Urinary proteomics in the assessment of chronic kidney disease

A pilot study on the differential urine proteomic profile of subjects with community-acquired acute kidney injury who recover versus those who do not recover completely at 4 months after hospital discharge

Background

Community-acquired acute kidney injury (CA-AKI) is a sudden structural damage and loss of kidney function in otherwise healthy individuals outside of hospital settings having high morbidity and mortality rates worldwide. Long-term sequelae of AKI involve an associated risk of progression to chronic kidney disease (CKD). Serum creatinine (SCr), the currently used clinical parameter for diagnosing AKI, varies greatly with age, gender, diet, and muscle mass. In the present study, we investigated the difference in urinary proteomic profile of subjects that recovered (R) and incompletely recovered (IR) from CA-AKI, 4 months after hospital discharge.

Methods

Study subjects were recruited from ongoing study of CA-AKI cohort. Patients with either sex or age > 18 years with no underline CKD were enrolled at the time of hospital discharge. Incomplete recovery from CA-AKI was defined as eGFR < 60 mL/min/1.73 m2 or dialysis dependence at 4 months after discharge. Second-morning urine samples were collected, and proteome analysis was performed with LC-MS/MS. Data were analyzed by Proteome Discoverer platform 2.2 (Thermo Scientific) using statistical and various bioinformatics tools for abundance of protein, cellular component, protein class and biological process were analyzed in the recovered and incompletely recovered groups.

Results

A total of 28 subjects (14 in each group) were enrolled. Collectively, 2019 peptides and proteins with 30 high-abundance proteins in the incompletely recovered group (R/IR <0.5, abundance ratio adj. p-value <0.05) and 11 high-abundance proteins in the incompletely recovered group (R/IR >2.0, abundance ratio adj. p-value <0.05) were identified. Tissue specificity analysis, GO enrichment analysis, and pathway enrichment analysis revealed significant proteins in both the groups that are part of different pathways and might be playing crucial role in renal recovery during the 4-month span after hospital discharge.

Conclusion

In conclusion, this study helped in identifying potential proteins and associated pathways that are either upregulated or downregulated at the time of hospital discharge in incompletely recovered CA-AKI patients that can be further investigated to check for their exact role in the disease progression or repair.

Late Presentation for Kidney Biopsy: Clinical Presentations and Laboratory Findings

Although the number of patients reaching end-stage kidney disease without a biopsy- proven diagnosis is increasing, kidney biopsies play a key role in diagnosing kidney disease. We analyzed prospective data from patients with kidney disease who underwent percutaneous native kidney biopsies from January 2006 to December 2017. Demographic data, clinical presentations, and the laboratory and radiological findings at the time of biopsy were analyzed. Of 530 patients, 42.8% were male. The mean age was 33.9 (32.8-34.9.2) years; 66.3% were aged 25-64 years. Edema was the main clinical presentation (61.9%), with clinical urine changes seen in 66.7%. Most (89.6%) were nondiabetic; 46.8% had high blood pressure or were on antihypertensive therapy. Most patients (77.5%) were in Stages I, II, and III, and 12.3% underwent hemodialysis at the time of admission. Most (54.4%) were obese. Low hemoglobin (31.8%), high triglycerides (30%), high total cholesterol (58.2%), low serum albumin (73.9%), nephrotic proteinuria (61.8.6%), and microscopic hematuria (79.8%) were the main laboratory findings. The immunological investigations showed that antinuclear antibodies, positive anti-double-stranded DNA (anti-dsDNA), and extractable nuclear antigens were positive in 29.6%, 20.7%, and 19.7%, respectively. Perinuclear antineutrophil cytoplasmic antibodies (ANCA) were positive in 9.6% and cytoplasmic ANCA were positive in 5.4%, whereas immunoglobulin A was detected in 4.6%. More than one- third of the patients had reached advanced chronic kidney disease (CKD) Stages IIIB, IV, and V. This indicates the need to increase awareness about CKD, greater utilization of kidney biopsies, and earlier investigations to enable accurate diagnoses, and proper and timely management.

Urine proteomics identifies biomarkers for diabetic kidney disease at different stages

BACKGROUND

Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the 'gold standard' for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress.

METHODS

In this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs.

RESULTS

We quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3.

CONCLUSIONS

Our study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

Large-Scale Proteomic Assessment of Urinary Extracellular Vesicles Highlights Their Reliability in Reflecting Protein Changes in the Kidney

BACKGROUND

Urinary extracellular vesicles (uEVs) are secreted into urine by cells from the kidneys and urinary tract. Although changes in uEV proteins are used for quantitative assessment of protein levels in the kidney or biomarker discovery, whether they faithfully reflect (patho)physiologic changes in the kidney is a matter of debate.

METHODS

Mass spectrometry was used to compare in an unbiased manner the correlations between protein levels in uEVs and kidney tissue from the same animal. Studies were performed on rats fed a normal or high K⁺ diet.

RESULTS

Absolute quantification determined a positive correlation (Pearson R=0.46 or 0.45, control or high K⁺ respectively, P<0.0001) between the approximately 1000 proteins identified in uEVs and corresponding kidney tissue. Transmembrane proteins had greater positive correlations relative to cytoplasmic proteins. Proteins with high correlations (R>0.9), included exosome markers Tsg101 and Alix. Relative quantification highlighted a monotonic relationship between altered transporter/channel abundances in uEVs and the kidney after dietary K⁺ manipulation. Analysis of genetic mouse models also revealed correlations between uEVs and kidney.

CONCLUSION

This large-scale unbiased analysis identifies uEV proteins that track the abundance of the parent proteins in the kidney. The data form a novel resource for the kidney community and support the reliability of using uEV protein changes to monitor specific physiologic responses and disease mechanisms.

Identification of Novel Biomarkers for Pre-diabetic Diagnosis Using a Combinational Approach

Reliable protein markers for pre-diabetes in humans are not clinically available. In order to identify novel and reliable protein markers for pre-diabetes in humans, healthy volunteers and patients diagnosed with pre-diabetes and stroke were recruited for blood collection. Blood samples were collected from healthy and pre-diabetic subjects 12 h after fasting. BMI was calculated from body weight and height. Fasting blood glucose (FBG), glycated hemoglobin (HbA1C), triglyceride (TG), total cholesterol, high-density lipoprotein, low-density lipoprotein (LDL), insulin and albumin were assayed by automated clinical laboratory methods. We used a quantitative proteomics approach to identify 1074 proteins from the sera of pre-diabetic and healthy subjects. Among them, 500 proteins were then selected using Mascot analysis scores. Further, 70 out of 500 proteins were selected via volcano plot analysis according to their statistical significance and average relative protein ratio. Eventually, 7 serum proteins were singled out as candidate markers for pre-diabetes due to their diabetic relevance and statistical significance. Immunoblotting data demonstrated that laminin subunit alpha 2 (LAMA2), mixed-lineage leukemia 4 (MLL4), and plexin domain containing 2 (PLXDC2) were expressed in pre-diabetic patients but not healthy volunteers. Receiver operating characteristic curve analysis indicated that the combination of the three proteins has greater diagnostic efficacy than any individual protein. Thus, LAMA2, MLL4 and PLXDC2 are novel and reliable serum protein markers for pre-diabetic diagnosis in humans.

Reclassification of chronic kidney disease patients for end-stage renal disease risk by proteinuria indexed to estimated glomerular filtration rate: multicentre prospective study in nephrology clinics

BACKGROUND

In non-dialysis chronic kidney disease (CKD), absolute proteinuria (Uprot) depends on the extent of kidney damage and residual glomerular filtration rate (GFR). We therefore evaluated, as compared with Uprot, the strength of association of proteinuria indexed to estimated GFR (eGFR) with end-stage renal disease (ESRD) risk.

METHODS

In a multi-cohort prospective study in 3957 CKD patients of Stages G3-G5 referred to nephrology clinics, we tested two multivariable Cox models for ESRD risk, with either Uprot (g/24 h) or filtration-adjusted proteinuria (F-Uprot) calculated as Uprot/eGFR ×100.

RESULTS

Mean ± SD age was 67 ± 14 years, males 60%, diabetics 29%, cardiovascular disease (CVD) 34%, eGFR 32 ± 13 mL/min/1.73 m2, median (interquartile range) Uprot 0.41 (0.12-1.29) g/24 h and F-Uprot 1.41 (0.36-4.93) g/24 h per 100 mL/min/1.73 m2 eGFR. Over a median follow-up of 44 months, 862 patients reached ESRD. At competing risk analysis, ESRD risk progressively increased when F-Uprot was 1.0-4.9 and ≥5.0 versus <1.0 g/24 h per 100 mL/min/1.73 m2 eGFR in Stages G3a-G4 (P < 0.001) and Stage G5 (P = 0.002). Multivariable Cox analysis showed that Uprot predicts ESRD in Stages G3a-G4 while in G5 the effect was not significant; conversely, F-Uprot significantly predicted ESRD at all stages. The F-Uprot model allowed a significantly better prediction versus the Uprot model according to Akaike information criterion. Net reclassification improvement was 12.2% (95% confidence interval 4.2-21.1), with higher reclassification in elderly, diabetes and CVD, as well as in diabetic nephropathy and glomerulonephritis, and in CKD Stages G4 and G5.

CONCLUSIONS

In patients referred to nephrology clinics, F-Uprot predicts ESRD at all stages of overt CKD and improves, as compared with Uprot, reclassification of patients for renal risk, especially in more advanced and complicated disease.

Urinary Metabolic Profile of Patients with Transfusion-Dependent β-Thalassemia Major Undergoing Deferasirox Therapy

INTRODUCTION

Renal dysfunction is a frequent complication in patients suffering from β-thalassemia major (β-TM). The aim of this study was to analyze the renal function and urine metabolomic profile of β-TM patients undergoing transfusions and deferasirox (DFX) therapy, in order to better characterize and shed light on the pathogenesis of renal disease in this setting.

METHODS AND SUBJECTS

40 patients affected by β-TM treated with DFX and 35 age- and gender-matched healthy controls were enrolled in the study. Renal function was assessed. Glomerular filtration rate (GFR) was estimated with CKD-EPI and Schwartz formula for adults and children, respectively. Renal tubular function and maximal urine concentration ability were tested. Urine specimens were analyzed by nuclear magnetic resonance spectroscopy to identify the urinary metabolite profiles.

RESULTS

The study of renal function in β-TM patients revealed normal estimated (e)GFR mean values and the albumin-to-creatinine ratio was <30 mg/g. The analysis of tubular function showed normal basal plasma electrolyte levels; 60% of patients presented hypercalciuria and many subjects showed defective urine concentration. Several amino acids, N-methyl compounds, and organic acids were overexcreted in the urine of thalassemic patients compared with controls.

DISCUSSION

The major finding of this work is that β-TM patients and controls exhibit different concentrations of some metabolites in the urine. Early recognition of urinary abnormalities may be useful to detect and prevent kidney damage.

Diffusion Weighted Magnetic Resonance Imaging in the Assessment of Renal Function and Parenchymal Changes in Chronic Kidney Disease: A Preliminary Study

Background

The aim of this study was to evaluate the feasibility of using intravoxel incoherent motion (IVIM) imaging for noninvasive assessment of pathologic changes in chronic kidney disease (CKD).

Material/Methods

Thirty-four patients with CKD and 20 healthy volunteers were examined on a 1.5 T magnetic resonance imaging (MRI) unit. The examination consisted of morphologic sequences and diffusion-weighted echo-planar sequence with 10 b values. Diffusion parameters were calculated with the use of mono- (apparent diffusion coefficient, ADC) and bi-exponential model: pure diffusion coefficient (D) and perfusion fraction (Fp). Blood samples to assess the serum creatinine level were taken immediately before examination. Ultrasound guided biopsies were performed in less than 30 days from MRI and were scored by an experienced nephropathologist. Parametrical unpaired t-test and ROC curve analysis were used to investigate differences in diffusion parameters in relation to estimated glomerular filtration rate (eGFR). Pearson’s correlation coefficients were calculated to assess relationship between diffusion parameters and laboratory and histopathological markers of renal damage. P-value <0.05 indicated statistical significance.

Results

Both ADC and D correlated positively with eGFR (respective r 0.74 and 0.72), however D showed a more significant correlation with histopathology: while D correlated negatively with parameters reflecting chronic glomerular (r −0.48) and tubulo-interstitial changes (r −0.47), ADC correlated only with interstitial infiltrations (r −0.44). Flow-related diffusion parameters showed high standard deviation.

Conclusions

IVIM imaging is sensitive to functional and morphologic changes in CKD. The separation of influence of Fp from true diffusion improves the assessment of chronic changes in renal parenchyma.

Urinary proteomics reveals association between pediatric nephrolithiasis and cardiovascular disease

PURPOSE

To study (1) the differences in the relative abundance of urinary proteins between children with kidney stones (RS) and hypercalciuria, hypocitraturia, normal metabolic work-up, and healthy controls (HC); (2) the association of these proteins with various diseases.

METHODS

Quantitative proteomic comparison of pooled urine from RS (N = 30, 24 females, mean age 12.95 ± 4.03 years) versus age- and gender-matched HC, using mass spectrometry. Relative protein abundance was estimated using spectral counting. Proteins of interest were selected using the following criteria: (1) ≥ 5 spectral counts; (2) ≥ twofold difference in spectral counts; and (3) ≤ 0.05 p value for the Fisher's Exact Test.

RESULTS

Of the 1813 proteins identified, 229 met the above criteria, with 162 proteins up-regulated in the RS group and 67 up-regulated in HC. The largest group of proteins (30 out of 229) was found to be associated with cardiovascular disease (CVD). Of those, 16 were involved in coagulation, fibrinolysis, and adhesion, 10 in inflammation, 5 in lipid transport and metabolism, and 4 in oxidative stress. All except two were exclusively found in children with hypercalciuria and hypocitraturia, and were not seen in children with normal metabolic work-up.

CONCLUSION

Using a proteomic approach, we found a significant association between hypercalciuric and hypocitraturic nephrolithiasis and CVD in children. The shared risk factors among both diseases are endothelial dysfunction and atherosclerosis caused by abnormal coagulation, adhesion, disturbance of lipid transport and metabolism, oxidative stress and inflammation. Further understanding of the pathophysiological link between nephrolithiasis and CVD is necessary for developing new therapeutic targets.

Cellular and molecular mechanisms of kidney fibrosis

Renal fibrosis is the final pathological process common to any ongoing, chronic kidney injury or maladaptive repair. It is considered as the underlying pathological process of chronic kidney disease (CKD), which affects more than 10% of world population and for which treatment options are limited. Renal fibrosis is defined by excessive deposition of extracellular matrix, which disrupts and replaces the functional parenchyma that leads to organ failure. Kidney's histological structure can be divided into three main compartments, all of which can be affected by fibrosis, specifically termed glomerulosclerosis in glomeruli, interstitial fibrosis in tubulointerstitium and arteriosclerosis and perivascular fibrosis in vasculature. In this review, we summarized the different appearance, cellular origin and major emerging processes and mediators of fibrosis in each compartment. We also depicted and discussed the challenges in translation of anti-fibrotic treatment to clinical practice and discuss possible solutions and future directions.

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.

Patterns of renal disease in South Korea: a 20-year review of a single-center renal biopsy database

Background: Several registries and centers have reported the results of renal biopsies from different parts of the world. As there are few data regarding the epidemiology of glomerulonephritis (GN) in South Korea, we conducted this study on renal biopsy findings during the last 20 years from a single center.

Methods: Data for 818 patients who underwent renal biopsy at our center between 1992 and 2011 were collected retrospectively. All kidney specimens were examined with light microscopy (LM) and immunofluorescent microscopy (IF).

Results: There were 818 cases of native kidney biopsies. In cases of primary GN, the most frequent type of renal pathology in adults (18–59 years) was mesangial proliferative GN (MsPGN, 34.5%) followed by IgA nephropathy (IgAN, 33.3%) and membranous GN (MGN, 8.8%). Indications in adults (18–59 years) were asymptomatic urinary abnormalities (75.3%) followed by nephrotic syndrome (19.8%) and acute kidney injury (AKI, 3.4%).

Conclusions: Among 818 renal biopsy specimens, MsPGN and IgAN were the most frequent biopsy-proven renal diseases. MGN was the third most common cause of primary GN and lupus nephritis (LN) was the most common secondary glomerular disease. Our data contribute to the epidemiology of renal disease in South Korea.

Microarray analysis reveals gene and microRNA signatures in diabetic kidney disease

The current study aimed to identify therapeutic gene and microRNA (miRNA) biomarkers for diabetic kidney disease (DKD). The public expression profile GSE30122 was used. Following data preprocessing, the limma package was used to select differentially-expressed genes (DEGs) in DKD glomeruli samples and tubuli samples and they were compared with corresponding controls. Then overlapping DEGs in glomeruli and tubuli were identified and enriched analysis was performed. In addition, protein‑protein interaction (PPI) network analysis as well as sub‑network analysis was conducted. miRNAs of the overlapping DEGs were investigated using WebGestal. A total of 139 upregulated and 28 downregulated overlapping DEGs were selected, which were primarily associated with pathways involved in extracellular matrix (ECM)‑receptor interactions and cytokine‑cytokine receptor interactions. CD44, fibronectin 1, C‑C motif chemokine ligand 5 and C‑X‑C motif chemokine receptor 4 were four primary nodes in the PPI network. miRNA (miR)‑17‑5p, miR‑20a and miR‑106a were important and nuclear receptor subfamily 4 group A member 3 (NR4A3), protein tyrosine phosphatase, receptor type O (PTPRO) and Kruppel like factor 9 (KLF9) were all predicted as target genes of the three miRNAs in the integrated miRNA‑target network. Several genes were identified in DKD, which may be involved in pathways such as ECM‑receptor interaction and cytokine‑cytokine receptor interaction. Three miRNAs may also be used as biomarkers for therapy of DKD, including miR‑17‑5p, miR‑20a and miR‑106a, with the predicted targets of NR4A3, PTPRO and KLF9.

Integration of Proteomics and Metabolomics in Exploring Genetic and Rare Metabolic Diseases

BACKGROUND

Inherited metabolic disorders or inborn errors of metabolism are caused by deficiency of enzymatic activities in the catabolism of amino acids, carbohydrates, or lipids. These disorders include aminoacidopathies, urea cycle defects, organic acidemias, defects of oxidation of fatty acids, and lysosomal storage diseases. Inborn errors of metabolism constitute a significant proportion of genetic diseases, particularly in children; however, they are individually rare. Clinical phenotypes are very variable, some of them remain asymptomatic, others manifest metabolic decompensation in neonatal age, and others encompass mental retardation at later age. The clinical manifestation of these disorders can involve different organs and/or systems. Some disorders are easily managed if promptly diagnosed and treated, whereas in other cases neither diet, vitamin therapy, nor transplantation appears to prevent multi-organ impairment.

SUMMARY

Here, we discuss the principal challenges of metabolomics and proteomics in inherited metabolic disorders. We review the recent developments in mass spectrometry-based proteomic and metabolomic strategies. Mass spectrometry has become the most widely used platform in proteomics and metabolomics because of its ability to analyze a wide range of molecules, its optimal dynamic range, and great sensitivity. The fast measurement of a broad spectrum of metabolites in various body fluids, also collected in small samples like dried blood spots, have been facilitated by the use of mass spectrometry-based techniques. These approaches have enabled the timely diagnosis of inherited metabolic disorders, thereby facilitating early therapeutic intervention. Due to its analytical features, proteomics is suited for the basic investigation of inborn errors of metabolism. Modern approaches enable detailed functional characterization of the pathogenic biochemical processes, as achieved by quantification of proteins and identification of their regulatory chemical modifications.

KEY MESSAGE

Mass spectrometry-based "omics" approaches most frequently used to study the molecular mechanisms underlying inherited metabolic disorders pathophysiology are described.

Biomarkers in Transplantation--Proteomics and Metabolomics

Modern multianalyte "omics" technologies allow for the identification of molecular signatures that confer significantly more information than measurement of a single parameter as typically used in current medical diagnostics. Proteomics and metabolomics bioanalytical assays capture a large set of proteins and metabolites in body fluids, cells, or tissues and, complementing genomics, assess the phenome. Proteomics and metabolomics contribute to the development of novel predictive clinical biomarkers in transplantation in 2 ways: they can be used to generate a diagnostic fingerprint or they can be used to discover individual proteins and metabolites of diagnostic potential. Much fewer metabolomics than proteomics biomarker studies in transplant patients have been reported, and, in contrast to proteomics discovery studies, new lead metabolite markers have yet to emerge. Most clinical proteomics studies have been discovery studies. Several of these studies have assessed diagnostic sensitivity and specificity. Nevertheless, none of these newly discovered protein biomarkers have yet been implemented in clinical decision making in transplantation. The currently most advanced markers discovered in proteomics studies in transplant patients are the chemokines CXCL-9 and CXCL-10, which have successfully been validated in larger multicenter trials in kidney transplant patients. These chemokines can be measured using standard immunoassay platforms, which should facilitate clinical implementation. Based on the published evidence, it is reasonable to expect that these chemokine markers can help guiding and individualizing immunosuppressive regimens, may be able to predict acute and chronic T-cell-mediated and antibody-mediated rejection, and may be useful tools for risk stratification of kidney transplant patients.

Plasma degradome affected by variable storage of human blood

Background

The successful application of—omics technologies in the discovery of novel biomarkers and targets of therapeutic interventions is facilitated by large collections of well curated clinical samples stored in bio banks. Mining the plasma proteome holds promise to improve our understanding of disease mechanisms and may represent a source of biomarkers. However, a major confounding factor for defining disease-specific proteomic signatures in plasma is the variation in handling and processing of clinical samples leading to protein degradation. To address this, we defined a plasma proteolytic signature (degradome) reflecting pre-analytical variability in blood samples that remained at ambient temperature for different time periods after collection and prior to processing.

Methods

We obtained EDTA blood samples from five healthy volunteers (n = 5), and blood tubes remained at ambient temperature for 30 min, 8, 24 and 48 h prior to centrifugation and isolation of plasma. Naturally occurred peptides derived from plasma samples were compared by label-free quantitative LC–MS/MS. To profile protein degradation, we analysed pooled plasma samples at T = 30 min and 48 h using PROTOMAP analysis. The proteolytic pattern of selected protein candidates was further validated by immunoblotting.

Results

A total of 820 plasma proteins were surveyed by PROTOMAP, and for 4 % of these, marked degradation was observed. We show distinct proteolysis patterns for talin-1, coagulation factor XI, complement protein C1r, C3, C4 and thrombospondin, and several proteins including S100A8, A9, annexin A1, profiling-1 and platelet glycoprotein V are enriched after 48 h blood storage at ambient temperature. In particular, thrombospondin protein levels increased after 8 h and proteolytic fragments appeared after 24 h storage time.

Conclusions

The overall impact of blood storage at ambient temperature for variable times on the plasma proteome and degradome is relatively minor, but in some cases can cause a potential bias in identifying and assigning relevant proteomic markers. The observed effects on the plasma proteome and degradome are predominantly triggered by limited leucocyte and platelet cell activation due to blood handling and storage. The baseline plasma degradome signature presented here can help filtering candidate protein markers relevant for clinical biomarker studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-016-9126-9) contains supplementary material, which is available to authorized users.

Renal allograft fibrosis: biology and therapeutic targets

Renal tubulointerstitial fibrosis is the final common pathway of progressive renal diseases. In allografts, it is assessed with tubular atrophy as interstitial fibrosis/tubular atrophy (IF/TA). IF/TA occurs in about 40% of kidney allografts at 3-6 months after transplantation, increasing to 65% at 2 years. The origin of renal fibrosis in the allograft is complex and includes donor-related factors, in particular in case of expanded criteria donors, ischemia-reperfusion injury, immune-mediated damage, recurrence of underlying diseases, hypertensive damage, nephrotoxicity of immunosuppressants, recurrent graft infections, postrenal obstruction, etc. Based largely on studies in the non-transplant setting, there is a large body of literature on the role of different cell types, be it intrinsic to the kidney or bone marrow derived, in mediating renal fibrosis, and the number of mediator systems contributing to fibrotic changes is growing steadily. Here we review the most important cellular processes and mediators involved in the progress of renal fibrosis, with a focus on the allograft situation, and discuss some of the challenges in translating experimental insights into clinical trials, in particular fibrosis biomarkers or imaging modalities.

Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression

BACKGROUND

Chronic kidney disease (CKD) is a growing public health concern, afflicting approximately one-tenth of adults in developed countries. However, the clinical need for an accurate test, such as a biomarker and/or peptide classifier, for predicting CKD progression and related adverse outcomes remains unaddressed. Recently, a proteomics approach based on capillary electrophoresis-mass spectrometry was employed to develop a urinary peptide-based high-dimensional classifier, namely CKD273, for predicting CKD progression.

OBJECTIVES

The study aims to critically appraise the evidence level of the CKD273 classifier's utility in predicting CKD progression, according to the Oxford Evidence-Based Medicine (EBM) and Strength of Recommendation Taxonomy (SORT) guidelines.

METHODS

Eligible studies were identified by a literature search of MEDLINE and Web of Science Expanded Core Collection databases. Limitations were set to prospective cohort studies evaluating the classifier's accuracy in predicting CKD progression. Data extraction was undertaken according to a predefined protocol by two independent reviewers. The EBM and SORT guidelines were applied to appraise the CKD273 classifier's utility for predicting CKD progression.

RESULTS

The query search results rendered four prospective cohort studies. The classifier performed independently of age, gender and the type of urine storage containers used. The classifier predicted the development of micro- or macroalbuminuria and rapid decline (i.e. >-5% annual decrease) in the estimated glomerular filtration rate. One study assessed the association of the classifier with end-stage renal disease and death but did not take confounding factors into account. The CKD273 classifier attained high evidence levels according to the EBM (score range 1b), supporting its utility for predicting CKD progression. However, lower scores were attained when the studies were scored according the SORT guidelines (score ranges 1-4).

CONCLUSIONS

Initial promising evidence supports the CKD273 classifier's utility in predicting CKD progression. The classifier's applicability should be corroborated with additional evidence arising from inception cohort studies assessing patient-oriented outcomes, which demonstrate its added value beyond currently available clinical risk predictors, as well as its cost-effectiveness in clinical practice.

The urinary proteome and metabonome differ from normal in adults with mitochondrial disease

We studied the extent and nature of renal involvement in a cohort of 117 adult patients with mitochondrial disease, by measuring urinary retinol-binding protein (RBP) and albumin; established markers of tubular and glomerular dysfunction, respectively. Seventy-five patients had the m.3243A>G mutation and the most frequent phenotypes within the entire cohort were 14 with MELAS, 33 with MIDD, and 17 with MERRF. Urinary RBP was increased in 29 of 75 of m.3243A>G patients, whereas albumin was increased in 23 of the 75. The corresponding numbers were 16 and 14, respectively, in the 42 non-m.3243A>G patients. RBP and albumin were higher in diabetic m.3243A>G patients than in nondiabetics, but there were no significant differences across the three major clinical phenotypes. The urine proteome (mass spectrometry) and metabonome (nuclear magnetic resonance) in a subset of the m.3243A>G patients were markedly different from controls, with the most significant alterations occurring in lysosomal proteins, calcium-binding proteins, and antioxidant defenses. Differences were also found between asymptomatic m.3243A>G carriers and controls. No patients had an elevated serum creatinine level, but 14% had hyponatremia, 10% had hypophosphatemia, and 14% had hypomagnesemia. Thus, abnormalities in kidney function are common in adults with mitochondrial disease, exist in the absence of elevated serum creatinine, and are not solely explained by diabetes.

The primary glomerulonephritides: a systems biology approach

Our understanding of the pathogenesis of most primary glomerular diseases, including IgA nephropathy, membranous nephropathy and focal segmental glomerulosclerosis, is limited. Advances in molecular technology now permit genome-wide, high-throughput characterization of genes and gene products from biological samples. Comprehensive examinations of the genome, transcriptome, proteome and metabolome (collectively known as omics analyses), have been applied to the study of IgA nephropathy, membranous nephropathy and focal segmental glomerulosclerosis in both animal models and human patients. However, most omics studies of primary glomerular diseases, with the exception of large genomic studies, have been limited by inadequate sample sizes and the lack of kidney-specific data sets derived from kidney biopsy samples. Collaborative efforts to develop a standardized approach for prospective recruitment of patients, scheduled monitoring of clinical outcomes, and protocols for sampling of kidney tissues will be instrumental in uncovering the mechanisms that drive these diseases. Integration of molecular data sets with the results of clinical and histopathological studies will ultimately enable these diseases to be characterized in a comprehensive and systematic manner, and is expected to improve the diagnosis and treatment of these diseases.

Metabolomics in chronic kidney disease

Chronic kidney disease (CKD) represents a major challenge to public healthcare. Traditional clinical biomarkers of renal function (blood urea nitrogen and serum creatinine) are not sensitive or specific enough and only increase significantly after the presence of substantial CKD. Therefore, more sensitive biomarkers of CKD are needed. CKD-specific biomarkers at an early disease stage and early diagnosis of specific renal diseases would enable improved therapeutic treatment and reduced the personal and financial burdens. The goal of metabolomics is to identify non-targeted, global small-molecule metabolite profiles of complex samples, such as biofluids and tissues. This method offers the potential for a holistic approach to clinical medicine, as well as improvements in disease diagnoses and the understanding of pathological mechanisms. This review article presents an overview of the recent developments in the field of metabolomics, followed by an in-depth discussion of its application to the study of CKD (primary, chronic glomerulonephritis such as IgA nephropathy; secondary, chronic renal injury such as diabetic nephropathy; chronic renal failure including end-stage kidney disease with and without undergoing replacement therapies, etc), including metabolomic analytical technologies, chemometrics, and metabolomics in experimental and clinical research. We describe the current status of the identification of metabolic biomarkers in CKD. Several markers have been confirmed across multiple studies to detect CKD earlier than traditional clinical chemical and histopathological methods. The application of metabolomics in CKD studies provides researchers the opportunity to gain new insights into metabolic profiling and pathophysiological mechanisms. Particular challenges in the field are presented and placed within the context of future applications of metabolomic approaches to the studies of CKD.

Proteome-base biomarkers in diabetes mellitus: progress on biofluids' protein profiling using mass spectrometry

The worldwide number of individuals suffering from diabetes mellitus (DM) has been projected to rise from 171 million in 2000 to 366 million in 2030. Identification of specific biomarkers for prediction and monitoring of DM is needed not only for the adequate screening diagnosis but also to assist the design of interventions to prevent or delay progression of this pathology and its attendant complications. Proteomic methods based on MS hold special promise for the identification of novel biomarkers that might form the foundation for new clinical tests, but to date, their contribution has been somehow unfruitful. Indeed, from more than 300 proteins found differently modulated in body fluids from diabetic patients, approximately 50 were validated with other approaches like ELISA or Western blotting and the clinical trials are being initiated to employ biofluids' proteomics (specifically urinary proteomics) in clinical decision. This review provides an overview of MS-based applications in the identification of potential biomarkers for DM, emphasizing the methodological challenges involved.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus

AIMS/HYPOTHESIS

Microalbuminuria is considered the first clinical sign of kidney dysfunction and is associated with a poor renal and cardiovascular prognosis in type 2 diabetes. Detection of patients who are prone to develop micro- or macroalbuminuria may represent an effective strategy to start or optimise therapeutic intervention. Here we assessed the value of a urinary proteomic-based risk score (classifier) in predicting the development and progression of microalbuminuria.

METHODS

We conducted a prospective case-control study. Cases (n = 44) and controls (n = 44) were selected from the PREVEND (Prevention of Renal and Vascular End-stage Disease) study and from the Steno Diabetes Center (Gentofte, Denmark). Cases were defined by transition from normo- to microalbuminuria or from micro- to macroalbuminuria over a follow-up of 3 years. Controls with no transitions in albuminuria were pair-matched for age, sex and albuminuria status. A model for the progression of albuminuria was built using a proteomic classifier based on 273 urinary peptides.

RESULTS

The proteomic classifier was independently associated with transition to micro- or macroalbuminuria (OR 1.35 [95% CI 1.02, 1.79], p = 0.035). The classifier predicted the development and progression of albuminuria on top of albuminuria and estimated GFR (eGFR, area under the receiver operating characteristic [ROC] curve increase of 0.03, p = 0.002; integrated discrimination index [IDI]: 0.105, p = 0.002). Fragments of collagen and α-2-HS-glycoprotein showed significantly different expression between cases and controls.

CONCLUSIONS/INTERPRETATION

Although limited by the relatively small sample size, these results suggest that analysis of a urinary biomarker set enables early renal risk assessment in patients with diabetes. Further work is required to confirm the role of urinary proteomics in the prevention of renal failure in diabetes.

Redox proteomics in study of kidney-associated hypertension: new insights to old diseases

SIGNIFICANCE

The kidney helps to maintain low blood pressure in the human body, and impaired kidney function is a common attribute of aging that is often associated with high blood pressure (hypertension). Kidney-related pathologies are important contributors (either directly or indirectly) to overall human mortality. In comparison with other organs, kidney has an unusually wide range of oxidative status, ranging from the well-perfused cortex to near-anoxic medulla.

RECENT ADVANCES

Oxidative stress has been implicated in many kidney pathologies, especially chronic kidney disease, and there is considerable research interest in oxidative stress biomarkers for earlier prediction of disease onset. Proteomics approaches have been taken to study of human kidney tissue, serum/plasma, urine, and animal models of hypertension.

CRITICAL ISSUES

Redox proteomics, in which oxidative post-translational modifications can be identified in protein targets of oxidative or nitrosative stress, has not been very extensively pursued in this set of pathologies.

FUTURE DIRECTIONS

Proteomics studies of kidney and related tissues have relevance to chronic kidney disease, and redox proteomics, in particular, represents an under-exploited toolkit for identification of novel biomarkers in this commonly occurring pathology.

Proteomics in hypertension and other cardiovascular diseases

Hypertension is a major cardiovascular risk factor with a multifactorial pathogenesis, including genetic and environmental factors. In addition to hypothesis-driven strategies, unbiased approaches such as genomics, proteomics, and metabolomics are useful tools to help unravel the pathophysiology of hypertension and associated organ damage. During development of cardiovascular disease the key organs and tissues undergo extensive functional and structural changes that are characterized by alterations in the amount and type of proteins that are expressed. Proteomic approaches study the expression of large numbers of proteins in organs, tissues, cells, and body fluids. A number of different proteomic platforms are available, many of which combine two methods to separate proteins and peptides after an initial digestion step. Identification of these peptides and changes in their expression in parallel with disease processes or medical treatment will help to identify as yet unknown pathophysiological pathways. There is also potential to use proteomic signatures as biomarkers of cardiovascular disease that will contribute to population screening, diagnosis of diseases and their severity, and monitoring of therapeutic interventions.

CE-MS for proteomics: Advances in interface development and application

Capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a powerful technique for the analysis of proteins and peptides. Over the past few years, significant progress has been made in the development of novel and more effective interfaces for hyphenating CE to MS. This review provides an overview of these new interfacing techniques for coupling CE to MS, covering the scientific literature from January 2007 to December 2011. The potential of these new CE-MS interfacing techniques is demonstrated within the field of (clinical) proteomics, more specifically "bottom-up" proteomics, by showing examples of the analysis of various biological samples. The relevant papers on CE-MS for proteomics are comprehensively summarized in tables, including, e.g. information on sample type and pretreatment, interfacing and MS detection mode. Finally, general conclusions and future perspectives are provided.

Biomarkers for diabetes prediction, pathogenesis or pharmacotherapy guidance? Past, present and future possibilities

An ideal biomarker should refine identification of those at risk of disease occurrence or progression, improve prediction of complications of disease, and/or guide and help tailor responses to different therapies. Biomarkers that give insights into disease pathogenesis are also of interest. With this in mind, this review describes biomarker studies relevant to diabetes, focusing on those conducted by the author, his colleagues and collaborators. The review highlights several points. (1) Novel biomarkers may not improve prediction of new-onset diabetes in a meaningful way beyond what can be achieved with simple measures combined with HbA(1c), and a sensible way ahead may be to combine diabetes and cardiovascular disease prediction using HbA(1c) and such measures. (2) In terms of disease pathogenesis, associations do not necessarily infer causality; potential for residual confounding and reverse causality should always be borne in mind. The potential relevance of such issues to understanding the relationship of some topical variables/pathways, namely adiponectin, inflammation and vitamin D, with diabetes will be highlighted. (3) How baseline and serial data on biomarkers arising from the liver have improved our understanding of the role of hepatic fat in diabetes pathogenesis will be explored. (4) Future goals for diabetes biomarker research should focus on predicting complications and determining subgroups who may respond better to particular therapies. (5) All novel biomarker research (regardless of analytical platforms used) needs to be tested against information available from commonly measured variables in clinical practice. Otherwise, many claims of clinical utility can be exaggerated. In summary, biomarker research in diabetes is continuing apace in a number of areas, but it remains to be seen whether the promise of biomarker research to improve the care of our patients becomes a reality.

Urinary metabonomics study on biochemical changes in an experimental model of chronic renal failure by adenine based on UPLC Q-TOF/MS

BACKGROUND

Chronic renal failure (CRF) is a serious clinical symptom, occurring as the end result of all kinds of chronic kidney disease and its pathophysiological mechanism is not yet well understood. We investigated the metabolic profiling of urine samples from CRF model rats to find potential disease biomarkers and research pathology of CRF.

METHODS

An animal model of CRF was produced by adenine. Metabolic profiling of the urine was performed by using ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC Q-TOF/MS). Acquired data were subjected to principal component analysis (PCA) for differentiating the CRF and the normal control groups. Potential biomarkers were screened by using S-plot and were identified by the accurate mass, isotopic pattern and MS(E) fragments information obtained from UPLC Q-TOF/MS analysis.

RESULTS

12 metabolites in urine were identified as potential biomarkers. Adenine-induced CRF rats were characterized by the increase of phytosphingosine, adrenosterone, tryptophan, 2,8-dihydroxyadenine, creatinine, and dihydrosphingosine together with the decrease of N-acetylleucine, 3-O-methyldopa, ethyl-N2-acetyl-L-argininate, dopamine, phenylalanine and kynurenic acid in urine. The altered metabolites demonstrated perturbations of amino acids metabolism, phospholipids metabolism and creatinine metabolism in CRF rats.

CONCLUSION

This work shows that metabonomics method is a valuable tool in CRF mechanism study and assists in clinical diagnosis of CRF.