Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

Purpose of review: Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years. Recent findings: Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, [alpha]-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression. Summary: Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making

Urinary biomarkers for renal tract malformations

Introduction: Renal tract malformations (RTMs) are congenital anomalies of the kidneys and urinary tract, which are the major cause of end-stage renal disease in children. Using immunoassay-based approaches (ELISA, western blot), individual urinary proteins including transforming growth factor β, tumor necrosis factor and monocyte attractant proteins 1 were found to be associated to RTMs. However, only mass spectrometry (MS) based methods leading to the identification of panels of protein-based markers composed of fragments of the extracellular matrix allowed the prediction of progression of RTMs and its complications. Areas covered: In this review, we summarized relevant studies identified in “Pubmed” using the keywords “urinary biomarkers” and “proteomics” and “renal tract malformations” or “hydronephrosis” or “ureteropelvic junction obstruction” or “posterior urethral valves” or “vesicoureteral reflux”. These publications represent studies on potential protein-based biomarkers, either individually or combined in panels, of RTMs in human and animal models. Expert commentary: Successful use in the clinic of these protein-based biomarkers will need to involve larger scale studies to reach sufficient power. Improved performance will potentially come from combining immunoassay- and MS-based markers

Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria

<p>Abstract</p> <p>Background</p> <p>Previously the angiotensin II receptor blocker Irbesartan has been demonstrated to reduce the risk for progression from microalbuminuria to macroalbuminuria in type 2 diabetic patients. The purpose of this study was to evaluate the effect of treatment with Irbesartan in type 2 diabetic patients with microalbuminuria on the urinary proteome.</p> <p>Methods</p> <p>High-resolution capillary-electrophoresis coupled to mass-spectrometry (CE-MS) was used to profile the low-molecular-weight proteome in urine of a subgroup of patients from a two year randomized irbesartan versus placebo therapy trial, which included hypertensive type 2 diabetic patients with microalbuminuria on ongoing antihypertensive medication (IRMA2-substudy).</p> <p>Results</p> <p>We demonstrate that the therapy with 300 mg Irbesartan daily over a period of two years results in significant changes of the urinary proteome. Both, a classifier developed previously that consists of urinary peptides indicative of chronic kidney disease, as well as several individual peptides changed significantly after treatment. These changes were not observed in the placebo-treated individuals. Most prominent are changes of urinary collagen fragments associated with progression of diabetic nephropathy, indicating normalization in urinary peptides.</p> <p>Conclusion</p> <p>CE-MS analysis of urine enabled identification of peptides as potential surrogate markers for renoprotection in microalbuminuric type 2 diabetic patients, which show persistent improvement after longterm treatment with Irbesartan. The results suggest that a major benefit of treatment by Irbesartan may be improvement of collagen turnover, reduction of fibrosis. They further suggest that urinary proteome analysis could be utilized to assess potential benefit of therapeutic intervention, providing statistically significant results even on a small population.</p

Left ventricular diastolic function in relation to the urinary proteome: a proof-of-concept study in a general population

Background: In previous studies, we identified two urinary proteomic classifiers, termed HF1 and HF2, which discriminated subclinical diastolic left ventricular (LV) dysfunction from normal. HF1 and HF2 combine information from 85 and 671 urinary peptides, mainly up- or down-regulated collagen fragments. We sought to validate these classifiers in a population study. Methods: In 745 people randomly recruited from a Flemish population (49.8 years; 51.3% women), we measured early and late diastolic peak velocities of mitral inflow (E and A) and mitral annular velocities (e' and a') by conventional and tissue Doppler echocardiography, and the urinary proteome by capillary electrophoresis coupled with mass spectrometry. Results: In the analyses adjusted for sex, age, body mass index, blood pressure, heart rate, LV mass index and intake of medications, we expressed effect sizes per 1-SD increment in the classifiers. HF1 was associated with 0.204 cm/s lower e' peak velocity (95% confidence interval, 0.057–0.351; p = 0.007) and 0.145 higher E/e' ratio (0.023–0.268; p = 0.020), while HF2 was associated with a 0.174 higher E/e' ratio (0.046–0.302; p = 0.008). According to published definitions, 67 (9.0%) participants had impaired LV relaxation and 96 (12.9%) had elevated LV filling pressure. The odds of impaired relaxation associated with HF1 was 1.38 (1.01–1.88; p = 0.043) and that of increased LV filling pressure associated with HF2 was 1.38 (1.00–1.90; p = 0.052). Conclusions: In a general population, the urinary proteome correlated with diastolic LV dysfunction, proving its utility for early diagnosis of this condition

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = −0.8031; p&#60;0.0001 and ρ = −0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = −0.6557; p = 0.0001 and ρ = −0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = −0.7752; p&#60;0.0001 and ρ = −0.8400; p&#60;0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data

Urinary peptidomics analysis reveals proteases involved in diabetic nephropathy

Mechanisms underlying the onset and progression of nephropathy in diabetic patients are not fully elucidated. Deregulation of proteolytic systems is a known path leading to disease manifestation, therefore we hypothesized that proteases aberrantly expressed in diabetic nephropathy (DN) may be involved in the generation of DN-associated peptides in urine. We compared urinary peptide profiles of DN patients (macroalbuminuric, n = 121) to diabetic patients with no evidence of DN (normoalbuminuric, n = 118). 302 sequenced, differentially expressed peptides (adjusted p-value &lt; 0.05) were analysed with the Proteasix tool predicting proteases potentially involved in their generation. Activity change was estimated based on the change in abundance of the investigated peptides. Predictions were correlated with transcriptomics (Nephroseq) and relevant protein expression data from the literature. This analysis yielded seventeen proteases, including multiple forms of MMPs, cathepsin D and K, kallikrein 4 and proprotein convertases. The activity of MMP-2 and MMP-9, predicted to be decreased in DN, was investigated using zymography in a DN mouse model confirming the predictions. Collectively, this proof-of-concept study links urine peptidomics to molecular changes at the tissue level, building hypotheses for further investigation in DN and providing a workflow with potential applications to other diseases

Proteomic evidence of biological aging in a child with a compound heterozygous ZMPSTE24 mutation

Background: Progeria-like syndromes offer a unique insight into ageing. Here we present the case of a boy affected with mandibuloacral dysplasia and compound heterozygous mutations in ZMPSTE24. Methods: Capillary electrophoresis-mass spectroscopy was used for proteome analysis to analyse peptides previously found to be differentially regulated in chronic kidney disease (273 peptides defining the CKD273 classifier), coronary artery disease (238 peptides defining the CAD238 classifier) and ageing (116 peptides defining the AGE116 classifier). Results: No evidence of renal disease was identified. Although the boy has no overt cardiovascular disease other than a raised carotid intima media thickness relative to his age, a proteomic classifier for the diagnosis of coronary artery disease was mildly raised. The biological age based on the proteomic AGE116 classifier was 24 years compared to the chronological ages of 5 and 10 years. In contrast, a control group of healthy children had a significantly lower (p&lt;0.0001) calculated mean age of 13. Conclusion: Urinary proteomic analysis is effective in confirming advanced biological age and to identify early evidence of renal or cardiovascular damage. Our case highlights the value of proteomic approaches in ageing research and may represent a method for non-invasive monitoring of the effects of early ageing

The use of urinary proteomics in the assessment of suitability of mouse models for ageing

Ageing is a complex process characterised by a systemic and progressive deterioration of biological functions. As ageing is associated with an increased prevalence of age-related chronic disorders, understanding its underlying molecular mechanisms can pave the way for therapeutic interventions and managing complications. Animal models such as mice are commonly used in ageing research as they have a shorter lifespan in comparison to humans and are also genetically close to humans. To assess the translatability of mouse ageing to human ageing, the urinary proteome in 89 wild-type (C57BL/6) mice aged between 8–96 weeks was investigated using capillary electrophoresis coupled to mass spectrometry (CE-MS). Using age as a continuous variable, 295 peptides significantly correlated with age in mice were identified. To investigate the relevance of using mouse models in human ageing studies, a comparison was performed with a previous correlation analysis using 1227 healthy subjects. In mice and humans, a decrease in urinary excretion of fibrillar collagens and an increase of uromodulin fragments was observed with advanced age. Of the 295 peptides correlating with age, 49 had a strong homology to the respective human age-related peptides. These ortholog peptides including several collagen (N = 44) and uromodulin (N = 5) fragments were used to generate an ageing classifier that was able to discriminate the age among both wild-type mice and healthy subjects. Additionally, the ageing classifier depicted that telomerase knock-out mice were older than their chronological age. Hence, with a focus on ortholog urinary peptides mouse ageing can be translated to human ageing

Peptide Fingerprinting of Alzheimer's Disease in Cerebrospinal Fluid: Identification and Prospective Evaluation of New Synaptic Biomarkers

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; Today, dementias are diagnosed late in the course of disease. Future treatments have to start earlier in the disease process to avoid disability requiring new diagnostic tools. The objective of this study is to develop a new method for the differential diagnosis and identification of new biomarkers of Alzheimer's disease (AD) using capillary-electrophoresis coupled to mass-spectrometry (CE-MS) and to assess the potential of early diagnosis of AD.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods and Findings:&lt;/b&gt; Cerebrospinal fluid (CSF) of 159 out-patients of a memory-clinic at a University Hospital suffering from neurodegenerative disorders and 17 cognitively-healthy controls was used to create differential peptide pattern for dementias and prospective blinded-comparison of sensitivity and specificity for AD diagnosis against the Criterion standard in a naturalistic prospective sample of patients. Sensitivity and specificity of the new method compared to standard diagnostic procedures and identification of new putative biomarkers for AD was the main outcome measure. CE-MS was used to reliably detect 1104 low-molecular-weight peptides in CSF. Training-sets of patients with clinically secured sporadic Alzheimer's disease, frontotemporal dementia, and cognitively healthy controls allowed establishing discriminative biomarker pattern for diagnosis of AD. This pattern was already detectable in patients with mild cognitive impairment (MCI). The AD-pattern was tested in a prospective sample of patients (n = 100) and AD was diagnosed with a sensitivity of 87% and a specificity of 83%. Using CSF measurements of beta-amyloid1-42, total-tau, and phospho(181)-tau, AD-diagnosis had a sensitivity of 88% and a specificity of 67% in the same sample. Sequence analysis of the discriminating biomarkers identified fragments of synaptic proteins like proSAAS, apolipoprotein J, neurosecretory protein VGF, phospholemman, and chromogranin A.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; The method may allow early differential diagnosis of various dementias using specific peptide fingerprints and identification of incipient AD in patients suffering from MCI. Identified biomarkers facilitate face validity for the use in AD diagnosis.&lt;/p&gt