Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine

Impact of Renal Denervation on Urinary Peptide-Based Biomarkers in Hypertension

BACKGROUND

Catheter-based renal denervation (RDN) reduces blood pressure in hypertension. Urinary peptides are associated with cardiovascular and renal disease and provide prognostic information. We aimed to investigate the effect of RDN on urinary peptide-based classifiers associated with chronic kidney and heart disease and to identify urinary peptides affected by RDN.

METHODS

This single-arm, single-center study included patients undergoing catheter-based RDN. Urine samples were collected before and 24 months after RDN and were analyzed using capillary electrophoresis coupled with mass spectrometry. Predefined urinary peptide-based classifiers for chronic kidney disease (CKD273), coronary artery disease (CAD238), and heart failure (HF1) were applied.

RESULTS

This study included 48 patients (33% female) with uncontrolled hypertension. At 24 months after RDN, systolic blood pressure (165±17 versus 148±20 mm Hg; P<0.0001), diastolic blood pressure (90±17 versus 81±13 mm Hg; P<0.0001), and mean arterial pressure (115±15 versus 103±13 mm Hg; P<0.0001) decreased significantly. A total of 103 urinary peptides from 37 different proteins, mostly collagens, altered following RDN. CAD238, a 238 coronary artery-specific polypeptide-based classifier, significantly improved following RDN (Cohen's d, -0.632; P=0.0001). The classification scores of HF1 (P=0.8295) and CKD273 (P=0.6293) did not change significantly.

CONCLUSIONS

RDN beneficially affected urinary peptides associated with coronary artery disease.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01888315.

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.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Gestational Age-Related Urinary Peptidome Changes in Preterm and Term Born Infants

INTRODUCTION

Preterm infants are at risk for a variety of somatic and neurological disorders. In recent years, biofluid proteomics has emerged as a potential diagnostic tool for biomarker analysis. The aim of this study was to determine gestational age (GA)-related patterns of the urinary peptidome in preterm infants for researching potential novel prognostic biomarkers.

METHODS

We performed urinary peptidomics in longitudinal samples of 24 preterm (mean GA weeks 28 + 1 [24+1-31 + 6]) and 27 term born controls (mean GA weeks 39 + 2 [37+0-41 + 1]) using capillary electrophoresis combined with mass spectrometry (CE-MS). Peptides were sequenced using CE-MS/MS or LC-MS/MS analysis and were deposited, matched, and annotated in a Microsoft SQL database for statistical analysis. We compared their abundance in urine of preterm and term born infants and performed a validation analysis as well as correlations to GA and clinical risk scores.

RESULTS

Our results confirmed significant differences in the abundance of peptides and the hypothesis of age-dependent urinary peptidome changes in preterm and term infants. In preterm infants, SLC38A10 (solute carrier family 38 member 10) is one of the most abundant peptides. Combined urinary peptides correlated with clinical risk scores (p < 0.05).

CONCLUSION

This is the first study reporting GA-related urinary peptidome changes of preterm infants detected by CE-MS and a modulation of the peptidome with GA. Further research is required to locate peptidome clusters correlated with specific clinical complications and long-term outcome. This may identify preterm infants at higher risk for adverse outcome who would benefit from early intervention.

Differentiating primary and secondary FSGS using non-invasive urine biomarkers

Background

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

Methods

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

Results

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

Conclusion

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

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.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Characterization of the internal working-life exposome using minimally and non-invasive sampling methods - a narrative review

During recent years, we are moving away from the 'one exposure, one disease'-approach in occupational settings and towards a more comprehensive approach, taking into account the totality of exposures during a life course by using an exposome approach. Taking an exposome approach however is accompanied by many challenges, one of which, for example, relates to the collection of biological samples. Methods used for sample collection in occupational exposome studies should ideally be minimally invasive, while at the same time sensitive, and enable meaningful repeated sampling in a large population and over a longer time period. This might be hampered in specific situations e.g., people working in remote areas, during pandemics or with flexible work hours. In these situations, using self-sampling techniques might offer a solution. Therefore, our aim was to identify existing self-sampling techniques and to evaluate the applicability of these techniques in an occupational exposome context by conducting a literature review. We here present an overview of current self-sampling methodologies used to characterize the internal exposome. In addition, the use of different biological matrices was evaluated and subdivided based on their level of invasiveness and applicability in an occupational exposome context. In conclusion, this review and the overview of self-sampling techniques presented herein can serve as a guide in the design of future (occupational) exposome studies while circumventing sample collection challenges associated with exposome studies.

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.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Strategies for capillary electrophoresis: Method development and validation for pharmaceutical and biological applications-Updated and completely revised edition

This review is in support of the development of selective, precise, fast, and validated capillary electrophoresis (CE) methods. It follows up a similar article from 1998, Wätzig H, Degenhardt M, Kunkel A. "Strategies for capillary electrophoresis: method development and validation for pharmaceutical and biological applications," pointing out which fundamentals are still valid and at the same time showing the enormous achievements in the last 25 years. The structures of both reviews are widely similar, in order to facilitate their simultaneous use. Focusing on pharmaceutical and biological applications, the successful use of CE is now demonstrated by more than 600 carefully selected references. Many of those are recent reviews; therefore, a significant overview about the field is provided. There are extra sections about sample pretreatment related to CE and microchip CE, and a completely revised section about method development for protein analytes and biomolecules in general. The general strategies for method development are summed up with regard to selectivity, efficiency, precision, analysis time, limit of detection, sample pretreatment requirements, and validation.

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.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

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.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure

Introduction

The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction.

Methods

The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients.

Results

More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction.

Conclusion

This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

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.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

BACKGROUND

The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways.

METHODS

In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores.

RESULTS

A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (&lt; 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p &lt; 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p &lt; 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p &lt; 0.001).

CONCLUSIONS

An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

Evaluation of the reliability of MS1-based approach to profile naturally occurring peptides with clinical relevance in urine samples

RATIONALE

The profiling of natural urinary peptides is a valuable indicator of kidney condition. While front-end separation limits the speed of peptidomic profiling, MS1-based results suffer from limited peptide coverage and specificity. Clinical studies on chronic kidney disease require an effective strategy to balance the trade-off between identification depth and throughput.

METHODS

CKD273, a urinary proteome classifier associated with chronic kidney disease, in samples from diabetic nephropathy patients was profiled in parallel using capillary electrophoresis-mass spectrometry (CE-MS), liquid chromatography with mass spectrometry (LC-MS), and matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). Through cross-comparison of results from MS1 of unfractionated peptides and elution-time-resolved MS1 as well as MS/MS in LC- and CE-MS approaches, we evaluated the contribution of false-positive identification to MS1-based identification and quantitation, and analyzed the benefit of front-end separation in terms of accuracy and efficiency.

RESULTS

In LC- and CE-MS, although MS1 data resulted in higher number of identifications than MS/MS, elution-time-dependent analysis revealed extensive interference by non-CKD273 peptides, which would contribute up to 50% to quantitation if they are not separated from genuine CKD273 peptides. In the absence of separation, MS1 data resulted in lower numbers of identifications and abundance pattern that significantly deviated from those by liquid chromatography with tandem mass spectrometry (LC-MS/MS) or capillary electrophoresis with tandem mass spectrometry (CE-MS/MS). CE showed higher identification efficiency even when less sample was used or achieved faster separation.

CONCLUSIONS

To ensure the reliability of MS1-based urinary peptide profiling, front-end separation should not be omitted, and elution time should be used in addition to intact mass for identification. Including MS/MS in data acquisition does not compromise the speed or identification number, while benefiting data reliability by providing real-time sequence verification.

Urinary proteome of dogs with renal disease secondary to leishmaniosis

Canine leishmaniosis is frequently associated with the development of renal disease. Its pathogenesis is complex and not fully understood. For this reason, this study aimed to describe the urinary proteome, and identify possible new biomarkers in dogs with kidney disease secondary to leishmaniosis. Urine samples were collected from 20 dogs, 5 from healthy dogs, and 15 from stages Leishvet III and IV. Urine samples were analyzed by UHPLC-MS/MS. The data are available via ProteomeXchange with identifier PXD029165. A total of 951 proteins were obtained. After bioinformatic analysis, 93 urinary proteins were altered in the study group. Enrichment analysis performed on these proteins showed an overrepresentation of the complement activation pathway, among others. Finally, 12 discriminant variables were found in dogs with renal disease secondary to leishmaniosis, highlighting C4a anaphylatoxin, apolipoprotein A-I, haptoglobin, leucine-rich alpha-2-glycoprotein 1, and beta-2-microglobulin. This study is the first to describe the urinary proteomics of dogs with renal disease caused by leishmaniosis, and it provides new possible biomarkers for the diagnosis and monitoring of this disease.

A Model to Detect Significant Prostate Cancer Integrating Urinary Peptide and Extracellular Vesicle RNA Data

There is a clinical need to improve assessment of biopsy-naïve patients for the presence of clinically significant prostate cancer (PCa). In this study, we investigated whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples collected within the Movember GAP1 Urine Biomarker study (n = 192) were analysed by both mass spectrometry-based urine-proteomics and NanoString gene-expression analysis (167 gene-probes). Cross-validated LASSO penalised regression and Random Forests identified a combination of clinical and urinary biomarkers for predictive modelling of significant disease (Gleason Score (Gs) ≥ 3 + 4). Four predictive models were developed: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, and ‘SoC’ (standard of care) clinical data models, alongside a fully integrated omics-model, deemed ‘ExoSpec’. ExoSpec (incorporating four gene transcripts, six peptides, and two clinical variables) is the best model for predicting Gs ≥ 3 + 4 at initial biopsy (AUC = 0.83, 95% CI: 0.77−0.88) and is superior to a standard of care (SoC) model utilising clinical data alone (AUC = 0.71, p < 0.001, 1000 resamples). As the ExoSpec Risk Score increases, the likelihood of higher-grade PCa on biopsy is significantly greater (OR = 2.8, 95% CI: 2.1−3.7). The decision curve analyses reveals that ExoSpec provides a net benefit over SoC and could reduce unnecessary biopsies by 30%.

Noninvasive Diagnosis of Acute Rejection in Renal Transplant Patients Using Mass Spectrometric Analysis of Urine Samples: A Multicenter Diagnostic Phase III Trial

Timely recognition and treatment of acute kidney graft rejection is important to prevent premature graft failure. A predefined urinary marker set for acute T cell–mediated rejection (TCMR) containing 14 peptides was tested for this purpose in a multicenter in-place validation study.

Preparative capillary electrophoresis (CE) fractionation of protein digests improves protein and peptide identification in bottom-up proteomics

Reversed-phase liquid chromatography (RPLC) is widely used to reduce sample complexity prior to mass spectrometry (MS) analysis in bottom-up proteomics. Improving peptide separation in complex samples enables lower-abundance proteins to be identified. Multidimensional separations that combine orthogonal separation modes improve protein and peptide identifications over RPLC alone. Here we report a preparative capillary electrophoresis (CE) fractionation method that combines CE and RPLC separations. Using this method, we demonstrate improved protein and peptide identification in a tryptic digest of E. coli cell lysate, with 132 ± 33% more protein identifications and 185 ± 65% more peptide identifications over non-fractionated samples. Fractionation enables detection of lower-abundance proteins in this complex sample. We demonstrate improved coverage of ovarian cancer biomarker MUC16 isolated from conditioned cell media, with 6.73% sequence coverage using CE fractionation compared to 2.74% coverage without preparative fractionation. This new method will allow researchers performing bottom-up proteomics to harness the advantages of CE separations while using widely available LC-MS/MS instrumentation.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Collagen-Derived Peptides in CKD: A Link to Fibrosis

Collagen is a major component of the extracellular matrix (ECM) and has an imminent role in fibrosis, in, among others, chronic kidney disease (CKD). Collagen alpha-1(I) (col1a1) is the most abundant collagen type and has previously been underlined for its contribution to the disease phenotype. Here, we examined 5000 urinary peptidomic datasets randomly selected from healthy participants or patients with CKD to identify urinary col1a1 fragments and study their abundance, position in the main protein, as well as their correlation with renal function. We identified 707 col1a1 peptides that differed in their amino acid sequence and/or post-translational modifications (hydroxyprolines). Well-correlated peptides with the same amino acid sequence, but a different number of hydroxyprolines, were combined into a final list of 503 peptides. These 503 col1a1 peptides covered 69% of the full col1a1 sequence. Sixty-three col1a1 peptides were significantly and highly positively associated (rho > +0.3) with the estimated glomerular filtration rate (eGFR), while only six peptides showed a significant and strong, negative association (rho < −0.3). A similar tendency was observed for col1a1 peptides associated with ageing, where the abundance of most col1a1 peptides decreased with increasing age. Collectively the results show a strong association between collagen peptides and loss of kidney function and suggest that fibrosis, potentially also of other organs, may be the main consequence of an attenuation of collagen degradation, and not increased synthesis.

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.

Reproducibility Evaluation of Urinary Peptide Detection Using CE-MS

In recent years, capillary electrophoresis coupled to mass spectrometry (CE-MS) has been increasingly applied in clinical research especially in the context of chronic and age-associated diseases, such as chronic kidney disease, heart failure and cancer. Biomarkers identified using this technique are already used for diagnosis, prognosis and monitoring of these complex diseases, as well as patient stratification in clinical trials. CE-MS allows for a comprehensive assessment of small molecular weight proteins and peptides (<20 kDa) through the combination of the high resolution and reproducibility of CE and the distinct sensitivity of MS, in a high-throughput system. In this study we assessed CE-MS analytical performance with regards to its inter- and intra-day reproducibility, variability and efficiency in peptide detection, along with a characterization of the urinary peptidome content. To this end, CE-MS performance was evaluated based on 72 measurements of a standard urine sample (60 for inter- and 12 for intra-day assessment) analyzed during the second quarter of 2021. Analysis was performed per run, per peptide, as well as at the level of biomarker panels. The obtained datasets showed high correlation between the different runs, low variation of the ten highest average individual log2 signal intensities (coefficient of variation, CV < 10%) and very low variation of biomarker panels applied (CV close to 1%). The findings of the study support the analytical performance of CE-MS, underlining its value for clinical application.

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.

CE-MS for Proteomics and Intact Protein Analysis

This chapter aims to explore various parameters involved in achieving high-end capillary electrophoresis hyphenated to mass spectrometry (CE-MS) analysis of proteins, peptides, and their posttranslational modifications. The structure of the topics discussed in this book chapter is conveniently mapped on the scheme of the CE-MS system itself, starting from sample preconcentration and injection techniques and finishing with mass analyzer considerations. After going through the technical considerations, a variety of relevant applications for this analytical approach are presented, including posttranslational modifications analysis, clinical biomarker discovery, and its growing use in the biotechnological industry.

Peptidomics and Capillary Electrophoresis

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

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.

Urine peptidome analysis in cardiorenal syndrome reflects molecular processes

The cardiorenal syndrome (CRS) is defined as the confluence of heart-kidney dysfunction. This study investigates the molecular differences at the level of the urinary peptidome between CRS patients and controls and their association to disease pathophysiology. The urinary peptidome of CRS patients (n = 353) was matched for age and sex with controls (n = 356) at a 1:1 ratio. Changes in the CRS peptidome versus controls were identified after applying the Mann-Whitney test, followed by correction for multiple testing. Proteasix tool was applied to investigate predicted proteases involved in CRS-associated peptide generation. Overall, 559 differentially excreted urinary peptides were associated with CRS patients. Of these, 193 peptides were specifically found in CRS when comparing with heart failure and chronic kidney disease urinary peptide profiles. Proteasix predicted 18 proteases involved in > 1% of proteolytic cleavage events including multiple forms of MMPs, proprotein convertases, cathepsins and kallikrein 4. Forty-four percent of the cleavage events were produced by 3 proteases including MMP13, MMP9 and MMP2. Pathway enrichment analysis supported that ECM-related pathways, fibrosis and inflammation were represented. Collectively, our study describes the changes in urinary peptides of CRS patients and potential proteases involved in their generation, laying the basis for further validation.

Pathophysiological Implications of Urinary Peptides in Hepatocellular Carcinoma

Simple Summary

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

Abstract

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

A Novel Urinary Proteomics Classifier for Non-Invasive Evaluation of Interstitial Fibrosis and Tubular Atrophy in Chronic Kidney Disease

Non-invasive urinary peptide biomarkers are able to detect and predict chronic kidney disease (CKD). Moreover, specific urinary peptides enable discrimination of different CKD etiologies and offer an interesting alternative to invasive kidney biopsy, which cannot always be performed. The aim of this study was to define a urinary peptide classifier using mass spectrometry technology to predict the degree of renal interstitial fibrosis and tubular atrophy (IFTA) in CKD patients. The urinary peptide profiles of 435 patients enrolled in this study were analyzed using capillary electrophoresis coupled with mass spectrometry (CE-MS). Urine samples were collected on the day of the diagnostic kidney biopsy. The proteomics data were divided into a training (n = 200) and a test (n = 235) cohort. The fibrosis group was defined as IFTA ≥ 15% and no fibrosis as IFTA < 10%. Statistical comparison of the mass spectrometry data enabled identification of 29 urinary peptides with differential occurrence in samples with and without fibrosis. Several collagen fragments and peptide fragments of fetuin-A and others were combined into a peptidomic classifier. The classifier separated fibrosis from non-fibrosis patients in an independent test set (n = 186) with area under the curve (AUC) of 0.84 (95% CI: 0.779 to 0.889). A significant correlation of IFTA and FPP_BH29 scores could be observed Rho = 0.5, p < 0.0001. We identified a peptidomic classifier for renal fibrosis containing 29 peptide fragments corresponding to 13 different proteins. Urinary proteomics analysis can serve as a non-invasive tool to evaluate the degree of renal fibrosis, in contrast to kidney biopsy, which allows repeated measurements during the disease course.

A urinary peptidomic profile predicts outcome in SARS-CoV-2-infected patients

BACKGROUND

COVID-19 prediction models based on clinical characteristics, routine biochemistry and imaging, have been developed, but little is known on proteomic markers reflecting the molecular pathophysiology of disease progression.

METHODS

The multicentre (six European study sites) Prospective Validation of a Proteomic Urine Test for Early and Accurate Prognosis of Critical Course Complications in Patients with SARS-CoV-2 Infection Study (Crit-COV-U) is recruiting consecutive patients (≥ 18 years) with PCR-confirmed SARS-CoV-2 infection. A urinary proteomic biomarker (COV50) developed by capillary-electrophoresis-mass spectrometry (CE-MS) technology, comprising 50 sequenced peptides and identifying the parental proteins, was evaluated in 228 patients (derivation cohort) with replication in 99 patients (validation cohort). Death and progression along the World Health Organization (WHO) Clinical Progression Scale were assessed up to 21 days after the initial PCR test. Statistical methods included logistic regression, receiver operating curve (ROC) analysis and comparison of the area under the curve (AUC).

FINDINGS

In the derivation cohort, 23 patients died, and 48 developed worse WHO scores. The odds ratios (OR) for death per 1 standard deviation (SD) increment in COV50 were 3·52 (95% CI, 2·02-6·13, p <0·0001) unadjusted and 2·73 (1·25-5·95, p = 0·012) adjusted for sex, age, baseline WHO score, body mass index (BMI) and comorbidities. For WHO scale progression, the corresponding OR were 2·63 (1·80-3·85, p<0·0001) and 3·38 (1·85-6·17, p<0·0001), respectively. The area under the curve (AUC) for COV50 as a continuously distributed variable was 0·80 (0·72-0·88) for mortality and 0·74 (0·66-0·81) for worsening WHO score. The optimised COV50 thresholds for mortality and worsening WHO score were 0·47 and 0·04 with sensitivity/specificity of 87·0 (74·6%) and 77·1 (63·9%), respectively. On top of covariates, COV50 improved the AUC, albeit borderline for death, from 0·78 to 0·82 (p = 0·11) and 0·84 (p = 0·052) for mortality and from 0·68 to 0·78 (p = 0·0097) and 0·75 (p = 0·021) for worsening WHO score. The validation cohort findings were confirmatory.

INTERPRETATION

This first CRIT-COV-U report proves the concept that urinary proteomic profiling generates biomarkers indicating adverse COVID-19 outcomes, even at an early disease stage, including WHO stages 1-3. These findings need to be consolidated in an upcoming final dataset.

FUNDING

The German Federal Ministry of Health funded the study.

Investigating the position of the separation capillary and emitter tube tips in a nanoflow sheath-liquid CE-ESI-MS interface to decouple the ESI potential

Robust decoupling of the ESI potential from the separation potential in CE-ESI-MS interfaces is very important for the high performance of the CE-ESI-MS devices and their applications for highly sensitive analyses of ionogenic compounds. In this study, we utilize a nanoflow sheath-liquid CE-ESI-MS interface composed of a quartz emitter and a separation fused silica capillary treated by etching, which are threaded to cross coupling for sheath liquid and electrode connection. Specifically, we have tested the ability of the interface to decouple the ESI potential from the separation potential at different positions of the separation capillary and ESI emitter tube tips. The interface with the separation capillary tip protruding the emitter tip by 20 μm did not provide sufficient robustness. The real ESI potential (delivered as 2.0 kV from the independent high voltage power supply HV2) ranged from 2.1 kV to 4.5 kV depending on the applied separation voltage (12.0-20.0 kV, provided by the power supply HV1) and electric conductivity of the background electrolyte (BGE) used. The interface robustness was partially improved when the capillary tip was aligned with the emitter tip. However, the complete decoupling of the spray and separation potentials was achieved only when the capillary tip was retracted 20 μm inside the emitter. In this arrangement, the ESI potential was stable and independent of both the separation potential (voltage) and the BGE conductivity. Moreover, this setting provided better sensitivity for the CE-ESI-MS analysis of selected drugs and benzylpyridinium cations than the setup with the capillary tip aligned with or protruding the emitter tip.

Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Possibilities and Perspectives

Capillary electrophoresis-mass spectrometry (CE-MS) is a very useful analytical technique for the selective and highly efficient profiling of polar and charged metabolites in a wide range of biological samples. Compared to other analytical techniques, the use of CE-MS in metabolomics is relatively low as the approach is still regarded as technically challenging and not reproducible. In this chapter, the possibilities of CE-MS for metabolomics are highlighted with special emphasis on the use of recently developed interfacing designs. The utility of CE-MS for targeted and untargeted metabolomics studies is demonstrated by discussing representative and recent examples in the biomedical and clinical fields. The potential of CE-MS for large-scale and quantitative metabolomics studies is also addressed. Finally, some general conclusions and perspectives are given on this strong analytical separation technique for probing the polar metabolome.

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.

Capillary electrophoresis electrospray ionization-mass spectrometry for peptidomics-based processing site determination

Proteolytic cleavage at specific sites is a key event that modulates protein functions in biological processes. These cleavage sites are identified through mass spectrometry-based peptidomics of overlapping peptide sequences. Here, we assessed to what extent a recent capillary electrophoresis (CE) system interfaced with electrospray ionization-mass spectrometry (ESI-MS) contributes to identifying endogenous peptides present in a biological sample. Peptides released by a human endocrine cell line stimulated for secretion was analyzed for uncovering potential processing sites created by proprotein convertases (PCs) that cleave precursors in the secretory pathway. CE-ESI-MS was conducted, in comparison to a standard liquid chromatography (LC)-ESI-MS platform. LC and CE complemented each other in elucidating processing sites that match PC consensus sequences from known substrates. We suggest that the precursors BIGH3, STC1, LFNG, QSOX1 and CYTC are potential substrates for PCs, and that a CE-ESI system would come in handy and garner greater recognition as a robust tool in peptidomics.

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.

Urinary Proteomics Associates with COVID-19 Severity: Pilot Proof-of-Principle Data and Design of a Multicentric Diagnostic Study

SARS-CoV-2 infection results in a mild-to-moderate disease course in most patients, allowing outpatient self-care and quarantine. However, in approx. 10% of cases a two- or three-phasic critical disease course with starting from day 7 to 10 is observed. To facilitate and plan outpatient care, biomarkers prognosing such worsening at an early stage appear of outmost importance. In this accelerated article, we report on the identification of urinary peptides significantly associated with SARS-CoV-2 infection, and the development of a multi-marker urinary peptide based test, COVID20, that may enable prognosis of critical and fatal outcomes in COVID-19 patients. COVID20 is composed of 20 endogenous peptides mainly derived from various collagen chains that enable differentiating moderate or severe disease from critical state or death with 83% sensitivity at 100% specificity. Based on the performance in this pilot study, testing in a prospective study on 1000 patients has been initiated. This article is protected by copyright. All rights reserved.

Enrichment of Collagen Fragments Using Dimeric Collagen Hybridizing Peptide for Urinary Collagenomics

Collagen remodeling in normal and pathologic conditions releases numerous collagen fragments into biological fluids. Although a few collagen fragments have been tested as biomarkers for disease indication, most occur at trace levels, making them nearly impossible to detect even with modern analytical tools. Here we report a new way to enrich collagen fragments that allows complete peptidomic analysis of collagen fragments in urine. Enrichment is made possible by dimeric collagen hybridizing peptides (CHPs) that bind collagen fragments originating from the triple helical regions of all collagen types with minimal sequence bias. LC-MS/MS analysis of enriched mouse urine revealed an average of 383 collagenous peptide fragments per sample (compared to 34 for unenriched sample), which could be mapped to all types of mouse collagens in the SwissProt database including FACITs and MACITs. Hierarchical clustering of a selected panel of the detected fragments separated osteopenic mice from healthy mice. The results demonstrate dimeric CHP's ability to enrich collagen fragments from biological fluid and its potential to aid peptidomics-based disease detection and biomarker discovery.

Urinary peptidomic biomarkers of renal function in heart transplant recipients

Background

Chronic kidney disease (CKD) is common in patients after heart transplantation (HTx). We assessed whether in HTx recipients the proteomic urinary classifier CKD273 or sequenced urinary peptides revealing the parental proteins correlated with the estimated glomerular filtration rate (eGFR).

Methods

In 368 HTx patients, we measured the urinary peptidome and analysed CKD273 and 48 urinary peptides with a detectable signal in >95% of participants. After 9.1 months (median), eGFR and the urinary biomarkers were reassessed.

Results

In multivariable Bonferroni-corrected analyses of the baseline data, a 1-SD increase in CKD273 was associated with a 11.4 [95% confidence interval (CI) 7.25–15.5] mL/min/1.73 m² lower eGFR and an odds ratio of 2.63 (1.56–4.46) for having eGFR <60 mL/min/1.73 m². While relating eGFR category at follow-up to baseline urinary biomarkers, CKD273 had higher (P = 0.007) area under the curve (0.75; 95% CI 0.70–0.80) than 24-h proteinuria (0.64; 95% CI 0.58–0.69), but additional adjustment for baseline eGFR removed significance of both biomarkers. In partial least squares analysis, the strongest correlates of the multivariable-adjusted baseline eGFR were fragments of collagen I (positive) and the mucin-1 subunit α (inverse). Associations between the changes in eGFR and the urinary markers were inverse for CKD273 and mucin-1 and positive for urinary collagen I.

Conclusions

With the exception of baseline eGFR, CKD273 was more closer associated with imminent renal dysfunction than 24-h proteinuria. Fragments of collagen I and mucin-1—respectively, positively and inversely associated with eGFR and change in eGFR—are single-peptide markers associated with renal dysfunction.

Identification of Urinary Biomarkers for Exercise-Induced Immunosuppression by iTRAQ Proteomics

Purpose

To identify noninvasive immune biomarkers of exercise-induced immunosuppression using the iTRAQ proteomics technique.

Methods

Fifteen healthy males were recruited and subjected to a four-week incremental treadmill running training program. After each week of training, WBC counts and CD4⁺ and CD8⁺ lymphocytes were measured to monitor the immune function status. iTRAQ proteomics technology was used to identify differential proteins and their characteristics in urine.

Results

Our data showed that the WBC counts, CD4⁺ lymphocytes, and CD4⁺/CD8⁺ ratio decreased by more than 10% after four weeks of training, suggesting exercise-induced immunosuppression. A total of 1854 proteins were identified in urine during the incremental running using the iTRAQ technology. Compared with the urine before training, there were 89, 52, 77, and 148 proteins significantly upregulated and 66, 27, 68, and 114 proteins significantly downregulated after each week, respectively. Among them, four upregulated proteins, SEMG-1, PIP, PDGFRL, and NDPK, increased their abundance with the increased exercise intensity. Bioinformatics analysis indicates that these proteins are involved in stress response and immune function.

Conclusion

Four weeks of incremental treadmill running induced immunosuppression in healthy males. By using iTRAQ proteomics, four proteins in the urine, SEMG-1, PIP, PDGFRL, and NDPK, were found to increase incrementally with the increased exercise intensity, which have the potential to be used as noninvasive immune biomarkers of exercise-induced immunosuppression.

Corona Isolation Method Matters: Capillary Electrophoresis Mass Spectrometry Based Comparison of Protein Corona Compositions Following On-Particle versus In-Solution or In-Gel Digestion

Increased understanding of the role of the nanomaterial protein corona in driving nanomaterial uptake into, and impacts on, cells and organisms, and the consequent need for characterization of the corona, has led to a flourishing of methods for isolation and analysis of the constituent proteins over the past decade. However, despite over 700 corona studies to date, very little is understood in terms of which methods provide the most precise and comprehensive characterization of the corona. With the increasing importance of the modeling of corona formation and its correlation with biological impacts, it is timely to properly characterize and validate the isolation approaches used to determine the protein corona. The current work introduces Capillary Electrophoresis with Electro Spray Ionization Mass Spectrometry (CESI-MS) as a novel method for protein corona characterizations and develops an on-particle tryptic digestion method, comparing peptide solubilization solutions and characterizing the recovery of proteins from the nanomaterial surface. The CESI-MS was compared to the gold standard nano-LC-MS for corona analysis and maintained a high degree of reproducibility, while increasing throughput by >3-fold. The on-particle digestion is compared to an in-solution digestion and an in-gel digestion of the protein corona. Interestingly, a range of different protein classes were found to be recovered to greater or lesser extents among the different methods. Apolipoproteins were detected at lower concentrations when a surfactant was used to solubilize peptides, whereas immunoglobulins in general have a high affinity for nanomaterials, and thus show lower recovery using on-particle digestion. The optimized on-particle digestion was validated using 6 nanomaterials and proved capable of recovering in excess of 97% of the protein corona. These are important factors to consider when designing corona studies and modeling corona formation and impacts, highlighting the significance of a comprehensive validation of nanomaterial corona analysis methods.

Urinary peptidome analyses for the diagnosis of chronic kidney disease in dogs

Chronic kidney disease (CKD) is clinically important in canine medicine. Current diagnostic tools lack sensitivity for detection of subclinical CKD. The aim of the present study was to evaluate urinary peptidome analysis for diagnosis of CKD in dogs. Capillary electrophoresis coupled to mass spectrometry analysis demonstrated presence of approximately 5400 peptides in dog urine. Comparison of urinary peptide abundance of dogs with and without CKD led to the identification of 133 differentially excreted peptides (adjusted P for each peptide <0.05). Sequence information was obtained for 35 of these peptides. This 35 peptide subset and the total group of 133 peptides were used to construct two predictive models of CKD which were subsequently validated by researchers masked to results in an independent cohort of 20 dogs. Both models diagnosed CKD with an area under the receiver operating characteristic (ROC) curve of 0.88 (95% confidence intervals [CI], 0.72-1.0). Most differentially excreted peptides represented fragments of collagen I, indicating possible association with fibrotic processes in CKD (similar to the equivalent human urinary peptide CKD model, CKD273). This first study of the urinary peptidome in dogs identified peptides that were associated with presence of CKD. Future studies are needed to validate the utility of this model for diagnosis and prediction of progression of canine CKD in a clinical setting.

CE-MS-based urinary biomarkers to distinguish non-significant from significant prostate cancer

Background

Prostate cancer progresses slowly when present in low risk forms but can be lethal when it progresses to metastatic disease. A non-invasive test that can detect significant prostate cancer is needed to guide patient management.

Methods

Capillary electrophoresis/mass spectrometry has been employed to identify urinary peptides that may accurately detect significant prostate cancer. Urine samples from 823 patients with PSA (<15 ng/ml) were collected prior to biopsy. A case–control comparison was performed in a training set of 543 patients (n_Sig = 98; n_non-Sig = 445) and a validation set of 280 patients (n_Sig = 48, n_non-Sig = 232). Totally, 19 significant peptides were subsequently combined by a support vector machine algorithm.

Results

Independent validation of the 19-biomarker model in 280 patients resulted in a 90% sensitivity and 59% specificity, with an AUC of 0.81, outperforming PSA (AUC = 0.58) and the ERSPC-3/4 risk calculator (AUC = 0.69) in the validation set.

Conclusions

This multi-parametric model holds promise to improve the current diagnosis of significant prostate cancer. This test as a guide to biopsy could help to decrease the number of biopsies and guide intervention. Nevertheless, further prospective validation in an external clinical cohort is required to assess the exact performance characteristics.