Urinary Proteomics and Outcomes in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Although several studies have addressed plasma proteomics in heart failure with preserved ejection fraction, limited data are available on the prognostic value of urinary proteomics. The objective of our study was to identify urinary proteins/peptides associated with death and heart failure admission in patients with heart failure with preserved ejection fraction.

METHODS AND RESULTS

The study population included participants enrolled in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial). The relationship between urine protein levels and the risk of death or heart failure admission was assessed using Cox regression, in both nonadjusted analyses and adjusting for urine creatinine levels, and the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) score. A total of 426 (12.4%) TOPCAT participants had urinary protein data and were included. There were 40 urinary proteins/peptides significantly associated with death or heart failure admission in nonadjusted analyses, 21 of which were also significant adjusted analyses. Top proteins in the adjusted analysis included ANGPTL2 (angiopoietin-like protein 2) (hazard ratio [HR], 0.5731 [95% CI, 0.47-0.7]; P=3.13E-05), AMY2A (α amylase 2A) (HR, 0.5496 [95% CI, 0.44-0.69]; P=0.0001), and DNASE1 (deoxyribonuclease-1) (HR, 0.5704 [95% CI, 0.46-0.71]; P=0.0002). Higher urinary levels of proteins involved in fibrosis (collagen VI α-1, collagen XV α-1), metabolism (pancreatic α-amylase 2A/B, mannosidase α class 1A member 1), and inflammation (heat shock protein family D member 1, inducible T cell costimulatory ligand) were associated with a lower risk of death or heart failure admission.

CONCLUSIONS

Our study identifies several novel associations between urinary proteins/peptides and outcomes in heart failure with preserved ejection fraction. Many of these associations are independent of clinical risk scores and may aid in risk stratification in this patient population.

Effect of urine alkalization on urinary inflammatory markers in cystinuric patients

Background

Cystinuria is associated with a high prevalence of chronic kidney disease (CKD). We previously described a urinary inflammatory-protein signature (UIS), including 38 upregulated proteins, in cystinuric patients (Cys-patients), compared with healthy controls (HC). This UIS was higher in Cys-patients with CKD. In the present observational study, we aimed to investigate the UIS in Cys-patients without CKD and patients with calcium nephrolithiasis (Lith-patients), versus HC and the effect of urine alkalization on the UIS of Cys-patients.

Methods

UIS was evaluated by nano-liquid chromatography coupled to high-resolution mass spectrometry in adult HC, Lith-patients and non-treated Cys-patients with an estimated glomerular filtration rate >60 mL/min/1.73 m2, and after a 3-month conventional alkalizing treatment in Cys-patients.

Results

Twenty-one Cys-patients [12 men, median age (interquartile range) 30.0 (25.0-44.0) years], 12 Lith-patients [8 men, 46.2 (39.5-54.2) years] and 7 HC [2 men, 43.1 (31.0-53.9) years] were included. Among the 38 proteins upregulated in our previous work, 11 proteins were also upregulated in Cys-patients compared with HC in this study (5 circulating inflammatory proteins and 6 neutrophil-derived proteins). This UIS was also found in some Lith-patients. Using this UIS, we identified two subclusters of Cys-patients (5 with a very high/high UIS and 16 with a moderate/low UIS). In the Cys-patients with very high/high UIS, urine alkalization induced a significant decrease in urinary neutrophil-derived proteins.

Conclusion

A high UIS is present in some Cys-patients without CKD and decreases under alkalizing treatment. This UIS could be a prognostic marker to predict the evolution towards CKD in cystinuria.

Quantitative comparison of the renal pelvic urine and bladder urine to examine modifications of the urine proteome by the lower urinary tract

PURPOSE

Urine proteome is a valuable reservoir of biomarkers for disease diagnosis and monitoring. Following formation as the plasma filtrate in the kidney, urine is progressively modified by the active reabsorption and secretion of the urinary tract. However, little is known about how the urine proteome changes as it passes along the urinary tract.

EXPERIMENTAL DESIGN

To investigate this, we compared the proteome composition of the renal pelvis urine (RPU) and individually self-voided bladder urine (BU) collected from seven unilateral urinary tract obstruction male patients by LC-MS/MS screening. To our knowledge, this is the first proteomic comparison of RPU and BU samples from the same individual.

RESULTS

Overall, RPU and BU proteomes did not exhibit proteins that were exclusively present in all samples of one urine type while in none of the other type. Nonetheless, BU had more overrepresented proteins that were observed at a higher frequency than RPU. Label-free quantitative analyses revealed BU-RPU differential proteins that are enriched in exosomes and extracellular proteins. However, the differences were not significant after corrections for multiple testing. Interestingly, we observed a significant increase of collagen peptides with hydroxyproline modifications in the BU samples, suggesting differences in protein modifications.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study revealed no substantial differences at the protein level between the BU and RPU samples. Future investigations with expanded cohorts would provide more insights about the urothelial-urinary interactions.

Dynamics of urine proteomics biomarker and disease progression in patients with IgA nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN) frequently leads to kidney failure. The urinary proteomics-based classifier IgAN237 may predict disease progression at the time of kidney biopsy. We studied whether IgAN237 also predicts progression later in the course of IgAN.

METHODS

Urine from patients with biopsy-proven IgAN was analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237-1, n = 103) and at follow-up (IgAN237-2, n = 89). Patients were categorized as "non-progressors" (IgAN237 ≤0.38) and "progressors" (IgAN237 >0.38). Estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio slopes were calculated.

RESULTS

Median age at biopsy was 44 years, interval between biopsy and IgAN237-1 was 65 months and interval between IgAN237-1 and IgAN237-2 was 258 days (interquartile range 71-531). IgAN237-1 and IgAN237-2 values did not differ significantly and were correlated (rho = 0.44, P < .001). Twenty-eight percent and 26% of patients were progressors based on IgAN237-1 and IgAN237-2, respectively. IgAN237 inversely correlated with chronic eGFR slopes (rho = -0.278, P = .02 for score-1; rho = -0.409, P = .002 for score-2) and with ±180 days eGFR slopes (rho = -0.31, P = .009 and rho = -0.439, P = .001, respectively). The ±180 days eGFR slopes were worse for progressors than for non-progressors (median -5.98 versus -1.22 mL/min/1.73 m2 per year for IgAN237-1, P < .001; -3.02 vs 1.08 mL/min/1.73 m2 per year for IgAN237-2, P = .0047). In multiple regression analysis baseline progressor/non-progressor according to IgAN237 was an independent predictor of eGFR180days-slope (P = .001).

CONCLUSION

The urinary IgAN237 classifier represents a risk stratification tool in IgAN also later in the course of the dynamic disease. It may guide patient management in an individualized manner.

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.

Urine proteomics by mass spectrometry identifies proteins involved in key pathogenic pathways in patients with juvenile dermatomyositis

OBJECTIVES

To identify and validate biomarkers in JDM patients using a multiplexing tandem mass tag urine proteome profiling approach.

METHODS

First morning void urine samples were collected from JDM patients (n = 20) and healthy control subjects (n = 21) and processed for analysis using a standardized liquid chromatography-tandem mass spectrometry approach. Biomarkers with significantly altered levels were correlated with clinical measures of myositis disease activity and damage. A subset of candidate biomarkers was validated using commercially available ELISA kits.

RESULTS

In total, 2348 proteins were detected in the samples, with 275 proteins quantified across all samples. Among the differentially altered proteins, cathepsin D and galectin-3 binding protein were significantly increased in the urine of JDM patients (adjusted P < 0.05), supporting previous findings in myositis patients. These two candidate biomarkers were confirmed with ELISAs. Cathepsin D positively correlated with Myositis Damage Index (r = 0.57, P < 0.05) and negatively correlated with the Childhood Myositis Assessment Scale (r = -0.54, P < 0.05). We also identified novel JDM candidate biomarkers involved with key features of myositis, including extracellular matrix remodelling proteins.

CONCLUSION

This study confirmed the presence of several proteins in the urine of JDM patients that were previously found to be elevated in the blood of myositis patients and identified novel candidate biomarkers that require validation. These results support the use of urine as a source for biomarker development in JDM.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

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.

[Comparison of methods for enriching urine proteins]

The abundance of proteins in human urine is low and easily to be masked by high-abundance proteins during mass spectrometry analysis. Development of efficient and highly selective enrichment methods is therefore a prerequisite for achieving deep coverage of urine protein markers. Notably, different experimental methods would affect the urine protein enrichment efficacy and the coverage of urine proteome. In this study, ultrafiltration, nitrocellulose membrane enrichment and saturated ammonium sulfate precipitation were used to process 10 mL urine samples from five healthy volunteers and five bladder cancer patients. The urine proteins were enriched and separate by SDS-PAGE to compare the purification efficiency of different methods. Moreover, the peptide identification effects of different purification methods were analyzed by mass spectrometry to determine the best method for enriching urine protein histones. Saturated ammonium sulfate precipitation method outperformed the ultrafiltration and the nitrocellulose membrane enrichment methods in terms of the protein enrichment efficacy and quality. The interference of highly abundant albumin was reduced, whereas the amount of low-abundance protein was increased, and the sensitivity of mass spectrometry identification was increased. The saturated ammonium sulfate precipitation method may be applied for large-scale urine processing for screening clinical diagnostic markers through proteomics.

Association of the chronic kidney disease urinary proteomic predictor CKD273 with clinical risk factors of graft failure in kidney allograft recipients

BACKGROUND

Kidney transplantation is the best treatment option for end stage kidney disease but is still associated with long term graft failure. In this study, we evaluated the application of urinary proteomics to identify grafts with high failure risk before initial decline of eGFR with irreversible graft changes.

METHODS

Fifty-two living donor kidney transplant recipients (KTR) with 8-years follow up were enrolled. All patients underwent clinical examination and had a routine laboratory screening at 3, 6, 12, 24, 36, 48 and 96 months post-transplantation, including creatinine, urea, albumin and 24h proteinuria. Graft function was estimated according to Nankivell. Urine samples at month 24 were analyzed by CE-MS followed by classification with the chronic kidney disease classifier CKD273.

RESULTS

CKD273 showed significant correlation with serum creatinine at every time point and moderate inverse correlation for the slope in glomerular filtration rates by Nankivell (r = -0.29, P = 0.05). Receiver operating characteristics analysis for graft loss and death within the next six years after proteomic analysis resulted in an area under curve value of 0.89 for CKD273 being superior to 0.67 for Nankivell eGFR. Stratification into CKD273 positive and negative patient groups revealed a hazard ratio of 16.5 for prevalence of graft loss in case of CKD273 positivity.

CONCLUSIONS

Using a representative KTR cohort with 8-years follow-up, we could demonstrate significant value of CKD273 for risk stratification of graft loss. This study provides the conceptual basis for further evaluation of CKD273 as prognostic tool for long-term graft function risk stratification by large prospective clinical trials.

Urine Proteomics Differentiate Primary Thrombotic Antiphospholipid Syndrome From Obstetric Antiphospholipid Syndrome

Antiphospholipid syndrome (APS) is a multisystem disorder characterized by thrombosis and/or recurrent fetal loss. This clinical phenotype heterogeneity may result in differences in response to treatment and prognosis. In this study, we aimed to identify primary thrombotic APS (TAPS) from primary obstetric APS (OAPS) using urine proteomics as a non-invasive method. Only patients with primary APS were enrolled in this study from 2016 to 2018 at a single clinical center in Shanghai. Urine samples from 15 patients with TAPS, 9 patients with OAPS, and 15 healthy controls (HCs) were collected and analyzed using isobaric tags for relative and absolute quantification (iTRAQ) labeling combined with liquid chromatography-tandem mass spectrometry analysis to identify differentially expressed proteins. Cluster analysis of urine proteomics identified differentiated proteins among the TAPS, OAPS, and HC groups. Urinary proteins were enriched in cytokine and cytokine receptor pathways. Representative secreted cytokines screened out (fold change >1.20, or <0.83, p<0.05) in these differentiated proteins were measured by enzyme-linked immunosorbent assay in a validation cohort. The results showed that the levels of C-X-C motif chemokine ligand 12 (CXCL12) were higher in the urine of patients with TAPS than in those with OAPS (p=0.035), while the levels of platelet-derived growth factor subunit B (PDGFB) were lower in patients with TAPS than in those with OAPS (p=0.041). In addition, correlation analysis showed that CXCL12 levels were positively correlated with immunoglobulin G anti-β2-glycoprotein I antibody (r=0.617, p=0.016). Our results demonstrated that urinary CXCL12 and PDGFB might serve as potential non-invasive markers to differentiate primary TAPS from primary OAPS.

An Ultrafast N-Glycoproteome Analysis Method Using Thermoresponsive Magnetic Fluid-Immobilized Enzymes

N-Glycosylation is one of the most common and important post-translational modification methods, and it plays a vital role in controlling many biological processes. Increasing discovery of abnormal alterations in N-linked glycans associated with many diseases leads to greater demands for rapid and efficient N-glycosylation profiling in large-scale clinical samples. In the workflow of global N-glycosylation analysis, enzymatic digestion is the main rate-limiting step, and it includes both protease digestion and peptide-N4–(N-acetyl-beta-glucosaminyl) asparagine amidase (PNGase) F deglycosylation. Prolonged incubation time is generally required because of the limited digestion efficiency of the conventional in-solution digestion method. Here, we propose novel thermoresponsive magnetic fluid (TMF)-immobilized enzymes (trypsin or PNGase F) for ultrafast and highly efficient proteome digestion and deglycosylation. Unlike other magnetic material-immobilized enzymes, TMF-immobilized enzymes display a unique temperature-triggered magnetic response behavior. At room temperature, a TMF-immobilized enzyme completely dissolves in an aqueous solution and forms a homogeneous system with a protein/peptide sample for efficient digestion but cannot be separated by magnetic force because of its excellent water dispersity. Above its lower critical solution temperature (LCST), thermoflocculation of a TMF-immobilized enzyme allows it to be easily recovered by increasing the temperature and magnetic force. Taking advantage of the unique homogeneous reaction of a TMF-immobilized enzyme, both protein digestion and glycopeptide deglycosylation can be finished within 3 min, and the whole sample processing time can be reduced by more than 20 times. The application of a TMF-immobilized enzyme in large-scale profiling of protein N-glycosylation in urine samples led to the successful identification of 2,197 N-glycopeptides and further demonstrated the potential of this strategy for fast and high-throughput analysis of N-glycoproteome in clinical samples.

Proteomics Profiling of the Urine of Patients with Hyperthyroidism after Anti-Thyroid Treatment

Hyperthyroidism, which is characterized by increased circulating thyroid hormone levels, alters the body’s metabolic and systemic hemodynamic balance and directly influences renal function. In this study, the urinary proteome of patients with hyperthyroidism was characterized using an untargeted proteomic approach with network analysis. Urine samples were collected from nine age-matched patients before and after carbimazole treatment. Differences in the abundance of urinary proteins between hyperthyroid and euthyroid states were determined using a 2D-DIGE coupled to MALDI-TOF mass spectrometry. Alterations in the abundance of urinary proteins, analyzed via Progenesis software, revealed a statistically significant difference in abundance in a total of 40 spots corresponding to 32 proteins, 25 up and 7 down (≥1.5-fold change, ANOVA, p ≤ 0.05). The proteins identified in the study are known to regulate processes associated with cellular metabolism, transport, and acute phase response. The notable upregulated urinary proteins were serotransferrin, transthyretin, serum albumin, ceruloplasmin, alpha-1B-glycoprotein, syntenin-1, and glutaminyl peptide cyclotransferase, whereas the three notable downregulated proteins were plasma kallikrein, protein glutamine gamma-glutamyl transferase, and serpin B3 (SERPINB3). Bioinformatic analysis using ingenuity pathway analysis (IPA) identified the dysregulation of pathways associated with cellular compromise, inflammatory response, cellular assembly, and organization and identified the involvement of the APP and AKT signaling pathways via their interactions with interleukins as the central nodes.

Urinary glycoproteomic profiling of non-muscle invasive and muscle invasive bladder carcinoma patients reveals distinct N-glycosylation pattern of CD44, MGAM, and GINM1

Clinical management of bladder carcinomas (BC) remains a major challenge and demands comprehensive multi-omics analysis for better stratification of the disease. Identification of patients on risk requires identification of signatures predicting prognosis risk of the patients. Understanding the molecular alterations associated with the disease onset and progression could improve the routinely used diagnostic and therapy procedures. In this study, we investigated the aberrant changes in N-glycosylation pattern of proteins associated with tumorigenesis as well as disease progression in bladder cancer. We integrated and compared global N-glycoproteomic and proteomic profile of urine samples from bladder cancer patients at different clinicopathological stages (non-muscle invasive and muscle-invasive patients [n = 5 and 4 in each cohort]) with healthy subjects (n = 5) using SPEG method. We identified 635 N-glycopeptides corresponding to 381 proteins and 543 N-glycopeptides corresponding to 326 proteins in NMIBC and MIBC patients respectively. Moreover, we identified altered glycosylation in 41 NMIBC and 21 MIBC proteins without any significant change in protein abundance levels. In concordance with the previously published bladder cancer cell line N-glycoproteomic data, we also observed dysregulated glycosylation in ECM related proteins. Further, we identified distinct N-glycosylation pattern of CD44, MGAM, and GINM1 between NMIBC and MIBC patients, which may be associated with disease progression in bladder cancer. These aberrant protein glycosylation events would provide a novel approach for bladder carcinoma diagnosis and further define novel mechanisms of tumor initiation and progression.

Quantitative proteomic analysis of a genetically induced prostate inflammation mouse model via custom 4-plex DiLeu isobaric labeling

Inflammation is involved in many prostate pathologies including infection, benign prostatic hyperplasia, and prostate cancer. Preclinical models are critical to our understanding of disease mechanisms, yet few models are genetically tractable. Here, we present a comparative quantitative proteomic analysis of urine from mice with and without prostate-specific inflammation induced by conditional prostate epithelial IL-1β expression. Relative quantification and sample multiplexing was achieved using custom 4-plex N,N-dimethyl leucine (DiLeu) isobaric tags and nanoflow ultrahigh-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. Each set of 4-plex DiLeu reagents allows four urine samples to be analyzed simultaneously, providing high-throughput and accurate quantification of urinary proteins. Proteins involved in the acute phase response, including haptoglobin, inter-α-trypsin inhibitor, and α1-antitrypsin 1-1, were differentially represented in the urine of mice with prostate inflammation. Mass spectrometry-based quantitative urinary proteomics represents a promising bioanalytical strategy for biomarker discovery and the elucidation of molecular mechanisms in urological research.

New biomarkers of Kawasaki disease identified by urine proteomic analysis

Kawasaki disease (KD) is an acute systemic vasculitis that mainly afflicts infants and young children. The symptoms of KD are similar to those of various febrile diseases. Here, we attempted to develop accurate diagnostic biomarkers of KD by performing urine proteomic analysis of samples from healthy controls, patients with KD, and patients with another febrile disease, pneumonia (two patients). We identified differentially expressed proteins (DEPs) in KD as compared to normal controls. We also constructed functional annotation and protein-protein interaction (PPI) networks of DEPs in KD and pneumonia. DEPs common to both KD and pneumonia were identified, as well as DEPs specific to KD. Compared to normal control, 43 and 62 DEPs were identified in KD and pneumonia, respectively. Serine hydroxymethyltransferase 1 is a hub protein of the KD-specific PPI network. Thirteen DEPs common to both KD and pneumonia and 30 DEPs specific to KD were identified. Of these, the expression of eight DEPs could cluster normal and pneumonia samples into one group and cluster KD samples into another group based on hierarchical clustering. Our study identified several DEPs that may play a role in KD and that may serve as diagnostic biomarkers to distinguish patients with KD from both normal control and other febrile diseases.

[Comparison of urinary proteomes among rats, guinea pigs and golden hamsters]

Biomarkers are the detectable changes associated with physiological or pathological changes. Urine as excreta of the body, without the mechanisms to maintain a homeostatic internal environment, can reflect a variety of changes in the body. Using animal models can simulate human disease processes, monitor disease changes, and provide clues to early diagnosis. Rats as commonly used model animals are not the dominant models for all disease, thus comparing the urinary proteins of rats with other animals to provide clues to the selection of other animal dominant models. In this study, urinary proteins were digested and profiled by liquid chromatography and tandem mass spectrometry (LC-MS/MS). The urinary proteins of rats, guinea pigs and golden hamsters were compared. The results showed that the number of urine proteins in the three different animals was different, and also different in every system of the body. This provides a basis for selecting the best animal models for different diseases.

Mining the human urine proteome for monitoring renal transplant injury

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

[Urine proteome study for the evaluation of age dynamics in healthy men]

We investigated the age dynamics of proteomic profile of urine in 52 healthy men aged 18 to 51 years. A special sample preparation was performed, followed by liquid chromatography-mass spectrometry. Liquid chromatography-mass spectrometry of the minor proteins was performed on a nano-HPLC Agilent 1100 system («Agilent Technologies Inc.», USA) in combination with a LTQ-FT Ultra mass spectrometer («Thermo Electron», Germany). A total of 259 proteins were identified. According to the TiGER database, a tissue origin was established for 141 proteins and identified 715 processes in which they participate. We found a significant positive correlation with age, the number of proteins (R=0,566; p-value=1,24E-05) and the weight of proteins (R=0,45; p value=8,17E-04). Identified 23 proteins were significantly more frequent in the urine of subjects with increasing age (p<0,05), and only one protein - RGSL, Regulator of G protein signaling protein-like (MW 125.69) - less frequently.

Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy

BACKGROUND

Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273).

METHODS

In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier.

RESULTS

We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found.

CONCLUSION

We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial.

Concentration and Preservation of Very Low Abundance Biomarkers in Urine, such as Human Growth Hormone (hGH), by Cibacron Blue F3G-A Loaded Hydrogel Particles

Urine is a potential source of diagnostic biomarkers for detection of diseases, and is a very attractive means of non-invasive biospecimen collection. Nonetheless, proteomic measurement in urine is very challenging because diagnostic biomarkers exist in very low concentration (usually below the sensitivity of common immunoassays) and may be subject to rapid degradation. Hydrogel nanoparticles functionalized with Cibacron Blue F3G-A (CB) have been applied to address these challenges for urine biomarker measurement. We chose one of the most difficult low abundance, but medically relevant, hormones in the urine: human growth hormone (hGH). The normal range of hGH in serum is 1 to 10 ng/mL but the urine concentration is suspected to be a thousand times less, well below the detection limit (50 pg/mL) of sensitive clinical hGH immunoassays. We demonstrate that CB particles can capture, preserve and concentrate hGH in urine at physiological salt and urea concentrations, so that hGH can be measured in the linear range of a clinical immunometric assay. Recombinant and cadaveric hGH were captured from synthetic and human urine, concentrated and measured with an Immulite chemiluminescent immunoassay. Values of hGH less than 0.05 ng/mL (the Immulite detection limit) were concentrated to 2 ng/mL, with a urine volume of 1 mL. Dose response studies using 10 mL of urine demonstrated that the concentration of hGH in the particle eluate was linearly dependent on the concentration of hGH in the starting solution, and that all hGH was removed from solution. Thus if the starting urine volume is 100 mL, the detection limit will be 0.1 pg/mL. Urine from a healthy donor whose serum hGH concentration was 1.34 ng/mL was studied in order detect endogenous hGH. Starting from a volume of 33 mL, the particle eluate had an hGH concentration of 58 pg/mL, giving an estimated initial concentration of hGH in urine of 0.175 pg/mL. The nanotechnology described here appears to have the desired precision, accuracy and sensitivity to support large scale clinical studies of urine hGH levels.

Urine proteomic profiling of pediatric nephrotic syndrome

The prognosis of pediatric nephrotic syndrome (NS) correlates with the responsiveness to glucocorticoid therapy. Steroid-resistant NS (SRNS) patients progress to end-stage renal disease, while steroid-sensitive NS (SSNS) and steroid-dependent (SDNS) patients do not. We have performed proteomic profiling of urine samples from a cross section of pediatric and adolescent subjects with SSNS, SRNS, and orthostatic proteinuria (OP) to identify urinary biomarkers of steroid resistance. We performed surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) on urine from 19 subjects with SSNS/SDNS in remission, 14 with SSNS/SDNS in relapse, 5 with SRNS in relapse, and 6 with OP. Genetic algorithm search of principal component space revealed a group of five peaks distinguishing SRNS subjects, with mass/charge (m/z) values of 3,917.07, 4,155.53, 6,329.68, 7,036.96, and 11,117.4. Our analyses identified the peak at m/z 11,117.4 with an accuracy of 95% for classifying SRNS. Multidimensional protein fractionation and mass spectrometric analysis of SRNS urine samples combined with immunodepletion identified the 11,117.4 protein as beta2-microglobulin (B2M). Using an unbiased protein profiling approach, we have validated previously reported findings of B2M as a biomarker associated with SRNS. Prospective studies are warranted to establish additional biomarkers that would be predictive of SRNS.