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Background: The risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression., Methods: We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi., Results: The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts., Conclusions: The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Background and Hypothesis: Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies., Methods: The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models., Results: In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state., Conclusion: Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Background: Focal segmental glomerulosclerosis (FSGS) is divided into genetic, primary (p), uncertain cause, and secondary (s) forms. The subclasses differ in management and prognosis with differentiation often being challenging. We aimed to identify specific urine proteins/peptides discriminating between clinical and biopsy-proven pFSGS and sFSGS., Methods: Sixty-three urine samples were collected in two different centers (19 pFSGS and 44 sFSGS) prior to biopsy. Samples were analysed using capillary electrophoresis-coupled mass spectrometry. For biomarker definition, datasets of age-/sex-matched normal controls (NC, n  = 98) and patients with other chronic kidney diseases (CKDs, n  = 100) were extracted from the urinary proteome database. Independent specificity assessment was performed in additional data of NC ( n  = 110) and CKD ( n  = 170)., Results: Proteomics data from patients with pFSGS were first compared to NC ( n  = 98). This resulted in 1179 biomarker ( P  < 0.05) candidates. Then, the pFSGS group was compared to sFSGS, and in a third step, pFSGS data were compared to data from different CKD etiologies ( n  = 100). Finally, 93 biomarkers were identified and combined in a classifier, pFSGS93. Total cross-validation of this classifier resulted in an area under the receiving operating curve of 0.95. The specificity investigated in an independent set of NC and CKD of other etiologies was 99.1% for NC and 94.7% for CKD, respectively. The defined biomarkers are largely fragments of different collagens (49%)., Conclusion: A urine peptide-based classifier that selectively detects pFSGS could be developed. Specificity of 95%-99% could be assessed in independent samples. Sensitivity must be confirmed in independent cohorts before routine clinical application., Competing Interests: J.S. is employee of Mosaiques Diagnostics. H.M. is co-founder and a shareholder of Mosaiques Diagnostics. B.H., M.W., and I.P. are employees of Travere Therapeutics., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Background: Immunoglobulin A nephropathy (IgAN) frequently leads to kidney failure. The urinary proteomics-based classifier IgAN237 may predict disease progression at the time of kidney biopsy. We studied whether IgAN237 also predicts progression later in the course of IgAN., Methods: Urine from patients with biopsy-proven IgAN was analyzed using capillary electrophoresis-mass spectrometry at baseline (IgAN237-1, n = 103) and at follow-up (IgAN237-2, n = 89). Patients were categorized as "non-progressors" (IgAN237 ≤0.38) and "progressors" (IgAN237 >0.38). Estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio slopes were calculated., Results: Median age at biopsy was 44 years, interval between biopsy and IgAN237-1 was 65 months and interval between IgAN237-1 and IgAN237-2 was 258 days (interquartile range 71-531). IgAN237-1 and IgAN237-2 values did not differ significantly and were correlated (rho = 0.44, P < .001). Twenty-eight percent and 26% of patients were progressors based on IgAN237-1 and IgAN237-2, respectively. IgAN237 inversely correlated with chronic eGFR slopes (rho = -0.278, P = .02 for score-1; rho = -0.409, P = .002 for score-2) and with ±180 days eGFR slopes (rho = -0.31, P = .009 and rho = -0.439, P = .001, respectively). The ±180 days eGFR slopes were worse for progressors than for non-progressors (median -5.98 versus -1.22 mL/min/1.73 m2 per year for IgAN237-1, P < .001; -3.02 vs 1.08 mL/min/1.73 m2 per year for IgAN237-2, P = .0047). In multiple regression analysis baseline progressor/non-progressor according to IgAN237 was an independent predictor of eGFR180days-slope (P = .001)., Conclusion: The urinary IgAN237 classifier represents a risk stratification tool in IgAN also later in the course of the dynamic disease. It may guide patient management in an individualized manner., (© The Author(s) 2023. Published by Oxford University Press on behalf of the ERA.)

Background: Kidney transplantation is the best treatment option for end-stage kidney disease but is still associated with long-term graft failure. In this study, we evaluated the application of urinary proteomics to identify grafts with high failure risk before initial decline of estimated glomerular filtration rate (eGFR) with irreversible graft changes., Methods: Fifty-two living donor kidney transplant recipients (KTR) with 8-year follow-up were enrolled. All patients underwent clinical examination and had a routine laboratory screening at 3, 6, 12, 24, 36, 48 and 96 months post-transplantation, including creatinine, urea, albumin and 24-h proteinuria. Graft function was estimated according to Nankivell. Urine samples at Month 24 were analysed by capillary electrophoresis coupled mass spectrometry followed by classification with the chronic kidney disease classifier CKD273., Results: CKD273 showed significant correlation with serum creatinine at every time point and moderate inverse correlation for the slope in glomerular filtration rates by Nankivell (r = -0.29, P = 0.05). Receiver operating characteristics analysis for graft loss and death within the next 6 years after proteomic analysis resulted in an area under curve value of 0.89 for CKD273 being superior to 0.67 for Nankivell eGFR. Stratification into CKD273-positive and -negative patient groups revealed a hazard ratio of 16.5 for prevalence of graft loss in case of CKD273 positivity., Conclusions: Using a representative KTR cohort with 8-year follow-up, we could demonstrate significant value of CKD273 for risk stratification of graft loss. This study provides the conceptual basis for further evaluation of CKD273 as a prognostic tool for long-term graft function risk stratification by large prospective clinical trials., (© The Author(s) 2021. Published by Oxford University Press on behalf of the ERA.)

Background: Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification., Methods: In this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models., Results: Of the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83-0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64-0.81)., Conclusions: A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone., (© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA.)

Cardiorenal syndrome (CRS) is defined by coexisting heart and renal dysfunctions. Malfunction of 1 organ may cause dysfunction of the other with variable causative disease that defines the type of CRS (1-5). Numerous studies showed that the prevalence of cardiovascular disease is increased in patients with chronic kidney disease (CKD). Similarly, CKD affects a large proportion of patients with heart failure. This overlap between primary heart or primary kidney disease blurs cause-effect inferences of the initiator/target organ. The classical subdivision of CRS in 5 categories does not provide pathophysiological suggestions for targeted intervention. It seems timely to revisit the value of CRS biomarkers in a pathophysiology-centered approach. We systematically reviewed the literature in CRS, which revealed 53 clinical studies describing the use of 44 biomarkers and 4 proteomic panels. All biomarkers are involved in at least one of the CRS comorbidities. Among the pathways affected, inflammation, aberrant glucose metabolism, neurohormonal activation, and oxidative stress are well described. There is growing evidence that fibrosis may be the "cornerstone" that unifies most of the pathways leading to CRS. Formation of excess fibrous connective tissue antedates CRS in many cases. This review highlights that biomarkers reflecting fibrosis may be of substantial clinical value in the early detection, prognostication, and guiding treatment of CRS. Biomarkers detecting changes in collagen turnover in the extracellular matrix of heart and kidney appear able to depict subclinical changes in the fibrotic remodeling of tissues and constitute a promising approach toward personalized intervention in CRS., (© American Journal of Hypertension, Ltd 2021. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1-G5., Methods: We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1-G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs)., Results: In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1-G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a >10-fold increased risk as compared with the lowest tertile., Conclusions: The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management., (© The Author(s) 2019. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The hepatokine fetuin-A, released by the human liver, promotes pro-inflammatory effects of perivascular fat. The involvement of inflammation in type 2 diabetes mellitus (T2DM) can affect the kidney and contribute to the development of diabetic kidney disease. Therefore we examined the association of urinary fetuin-A protein fragments with renal damage in T2DM patients., Methods: Urinary peptides of 1491 individuals using proteome data available from the human urine proteome database were analysed. Prediction of proteases involved in urinary peptide generation was performed using the Proteasix tool., Results: We identified 14 different urinary protein fragments that belong to the region of the connecting peptide (amino acid 301-339) of the total fetuin-A protein. Calpains (CAPN1 and CAPN2), matrix metalloproteinase and pepsin A-3 were identified as potential proteases that were partially confirmed by previous in vitro studies. Combined fetuin-A peptides (mean of amplitudes) were significantly increased in T2DM patients with kidney disease and to a lesser extent with cardiovascular risk. Furthermore, fetuin-A peptide levels displayed a significant negative correlation with baseline estimated glomerular filtration rate (eGFR) values ( r  = -0.316, P < 0.0001) and with the slope (%) of eGFR per year ( r  = -0.096, P = 0.023). A multiple regression model including fetuin-A peptide and albuminuria resulted in a significantly improved correlation with eGFR ( r  = -0.354, P < 0.0001) compared with albuminuria, indicating an added value of this novel biomarker., Conclusions: The urinary proteome analysis demonstrated the association of fetuin-A peptides with impaired kidney function in T2DM patients. Furthermore, fetuin-A peptides displayed early signs of kidney damage before albuminuria appeared and therefore can be used as markers for kidney disease detection., (© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA.)

Background: Nephropathy related to obesity lacks a pathophysiological understanding and definite diagnostic pathways by biomarkers., Methods: In this study we investigated the association between urinary peptides and body mass index (BMI) and renal function in proteome data sets from 4015 individuals., Results: A total of 365 urinary peptides were identified to be significantly associated with BMI. The majority of these peptides were collagen fragments. In addition, most of the peptides also demonstrated a significant concordant association with estimated glomerular filtration rate (eGFR) in the investigated cohort, with the presence of diabetes exhibiting no significant association. A new classifier was developed, based on 150 urinary peptides, that enabled the distinction of non-obese subjects with preserved kidney function from obese, non-diabetic subjects with eGFR >45 mL/min/1.73 m 2 in an independent cohort, with an area under the curve of 0.93., Conclusions: On a molecular level, the data strongly suggest a link between obesity and fibrosis, which may be a major cause of obesity-related nephropathy., (© The Author(s) 2020. Published by Oxford University Press on behalf of ERA-EDTA.)

Background: Mutations in Melanoma Antigen-encoding Gene D2 (MAGED2) promote tubular dysfunction, suggesting that MAGE proteins may play a role in kidney pathophysiology. We have characterized the expression and regulation of MAGE genes in normal kidneys and during kidney disease., Methods: The expression of MAGE genes and their encoded proteins was explored by systems biology multi-omics (kidney transcriptomics and proteomics) in healthy adult murine kidneys and following induction of experimental acute kidney injury (AKI) by a folic acid overdose. Changes in kidney expression during nephrotoxic AKI were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blot and immunohistochemistry. Factors regulating gene expression were studied in cultured tubular cells., Results: Five MAGE genes (MAGED1, MAGED2, MAGED3, MAGEH1, MAGEE1) were expressed at the mRNA level in healthy adult mouse kidneys, as assessed by RNA-Seq. Additionally, MAGED2 was significantly upregulated during experimental AKI as assessed by array transcriptomics. Kidney proteomics also identified MAGED2 as upregulated during AKI. The increased kidney expression of MAGED2 mRNA and protein was confirmed by qRT-PCR and western blot, respectively, in murine folic acid- and cisplatin-induced AKI. Immunohistochemistry located MAGED2 to tubular cells in experimental and human kidney injury. Tubular cell stressors [serum deprivation and the inflammatory cytokine tumour necrosis factor-like weak inducer of apoptosis (TWEAK)] upregulated MAGED2 in cultured tubular cells., Conclusions: MAGED2 is upregulated in tubular cells in experimental and human kidney injury and is increased by stressors in cultured tubular cells. This points to a role of MAGED2 in tubular cell injury during kidney disease that should be dissected by carefully designed functional approaches., (© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: Chronic kidney disease (CKD) is common in patients after heart transplantation (HTx). We assessed whether in HTx recipients the proteomic urinary classifier CKD273 or sequenced urinary peptides revealing the parental proteins correlated with the estimated glomerular filtration rate (eGFR)., Methods: In 368 HTx patients, we measured the urinary peptidome and analysed CKD273 and 48 urinary peptides with a detectable signal in >95% of participants. After 9.1 months (median), eGFR and the urinary biomarkers were reassessed., Results: In multivariable Bonferroni-corrected analyses of the baseline data, a 1-SD increase in CKD273 was associated with a 11.4 [95% confidence interval (CI) 7.25-15.5] mL/min/1.73 m2 lower eGFR and an odds ratio of 2.63 (1.56-4.46) for having eGFR <60 mL/min/1.73 m2. While relating eGFR category at follow-up to baseline urinary biomarkers, CKD273 had higher (P = 0.007) area under the curve (0.75; 95% CI 0.70-0.80) than 24-h proteinuria (0.64; 95% CI 0.58-0.69), but additional adjustment for baseline eGFR removed significance of both biomarkers. In partial least squares analysis, the strongest correlates of the multivariable-adjusted baseline eGFR were fragments of collagen I (positive) and the mucin-1 subunit α (inverse). Associations between the changes in eGFR and the urinary markers were inverse for CKD273 and mucin-1 and positive for urinary collagen I., Conclusions: With the exception of baseline eGFR, CKD273 was more closer associated with imminent renal dysfunction than 24-h proteinuria. Fragments of collagen I and mucin-1-respectively, positively and inversely associated with eGFR and change in eGFR-are single-peptide markers associated with renal dysfunction., (© The Author(s) 2018. Published by Oxford University Press on behalf of ERA-EDTA.)

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

Background: The mineralocorticoid receptor antagonist spironolactone significantly reduces albuminuria in patients with diabetes. Prior studies have shown large between-patient variability in albuminuria treatment response. We previously developed and validated a urinary proteomic classifier that predicts onset and progression of chronic kidney disease. Here, we tested whether the proteomic classifier based on 273 urinary peptides (CKD273) predicts albuminuria response to spironolactone treatment., Methods: We performed a post hoc analysis in a double-blind randomized clinical trial with allocation to either spironolactone 12.5-50 mg/day (n = 57) or placebo (n = 54) for 16 weeks. Patients were diagnosed with type 2 diabetes and resistant hypertension. Treatment was an adjunct to renin-angiotensin system inhibition. Primary endpoint was the percentage change in urine albumin to creatinine ratio (UACR). Capillary electrophoresis mass spectrometry was used to quantify urinary peptides at baseline. The previously validated combination of 273 known urinary peptides was used as proteomic classifier., Results: Spironolactone reduced UACR relative to placebo by 50%, although with a large between-patient variability in UACR response (5th to 95th percentile, 7 to 312%). An interaction was detected between CKD273 and treatment assignment (β = -1.09, P = 0.026). Higher values of CKD273 at baseline were associated with a larger reduction in UACR in the spironolactone group (β = -0.70, P = 0.049), but not in the placebo group (β = 0.39, P = 0.25). Stratified in tertiles of baseline CKD273, reduction in UACR was greater in the highest tertile, 63% (95% confidence interval: 35-79%), as compared with the two other tertiles combined, 16% (-17 to 40%) (P = 0.011)., Conclusions: A urinary proteomics classifier can be used to identify individuals with type 2 diabetes who are more likely to show an albuminuria-lowering response to spironolactone treatment. These results suggest that urinary proteomics may be a valuable tool to tailor therapy, but confirmation in a larger clinical trial is required., (© The Authors 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease., Methods: We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers., Results: For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology., Conclusions: Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: Early prevention of diabetic nephropathy is not successful as early interventions have shown conflicting results, partly because of a lack of early and precise indicators of disease development. Urinary proteomics has shown promise in this regard and could identify those at high risk who might benefit from treatment. In this study we investigate its utility in a large type 2 diabetic cohort with normoalbuminuria., Methods: We performed a post hoc analysis in the Diabetic Retinopathy Candesartan Trials (DIRECT-Protect 2 study), a multi centric randomized clinical controlled trial. Patients were allocated to candesartan or placebo, with the aim of slowing the progression of retinopathy. The secondary endpoint was development of persistent microalbuminuria (three of four samples). We used a previously defined chronic kidney disease risk score based on proteomic measurement of 273 urinary peptides (CKD273-classifier). A Cox regression model for the progression of albuminuria was developed and evaluated with integrated discrimination improvement (IDI), continuous net reclassification index (cNRI) and receiver operating characteristic curve statistics., Results: Seven hundred and thirty-seven patients were analysed and 89 developed persistent microalbuminuria (12%) with a mean follow-up of 4.1 years. At baseline the CKD273-classifier predicted development of microalbuminuria during follow-up, independent of treatment (candesartan/placebo), age, gender, systolic blood pressure, urine albumin excretion rate, estimated glomerular filtration rate, HbA1c and diabetes duration, with hazard ratio 2.5 [95% confidence interval (CI) 1.4-4.3; P = 0.002] and area under the curve 0.79 (95% CI 0.75-0.84; P < 0.0001). The CKD273-classifier improved the risk prediction (relative IDI 14%, P = 0.002; cNRI 0.10, P = 0.043)., Conclusions: In this cohort of patients with type 2 diabetes and normoalbuminuria from a large intervention study, the CKD273-classifier was an independent predictor of microalbuminuria. This may help identify high-risk normoalbuminuric patients for preventive strategies for diabetic nephropathy., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The excessive accumulation of extracellular matrix (ECM) in the renal tubulointerstitium is a key component of chronic renal damage in lupus nephritis (LN) and a critical determinant of the disease progression to renal failure. Detection of fibrosis requires renal biopsy and is therefore limited by high risks associated with an invasive procedure. This study explores whether a unique LN urinary peptidome can be identified and whether LN-specific alteration reflects the underlying fibrogenic process of altered ECM turnover., Method: Urinary peptides were analyzed for 36 LN and 35 nonrenal systemic lupus erythematosus (SLE) subjects and 58 healthy volunteers (HVs)., Results: In total, 70 collagen and 230 noncollagen peptides were significantly changed between LN and nonrenal SLE and between LN and HV and defined as 'LN peptides'; 14 proteases associated with observed LN collagen peptides were identified and activities in 9 proteases were significantly different between LN and nonrenal SLE; 28 collagen peptides were correlated with at least one parameter of clinical renal dysfunction or histolopathology., Conclusion: Urinary peptidomic alterations likely reflect pathogenic pathways involving ECM turnover in LN kidneys and potentially could be developed as biomarkers to monitor renal disease progression., (© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

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., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

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.

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., (© The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

The quest for the ideal therapeutic target in chronic kidney disease (CKD) has been riddled with many obstacles stemming from the molecular complexity of the disease and its co-morbidities. Recent advances in omics technologies and the resulting amount of available data encompassing genomics, proteomics, peptidomics, transcriptomics and metabolomics has created an opportunity for integrating omics datasets to build a comprehensive and dynamic model of the molecular changes in CKD for the purpose of biomarker and drug discovery. This article reviews relevant concepts in omics data integration using systems biology, a mathematical modelling method that globally describes a biological system on the basis of its modules and the functional connections that govern their behaviour. The review describes key databases and bioinformatics tools, as well as the challenges and limitations of the current state of the art, along with practical application to CKD therapeutic target discovery. Moreover, it describes how systems biology and visualization tools can be used to generate clinically relevant molecular models with the capability to identify specific disease pathways, recognize key events in disease development and track disease progression., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: The reduced glomerular filtration rate in the advanced stages of chronic kidney disease (CKD) leads to plasma accumulation of uraemic retention solutes including proteins. It has been hypothesized that these changes may, at least in part, be responsible for CKD-associated morbidity and mortality. However, most studies focused on the role of individual proteins, while a holistic, large-scale, integrative approach may generate significant additional insight., Methods: In a discovery study, we analysed the plasma proteome of patients with stage 2-3 CKD (n = 14) and stage 5 CKD with haemodialysis (HD) (n = 15), using high-resolution LC-MS/MS analysis. Selected results were validated in a cohort of 40 patients with different CKD stages with or without HD, using ELISA., Results: Of a total of 2054 detected proteins, 127 displayed lower, while 206 displayed higher abundance in the plasma of patients on HD. Molecular pathway analysis confirmed the modification of known processes involved in CKD complications, including decreased haemostasis and increased inflammation, complement activation and vascular damage. In addition, we identified the plasma increase during CKD progression of lysozyme C and leucine-rich alpha-2 glycoprotein, two proteins related to vascular damage and heart failure. High level of leucine-rich alpha-2 glycoprotein was associated with higher mortality in stage 5 CKD patients on HD., Conclusions: This study provides for the first time a comprehensive assessment of CKD plasma proteome, contributing to new knowledge and potential markers of CKD. These results will serve as a basis for future studies investigating the relevance of these molecules in CKD associated morbidity and mortality., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Motivation: A special characteristic of data from molecular biology is the frequent occurrence of zero intensity values which can arise either by true absence of a compound or by a signal that is below a technical limit of detection., Results: While so-called two-part tests compare mixture distributions between groups, one-part tests treat the zero-inflated distributions as left-censored. The left-inflated mixture model combines these two approaches. Both types of distributional assumptions and combinations of both are considered in a simulation study to compare power and estimation of log fold change. We discuss issues of application using an example from peptidomics.The considered tests generally perform best in scenarios satisfying their respective distributional assumptions. In the absence of distributional assumptions, the two-part Wilcoxon test or the empirical likelihood ratio test is recommended. Assuming a log-normal subdistribution the left-inflated mixture model provides estimates for the proportions of the two considered types of zero intensities., Availability: R code is available at http://cemsiis.meduniwien.ac.at/en/kb/science-research/software/, (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: We investigate whether the urinary proteome refines the diagnosis of renal dysfunction, which affects over 10% of the adult population., Methods: We measured serum creatinine, estimated glomerular filtration rate (eGFR) and 24-h albuminuria in 797 people randomly recruited from a population. We applied capillary electrophoresis coupled with mass spectrometry to measure multi-dimensional urinary proteomic classifiers developed for renal dysfunction (CKD273) or left ventricular dysfunction (HF1 and HF2). Renal function was followed up in 621 participants and the incidence of cardiovascular events in the whole study population., Results: In multivariable-adjusted cross-sectional analyses, higher biomarker levels analysed separately or combined by principal component analysis into a single factor (SF), correlated (P ≤ 0.010) with worse renal function. Over 4.8 years, higher HF1 and SF predicted (P ≤ 0.014) lowering of eGFR; higher HF2 predicted (P ≤ 0.049) increase in serum creatinine and decrease eGFR. HF1, HF2 and SF predicted progression from CKD Stages 2 or ≤2 to Stage ≥3, with risk estimates for a 1-SD increment in the urinary biomarkers ranging from 38 to 71% (P ≤ 0.039). HF1, HF2 and SF yielded a net reclassification improvement of 31-51% (P ≤ 0.029). Over 6.1 years, 47 cardiovascular events occurred. HF2 and SF, independent of baseline eGFR, 24-h albuminuria and other covariables were significant predictors of cardiovascular complications with risk estimates for 1-SD increases ranging from 32 to 41% (P ≤ 0.047)., Conclusions: The urinary proteome refines the diagnosis of existing or progressing renal dysfunction and predicts cardiovascular complications., (© The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Background: Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The 'Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273)., Methods: In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier., Results: We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found., Conclusion: We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY trial., (© The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Aims: Despite the significant heart failure (HF) burden on society, easily applicable screening techniques, particularly for the early detection of asymptomatic left ventricular (LV) dysfunction, are lacking. The present study aimed to identify and test a set of urinary polypeptides that may indicate early LV diastolic dysfunction as defined by echocardiography in hypertensive patients in a cross-sectional case-control study nested within the FLEMish study on ENvironment, Genes and Health Outcome (FLEMENGHO)., Methods and Results: To identify potentially discriminating urinary biomarkers for LV diastolic dysfunction, we applied capillary electrophoresis coupled to mass spectrometry. In the discovery set, we compared 19 hypertensive patients with asymptomatic LV diastolic dysfunction with 19 healthy controls. In the absence of adjustment for multiple testing, 85 urinary peptides were different between cases and controls at a P-value of <0.033. With adjustment for multiple testing, three potential biomarkers remained significantly different between cases and controls (P ≤ 0.02). We combined the 85 potential biomarkers in a high-dimensional model (classifier), which we applied in a blinded manner to an independent test set of 16 hypertensive patients with symptomatic HF and 16 healthy controls. Upon unblinding, the area under the receiver operating characteristic curve (AUC) of the HF classification was 0.84 (95% CI: 0.70-0.98; P= 0.001)., Conclusion: In asymptomatic hypertensive patients with LV diastolic dysfunction, we identified a set of urinary polypeptides specific for essential hypertension with LV diastolic dysfunction that subsequently distinguished hypertensive patients with overt HF from healthy controls.

Background: The renal Fanconi syndrome (FS) is characterized by renal glucosuria, loss of electrolytes, bicarbonate and lactate, generalized hyperaminoaciduria and low-molecular-weight proteinuria. We studied the urinary low-molecular-weight proteome to identify excreted peptides indicative of a pathogenetic mechanism leading to tubular dysfunction., Methods: We established a urinary proteome pattern using capillary electrophoresis mass spectrometry (CE-MS) of 7 paediatric patients with cystinosis and 6 patients with ifosfamide-induced FS as the study group, and 54 healthy volunteers and 45 patients suffering from other renal diseases such as lupus nephritis (n = 8), focal segmental glomerulosclerosis (n = 27), minimal change disease (n = 7) and membranous glomerulonephritis (n = 3) as controls. Consequently, we conducted a blinded study consisting of 11 FS patients and 9 patients with renal disease other than FS. Additionally, we applied this pattern to 294 previously measured samples of patients with different renal diseases. Amino acid sequences of some marker proteins were obtained., Results: Specificity for detecting FS was 89% and sensitivity was 82%. The marker peptides constituting the proteome pattern are fragments derived from osteopontin, uromodulin and collagen alpha-1., Conclusions: CE-MS can be used to diagnose FS in paediatric patients and might be a future tool for the non-invasive diagnosis of FS. The reduced amount of the marker proteins osteopontin and uromodulin indicates loss of function of tubular excretion in all patients suffering from FS regardless of the underlying cause. In addition, the six different fragments of the collagen alpha-1 (I) chain were either elevated or reduced in the urine. This indicates a change of proteases in collagen degradation as observed in interstitial fibrosis. These changes were prominent irrespectively of the stages of FS. This indicates fibrosis as an early starting pathogenetic reason for the development of renal insufficiency in FS patients.

Background: Uraemic toxicity results in the dysfunction of many organ systems, provoking an increase in morbidity and mortality. To date, only approximately 90 uraemic retention solutes have been described. To examine unknown uraemic substances thoroughly, the identification of as many compounds as possible in the ultrafiltrate and/or plasma of patients would lead to a less biased definition of the uraemic retention process compared with what is proposed today., Methods: We describe the application of a novel proteomic tool for the identification of a large number of molecules present in ultrafiltrate from uraemic and normal plasma obtained with high- or low-flux membranes. Separation by capillary electrophoresis was coupled on-line to a mass spectrometer, yielding identification of polypeptides based on their molecular weight., Results: Between 500 and >1000 polypeptides with a molecular weight ranging from 800 to 10,000 Da could be detected in individual samples, and were identified via their mass and their particular migration time in capillary electrophoresis. In ultrafiltrate from uraemic plasma, 1394 polypeptides were detected in the high-flux vs 1046 in the low-flux samples, while in ultrafiltrate from normal plasma, 544 polypeptides vs 490 were found in ultrafiltrate from normal plasma obtained from membranes with comparable cut-off. In addition, polypeptides >5 kDa were virtually only detected in the uraemic ultrafiltrate from the high-flux membrane (n = 28 vs n = 5 with the low-flux membrane). To demonstrate the feasibility of further characterizing the detected molecules, polypeptides present exclusively in uraemic ultrafiltrate were chosen for sequencing analyses. A 950.6 Da polypeptide was identified as a fragment of the salivary proline-rich protein. A 1291.8 Da fragment was derived from alpha-fibrinogen., Conclusion: The data presented here strongly suggest that the application of proteomic approaches such as capillary electrophoresis and mass spectrometry will result in the identification of many more uraemic solutes than those known at present. This could enable the introduction of more direct elimination strategies, since it is possible to obtain an extended appreciation of the removal capacities of particular dialyser membranes.

We examined the role of protein kinase C (PKC) for the generation of arginine-vasopressin (AVP)-linked Ca2+ oscillations in beta-cells (HIT-T15). Activation of PKC by phorbol-12,13-dibutyrate (PDBu) reduced the frequency and finally abolished AVP-induced Ca2+ oscillations. The PKC inhibitors Gö 6976, Ro-32-0432, or chelerythrine converted Ca2+ oscillations to a plateau-like rise in cytosolic free Ca2+, and PKC down-regulation reduced the percentage of cells exhibiting AVP-induced Ca2+ oscillations. Several mechanisms were identified by which PKC could exert feedback on the AVP-linked Ca2+ oscillator. PDBu, but not the PKC inhibitors, inhibited AVP-stimulated inositol 1,4,5-trisphosphate production and mobilization of internal Ca2+. Ca2+ influx through voltage-sensitive Ca2+ channels was attenuated by PDBu and PKC inhibitors, indicating complex PKC-dependent regulation of voltage-sensitive Ca2+ channels involving stimulatory as well as inhibitory components. Furthermore, AVP caused oscillatory translocation of yellow fluorescent protein (YFP)-tagged PKCalpha and PKCbetaIota to the plasma membrane, which paralleled the Ca2+ oscillations in single cells. Repetitive translocation of YFP-PKCalpha and -PKCbetaIota could also be elicited by repetitive release of caged Ca2+. By contrast, AVP-stimulated translocation of YFP-PKCepsilon was monophasic, not synchronized with Ca2+ oscillations, and could not be mimicked by release of caged Ca2+. In conclusion, undisturbed activation of PKCs is a necessary intermediate to generate or maintain AVP-induced Ca2+ oscillations in pancreatic beta-cells. The data further suggest that classical PKCs, predominantly by inhibition of inositol 1,4,5-trisphosphate production, provide the negative feedback required for AVP-induced Ca2+ oscillations to occur that is mediated by their repetitive activation by oscillating Ca2+ concentrations.