urinary proteomic classifier HF1, compared with BNP, in HF. HF1 consists of 85 unique urinary peptide fragments thought, mainly, to reflect collagen turnover.

METHODS AND RESULTS

We performed urinary proteome analysis using capillary electrophoresis coupled with mass spectrometry in 829 participants. Of these, 622 had HF (504 had chronic HF and 118 acute HF) and 207 were controls (62 coronary heart disease patients without HF and 145 healthy controls). The area under the receiver operating characteristic (ROC) curve (AUC) using HF1 for the diagnosis of HF (cases vs. controls) was 0.94 (95% CI, 0.92-0.96). This compared with an AUC for BNP of 0.98 (95% CI, 0.97-0.99). Adding HF1 to BNP increased the AUC to 0.99 (0.98-0.99), P < 0.001, and led to a net reclassification improvement of 0.67 (95% CI, 0.54-0.77), P < 0.001. Among 433 HF patients followed up for a median of 989 days, we observed 186 deaths. HF1 had poorer predictive value to BNP for all-cause mortality and did not add prognostic information when combined with BNP.

CONCLUSIONS

The urinary proteomic classifier HF1 performed as well, diagnostically, as BNP and provided incremental diagnostic information when added to BNP. HF1 had less prognostic utility than BNP.

Proteomic characterization of obesity-related nephropathy

Background

Nephropathy related to obesity lacks a pathophysiological understanding and definite diagnostic pathways by biomarkers.

Methods

In this study we investigated the association between urinary peptides and body mass index (BMI) and renal function in proteome data sets from 4015 individuals.

Results

A total of 365 urinary peptides were identified to be significantly associated with BMI. The majority of these peptides were collagen fragments. In addition, most of the peptides also demonstrated a significant concordant association with estimated glomerular filtration rate (eGFR) in the investigated cohort, with the presence of diabetes exhibiting no significant association. A new classifier was developed, based on 150 urinary peptides, that enabled the distinction of non-obese subjects with preserved kidney function from obese, non-diabetic subjects with eGFR >45 mL/min/1.73 m² in an independent cohort, with an area under the curve of 0.93.

Conclusions

On a molecular level, the data strongly suggest a link between obesity and fibrosis, which may be a major cause of obesity-related nephropathy.

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care

Rationale

The urinary proteome reflects molecular drivers of disease.

Objectives

To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality.

Methods

In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses.

Measurements and main results

In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+ 1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis.

Conclusions

The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.