Haploinsufficiency of the mouse Tshz3 gene leads to kidney defects.

Renal tract defects and autism spectrum disorder (ASD) deficits represent the phenotypic core of the 19q12 deletion syndrome caused by the loss of one copy of the TSHZ3 gene. Although a proportion of Tshz3 heterozygous (Tshz3+/lacZ) mice display ureteral defects, no kidney defects have been reported in these mice. The purpose of this study was to characterize the expression of Tshz3 in adult kidney as well as the renal consequences of embryonic haploinsufficiency of Tshz3 by analyzing the morphology and function of Tshz3 heterozygous adult kidney. Here, we described Tshz3 expression in the smooth muscle and stromal cells lining the renal pelvis, the papilla and glomerular endothelial cells (GEnCs) of the adult kidney as well as in the proximal nephron tubules in neonatal mice. Histological analysis showed that Tshz3+/lacZ adult kidney had an average of 29% fewer glomeruli than wild-type kidney. Transmission electron microscopy of Tshz3+/lacZ glomeruli revealed a reduced thickness of the glomerular basement membrane and a larger foot process width. Compared to wild type, Tshz3+/lacZ mice showed lower blood urea, phosphates, magnesium and potassium at 2 months of age. At the molecular level, transcriptome analysis identified differentially expressed genes related to inflammatory processes in Tshz3+/lacZ compare to wild-type (control) adult kidneys. Lastly, analysis of the urinary peptidome revealed 33 peptides associated with Tshz3+/lacZ adult mice. These results provide the first evidence that in the mouse Tshz3 haploinsufficiency leads to cellular, molecular and functional abnormalities in the adult mouse kidney.

Amniotic fluid peptides predict postnatal kidney survival in developmental kidney disease.

Although a rare disease, bilateral congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of end stage kidney disease in children. Ultrasound-based prenatal prediction of postnatal kidney survival in CAKUT pregnancies is far from accurate. To improve prediction, we conducted a prospective multicenter peptidome analysis of amniotic fluid spanning 140 evaluable fetuses with CAKUT. We identified a signature of 98 endogenous amniotic fluid peptides, mainly composed of fragments from extracellular matrix proteins and from the actin binding protein thymosin-ß4. The peptide signature predicted postnatal kidney outcome with an area under the curve of 0.96 in the holdout validation set of patients with CAKUT with definite endpoint data. Additionally, this peptide signature was validated in a geographically independent sub-cohort of 12 patients (area under the curve 1.00) and displayed high specificity in non-CAKUT pregnancies (82 and 94% in 22 healthy fetuses and in 47 fetuses with congenital cytomegalovirus infection respectively). Change in amniotic fluid thymosin-ß4 abundance was confirmed with ELISA. Knockout of thymosin-ß4 in zebrafish altered proximal and distal tubule pronephros growth suggesting a possible role of thymosin ß4 in fetal kidney development. Thus, recognition of the 98-peptide signature in amniotic fluid during diagnostic workup of prenatally detected fetuses with CAKUT can provide a long-sought evidence base for accurate management of the CAKUT disorder that is currently unavailable.

Proteomics and personalized medicine: a focus on kidney disease.

Introduction: Inter-individual variability in response to drug treatment has induced an increased demand for decisions via personalize medicine. Also, the contribution of proteomics to the era of personalized medicine would seem to be vital in improving therapeutic outcomes. Areas covered: We review validated biomarkers discovered by proteomics techniques and their use in personalized medicine with the focus on kidney diseases. We discuss this topic with a special emphasis on recent publications and relevant initiatives and depict some limitations that remain for personalized medicine. Expert opinion: The development of highly accurate biomarkers is essential for optimizing the management of kidney diseases. Various biomarkers of kidney diseases have been identified using proteomic techniques. However, only a few of these biomarkers showed the potential to be used in clinical practice concerning personalized medicine. Therefore, it becomes evident that the combination of multiple biomarkers confers higher accuracy and the ability to depict complex pathophysiological conditions, a prerequisite for personalized treatment. CKD273, a multimarker panel for early CKD detection may serve as a first example for personalized medicine in nephrology. Based on this successful example, proteomics is expected to develop into the key technology to guide personalized intervention.

Urinary proteomics to diagnose chronic active antibody-mediated rejection in pediatric kidney transplantation - a pilot study.

Chronic antibody-mediated rejection (cABMR) is the main cause of long-term renal graft loss. Late-stage diagnosis is made by detecting donor-specific antibodies (DSA) in blood combined with typical histomorphological lesions in renal allografts. There is a need for noninvasive biomarkers for cABMR that might permit screening and earlier diagnosis. In a case control study of 24 pediatric renal transplant recipients, urine samples were analyzed using capillary electrophoresis and mass spectrometry. Patients were matched with 36 pediatric renal transplant patients without cABMR. Statistical analysis used the nonparametric Wilcoxon test to identify 79 significant biomarkers, which were combined to a support vector machine-based classifier. After validation in an independent test cohort of eight pediatric patients with and 12 without cABMR, the area under the receiver operating characteristic (ROC) curve (AUC) for detection of cABMR was 0.92 (95% CI 0.71-0.99) with a sensitivity of 100% (95% CI 63-100%) and a specificity of 75% (95% CI 43-95%). Combining this classifier with the urinary proteomic marker CKD273 improved the detection of patients with cABMR with misclassification in only 2/20 of the patients. These data indicate that a biomarker pattern derived from urinary proteomics allows the detection of cABMR in pediatric renal transplant recipients with high sensitivity and moderate specificity.

Predicting albuminuria response to spironolactone treatment with urinary proteomics in patients with type 2 diabetes and hypertension.

Background: The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in patients with diabetes. Prior studies have shown large between-patient variability in albuminuria treatment response. We previously developed and validated a urinary proteomic classifier that predicts onset and progression of chronic kidney disease. Here, we tested whether the proteomic classifier based on 273 urinary peptides (CKD273) predicts albuminuria response to spironolactone treatment. Methods: We performed a post hoc analysis in a double-blind randomized clinical trial with allocation to either spironolactone 12.5-50 mg/day (n = 57) or placebo (n = 54) for 16 weeks. Patients were diagnosed with type 2 diabetes and resistant hypertension. Treatment was an adjunct to renin-angiotensin system inhibition. Primary endpoint was the percentage change in urine albumin to creatinine ratio (UACR). Capillary electrophoresis mass spectrometry was used to quantify urinary peptides at baseline. The previously validated combination of 273 known urinary peptides was used as proteomic classifier. Results: Spironolactone reduced UACR relative to placebo by 50%, although with a large between-patient variability in UACR response (5th to 95th percentile, 7 to 312%). An interaction was detected between CKD273 and treatment assignment (ß = -1.09, P = 0.026). Higher values of CKD273 at baseline were associated with a larger reduction in UACR in the spironolactone group (ß = -0.70, P = 0.049), but not in the placebo group (ß = 0.39, P = 0.25). Stratified in tertiles of baseline CKD273, reduction in UACR was greater in the highest tertile, 63% (95% confidence interval: 35-79%), as compared with the two other tertiles combined, 16% (-17 to 40%) (P = 0.011). Conclusions: A urinary proteomics classifier can be used to identify individuals with type 2 diabetes who are more likely to show an albuminuria-lowering response to spironolactone treatment. These results suggest that urinary proteomics may be a valuable tool to tailor therapy, but confirmation in a larger clinical trial is required.

Urinary peptide biomarker panel associated with an improvement in estimated glomerular filtration rate in chronic kidney disease patients.

Background: An improvement in the glomerular filtration rate (GFR) of chronic kidney disease patients has been an underestimated clinical outcome. Although this may be considered as an unexpected disease course, it may provide some insights into possible mechanisms underlying disease remission and/or regression. Therefore, our aim was to identify urinary peptide biomarkers associated with an improvement in estimated GFR (eGFR) over time and to improve patient stratification. Methods: Capillary electrophoresis coupled with mass spectrometry (CE-MS) was employed to evaluate the urine peptidome of patients with different types of renal diseases. In total, 376 patients with a slope/year between -1.5% and +1.5% were designated as non-progressors or stable, while 177 patients with a > 5% slope/year were designated as patients with an improved eGFR for state-of-art biomarker discovery and validation. Results: We detected 384 significant peptide fragments by comparing the CE-MS data of the stable patients and those with improved renal function in our development cohort. Of these 384, a set of 141 peptides with available amino acid sequence information were used to generate a support vector machine-based classification panel. The biomarker panel was applied to our validation cohort, achieving a moderate area under the curve (AUC) value of 0.85 (81% sensitivity and 81% specificity). The majority of the peptides (78%) from the diagnostic panel arose from different types of collagen. Conclusions: We have developed a panel of urinary peptide markers able to discriminate those patients predisposed to improve their kidney function over time and possibly be treated with more specific or less aggressive therapy.

A urinary proteome-based classifier for the early detection of decline in glomerular filtration.

BACKGROUND: Chronic kidney disease (CKD) progression is currently assessed by a decline in estimated glomerular filtration rate (eGFR) and/or an increase in urinary albumin excretion (UAE). However, these markers are considered either to be late-stage markers or to have low sensitivity or specificity. In this study, we investigated the performance of the urinary proteome-based classifier CKD273, compared with UAE, in a number of different narrow ranges of CKD severity, with each range separated by an eGFR of 10 mL/min/1.73 m 2 . METHODS: A total of 2672 patients with different CKD stages were included in the study. Of these, 394 individuals displayed a decline in eGFR of >5 mL/min/1.73 m 2 /year (progressors) and the remaining individuals were considered non-progressors. For all samples, UAE values and CKD273 classification scores were obtained. To assess UAE values and CKD273 scores at different disease stages, the cohort was divided according to baseline eGFRs of ≥80, 70-79, 60-69, 50-59, 40-49, 30-39 and <29 mL/min/1.73 m 2 . In addition, areas under the curve for CKD273 and UAE were calculated. RESULTS: In early stage CKD, the urinary proteome-based classifier performed significantly better than UAE in detecting progressors. In contrast, UAE performed better in patients with late-stage CKD. No significant difference in performance was found between CKD273 and UAE in patients with moderately reduced renal function. CONCLUSIONS: These results suggest that urinary peptides, as combined in the CKD273 classifier, allow the detection of progressive CKD at early stages, a point where therapeutic intervention is more likely to be effective. However, late-stage disease, where irreversible damage of the kidney is already present, is better detected by UAE.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis.

BACKGROUND: In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease. METHODS: We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers. RESULTS: For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology. CONCLUSIONS: Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

Urinary proteomics predict onset of microalbuminuria in normoalbuminuric type 2 diabetic patients, a sub-study of the DIRECT-Protect 2 study.

BACKGROUND: Early prevention of diabetic nephropathy is not successful as early interventions have shown conflicting results, partly because of a lack of early and precise indicators of disease development. Urinary proteomics has shown promise in this regard and could identify those at high risk who might benefit from treatment. In this study we investigate its utility in a large type 2 diabetic cohort with normoalbuminuria. METHODS: We performed a post hoc analysis in the Diabetic Retinopathy Candesartan Trials (DIRECT-Protect 2 study), a multi centric randomized clinical controlled trial. Patients were allocated to candesartan or placebo, with the aim of slowing the progression of retinopathy. The secondary endpoint was development of persistent microalbuminuria (three of four samples). We used a previously defined chronic kidney disease risk score based on proteomic measurement of 273 urinary peptides (CKD273-classifier). A Cox regression model for the progression of albuminuria was developed and evaluated with integrated discrimination improvement (IDI), continuous net reclassification index (cNRI) and receiver operating characteristic curve statistics. RESULTS: Seven hundred and thirty-seven patients were analysed and 89 developed persistent microalbuminuria (12%) with a mean follow-up of 4.1 years. At baseline the CKD273-classifier predicted development of microalbuminuria during follow-up, independent of treatment (candesartan/placebo), age, gender, systolic blood pressure, urine albumin excretion rate, estimated glomerular filtration rate, HbA1c and diabetes duration, with hazard ratio 2.5 [95% confidence interval (CI) 1.4-4.3; P = 0.002] and area under the curve 0.79 (95% CI 0.75-0.84; P < 0.0001). The CKD273-classifier improved the risk prediction (relative IDI 14%, P = 0.002; cNRI 0.10, P = 0.043). CONCLUSIONS: In this cohort of patients with type 2 diabetes and normoalbuminuria from a large intervention study, the CKD273-classifier was an independent predictor of microalbuminuria. This may help identify high-risk normoalbuminuric patients for preventive strategies for diabetic nephropathy.

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 ß, 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 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, α-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.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Urine as a source for clinical proteome analysis: from discovery to clinical application.

The success of clinical proteome analysis should be assessed based on the clinical impact following implementation of findings. Although there have been several technological advancements in mass spectrometry in the last years, these have not resulted in similar advancements in clinical proteomics. In addition, application of proteomic biomarkers in clinical diagnostics and practical improvement in the disease management is extremely rare. In this review, we discuss the relevant issues associated with identification of robust biomarkers of clinical value. Urine appears to be an ideal source of biomarkers, for theoretical, methodological, and practical reasons. Therefore, this review is focused on the search for biomarkers in urine within the last decade. Urine can be used for non-invasive assessment of a variety of diseases including those affecting the urogenital tract and also other pathologies such as cardiovascular disease or appendicitis. We also discuss the importance of data validation, an essential step in translating biomarkers into the clinical practice. Furthermore, we examine several examples of apparently successful proteomic biomarker discovery studies and their implications for disease diagnosis, prognosis, and therapy evaluation. We also discuss some current challenges in this field and reflect on future research prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria.

BACKGROUND: The contrast between a high prevalence of chronic kidney disease (CKD) and the low incidence of end-stage renal disease highlights the need for new biomarkers of progression beyond albuminuria testing. Urinary proteomics is a promising method, but more studies focusing on progression rate and patients with hypertensive nephropathy are needed. RESULTS: We analyzed urine samples with capillary electrophoresis coupled to a mass-spectrometer from 18 well characterized patients with CKD stage 4-5 (of whom six with hypertensive nephropathy) and 17 healthy controls. Classification scores based on a previously developed panel of 273 urinary peptides were calculated and compared to urine albumin dipstick results. Urinary proteomics classified CKD with a sensitivity of 0.95 and specificity of 1.00. Overall diagnostic accuracy (area under ROC curve) was 0.98, which was better than for albuminuria (0.85, p = 0.02). Results for hypertensive nephropathy were similar to other CKD diagnoses. Adding the proteomic score to an albuminuria model improved detection of rapid kidney function decline (>4 ml/min/1.73 m(2) per year) substantially: area under ROC curve increased from 0.762 to 0.909 (p = 0.042), and 38% of rapid progressors were correctly reclassified to a higher risk and 55% of slow progressors were correctly reclassified to a lower risk category. Reduced excretion of collagen types I-III, uromodulin, and other indicators of interstitial inflammation, fibrosis and tubular dysfunction were associated with CKD diagnosis and rapid progression. Patients with hypertensive nephropathy displayed the same findings as other types of CKD. CONCLUSIONS: Urinary proteomic analyses had a high diagnostic accuracy for CKD, including hypertensive nephropathy, and strongly improved identification of patients with rapid kidney function decline beyond albuminuria testing.

The urinary proteome as correlate and predictor of renal function in a population study.

BACKGROUND: We investigate whether the urinary proteome refines the diagnosis of renal dysfunction, which affects over 10% of the adult population. METHODS: We measured serum creatinine, estimated glomerular filtration rate (eGFR) and 24-h albuminuria in 797 people randomly recruited from a population. We applied capillary electrophoresis coupled with mass spectrometry to measure multi-dimensional urinary proteomic classifiers developed for renal dysfunction (CKD273) or left ventricular dysfunction (HF1 and HF2). Renal function was followed up in 621 participants and the incidence of cardiovascular events in the whole study population. RESULTS: In multivariable-adjusted cross-sectional analyses, higher biomarker levels analysed separately or combined by principal component analysis into a single factor (SF), correlated (P ≤ 0.010) with worse renal function. Over 4.8 years, higher HF1 and SF predicted (P ≤ 0.014) lowering of eGFR; higher HF2 predicted (P ≤ 0.049) increase in serum creatinine and decrease eGFR. HF1, HF2 and SF predicted progression from CKD Stages 2 or ≤2 to Stage ≥3, with risk estimates for a 1-SD increment in the urinary biomarkers ranging from 38 to 71% (P ≤ 0.039). HF1, HF2 and SF yielded a net reclassification improvement of 31-51% (P ≤ 0.029). Over 6.1 years, 47 cardiovascular events occurred. HF2 and SF, independent of baseline eGFR, 24-h albuminuria and other covariables were significant predictors of cardiovascular complications with risk estimates for 1-SD increases ranging from 32 to 41% (P ≤ 0.047). CONCLUSIONS: The urinary proteome refines the diagnosis of existing or progressing renal dysfunction and predicts cardiovascular complications.

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.

Urine proteome analysis reflects atherosclerotic disease in an ApoE-/- mouse model and allows the discovery of new candidate biomarkers in mouse and human atherosclerosis.

Noninvasive diagnosis of atherosclerosis via single biomarkers has been attempted but remains elusive. However, a previous polymarker or pattern approach of urine polypeptides in humans reflected coronary artery disease with high accuracy. The aim of the current study is to use urine proteomics in ApoE(-/-) mice to discover proteins with pathophysiological roles in atherogenesis and to identify urinary polypeptide patterns reflecting early stages of atherosclerosis. Urine of ApoE(-/-) mice either on high fat diet (HFD) or chow diet was collected over 12 weeks; urine of wild type mice on HFD was used to exclude diet-related proteome changes. Capillary electrophoresis coupled to mass spectrometry (CE-MS) of samples identified 16 polypeptides specific for ApoE(-/-) mice on HFD. In a blinded test set, these polypeptides allowed identification of atherosclerosis at a sensitivity of 90% and specificity of 100%, as well as monitoring of disease progression. Sequencing of the discovered polypeptides identified fragments of α(1)-antitrypsin, epidermal growth factor (EGF), kidney androgen-regulated protein, and collagen. Using immunohistochemistry, α(1)-antitrypsin, EGF, and collagen type I were shown to be highly expressed in atherosclerotic plaques of ApoE(-/-) mice on HFD. Urinary excretion levels of collagen and α(1)-antitrypsin fragments also significantly correlated with intraplaque collagen and α(1)-antitrypsin content, mirroring plaque protein expression in the urine proteome. To provide further confirmation that the newly identified proteins are relevant in humans, the presence of collagen type I, α(1)-antitrypsin, and EGF was also confirmed in human atherosclerotic disease. Urine proteome analysis in mice exemplifies the potential of a novel multimarker approach for the noninvasive detection of atherosclerosis and monitoring of disease progression. Furthermore, this approach represents a novel discovery tool for the identification of proteins relevant in murine and human atherosclerosis and thus also defines potential novel therapeutic targets.

Small peptide CSF fingerprint of amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by abnormal protein aggregation in the motor neurons. Present and earlier proteomic studies to characterize peptides in cerebrospinal fluid (CSF) associated with motoneuron pathology did not target low molecular weight proteins and peptides. We hypothesized that specific changes in CSF peptides or low molecular weight proteins are significantly altered in ALS, and that these changes may support deciphering molecular pathophysiology and even guide approaches towards therapeutic interventions. METHODS: Cerebrospinal fluid (CSF) from 50 ALS patients and 50 non-ALS controls was collected, centrifuged immediately after collection, aliquoted into polypropylene test tubes, frozen within 30-40 min after the puncture, and stored at -80°C until use. Peptides were sequenced using capillary electrophoresis or liquid chromatography/mass spectrometry (CE-MS/MS or LC-MS/MS). FINDINGS: In the CSF of 50 patients and 50 non-ALS controls 33 peptides were found, of which 14 could be sequenced using a non-lytic single-pot proteomic detection method, CE/MS. ALS deregulated peptides vs. controls included Integral membrane protein 2B, Neurosecretory protein VGF, Osteopontin, Neuroendocrine protein 7B2 (Secretogranin-V), EGF-containing fibulin-like extracellular matrix protein 1, Xylosyltransferase 1 XT-1, Chromogranin-A, Superoxide dismutase SOD-1, Secretogranin-1 (Chromogranin B), NR2F2 Nuclear Receptor Subfamily 2 Group F Member 2 and Collagen alpha-1(VII) chain. INTERPRETATION: Most striking deregulations in CSF from ALS patients were found in VGF, Osteopontin, SOD-1 and EFEMP1 peptides. No associations of disease severity, duration and region of onset with sequenced peptides were found.

Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation.

In this study, we have developed Proteasix, an open-source peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. We developed a curated cleavage site (CS) database, containing 3500 entries about human protease/CS combinations. On top of this database, we built a tool, Proteasix, which allows CS retrieval and protease associations from a list of peptides. To establish the proof of concept of the approach, we used a list of 1388 peptides identified from human urine samples, and compared the prediction to the analysis of 1003 randomly generated amino acid sequences. Metalloprotease activity was predominantly involved in urinary peptide generation, and more particularly to peptides associated with extracellular matrix remodelling, compared to proteins from other origins. In comparison, random sequences returned almost no results, highlighting the specificity of the prediction. This study provides a tool that can facilitate linking of identified protein fragments to predicted protease activity, and therefore into presumed mechanisms of disease. Experiments are needed to confirm the in silico hypotheses; nevertheless, this approach may be of great help to better understand molecular mechanisms of disease, and define new biomarkers, and therapeutic targets.

IMAC fractionation in combination with LC-MS reveals H2B and NIF-1 peptides as potential bladder cancer biomarkers.

Improvement in bladder cancer (BC) management requires more effective diagnosis and prognosis of disease recurrence and progression. Urinary biomarkers attract special interest because of the noninvasive means of urine collection. Proteomic analysis of urine entails the adoption of a fractionation methodology to reduce sample complexity. In this study, we applied immobilized metal affinity chromatography in combination with high-resolution LC-MS/MS for the discovery of native urinary peptides potentially associated with BC aggressiveness. This approach was employed toward urine samples from patients with invasive BC, noninvasive BC, and benign urogenital diseases. A total of 1845 peptides were identified, corresponding to a total of 638 precursor proteins. Specific enrichment for proteins involved in nucleosome assembly and for zinc-finger transcription factors was observed. The differential expression of two candidate biomarkers, histone H2B and NIF-1 (zinc finger 335) in BC, was verified in independent sets of urine samples by ELISA and by immunohistochemical analysis of BC tissue. The results collectively support changes in the expression of both of these proteins with tumor progression, suggesting their potential role as markers for discriminating BC stages. In addition, the data indicate a possible involvement of NIF-1 in BC progression, likely as a suppressor and through interactions with Sox9 and HoxA1.