Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease

Precision Medicine and Personalized Medicine in Cardiovascular Disease

Precision medicine aims to offer "the right treatment to the right patient at the right time." In cardiovascular medicine the potential of precision medicine applies to all stages of the disease development and includes risk prediction, preventative measures, and targeted therapeutic approaches. Precision medicine will benefit from new developments in the area of genomics and other omics but equally heavily depends on established biomarkers, functional tests, and imaging. Cardiovascular medicine often relies on noninvasive diagnostic procedures and symptom-based disease management. In contrast, other clinical disciplines including oncology and immunology have already moved to molecular diagnostics that lend themselves to precision medicine approaches. There are opportunities to implement precision medicine approaches by focusing on common diseases such as hypertension, conditions with diagnostic and prognostic uncertainty such as angina, and conditions that are associated with high mortality and involve costly and potentially harmful interventions such as dilated cardiomyopathy and cardiac resynchronization therapy. Sex and gender issues have not yet been fully explored in precision medicine although the opportunity to use molecular data to more accurately manage men and women with cardiovascular disease has been acknowledged. A mindshift is required in order to fully exploit the potential of precision medicine to tackle the global burden of cardiovascular diseases.

Proteomics for Biomarker Identification and Clinical Application in Kidney Disease

Treatment effectiveness for kidney disease is limited by lack of accuracy, sensitivity, specificity of diagnostic, prognostic, and therapeutic biomarkers. The gold standard test renal biopsy along with serum creatinine and proteinuria is often necessary to establish a diagnosis, particularly in glomerular disease. Proteomics has become a powerful tool for novel biomarker discovery in kidney disease. Novel proteomics offer earlier and more accurate diagnosis of renal pathology than possible with traditional biomarkers such as serum creatinine and urine protein. In addition, proteomic biomarkers could also be useful to choose the most suitable therapeutic targets. This review focuses on the current status of proteomic biomarkers from animal models (5/6 nephrectomy, unilateral ureteral obstruction, and diabetic nephropathy) and human studies (chronic kidney disease, glomerular diseases, transplantation, dialysis, acute and drug-induced kidney injury) to assess relevant findings and clinical usefulness. Current issues and problems related to the discovery, validation, and clinical application of proteomic biomarkers are discussed. We also describe several proteomic strategies highlighting technologic advancements, specimen selection, data processing and analysis. This review might provide help in future proteomic studies to improve the diagnosis and management of kidney disease.

Origins, Technological Development, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.

Renal fibrosis: Recent translational aspects

Renal fibrogenesis is the common final pathway to all renal injuries that consequently leads to Chronic Kidney Disease (CKD). Renal fibrogenesis corresponds to the replacement of renal functional tissue by extra-cellular matrix proteins, mainly collagens, that ultimately impairs kidney function. Blockade of the renin angiotensin system by Angiotensin Converting Enzyme inhibitors (ACEi) or Angiotensin Receptor Blockers (ARBs) was the first strategy that proved efficient to blunt the development of renal fibrogenesis independently of its systemic action on blood pressure. Although this strategy has been published 20years ago, there is to date no novel therapeutic targets that are both safe and efficient in hindering renal fibrogenesis and CKD in humans, nor there is any new biomarker to precisely quantify this process. In our review, we will focus on the most recent pathways leading to fibrogenesis which have a high therapeutic potential in humans and on the most promising biomarkers of renal fibrosis.

Diagnosis of diabetic kidney disease: state of the art and future perspective

Approximately 20% to 40% of patients with type 1 or type 2 diabetes mellitus develop diabetic kidney disease. This is a clinical syndrome characterized by persistent albuminuria (> 300 mg/24 h, or > 300 mg/g creatinine), a relentless decline in glomerular filtration rate (GFR), raised arterial blood pressure, and enhanced cardiovascular morbidity and mortality. There is a characteristic histopathology. In classical diabetic nephropathy, the first clinical sign is moderately increased urine albumin excretion (microalbuminuria: 30-300 mg/24 h, or 30-300 mg/g creatinine; albuminuria grade A2). Untreated microalbuminuria will gradually worsen, reaching clinical proteinuria or severely increased albuminuria (albuminuria grade A3) over 5 to 15 years. The GFR then begins to decline, and without treatment, end-stage renal failure is likely to result in 5 to 7 years. Although albuminuria is the first sign of diabetic nephropathy, the first symptom is usually peripheral edema, which occurs at a very late stage. Regular, systematic screening for diabetic kidney disease is needed in order to identify patients at risk of or with presymptomatic diabetic kidney disease. Annual monitoring of urinary albumin-to-creatinine ratio, estimated GFR, and blood pressure is recommended. Several new biomarkers or profiles of biomarkers have been investigated to improve prognostic and diagnostic precision, but none have yet been implemented in routine clinical care. In the future such techniques may pave the way for personalized treatment.

Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies?

Chronic kidney disease (CKD) is a prevalent cause of morbidity and mortality worldwide. A hallmark of CKD progression is renal fibrosis characterized by excessive accumulation of extracellular matrix (ECM) proteins. In this study, we aimed to investigate the correlation of the urinary proteome classifier CKD273 and individual urinary peptides with the degree of fibrosis. In total, 42 kidney biopsies and urine samples were examined. The percentage of fibrosis per total tissue area was assessed in Masson trichrome stained kidney tissues. The urinary proteome was analysed by capillary electrophoresis coupled to mass spectrometry. CKD273 displayed a significant and positive correlation with the degree of fibrosis (Rho = 0.430, P = 0.0044), while the routinely used parameters (glomerular filtration rate, urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio) did not (Rho = -0.222; -0.137; -0.070 and P = 0.16; 0.39; 0.66, respectively). We identified seven fibrosis-associated peptides displaying a significant and negative correlation with the degree of fibrosis. All peptides were collagen fragments, suggesting that these may be causally related to the observed accumulation of ECM in the kidneys. CKD273 and specific peptides are significantly associated with kidney fibrosis; such an association could not be detected by other biomarkers for CKD. These non-invasive fibrosis-related biomarkers can potentially be implemented in future trials.

Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker

Introduction

CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to <60 ml/min per 1.73 m².

Methods

In analyses of 2087 individuals from 6 cohorts (46.4% women; 73.5% with diabetes; mean age, 46.1 years; eGFR ≥ 60 ml/min per 1.73 m², 100%; urinary albumin excretion rate [UAE] ≥20 μg/min, 6.2%), we accounted for cohort, sex, age, mean arterial pressure, diabetes, and eGFR at baseline and expressed associations per 1-SD increment in urinary biomarkers.

Results

Over 5 (median) follow-up visits, eGFR decreased more with higher baseline CKD273 than UAE (1.64 vs. 0.82 ml/min per 1.73 m²; P < 0.0001). Over 4.6 years (median), 390 participants experienced a first renal endpoint (eGFR decrease by ≥10 to <60 ml/min per 1.73 m²), and 172 experienced an endpoint sustained over follow-up. The risk of a first and sustained renal endpoint increased with UAE (hazard ratio ≥ 1.23; P ≤ 0.043) and CKD273 (≥ 1.20; P ≤ 0.031). UAE (≥20 μg/min) and CKD273 (≥0.154) thresholds yielded sensitivities of 30% and 33% and specificities of 82% and 83% (P ≤ 0.0001 for difference between UAE and CKD273 in proportion of correctly classified individuals). As continuous markers, CKD273 (P = 0.039), but not UAE (P = 0.065), increased the integrated discrimination improvement, while both UAE and CKD273 improved the net reclassification index (P ≤ 0.0003), except for UAE per threshold (P = 0.086).

Discussion

In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target.

The effect of sodium/glucose cotransporter 2 (SGLT2) inhibition on the urinary proteome

Treatment with empagliflozin, an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), is associated with slower progression of diabetic kidney disease. In this analysis, we explored the hypothesis that empagliflozin may have an impact on urinary peptides associated with chronic kidney disease (CKD). In this post-hoc, exploratory analysis, we investigated urine samples obtained from 40 patients with uncomplicated type 1 diabetes (T1D) before and after treatment with empagliflozin for 8 weeks to for significant post-therapy changes in urinary peptides. We further assessed the association of these changes with CKD in an independent cohort, and with a previously established urinary proteomic panel, termed CKD273. 107 individual peptides significantly changed after treatment. The majority of the empagliflozin-induced changes were in the direction of “CKD absent” when compare to patients with CKD and controls. A classifier consisting of these 107 peptides scored significantly different in controls, in comparison to CKD patients. However, empagliflozin did not impact the CKD273 classifier. Our data indicate that empagliflozin induces multiple significant changes in the urinary proteomic markers such as mucin and clusterin. The relationship between empagliflozin-induced proteomic changes and clinical outcomes merits further investigation.

Urine peptidome analysis predicts risk of end-stage renal disease and reveals proteolytic pathways involved in autosomal dominant polycystic kidney disease progression

Background

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by slowly progressive bilateral renal cyst growth ultimately resulting in loss of kidney function and end-stage renal disease (ESRD). Disease progression rate and age at ESRD are highly variable. Therapeutic interventions therefore require early risk stratification of patients and monitoring of disease progression in response to treatment.

Methods

We used a urine peptidomic approach based on capillary electrophoresis-mass-spectrometry (CE-MS) to identify potential biomarkers reflecting the risk for early progression to ESRD in the Consortium of Radiologic Imaging in Polycystic Kidney Disease (CRISP) cohort.

Results

A biomarker-based classifier consisting of 20 urinary peptides allowed the prediction of ESRD within 10-13 years of follow-up in patients 24-46 years of age at baseline. The performance of the biomarker score approached that of height-adjusted total kidney volume (htTKV) and the combination of the biomarker panel with htTKV improved prediction over either one alone. In young patients (<24 years at baseline), the same biomarker model predicted a 30 mL/min/1.73 m 2 glomerular filtration rate decline over 8 years. Sequence analysis of the altered urinary peptides and the prediction of the involved proteases by in silico analysis revealed alterations in distinct proteolytic pathways, in particular matrix metalloproteinases and cathepsins.

Conclusion

We developed a urinary test that accurately predicts relevant clinical outcomes in ADPKD patients and suggests altered proteolytic pathways involved in disease progression.

Comparative proteome and peptidome analysis of the cephalic fluid secreted by Arapaima gigas (Teleostei: Osteoglossidae) during and outside parental care

Parental investment in Arapaima gigas includes nest building and guarding, followed by a care provision when a cephalic fluid is released from the parents’ head to the offspring. This fluid has presumably important functions for the offspring but so far its composition has not been characterised. In this study the proteome and peptidome of the cephalic secretion was studied in parental and non-parental fish using capillary electrophoresis coupled to mass spectrometry (CE-MS) and GeLC-MS/MS analyses. Multiple comparisons revealed 28 peptides were significantly different between males and parental males (PC-males), 126 between females and parental females (PC-females), 51 between males and females and 9 between PC-males and PC-females. Identification revealed peptides were produced in the inner ear (pcdh15b), eyes (tetraspanin and ppp2r3a), central nervous system (otud4, ribeye a, tjp1b and syn1) among others. A total of 422 proteins were also identified and gene ontology analysis revealed 28 secreted extracellular proteins. From these, 2 hormones (prolactin and stanniocalcin) and 12 proteins associated to immunological processes (serotransferrin, α-1-antitrypsin homolog, apolipoprotein A-I, and others) were identified. This study provides novel biochemical data on the lateral line fluid which will enable future hypotheses-driven experiments to better understand the physiological roles of the lateral line in chemical communication.

Multiplexed Temporal Quantification of the Exercise-regulated Plasma Peptidome

Exercise is extremely beneficial to whole body health reducing the risk of a number of chronic human diseases. Some of these physiological benefits appear to be mediated via the secretion of peptide/protein hormones into the blood stream. The plasma peptidome contains the entire complement of low molecular weight endogenous peptides derived from secretion, protease activity and PTMs, and is a rich source of hormones. In the current study we have quantified the effects of intense exercise on the plasma peptidome to identify novel exercise regulated secretory factors in humans. We developed an optimized 2D-LC-MS/MS method and used multiple fragmentation methods including HCD and EThcD to analyze endogenous peptides. This resulted in quantification of 5,548 unique peptides during a time course of exercise and recovery. The plasma peptidome underwent dynamic and large changes during exercise on a time-scale of minutes with many rapidly reversible following exercise cessation. Among acutely regulated peptides, many were known hormones including insulin, glucagon, ghrelin, bradykinin, cholecystokinin and secretogranins validating the method. Prediction of bioactive peptides regulated with exercise identified C-terminal peptides from Transgelins, which were increased in plasma during exercise. In vitro experiments using synthetic peptides identified a role for transgelin peptides on the regulation of cell-cycle, extracellular matrix remodeling and cell migration. We investigated the effects of exercise on the regulation of PTMs and proteolytic processing by building a site-specific network of protease/substrate activity. Collectively, our deep peptidomic analysis of plasma revealed that exercise rapidly modulates the circulation of hundreds of bioactive peptides through a network of proteases and PTMs. These findings illustrate that peptidomics is an ideal method for quantifying changes in circulating factors on a global scale in response to physiological perturbations such as exercise. This will likely be a key method for pinpointing exercise regulated factors that generate health benefits.

Current state of the art for enhancing urine biomarker discovery

INTRODUCTION

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state.

AREAS COVERED

High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Expert commentary: Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals.

A Urinary Fragment of Mucin-1 Subunit α Is a Novel Biomarker Associated With Renal Dysfunction in the General Population

INTRODUCTION

Sequencing peptides included in the urinary proteome identifies the parent proteins and may reveal mechanisms underlying the pathophysiology of chronic kidney disease.

METHODS

In 805 randomly recruited Flemish individuals (50.8% women; mean age, 51.1 years), we determined the estimated glomerular filtration rate (eGFR) from serum creatinine using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. We categorized eGFR according to the National Kidney Foundation Kidney Disease Outcomes Quality Initiative guideline. We analyzed 74 sequenced urinary peptides with a detectable signal in more than 95% of participants. Follow-up measurements of eGFR were available in 597 participants.

RESULTS

In multivariable analyses, baseline eGFR decreased (P ≤ 0.022) with urinary fragments of mucin-1 (standardized association size expressed in ml/min/1.73 m², -4.48), collagen III (-2.84), and fibrinogen (-1.70) and was bi-directionally associated (P ≤ 0.0006) with 2 urinary collagen I fragments (+2.28 and -3.20). The eGFR changes over 5 years (follow-up minus baseline) resulted in consistent estimates (P ≤ 0.025) for mucin-1 (-1.85), collagen (-1.37 to 1.43) and fibrinogen (-1.45) fragments. Relative risk of having or progressing to eGFR <60 ml/min/1.73 m² was associated with mucin-1. Partial least-squares analysis confirmed mucin-1 as the strongest urinary marker associated with decreased eGFR, with a score of 2.47 compared with 1.80 for a collagen I fragment as the next contender. Mucin-1 predicted eGFR decline to <60 ml/min/1.73 m² over and above microalbuminuria (P = 0.011) and retained borderline significance (P = 0.05) when baseline eGFR was accounted for.

DISCUSSION

In the general population, mucin-1 subunit α, an extracellular protein that is shed from renal tubular epithelium, is a novel biomarker associated with renal dysfunction.

Use of Biomarkers in the Evaluation and Treatment of Hypertensive Patients

The current definition of hypertension is based on blood pressure values, and blood pressure also drives treatment decisions, is the most important treatment monitoring tool and helps estimating risk of hypertension-related organ damage. In an era of precision medicine, additional biomarkers are needed in the diagnosis and management of patients with hypertension. In this review, we outline the areas in which functional, imaging and circulating biomarkers could help in a more individualised definition of hypertension and associated risk. We will cover biomarkers for diagnosis; of pathophysiology and prediction of hypertension; response to treatment, organ damage; and to monitor treatment. A clear focus is on the vasculature, the heart and the kidneys, whereas we see a need to further develop biomarkers of cerebral function in order to diagnose cognition deficits and monitor changes in cognition in the future to support addressing the growing burden of hypertension-associated vascular dementia.

Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome

In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), miRNA, ncRNA, and lincRNA biomarkers and proteomic and metabolomic biomarkers. Complicated pathomechanisms of CKD development and progression require not a single marker but their combination in order to mirror all types of alterations occurring in the course of this disease. It seems that in the not so distant future, conventional markers may be exchanged for new ones, however, confirmation of their efficacy, sensitivity and specificity as well as the reduction of analysis costs are required.

A combined bile and urine proteomic test for cholangiocarcinoma diagnosis in patients with biliary strictures of unknown origin

BACKGROUND

Detection of cholangiocarcinoma (CC) remains a diagnostic challenge particularly in patients with primary sclerosing cholangitis (PSC). We recently established diagnostic peptide marker models in bile and urine to detect CC. Our aim was to combine both models to reach a higher diagnostic accuracy of CC diagnosis.

METHODS

Bile (BPA) and urine (UPA) proteome analysis by capillary electrophoresis mass spectrometry was performed in a case-control phase II study on 87 patients (36 CC including 13 with CC on top of PSC, 33 PSC and 18 other benign disorders). A logistic regression model with both analyses was developed and subsequently validated in a prospective cohort of 45 patients.

RESULTS

In the retrospective study, single BPA and UPA showed sensitivities of 83 and 89 % and specificities of 80 and 86 % with an area under the curve (AUC) value of 0.85 and 0.93. If CC was defined as positive UPA and BPA the combination resulted in a sensitivity of 72 % and a specificity of 96 %. The logistic regression model resulted in an increase in sensitivity to 92 % at 84 % specificity with an AUC of 0.96. Applied to the prospective study cohort, the logistic regression model was superior in its sensitivity (94%) and specificity (76%) over single BPA (63% sensitivity, 69% specificity) and UPA (81% sensitivity, 72% specificity) with an AUC of 0.84.

CONCLUSION

Our logistic regression model enables CC diagnosis with a higher accuracy than currently available diagnostic tools leading potentially to an earlier diagnosis.

Analysis of Urine Composition in Type II Diabetic Mice after Intervention Therapy Using Holothurian Polypeptides

Hydrolysates and peptide fractions (PF) obtained from sea cucumber with commercial enzyme were studied on the hyperglycemic and renal protective effects on db/db rats using urine metabolomics. Compared with the control group the polypeptides from the two species could significantly reduce the urine glucose and urea. We also tried to address the compositions of highly expressed urinary proteins using a proteomics approach. They were serum albumins, AMBP proteins, negative trypsin, elastase, and urinary protein, GAPDH, a receptor of urokinase-type plasminogen activator (uPAR), and Ig kappa chain C region. We used the electronic nose to quickly detect changes in the volatile substances in mice urine after holothurian polypeptides (HPP) fed, and the results show it can identify the difference between treatment groups with the control group without overlapping. The protein express mechanism of HPP treating diabetes was discussed, and we suggested these two peptides with the hypoglycemic and renal protective activity might be utilized as nutraceuticals.

Personalized Medicine: New Perspectives for the Diagnosis and the Treatment of Renal Diseases

The prevalence of renal diseases is rising and reaching 5-15% of the adult population. Renal damage is associated with disturbances of body homeostasis and the loss of equilibrium between exogenous and endogenous elements including drugs and metabolites. Studies indicate that renal diseases are influenced not only by environmental but also by genetic factors. In some cases the disease is caused by mutation in a single gene and at that time severity depends on the presence of one or two mutated alleles. In other cases, renal disease is associated with the presence of alteration within a gene or genes, but environmental factors are also necessary for the development of disease. Therefore, it seems that the analysis of genetic aspects should be a natural component of clinical and experimental studies. The goal of personalized medicine is to determine the right drug, for the right patient, at the right time. Whole-genome examinations may help to change the approach to the disease and the patient resulting in the creation of "personalized medicine" with new diagnostic and treatment strategies designed on the basis of genetic background of each individual. The identification of high-risk patients in pharmacogenomics analyses will help to avoid many unwarranted side effects while optimizing treatment efficacy for individual patients. Personalized therapies for kidney diseases are still at the preliminary stage mainly due to high costs of such analyses and the complex nature of human genome. This review will focus on several areas of interest: renal disease pathogenesis, diagnosis, treatment, rate of progression and the prediction of prognosis.

Diagnostic and prognostic tests in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune inflammatory disease characterized by autoantibodies directed against numerous self-nuclear antigens. Because of the heterogeneous nature of lupus, it has been challenging to identify markers that are sensitive and specific enough for its diagnosis and monitoring. However, with the sequencing of the human genome, rapid development of high-throughput approaches has allowed for a better understanding of the etiopathogenesis of complex diseases, including SLE. Here we present a review of the latest advancements in biomarker discovery during the "omics" era, using these novel technologies, for assisting in the diagnosis and prognosis of patients with SLE.

CancerPDF: A repository of cancer-associated peptidome found in human biofluids

CancerPDF (Cancer Peptidome Database of bioFluids) is a comprehensive database of endogenous peptides detected in the human biofluids. The peptidome patterns reflect the synthesis, processing and degradation of proteins in the tissue environment and therefore can act as a gold mine to probe the peptide-based cancer biomarkers. Although an extensive data on cancer peptidome has been generated in the recent years, lack of a comprehensive resource restrains the facility to query the growing community knowledge. We have developed the cancer peptidome resource named CancerPDF, to collect and compile all the endogenous peptides isolated from human biofluids in various cancer profiling studies. CancerPDF has 14,367 entries with 9,692 unique peptide sequences corresponding to 2,230 unique precursor proteins from 56 high-throughput studies for ~27 cancer conditions. We have provided an interactive interface to query the endogenous peptides along with the primary information such as m/z, precursor protein, the type of cancer and its regulation status in cancer. To add-on, many web-based tools have been incorporated, which comprise of search, browse and similarity identification modules. We consider that the CancerPDF will be an invaluable resource to unwind the potential of peptidome-based cancer biomarkers. The CancerPDF is available at the web address http://crdd.osdd.net/raghava/cancerpdf/.

Clinical proteomics in kidney disease as an exponential technology: heading towards the disruptive phase

Exponential technologies double in power or processing speed every year, whereas their cost halves. Deception and disruption are two key stages in the development of exponential technologies. Deception occurs when, after initial introduction, technologies are dismissed as irrelevant, while they continue to progress, perhaps not as fast or with so many immediate practical applications as initially thought. Twenty years after the first publications, clinical proteomics is still not available in most hospitals and some clinicians have felt deception at unfulfilled promises. However, there are indications that clinical proteomics may be entering the disruptive phase, where, once refined, technologies disrupt established industries or procedures. In this regard, recent manuscripts in CKJ illustrate how proteomics is entering the clinical realm, with applications ranging from the identification of amyloid proteins in the pathology lab, to a new generation of urinary biomarkers for chronic kidney disease (CKD) assessment and outcome prediction. Indeed, one such panel of urinary peptidomics biomarkers, CKD273, recently received a Food and Drug Administration letter of support, the first ever in the CKD field. In addition, a must-read resource providing information on kidney disease-related proteomics and systems biology databases and how to access and use them in clinical decision-making was also recently published in CKJ.

Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease

Capillary electrophoresis coupled with mass spectrometry (CE-MS) has been used as a platform for discovery and validation of urinary peptides associated with chronic kidney disease (CKD). CKD affects ∼ 10% of the population, with high associated costs for treatments. A urinary proteome-based classifier (CKD273) has been discovered and validated in cross-sectional and longitudinal studies to assess and predict the progression of CKD. It has been implemented in studies employing cohorts of > 1000 patients. CKD273 is commercially available as an in vitro diagnostic test for early detection of CKD and is currently being used for patient stratification in a multicentre randomized clinical trial (PRIORITY). The validity of the CKD273 classifier has recently been evaluated applying the Oxford Evidence-Based Medicine and Southampton Oxford Retrieval Team guidelines and a letter of support for CKD273 was issued by the US Food and Drug Administration. In this article we review the current evidence published on CKD273 and the challenges associated with implementation. Definition of a possible surrogate early endpoint combined with CKD273 as a biomarker for patient stratification currently appears as the most promising strategy to enable the development of effective drugs to be used at an early time point when intervention can still be effective.

The role of laboratory testing in detection and classification of chronic kidney disease: national recommendations

Chronic kidney disease (CKD) is a common clinical condition with significant adverse consequences for the patient and it is recognized as a significant public health problem. The role of laboratory medicine in diagnosis and management of CKD is of great importance: the diagnosis and staging are based on estimation of glomerular filtration rate (eGFR) and assessment of albuminuria (or proteinuria). Therefore, the joint working group of the Croatian society of medical biochemistry and laboratory medicine and Croatian chamber of medical biochemists for laboratory diagnostics in CKD issued this national recommendation regarding laboratory diagnostics of CKD.
Key factors for laboratories implementing the national guidelines for the diagnosis and management of CKD are:
1. Ensure good communication between laboratory professionals and clinicians, such as nephrologists or specialists in general/family medicine,
2. Ensure all patients are provided with the same availability of laboratory diagnostics,
3. Ensure creatinine assays are traceable to isotope dilution mass spectrometry (IDMS) method and have minimal bias and acceptable imprecision,
4. Select the appropriate GFR estimating formula. Recommended equation is the 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD - EPI) equation,
5. In reporting the key laboratory tests (creatinine, eGFR, urine albumin-to-creatinine ratio, urine protein-to-creatinine ratio) use the appropriate reporting units,
6. Provide adequate information on limitations of creatinine measurement.
The manuscript has been organized to identify critical points in laboratory tests used in basic laboratory diagnostics of CKD and is based on the Kidney Disease: Improving Global Outcomes (KDIGO) 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease.

Human Antimicrobial Peptides in Bodily Fluids: Current Knowledge and Therapeutic Perspectives in the Postantibiotic Era

Antimicrobial peptides (AMPs) are an integral part of the innate immune defense mechanism of many organisms. Due to the alarming increase of resistance to antimicrobial therapeutics, a growing interest in alternative antimicrobial agents has led to the exploitation of AMPs, both synthetic and isolated from natural sources. Thus, many peptide-based drugs have been the focus of increasing attention by many researchers not only in identifying novel AMPs, but in defining mechanisms of antimicrobial peptide activity as well. Herein, we review the available strategies for the identification of AMPs in human body fluids and their mechanism(s) of action. In addition, an overview of the distribution of AMPs across different human body fluids is provided, as well as its relation with microorganisms and infectious conditions.

Addition of nonalbumin proteinuria to albuminuria improves prediction of type 2 diabetic nephropathy progression

BACKGROUND

Albuminuria is generally accepted as a sensitive marker of diabetic nephropathy but has limitations in predicting its progression. The aim of this study was to evaluate the use of nonalbumin proteinuria in addition to albuminuria for predicting the progression of type 2 diabetic nephropathy.

METHODS

In this retrospective observational study, the urine albumin-to-creatinine ratio (ACR) and the nonalbumin protein-to-creatinine ratio (NAPCR) were measured in 325 patients with type 2 diabetes and estimated glomerular filtration rates (eGFR) ≥30 mL/min/1.73 m². The patients were divided into four groups based on the cutoff points for the urinary ACR (30 mg/g) and NAPCR (120 mg/g). The renal outcomes were chronic kidney disease (CKD) progression and accelerated eGFR decline.

RESULTS

During the 4.3-year follow-up period, 25 (7.7%) patients showed CKD progression and 69 (21.2%) patients showed accelerated eGFR decline. After adjusting for nine clinical parameters, the group with a NAPCR greater than 120 mg/g exhibited higher cumulative incidences of CKD progression (hazard ratio 6.84; P = 0.001) and accelerated eGFR decline (hazard ratio 1.95; P = 0.011) than the group with a NAPCR < 120 mg/g. In patients with normoalbuminuria, the group with NAPCR levels greater than 120 mg/g also exhibited a higher cumulative incidence than that with NAPCR levels <120 mg/g of CKD progression (hazard ratio 21.82; P = 0.005). The addition of NAPCR to ACR improved the model fit for CKD progression and accelerated eGFR decline.

CONCLUSION

Nonalbumin proteinuria showed additional value over and above that of albuminuria for predicting the progression of CKD in patients with type 2 diabetes.

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 Proteomics for Diagnosis and Monitoring of Diabetic Nephropathy

The last decade has seen a surge in publications describing novel biomarkers for early detection of diabetic nephropathy (DN), but as yet none have outperformed albuminuria in well-designed prospective studies. This is partially attributable to our incomplete understanding of the many complex interrelated mechanisms underlying DN development, a heterogeneous process unlikely to be captured by a single biomarker. Proteomics offers the advantage of simultaneously analysing the entire protein content of a biological sample, and the technique has gained attention as a potential tool for a more accurate diagnosis of disease at an earlier stage as well as a means by which to unravel the pathogenesis of complex diseases such as DN using an untargeted approach. This review will discuss the potential of proteomics as both a clinical and research tool, evaluating exploratory work in animal models as well as diagnostic potential in human subjects.

The proteome pattern cGvHD_MS14 allows early and accurate prediction of chronic GvHD after allogeneic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) may be curative, but is associated with significant morbidity and mortality. Chronic graft-versus-host disease (cGvHD), characterized by inflammation and fibrosis of multiple target organs, considerably contributes to the morbidity and mortality even years after allo-HSCT. Diagnosis of cGvHD is based on clinical features and histology of biopsies. Here, we report the generation of a urinary cGvHD-specific proteome-pattern (cGvHD_MS14) established by capillary electrophoresis-mass spectrometry to predict onset and severity of cGvHD as an unbiased laboratory test. cGvHD_MS14 was evaluated on samples from 412 patients collected prospectively in four transplant centers. Sensitivity and specificity was 84 and 76% by cGvHD_MS14 classification. Sensitivity further increased to 93% by combination of cGvHD_MS14 with relevant clinical variables to a logistic regression model. cGvHD was predicted up to 55 days prior to clinical diagnosis. Acute GvHD is not recognized by cGvHD_MS14. cGvHD_MS14 consists of 14 differentially excreted peptides, six of those have been sequenced to date and are fragments from thymosin β-4, eukaryotic translation initiation factor 4γ2, fibrinogen β-chain or collagens. In conclusion, the cGvHD_MS14-pattern allows early, highly sensitive and specific prediction of cGvHD as an independent diagnostic criterion of clinical diagnosis potentially allowing early therapeutic intervention.

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice.

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 1. High abundance proteins, acute phase proteins and peptidomics

A peptidomic investigation of milk from an experimental model of Streptococcus uberis mastitis in dairy cows has incorporated a study of milk high abundance and acute phase (APP) proteins as well as analysis of low molecular weight peptide biomarkers. Intramammary infection (IMI) with S. uberis caused a shift in abundance from caseins, β-lactoglobulin and α-lactalbumin to albumin, lactoferrin and IgG with the increase in lactoferrin occurring last. The APP response of haptoglobin, mammary associated serum amyloid A3 and C-reactive protein occurred between 30-48 hours post challenge with peak concentrations of APPs at 72-96 hours post challenge and declined thereafter at a rate resembling the fall in bacterial count rather than the somatic cell count. A peptide biomarker panel for IMI based on capillary electrophoresis and mass spectrometry was developed. It comprised 77 identified peptides (IMI77) composed mainly of casein derived peptides but also including peptides of glycosylation dependent cell adhesion molecule and serum amyloid A. The panel had a biomarker classification score that increased from 36 hour to 81 hour post challenge, significantly differentiating infected from non-infected milk, thus suggesting potential as a peptide biomarker panel of bovine mastitis and specifically that of S. uberis origin. The use of omic technology has shown a multifactorial cross system reaction in high and low abundance proteins and their peptide derivatives with changes of over a thousand fold in analyte levels in response to S. uberis infection.

Early detection of diabetic kidney disease: Present limitations and future perspectives

Diabetic kidney disease (DKD) is one of the most common diabetic complications, as well as the leading cause of chronic kidney disease and end-stage renal disease around the world. To prevent the dreadful consequence, development of new assays for diagnostic of DKD has always been the priority in the research field of diabetic complications. At present, urinary albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are the standard methods for assessing glomerular damage and renal function changes in clinical practice. However, due to diverse tissue involvement in different individuals, the so-called “non-albuminuric renal impairment” is not uncommon, especially in patients with type 2 diabetes. On the other hand, the precision of creatinine-based GFR estimates is limited in hyperfiltration status. These facts make albuminuria and eGFR less reliable indicators for early-stage DKD. In recent years, considerable progress has been made in the understanding of the pathogenesis of DKD, along with the elucidation of its genetic profiles and phenotypic expression of different molecules. With the help of ever-evolving technologies, it has gradually become plausible to apply the thriving information in clinical practice. The strength and weakness of several novel biomarkers, genomic, proteomic and metabolomic signatures in assisting the early diagnosis of DKD will be discussed in this article.

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction

Background

Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF.

Methods and Results

Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin.

Conclusion

CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Pro: urine proteomics as a liquid kidney biopsy: no more kidney punctures!

In this article, the benefits of urinary proteomics in comparison with kidney biopsy are discussed. The majority of urinary proteins are generated by the kidney, hence the urinary proteome holds substantial information on the kidney, and assessment of the urinary proteome could be considered a 'liquid biopsy'. The main question is how well the information contained in the urinary proteome can be assessed today, if it is ready to be routinely used, and what are the advantages and possible disadvantages in comparison with current standards. Since chronic kidney disease (CKD) is by far the largest area in nephrology based on the number of patients affected, the focus of this article is on CKD. Substantial progress was made in the last decade in urinary proteomics, and today we have comparable urinary proteome datasets of tens of thousands of subjects available. Clinical proteomics studies in CKD including close to, or even exceeding, 1000 subjects have recently been published, demonstrating a benefit over the current state-of-the-art in diagnosis and especially prognosis. The first large multicentric randomized controlled intervention trial aiming at preventing CKD by employing urinary proteomics-guided intervention has been initiated recently. These data provide ample evidence for the utility and value of urinary proteomics in nephrology. A further consideration is that the purpose of the biopsy, be it 'liquid' or 'solid', is to guide intervention. However, essentially all drug targets are proteins, not microscopic structures. Therefore, obtaining information on the proteome to guide intervention appears to be the most appropriate approach. Presenting more detailed evidence, I argue that urinary proteome analysis can, in most cases, be employed to guide therapeutic intervention, can be repeated multiple times as it is without any direct risk or discomfort and can be considered as a liquid biopsy.

Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study

Background

Acute kidney injury (AKI) is a prominent problem in hospitalized patients and associated with increased morbidity and mortality. Clinical medicine is currently hampered by the lack of accurate and early biomarkers for diagnosis of AKI and the evaluation of the severity of the disease.

In 2010, we established a multivariate peptide marker pattern consisting of 20 naturally occurring urinary peptides to screen patients for early signs of renal failure. The current study now aims to evaluate if, in a different study population and potentially various AKI causes, AKI can be detected early and accurately by proteome analysis.

Methods

Urine samples from 60 patients who developed AKI after cardiac surgery were analyzed by capillary electrophoresis-mass spectrometry (CE-MS). The obtained peptide profiles were screened by the AKI peptide marker panel for early signs of AKI. Accuracy of the proteomic model in this patient collective was compared to that based on urinary neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) ELISA levels. Sixty patients who did not develop AKI served as negative controls.

Results

From the 120 patients, 110 were successfully analyzed by CE-MS (59 with AKI, 51 controls). Application of the AKI panel demonstrated an AUC in receiver operating characteristics (ROC) analysis of 0.81 (95 % confidence interval: 0.72–0.88). Compared to the proteomic model, ROC analysis revealed poorer classification accuracy of NGAL and KIM-1 with the respective AUC values being outside the statistical significant range (0.63 for NGAL and 0.57 for KIM-1).

Conclusions

This study gives further proof for the general applicability of our proteomic multimarker model for early and accurate prediction of AKI irrespective of its underlying disease cause.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1344-z) contains supplementary material, which is available to authorized users.

Development of a MALDI MS-based platform for early detection of acute kidney injury

PURPOSE

Septic acute kidney injury (AKI) is associated with poor outcome. This can partly be attributed to delayed diagnosis and incomplete understanding of the underlying pathophysiology. Our aim was to develop an early predictive test for AKI based on the analysis of urinary peptide biomarkers by MALDI-MS.

EXPERIMENTAL DESIGN

Urine samples from 95 patients with sepsis were analyzed by MALDI-MS. Marker search and multimarker model establishment were performed using the peptide profiles from 17 patients with existing or within the next 5 days developing AKI and 17 with no change in renal function. Replicates of urine sample pools from the AKI and non-AKI patient groups and normal controls were also included to select the analytically most robust AKI markers.

RESULTS

Thirty-nine urinary peptides were selected by cross-validated variable selection to generate a support vector machine multidimensional AKI classifier. Prognostic performance of the AKI classifier on an independent validation set including the remaining 61 patients of the study population (17 controls and 44 cases) was good with an area under the receiver operating characteristics curve of 0.82 and a sensitivity and specificity of 86% and 76%, respectively.

CONCLUSION AND CLINICAL RELEVANCE

A urinary peptide marker model detects onset of AKI with acceptable accuracy in septic patients. Such a platform can eventually be transferred to the clinic as fast MALDI-MS test format.

Misclassification Errors in Unsupervised Classification Methods. Comparison Based on the Simulation of Targeted Proteomics Data

Unsupervised classification methods are gaining acceptance in omics studies of complex common diseases, which are often vaguely defined and are likely the collections of disease subtypes. Unsupervised classification based on the molecular signatures identified in omics studies have the potential to reflect molecular mechanisms of the subtypes of the disease and to lead to more targeted and successful interventions for the identified subtypes. Multiple classification algorithms exist but none is ideal for all types of data. Importantly, there are no established methods to estimate sample size in unsupervised classification (unlike power analysis in hypothesis testing). Therefore, we developed a simulation approach allowing comparison of misclassification errors and estimating the required sample size for a given effect size, number, and correlation matrix of the differentially abundant proteins in targeted proteomics studies. All the experiments were performed in silico. The simulated data imitated the expected one from the study of the plasma of patients with lower urinary tract dysfunction with the aptamer proteomics assay Somascan (SomaLogic Inc, Boulder, CO), which targeted 1129 proteins, including 330 involved in inflammation, 180 in stress response, 80 in aging, etc. Three popular clustering methods (hierarchical, k-means, and k-medoids) were compared. K-means clustering performed much better for the simulated data than the other two methods and enabled classification with misclassification error below 5% in the simulated cohort of 100 patients based on the molecular signatures of 40 differentially abundant proteins (effect size 1.5) from among the 1129-protein panel.