Improved prenatal assessment of kidney disease using multiple ultrasound features.

BACKGROUND AND HYPOTHESIS: Congenital anomalies of the kidney and the urinary tract (CAKUT), often discovered in utero, cover a wide spectrum of outcomes ranging from normal postnatal kidney function to fetal death. The current ultrasound workup does not allow for an accurate assessment of the outcome. The present study aimed to significantly improve the ultrasound-based prediction of postnatal kidney survival in CAKUT. METHODS: Histological analysis of kidneys of 15 CAKUT fetuses was performed to better standardize the ultrasound interpretation of dysplasia and cysts. Ultrasound images of 140 CAKUT fetuses with 2-year postnatal follow-up were annotated for amniotic fluid volume and kidney number, size, dysplasia and/or cysts using standardized ultrasound readout. Association of ultrasound features and clinical data (sex and age at diagnosis) with postnatal kidney function was studied using logistic regression. Amniotic fluid proteome associated to kidney dysplasia or cysts was characterized by mass spectrometry. RESULTS: Histologically, poor ultrasound corticomedullary differentiation was associated to dysplastic lesions and ultrasound hyperechogenicity was associated to the presence of microcysts. Of all ultrasound and clinical parameters, reduced amniotic volume, dysplasia and cysts were the best predictors of poor outcome (Odd ratio = 57 [95%CI: 11-481], 20 [3-225] and 7 [1-100], respectively). Their combination into an algorithm improved prediction of postnatal kidney function compared to amniotic volume alone (area under the ROC curve = 0.92 [0.86-0.98] in a 10-fold cross validation). Dysplasia and cysts were correlated (Cramer's V coefficient = 0.44, p<0.0001), but amniotic fluid proteome analysis revealed that they had distinct molecular origin (extracellular matrix and cell contacts versus cellular death, respectively), probably explaining the additivity of their predictive performances. CONCLUSION: Antenatal clinical advice for CAKUT pregnancies can be improved by a more standardized and combined interpretation of ultrasound data.

Leucine-Rich Alpha-2 Glycoprotein 1 Accumulates in Complicated Atherosclerosis and Promotes Calcification.

Atherosclerosis is the primary cause of cardiovascular disease. The development of plaque complications, such as calcification and neo-angiogenesis, strongly impacts plaque stability and is a good predictor of mortality in patients with atherosclerosis. Despite well-known risk factors of plaque complications, such as diabetes mellitus and chronic kidney disease, the mechanisms involved are not fully understood. We and others have identified that the concentration of circulating leucine-rich α-2 glycoprotein 1 (LRG1) was increased in diabetic and chronic kidney disease patients. Using apolipoprotein E knockout mice (ApoE-/-) (fed with Western diet) that developed advanced atherosclerosis and using human carotid endarterectomy, we showed that LRG1 accumulated into an atherosclerotic plaque, preferentially in calcified areas. We then investigated the possible origin of LRG1 and its functions on vascular cells and found that LRG1 expression was specifically enhanced in endothelial cells via inflammatory mediators and not in vascular smooth muscle cells (VSMC). Moreover, we identified that LRG1 was able to induce calcification and SMAD1/5-signaling pathways in VSMC. In conclusion, our results identified for the first time that LRG1 is a direct contributor to vascular calcification and suggest a role of this molecule in the development of plaque complications in patients with atherosclerosis.

Mapping of the amniotic fluid proteome of fetuses with congenital anomalies of the kidney and urinary tract identifies plastin 3 as a protein involved in glomerular integrity.

Congenital anomalies of the kidney and the urinary tract (CAKUT) are the first cause of chronic kidney disease in childhood. Several genetic and environmental origins are associated with CAKUT, but most pathogenic pathways remain elusive. Considering the amniotic fluid (AF) composition as a proxy for fetal kidney development, we analyzed the AF proteome from non-severe CAKUT (n = 19), severe CAKUT (n = 14), and healthy control (n = 22) fetuses using LC-MS/MS. We identified 471 significant proteins that discriminated the three AF groups with 81% precision. Among them, eight proteins independent of gestational age (CSPG4, LMAN2, ENDOD1, ANGPTL2, PRSS8, NGFR, ROBO4, PLS3) were associated with both the presence and the severity of CAKUT. Among those, five were part of a protein-protein interaction network involving proteins previously identified as being potentially associated with CAKUT. The actin-bundling protein PLS3 (plastin 3) was the only protein displaying a gradually increased AF abundance from control, via non-severe, to severe CAKUT. Immunohistochemistry experiments showed that PLS3 was expressed in the human fetal as well as in both the fetal and the postnatal mouse kidney. In zebrafish embryos, depletion of PLS3 led to a general disruption of embryonic growth including reduced pronephros development. In postnatal Pls3-knockout mice, kidneys were macroscopically normal, but the glomerular ultrastructure showed thickening of the basement membrane and fusion of podocyte foot processes. These structural changes were associated with albuminuria and decreased expression of podocyte markers including Wilms' tumor-1 protein, nephrin, and podocalyxin. In conclusion, we provide the first map of the CAKUT AF proteome that will serve as a reference for future studies. Among the proteins strongly associated with CAKUT, PLS3 did surprisingly not specifically affect nephrogenesis but was found as a new contributor in the maintenance of normal kidney function, at least in part through the control of glomerular integrity. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury.

BACKGROUND: The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways. METHODS: In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores. RESULTS: A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (< 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p < 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p < 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p < 0.001). CONCLUSIONS: An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

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

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

How can artificial intelligence be used for peptidomics?

INTRODUCTION: Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED: The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1). EXPERT OPINION: Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).

The low affinity p75 neurotrophin receptor is down-regulated in congenital anomalies of the kidney and the urinary tract: Possible involvement in early nephrogenesis.

Congenital Anomalies of the Kidney and of the Urinary Tract (CAKUT) cover a broad range of disorders including abnormal kidney development caused by defective nephrogenesis. Here we explored the possible involvement of the low affinity p75 neurotrophin receptor (p75NTR) in CAKUT and nephrogenesis. In mouse, p75NTR was highly expressed in fetal kidney, located within cortical early nephrogenic bodies, and decreased rapidly after birth. In human control fetal kidney, p75NTR was also located within the early nephrogenic bodies as well as in the mature glomeruli, presumably in the mesangium. In CAKUT fetal kidneys, the kidney cortical structure and the localization of p75NTR were often disorganized, and quantification of p75NTR in amniotic fluid revealed a significant reduction in CAKUT compared to control. Finally, invalidation of p75NTR in zebrafish embryo with an antisense morpholino significantly altered pronephros development. Our results indicate that renal p75NTR is altered in CAKUT fetuses, and could participate to early nephrogenesis.

Prevalence of atheromatous and non-atheromatous cardiovascular disease by age in chronic kidney disease.

BACKGROUND: Although chronic kidney disease (CKD) and age are major risk factors for cardiovascular disease (CVD), little is known about the relative proportions of atheromatous and non-atheromatous CVD by age in CKD patients. METHODS: We used baseline data from the French Chronic Kidney Disease-Renal Epidemiology and Information Network (CKD-REIN) cohort of 3033 patients (65% men) with CKD Stages 3-4 to study crude and adjusted associations between age, the estimated glomerular filtration rate (eGFR), atheromatous CVD (coronary artery disease, peripheral artery disease and stroke) and non-atheromatous CVD (heart failure, cardiac arrhythmia and valvular heart disease). RESULTS: Mean age was 66.8 and mean Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) eGFR was 32.9 mL/min/1.73 m2. In the <65, (65-74), (75-84) and ≥85 year age groups, the prevalence was, respectively, 18.7, 35.5, 42.9 and 37.8% for atheromatous CVD, and 14.9, 28.4, 38.1 and 56.4% for non-atheromatous CVD. After adjusting for albuminuria, sex and CVD risk factors, the odds ratio (OR) [95% confidence interval (CI)] for (65-74), (75-84) and ≥85 age groups (compared with the <65 group) was, respectively, 1.99 (1.61-2.46), 2.89 (2.30-3.62), 2.72 (1.77-4.18) for atheromatous CVD and 2.07 (1.66-2.58), 3.15 (2.50-3.97), 7.04 (4.67-10.61) for non-atheromatous CVD. Compared with patients with an eGFR ≥30 mL/min/1.73 m2, those with an eGFR <30 mL/min/1.73 m2 had a higher OR for atheromatous CVD [1.21 (1.01-1.44)] and non-atheromatous CVD [1.16 (0.97-1.38)]. CONCLUSIONS: In this large cohort of CKD patients, both atheromatous and non-atheromatous CVD were highly prevalent and more frequent in older patients. In a given age group, the prevalence of atheromatous and non-atheromatous CVD was similar (except for a greater prevalence of non-atheromatous CVD after 85).

A single-center study to evaluate the efficacy of a fetal urine peptide signature predicting postnatal renal outcome in fetuses with posterior urethral valves.

BACKGROUND: Posterior urethral valves (PUVs) account for 17% of pediatric renal failure. The management of pregnancies involving fetuses with PUV is hampered by the fact that current clinical parameters obtained from fetal ultrasound and/or fetal urine biochemistry are insufficient to predict postnatal renal function. We previously have developed a fetal urine peptide signature (12PUV) that predicted with high precision postnatal renal failure at 2 years of age in fetuses with PUV. Here, we evaluated the accuracy of this signature to predict postnatal renal outcome in fetuses with PUV in an independent single-center study. METHODS: Thirty-three women carrying fetuses with suspected PUV were included. Twenty-five fetuses received vesicoamniotic shunts during pregnancy. PUV was confirmed postnatally in 23 patients. Of those 23 fetuses, 2 were lost in follow-up. Four and 3 patients died in the pre- and perinatal periods, respectively. Follow-up renal function at 6 months of age was obtained for the remaining 14 patients. The primary outcome was early renal failure, defined by an eGFR < 60 mL/min/1.73 m2 before 6 months of age or pre- or perinatal death. RESULTS: The peptide signature predicted postnatal renal outcome in postnatally confirmed PUV fetuses with an AUC of 0.94 (95%CI 0.74-1.0) and an accuracy of 90% (95%CI 78-100). The signature predicted postnatal renal outcome for the suspected PUV cases with an AUC of 0.89 (95%CI 0.72-0.97) and an accuracy of 84% (95%CI 71-97). CONCLUSIONS: This single-center study confirms the predictive power of the previously identified 12PUV fetal urinary peptide signature.

Reviewing Mechanistic Peptidomics in Body Fluids Focusing on Proteases.

The comprehension of how protease networks sculpt proteomes might help to disclose the functional annotation of the peptidome in health and disease. Envisioning to add new insights on the protease networks involved in the regulation of body fluid peptidomes, the authors apply Proteasix software to predict the proteases involved in the generation of the naturally occurring peptides present in six of the most studied human body fluids. Peptidome data is collected from the databases and from experimental studies. The analysis highlights 132 putative proteases from four families with the predominance of serine proteases and metalloproteases. From these, 49 proteases seem to be common to all fluids and are mostly associated to extracellular matrix organization as well as protein/peptide hormone processing. Data analysis also emphasizes: i) the similarity between plasma and CSF protease profiles; ii) that saliva and tears share proteases involved in the generation of peptides with antimicrobial activity; iii) that urine is the body fluid with the highest number of unique putative proteases, precluding an easy tracing of proteolytic events in this case. Taken together, the analysis emphasizes the intricate modus operandi of proteases, challenged by the interconnected pathways and amplification cascades in which they are involved.

Identification of Symptomatic Fetuses Infected with Cytomegalovirus Using Amniotic Fluid Peptide Biomarkers.

Cytomegalovirus (CMV) is the most common cause of congenital infection, and is a major cause of sensorineural hearing loss and neurological disabilities. Evaluating the risk for a CMV infected fetus to develop severe clinical symptoms after birth is crucial to provide appropriate guidance to pregnant women who might have to consider termination of pregnancy or experimental prenatal medical therapies. However, establishing the prognosis before birth remains a challenge. This evaluation is currently based upon fetal imaging and fetal biological parameters, but the positive and negative predictive values of these parameters are not optimal, leaving room for the development of new prognostic factors. Here, we compared the amniotic fluid peptidome between asymptomatic fetuses who were born as asymptomatic neonates and symptomatic fetuses who were either terminated in view of severe cerebral lesions or born as severely symptomatic neonates. This comparison allowed us to identify a 34-peptide classifier in a discovery cohort of 13 symptomatic and 13 asymptomatic neonates. This classifier further yielded 89% sensitivity, 75% specificity and an area under the curve of 0.90 to segregate 9 severely symptomatic from 12 asymptomatic neonates in a validation cohort, showing an overall better performance than that of classical fetal laboratory parameters. Pathway analysis of the 34 peptides underlined the role of viral entry in fetuses with severe brain disease as well as the potential importance of both beta-2-microglobulin and adiponectin to protect the injured fetal brain infected with CMV. The results also suggested the mechanistic implication of the T calcium channel alpha-1G (CACNA1G) protein in the development of seizures in severely CMV infected children. These results open a new field for potential therapeutic options. In conclusion, this study demonstrates that amniotic fluid peptidome analysis can effectively predict the severity of congenital CMV infection. This peptidomic classifier may therefore be used in clinical settings during pregnancy to improve prenatal counseling.

Increased urine acylcarnitines in diabetic ApoE-/- mice: Hydroxytetradecadienoylcarnitine (C14:2-OH) reflects diabetic nephropathy in a context of hyperlipidemia.

Hyperlipidemia is a risk factor for initiation and progression of diabetic nephropathy but the metabolic pathways altered in the diabetic kidney in a context of hyperlipidemia remain incompletely described. Assuming that changes in urine composition reflect the alteration of renal metabolism and function, we analyzed the urine metabolite composition of diabetic (streptozotocin-treatment) and control (non diabetic) ApoE-/- mice fed a high cholesterol diet using targeted quantitative metabolomics. Urine metabolome was also compared to the plasma metabolome of the same animals. As previously shown, urine albuminuria/urine creatinine ratio (uACR) and glomerular area and plasma lipids (cholesterol, triglycerides) were more elevated in diabetic mice compared to control. After adjustment to urine creatinine, the abundance of 52 urine metabolites was significantly different in diabetic mice compared to control. Among them was a unique metabolite, C14:2-OH (3-hydroxytetradecadienoylcarnitine) that, in diabetic mice, was positively and significantly correlated with uACR, glomerular hypertrophy, blood glucose and plasma lipids. That metabolite was not detected in plasma. C14:2-OH is a long-chain acylcarnitine reminiscent of altered fatty acid beta oxidation. Other acylcarnitines, particularly the short chains C3-OH, C3-DC, C4:1, C5-DC, C5-M-DC, C5-OH that are reminiscent of altered oxidation of branched and aromatic amino acids were also exclusively detected in urine but were only correlated with plasma lipids. Finally, the renal gene expression of several enzymes involved in fatty acid and/or amino acid oxidation was significantly reduced in diabetic mice compared to control. This included the bifunctional enoyl-CoA hydratase/3-hydroxyacyl-CoA (Ehhadh) that might play a central role in C14:2-OH production. This study indicate that the development of diabetes in a context of hyperlipidemia is associated with a reduced capacity of kidney to oxidize fatty acids and amino acids with the consequence of an elevation of urinary acetylcarnitines including C14:2-OH that specifically reflects diabetic nephropathy.

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

Background: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by slowly progressive bilateral renal cyst growth ultimately resulting in loss of kidney function and end-stage renal disease (ESRD). Disease progression rate and age at ESRD are highly variable. Therapeutic interventions therefore require early risk stratification of patients and monitoring of disease progression in response to treatment. Methods: We used a urine peptidomic approach based on capillary electrophoresis-mass-spectrometry (CE-MS) to identify potential biomarkers reflecting the risk for early progression to ESRD in the Consortium of Radiologic Imaging in Polycystic Kidney Disease (CRISP) cohort. Results: A biomarker-based classifier consisting of 20 urinary peptides allowed the prediction of ESRD within 10-13 years of follow-up in patients 24-46 years of age at baseline. The performance of the biomarker score approached that of height-adjusted total kidney volume (htTKV) and the combination of the biomarker panel with htTKV improved prediction over either one alone. In young patients (<24 years at baseline), the same biomarker model predicted a 30 mL/min/1.73 m 2 glomerular filtration rate decline over 8 years. Sequence analysis of the altered urinary peptides and the prediction of the involved proteases by in silico analysis revealed alterations in distinct proteolytic pathways, in particular matrix metalloproteinases and cathepsins. Conclusion: We developed a urinary test that accurately predicts relevant clinical outcomes in ADPKD patients and suggests altered proteolytic pathways involved in disease progression.

The role of urinary peptidomics in kidney disease research.

Urinary peptidomics focuses on endogenous urinary peptide content. Many studies now show the usefulness of this approach for the discovery and validation of biomarkers in kidney diseases that are as varied as chronic kidney disease, acute kidney injury, congenital anomalies of the kidney and the urinary tract, and polycystic kidney disease. Most studies focus on chronic kidney disease and demonstrate that urinary peptidome analysis can substantially contribute to early detection and stratification of patients with chronic kidney disease. A number of multicenter studies are ongoing that aim further validation in a clinical setting and broaden the applicability of urinary peptides. The association of urinary peptides with kidney disease also starts to deliver information on the pathophysiology of kidney disease with emphasis on extracellular matrix remodeling. Bioinformatic peptide centric tools have been developed that allow to model the changes in protease activity involved in kidney disease, based on the urinary peptidome content. A novel application of urinary peptidome analysis is the back-translation of results obtained in humans to animals for animal model validation and improvement of readout in these preclinical models. In conclusion, urinary peptidomics not only contribute to detection and stratification of kidney disease in the clinic, but might also create a new impulse in drug discovery through better insight in the pathophysiology of disease and optimized translatability of animal models.

Epidermal growth factor and kidney disease: a long-lasting story.

Epidermal growth factor has been previously associated with kidney disease. In this issue of Kidney International, Betz et al. (2016) link urinary epidermal growth factor abundance with an increased risk of the development of diabetic nephropathy in a novel animal model of diabetic nephropathy and in a large cohort of patients with type 2 diabetes. Although the clinical value of these observations needs to be confirmed in further studies, these observations further strengthen the tight link between epidermal growth factor and kidney disease.

Urinary peptidomics provides a noninvasive humanized readout of diabetic nephropathy in mice.

Nephropathy is among the most frequent complications of diabetes and the leading cause of end-stage renal disease. Despite the success of novel drugs in animal models, the majority of the subsequent clinical trials employing those drugs targeting diabetic nephropathy failed. This lack of translational value may in part be due to an inadequate comparability of human disease and animal models that often capture only a few aspects of disease. Here we overcome this limitation by developing a multimolecular noninvasive humanized readout of diabetic nephropathy based on urinary peptidomics. The disease-modified urinary peptides of 2 type 2 diabetic nephropathy mouse models were identified and compared with previously validated urinary peptide markers of diabetic nephropathy in humans to generate a classifier composed of 21 ortholog peptides. This classifier predicted the response to disease and treatment with inhibitors of the renin-angiotensin system in mice. The humanized classifier was significantly correlated with glomerular lesions. Using a human type 2 diabetic validation cohort of 207 patients, the classifier also distinguished between patients with and without diabetic nephropathy, and their response to renin-angiotensin system inhibition. Thus, a combination of multiple molecular features common to both human and murine disease could provide a significant change in translational drug discovery research in type 2 diabetic nephropathy.

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

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

Label-free quantitative urinary proteomics identifies the arginase pathway as a new player in congenital obstructive nephropathy.

Obstructive nephropathy is a frequently encountered situation in newborns. In previous studies, the urinary peptidome has been analyzed for the identification of clinically useful biomarkers of obstructive nephropathy. However, the urinary proteome has not been explored yet and should allow additional insight into the pathophysiology of the disease. We have analyzed the urinary proteome of newborns (n = 5/group) with obstructive nephropathy using label free quantitative nanoLC-MS/MS allowing the identification and quantification of 970 urinary proteins. We next focused on proteins exclusively regulated in severe obstructive nephropathy and identified Arginase 1 as a potential candidate molecule involved in the development of obstructive nephropathy, located at the crossroad of pro- and antifibrotic pathways. The reduced urinary abundance of Arginase 1 in obstructive nephropathy was verified in independent clinical samples using both Western blot and MRM analysis. These data were confirmed in situ in kidneys obtained from a mouse obstructive nephropathy model. In addition, we also observed increased expression of Arginase 2 and increased total arginase activity in obstructed mouse kidneys. mRNA expression analysis of the related arginase pathways indicated that the pro-fibrotic arginase-related pathway is activated during obstructive nephropathy. Taken together we have identified a new actor in the development of obstructive nephropathy in newborns using quantitative urinary proteomics and shown its involvement in an in vivo model of disease. The present study demonstrates the relevance of such a quantitative urinary proteomics approach with clinical samples for a better understanding of the pathophysiology and for the discovery of potential therapeutic targets.