Urinary peptide panel for prognostic assessment of bladder cancer relapse.

Non-invasive tools stratifying bladder cancer (BC) patients according to the risk of relapse are urgently needed to guide clinical intervention. As a follow-up to the previously published study on CE-MS-based urinary biomarkers for BC detection and recurrence monitoring, we expanded the investigation towards BC patients with longitudinal data. Profiling datasets of BC patients with follow-up information regarding the relapse status were investigated. The peptidomics dataset (n = 98) was split into training and test set. Cox regression was utilized for feature selection in the training set. Investigation of the entire training set at the single peptide level revealed 36 peptides being strong independent prognostic markers of disease relapse. Those features were further integrated into a Random Forest-based model evaluating the risk of relapse for BC patients. Performance of the model was assessed in the test cohort, showing high significance in BC relapse prognosis [HR = 5.76, p-value = 0.0001, c-index = 0.64]. Urinary peptide profiles integrated into a prognostic model allow for quantitative risk assessment of BC relapse highlighting the need for its incorporation in prospective studies to establish its value in the clinical management of BC.

Urinary proteome signature of Renal Cysts and Diabetes syndrome in children.

Renal Cysts and Diabetes Syndrome (RCAD) is an autosomal dominant disorder caused by mutations in the HNF1B gene encoding for the transcriptional factor hepatocyte nuclear factor-1B. RCAD is characterized as a multi-organ disease, with a broad spectrum of symptoms including kidney abnormalities (renal cysts, renal hypodysplasia, single kidney, horseshoe kidneys, hydronephrosis), early-onset diabetes mellitus, abnormal liver function, pancreatic hypoplasia and genital tract malformations. In the present study, using capillary electrophoresis coupled to mass spectrometry (CE-MS), we investigated the urinary proteome of a pediatric cohort of RCAD patients and different controls to identify peptide biomarkers and obtain further insights into the pathophysiology of this disorder. As a result, 146 peptides were found to be associated with RCAD in 22 pediatric patients when compared to 22 healthy age-matched controls. A classifier based on these peptides was generated and further tested on an independent cohort, clearly discriminating RCAD patients from different groups of controls. This study demonstrates that the urinary proteome of pediatric RCAD patients differs from autosomal dominant polycystic kidney disease (PKD1, PKD2), congenital nephrotic syndrome (NPHS1, NPHS2, NPHS4, NPHS9) as well as from chronic kidney disease conditions, suggesting differences between the pathophysiology behind these disorders.

Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers.

PURPOSE: Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic. EXPERIMENTAL DESIGN: The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma. RESULTS: A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects. CONCLUSIONS AND CLINICAL RELEVANCE: Through the established glycoproteomics workflow, intact O- and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

Comparison of Urine and Plasma Peptidome Indicates Selectivity in Renal Peptide Handling.

PURPOSE: Urine is considered to be produced predominantly as a result of plasma filtration in the kidney. However, the origin of the native peptides present in urine has never been investigated in detail. Therefore, the authors aimed to obtain a first insight into the origin of urinary peptides based on a side-by-side comprehensive analysis of the plasma and urine peptidome. METHODS: Twenty-two matched urine and plasma samples are analyzed for their peptidome using capillary electrophoresis coupled to mass spectrometry (CE-MS; for relative quantification) and CE or LC coupled to tandem mass spectrometry (CE- or LC-MS/MS; for peptide identification). The overlap and association of abundance of the different peptides present in these two body fluids are evaluated. RESULTS: The authors are able to identify 561 plasma and 1461 urinary endogenous peptides. Only 90 peptides are detectable in both urine and plasma. No significant correlation is found when comparing the abundance of these common peptides, with the exception of collagen fragments. This observation is also supported when comparing published peptidome data from both plasma and urine. CONCLUSIONS AND CLINICAL RELEVANCE: Most of the plasma peptides are not detectable in urine, possibly due to tubular reabsorption. The majority of urinary peptides may in fact originate in the kidney. The notable exception is collagen fragments, which indicates potential selective exclusion of these peptides from tubular reabsorption. Experimental verification of this hypothesis is warranted.

Urinary peptidomics in kidney disease and drug research.

INTRODUCTION: Due to its close connection with the renal system, urine is considered a valuable source of information in kidney disease research. Peptidomics methods focus on the discovery of endogenous peptides, given their wide range of biological functions and diagnostic and therapeutic potential. Representing a non-invasive and sensitive method, technological prospects of urinary peptidomics should be evaluated in the context of drug discovery and research. Areas covered: This review describes urinary peptidomics with focus on its application in drug research in the field of kidney diseases. The authors provide an overview of current achievements and potential future applications. Expert opinion: The urinary peptidome is a dynamically changing source of information, able to reflect sudden and long-term changes affecting the renal system. Studies utilizing urinary peptidomics techniques have demonstrated their value in patient stratification and detection of early pathological changes in kidney disease. Serving as a liquid biopsy, urinary peptides are an invaluable tool for drug response monitoring. Nevertheless, peptidomics is largely underexplored in drug research in general, as evidenced by the scarce number of scientific publications on this topic. Further progress will be driven by the successful validation of current discoveries and continued efforts to improve the translation of results into therapeutic applications.

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 < 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.

Identification of novel molecular signatures of IgA nephropathy through an integrative -omics analysis.

IgA nephropathy (IgAN) is the most prevalent among primary glomerular diseases worldwide. Although our understanding of IgAN has advanced significantly, its underlying biology and potential drug targets are still unexplored. We investigated a combinatorial approach for the analysis of IgAN-relevant -omics data, aiming at identification of novel molecular signatures of the disease. Nine published urinary proteomics datasets were collected and the reported differentially expressed proteins in IgAN vs. healthy controls were integrated into known biological pathways. Proteins participating in these pathways were subjected to multi-step assessment, including investigation of IgAN transcriptomics datasets (Nephroseq database), their reported protein-protein interactions (STRING database), kidney tissue expression (Human Protein Atlas) and literature mining. Through this process, from an initial dataset of 232 proteins significantly associated with IgAN, 20 pathways were predicted, yielding 657 proteins for further analysis. Step-wise evaluation highlighted 20 proteins of possibly high relevance to IgAN and/or kidney disease. Experimental validation of 3 predicted relevant proteins, adenylyl cyclase-associated protein 1 (CAP1), SHC-transforming protein 1 (SHC1) and prolylcarboxypeptidase (PRCP) was performed by immunostaining of human kidney sections. Collectively, this study presents an integrative procedure for -omics data exploitation, giving rise to biologically relevant results.

PeptiCKDdb-peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease.

The peptiCKDdb is a publicly available database platform dedicated to support research in the field of chronic kidney disease (CKD) through identification of novel biomarkers and molecular features of this complex pathology. PeptiCKDdb collects peptidomics and proteomics datasets manually extracted from published studies related to CKD. Datasets from peptidomics or proteomics, human case/control studies on CKD and kidney or urine profiling were included. Data from 114 publications (studies of body fluids and kidney tissue: 26 peptidomics and 76 proteomics manuscripts on human CKD, and 12 focusing on healthy proteome profiling) are currently deposited and the content is quarterly updated. Extracted datasets include information about the experimental setup, clinical study design, discovery-validation sample sizes and list of differentially expressed proteins (P-value < 0.05). A dedicated interactive web interface, equipped with multiparametric search engine, data export and visualization tools, enables easy browsing of the data and comprehensive analysis. In conclusion, this repository might serve as a source of data for integrative analysis or a knowledgebase for scientists seeking confirmation of their findings and as such, is expected to facilitate the modeling of molecular mechanisms underlying CKD and identification of biologically relevant biomarkers.Database URL: www.peptickddb.com.

Omics databases on kidney disease: where they can be found and how to benefit from them.

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease.

The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression.

Report on the Workshop and Regular Meeting of the Imode-CKD and Bcmolmed Marie Curie Training and Research Programs.

A Workshop and Regular Meeting of the Marie Curie Training and Research Programs iMODECKD (Identification of the Molecular Determinants of established Chronic Kidney Disease) and BCMolMed (Molecular Medicine for Bladder Cancer) was held from 20-22 March at the Macedonian Academy of Science and Arts (MASA). The meeting was hosted by the participating center University of Skopje (SKO) - Goce Spasovski and MASA - Momir Polenakovic (R. Macedonia). The representative from MASA proteomic research center - Katerina Davalieva (R. Macedonia) had presentation on proteomic research in prostate cancer (PCa). 40 researchers from 13 different countries participated at the meeting. The Workshop was devoted on "Chronic Kidney Disease: Clinical Management issues", and consisted of 15 oral presentations given by nephrologists and experts in the field of CKD. Raymond Vanholder (Belgium) - past president of ERA-EDTA had a keynote lecture on "CKD: Questions that need to be answered and are not (or at least not entirely)". The workshop continued in four sessions with lectures from Alberto Ortiz (Spain), Olivera Stojceva-Taneva (R. Macedonia), Dimitrios Goumenos (Greece), Joachim Beige (Germany), Marian Klinger (Poland), Goce Spasovski (R. Macedonia), Joachim Jankowski (Germany), Adalbert Schiller (Romania), Robert Johnson (USA), Franco Ferrario (Italy), Ivan Rychlik (Czech Republic), Fulvio Magni (Italy) and Giovambattista Capasso (Italy), all covering a training theme. Within the meeting there were two lectures on complimentary skills for ethics in science and career advancement from two principal investigators - Goce Spasovski (R. Macedonia) and Joost Schanstra (France). During the Regular Meeting, 13 PhD students i.e. Early Stage Researchers and one Experienced Researcher from both Programs presented their work and progress within iMODE-CKD and BCMolMed projects. This meeting was a great opportunity to exchange experience and ideas in the field of systems biology approaches and translational medicine and planning future collaboration.