A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

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.

Discovery of novel candidate urinary protein biomarkers for prostate cancer in a multiethnic cohort of South African patients via label-free mass spectrometry

PURPOSE

Improvement in diagnostic accuracy of prostate cancer (PCa) progression using MS-based methods to analyze biomarkers in our African, Caucasian, and Mixed Ancestry patients can advance early detection and treatment monitoring.

EXPERIMENTAL DESIGN

MS-based proteomic analysis of pooled (N = 36) and individual samples (N = 45) of PCa, benign prostatic hyperplasia, normal healthy controls, and patients with other uropathies was used to identify differences in proteomics profile. Samples were analyzed for potential biomarkers and proteome coverage in African, Caucasian, and Mixed Ancestry PCa patients.

RESULTS

A total of 1102 and 5595 protein groups and nonredundant peptides, respectively, were identified in the pooling experiments (FDR = 0.01). Twenty potential biomarkers in PCa were identified and fold differences ± 2SD were observed in 17 proteins using intensity-based absolute quantification. Analysis of 45 individual samples yielded 1545 and 9991 protein groups and nonredundant peptides, respectively. Seventy-three (73) proteins groups, including existing putative PCa biomarkers, were found to be potential biomarkers of PCa by label-free quantification and demonstrated ethnic trends within our PCa cohort.

CONCLUSION AND CLINICAL RELEVANCE

Urinary proteomics is a promising route to PCa biomarker discovery and may serve as source of ethnic-related biomarkers of PCa.

Contextualised urinary biomarker analysis facilitates diagnosis of paediatric obstructive sleep apnoea

BACKGROUND

Intrinsic variance of the urine proteome limits the discriminative power of proteomic analysis and complicates potential biomarker detection in the context of paediatric sleep disorders.

METHODS AND RESULTS

Using a rigorous workflow for proteomic analysis of urine, we demonstrate that gender and diurnal effects constitute two important sources of variability in healthy children. In the context of disease, complex pathophysiological perturbations magnify these proteomic differences and therefore require contextualised biomarker analysis. Indeed, by performing biomarker discovery in a gender- and diurnal-dependent manner, we identified ∼30-fold more candidate biomarkers of paediatric obstructive sleep apnoea (OSA), a highly prevalent condition in children characterised by repetitive episodes of intermittent hypoxia and hypercapnia, and sleep fragmentation in the context of recurrent upper airway obstructive events during sleep. Remarkably, biomarkers were highly specific for gender and sampling time as poor overlap (∼3%) was observed in the proteins identified in boys and girls across morning and bedtime samples.

CONCLUSIONS

As no clinical basis to explain gender-specific effects in OSA or healthy children is apparent, we propose that implementation of contextualised biomarker strategies will be applicable to a broad range of human diseases, and may be specifically applicable to paediatric OSA.

A method for isolation and identification of urinary biomarkers in patients with diabetic nephropathy

PURPOSE

The poor performance of current tests for predicting the onset, progression and treatment response of diabetic nephropathy has engendered a search for more sensitive and specific urinary biomarkers. Our goal was to develop a new method for protein biomarker discovery in urine from these patients.

EXPERIMENTAL DESIGN

We analyzed urine from normal subjects and patients with early and advanced nephropathy. Proteins were separated using a novel analysis process including immunodepletion of high-abundance proteins followed by two-stage LC fractionation of low-abundance proteins. The proteins in the fractions were sequenced using MS/MS.

RESULTS

Immunodepletion of selected high-abundance proteins followed by two-stage LC produced approximately 700 fractions, each less complex and more amenable to analysis than the mixture and requiring minimal processing for MS identification. Comparison of fractions between normal and diabetic nephropathy subjects revealed several low-abundance proteins that reproducibly distinguished low glomerular filtration rate (GFR) from both high GFR diabetic and normal subjects, including uteroglobin, a protein previously associated with renal scarring.

CONCLUSIONS AND CLINICAL RELEVANCE

We developed a novel method to identify low-abundance urinary proteins that enables the discovery of potential biomarkers to improve the diagnosis and management of patients with diabetic nephropathy.

Multiplexed quantification of 63 proteins in human urine by multiple reaction monitoring-based mass spectrometry for discovery of potential bladder cancer biomarkers

Three common urological diseases are bladder cancer, urinary tract infection, and hematuria. Seventeen bladder cancer biomarkers were previously discovered using iTRAQ - these findings were verified by MRM-MS in this current study. Urine samples from 156 patients with hernia (n=57, control), bladder cancer (n=76), or urinary tract infection/hematuria (n=23) were collected and subjected to multiplexed LC-MRM/MS to determine the concentrations of 63 proteins that are normally considered to be plasma proteins, but which include proteins found in our earlier iTRAQ study. Sixty-five stable isotope-labeled standard proteotypic peptides were used as internal standards for 63 targeted proteins. Twelve proteins showed higher concentrations in the bladder cancer group than in the hernia and the urinary tract infection/hematuria groups, and thus represent potential urinary biomarkers for detection of bladder cancer. Prothrombin had the highest AUC (0.796), with 71.1% sensitivity and 75.0% specificity for differentiating bladder cancer (n=76) from non-cancerous (n=80) patients. The multiplexed MRM-MS data was used to generate a six-peptide marker panel. This six-peptide panel (afamin, adiponectin, complement C4 gamma chain, apolipoprotein A-II precursor, ceruloplasmin, and prothrombin) can discriminate bladder cancer subjects from non-cancerous subjects with an AUC of 0.814, with a 76.3% positive predictive value, and a 77.5% negative predictive value. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.

Shotgun proteomics identifies proteins specific for acute renal transplant rejection

PURPOSE

Acute rejection (AR) remains the primary risk factor for renal transplant outcome; development of non-invasive diagnostic biomarkers for AR is an unmet need.

EXPERIMENTAL DESIGN

We used shotgun proteomics applying LC-MS/MS and ELISA to analyze a set of 92 urine samples, from patients with AR, stable grafts (STA), proteinuria (NS), and healthy controls.

RESULTS

A total of 1446 urinary proteins (UP) were identified along with a number of nonspecific proteinuria-specific, renal transplantation specific and AR-specific proteins. Relative abundance of identified UP was measured by protein-level spectral counts adopting a weighted fold-change statistic, assigning increased weight for more frequently observed proteins. We have identified alterations in a number of specific UP in AR, primarily relating to MHC antigens, the complement cascade and extra-cellular matrix proteins. A subset of proteins (uromodulin, SERPINF1 and CD44), have been further cross-validated by ELISA in an independent set of urine samples, for significant differences in the abundance of these UP in AR.

CONCLUSIONS AND CLINICAL RELEVANCE

This label-free, semi-quantitative approach for sampling the urinary proteome in normal and disease states provides a robust and sensitive method for detection of UP for serial, non-invasive clinical monitoring for graft rejection after kidney transplantation.

Discovery of novel bladder cancer biomarkers by comparative urine proteomics using iTRAQ technology

A urine sample preparation workflow for the iTRAQ (isobaric tag for relative and absolute quantitation) technique was established. The reproducibility of this platform was evaluated and applied to discover proteins with differential levels between pooled urine samples from nontumor controls and three bladder cancer patient subgroups with different grades/stages (a total of 14 controls and 23 cancer cases in two multiplex iTRAQ runs). Combining the results of two independent clinical sample sets, a total of 638 urine proteins were identified. Among them, 55 proteins consistently showed >2-fold differences in both sample sets. Western blot analyses of individual urine samples confirmed that the levels of apolipoprotein A-I (APOA1), apolipoprotein A-II, heparin cofactor 2 precursor and peroxiredoxin-2 were significantly elevated in bladder cancer urine specimens (n = 25-74). Finally, we quantified APOA1 in a number of urine samples using a commercial ELISA and confirmed again its potential value for diagnosis (n = 126, 94.6% sensitivity and 92.0% specificity at a cutoff value of 11.16 ng/mL) and early detection (n = 71, 83.8% sensitivity and 94.0% specificity). Collectively, our results provide the first iTRAQ-based quantitative profile of bladder cancer urine proteins and represent a valuable resource for the discovery of bladder cancer markers.