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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.

Background The increased prevalence of cardiovascular disease (CVD) indicates a demand for novel therapeutic approaches. Proteome analysis of vascular tissues from animal models and humans with CVD could lead to the identification of novel druggable targets. Methods LC-MS/MS analysis of thoracic aortas from three mouse models of non-diabetic and diabetic (streptozotocin (STZ)-induced) atherosclerosis followed by bioinformatics/pathway analysis was performed. Selected findings were confirmed by proteomics analysis of human vessels from patients with CVD as well as in vitro studies (migration, proliferation, angiogenesis assays) using endothelial (HUVEC) cells. Findings Comparative tissue proteomics of low density lipoprotein receptor deficient (Ldlr−/−) and diabetic Ldlr−/− (Ldlr−/−STZ) with wild type (WT) animals led to the identification of 284 differentially expressed proteins in both models. Among them, 177 proteins were also differentially expressed in diabetic apolipoprotein E deficient (ApoE−/−STZ) mice, suggesting expression changes associated with atherosclerosis independent of the model used. These proteins recapitulated the hallmarks of atherosclerosis. Comparison of these findings with differentially expressed proteins in human vessels with CVD enabled shortlisting of six commonly dysregulated proteins. Among them, lysine-specific demethylase 5D (KDM5D) exhibited pronounced overexpression accompanied by a reduction in the protein levels of its substrate, the trimethylated lysine 4 of histone H3 (H3K4me3), in patients with CVD. Functional interference studies applying a KDM5 inhibitor on HUVEC reduced cell proliferation, migration and tube-forming ability in vitro. Interpretation This high-throughput proteomics strategy identified KDM5 histone demethylases being potentially involved in CVD, possibly by affecting H3K4 methylation. Fund [SysVasc, HEALTH-2013 603288], [ERA-CVD PROACT: ANR-17-ECVD-0006, 01KL1805], [FRM, DEQ20170336759].

International audience; Preclinical animal models are extensively used in nephrology. In this review, the utility of performing proteome analysis of kidney tissue or urine in such models and transfer of the results to human application has been assessed. Analysis of the literature identified 68 relevant publications. Pathway analysis of the reported proteins clearly indicated links with known biological processes in kidney disease providing validation of the observed changes in the preclinical models. However, although most studies focused on the identification of early markers of kidney disease or prediction of its progression, none of the identified makers has made it to substantial validation in the clinic or at least in human samples. Especially in renal disease where urine is an abundant source of biomarkers of diseases of the kidney and the urinary tract, it therefore appears that the focus should be on human material based discovery studies. In contrast, the most valid information of proteome analysis of preclinical models in nephrology for translation in human disease resides in studies focusing on drug evaluation, both efficacy for translation to the clinic and for mechanistic insight.

International audience; Although cardiovascular disease (CVD) is the leading cause of morbimortality worldwide, promising new drug candidates are lacking. We compared the arterial high-resolution proteome of patients with advanced versus early-stage CVD to predict, from a library of small bioactive molecules, drug candidates able to reverse this disease signature. Of the approximately 4000 identified proteins, 100 proteins were upregulated and 52 were downregulated in advanced-stage CVD. Arachidonyl trifluoromethyl ketone (AACOCF3), a cytosolic phospholipase A2 (cPLA2) inhibitor was predicted as the top drug able to reverse the advanced-stage CVD signature. Vascular cPLA2 expression was increased in patients with advanced-stage CVD. Treatment with AACOCF3 significantly reduced vascular calcification in a cholecalciferol-overload mouse model and inhibited osteoinductive signaling in vivo and in vitro in human aortic smooth muscle cells. In conclusion, using a systems biology approach, we have identified a potentially new compound that prevented typical vascular calcification in CVD in vivo. Apart from the clear effect of this approach in CVD, such strategy should also be able to generate novel drug candidates in other complex diseases.

Increased incidence of chronic kidney disease (CKD) with consecutive progression to end-stage renal disease represents a significant burden to healthcare systems. Renal tubulointerstitial fibrosis (TIF) is a classical hallmark of CKD and is well correlated with the loss of renal function. The bioactive lysophospholipid lysophosphatidic acid (LPA), acting through specific G-protein-coupled receptors, was previously shown to be involved in TIF development in a mouse model of unilateral ureteral obstruction. Here, we study the role of LPA in a mouse subjected to subtotal nephrectomy (SNx), a more chronic and progressive model of CKD. Five months after surgical nephron reduction, SNx mice showed massive albuminuria, extensive TIF, and glomerular hypertrophy when compared to sham-operated animals. Urinary and plasma levels of LPA were analyzed using liquid chromatography tandem mass spectrometry. LPA was significantly increased in SNx urine, not in plasma, and was significantly correlated with albuminuria and TIF. Moreover, SNx mice showed significant downregulation in the renal expression of lipid phosphate phosphohydrolases (LPP1, 2, and 3) that might be involved in reduced LPA bioavailability through dephosphorylation. We concluded that SNx increases urinary LPA through a mechanism that could involve co-excretion of plasma LPA with albumin associated with a reduction of its catabolism in the kidney. Because of the previously demonstrated profibrotic activity of LPA, the association of urinary LPA with TIF suggests the potential involvement of LPA in the development of advanced CKD in the SNx mouse model. Targeting LPA metabolism might represent an interesting approach in CKD treatment.

Objectives Differentiation of pancreatic cancer (PCA) from chronic pancreatitis (CP) is challenging. We searched for peptide markers in urine to develop a diagnostic peptide marker model. Methods Capillary electrophoresis-mass spectrometry was used to search for peptides in urine of patients with PCA (n = 39) or CP (n = 41). Statistical different peptides were included in a peptide multimarker model. Peptide markers were sequence identified and validated by immunoassay and immunohistochemistry (IHC). Results Applied to a validation cohort of 54 patients with PCA and 52 patients with CP, the peptide model correctly classified 47 patients with PCA and 44 patients with CP (area under the curve, 0.93; 87% sensitivity; 85% specificity). All 5 patients with PCA with concomitant CP were classified positive. Urine proteome analysis outperformed carbohydrate antigen 19-9 (area under the curve, 0.84) by a 15% increase in sensitivity at the same specificity. From 99 healthy subjects, only four were misclassified. Fetuin-A was the most prominent peptide marker source for PCA as verified by immunoassay and IHC. In silico protease mapping of the peptide markers' terminal sequences pointed to increased meprin-A activity in PCA, which in IHC was associated with neoangiogenesis. Conclusions Urinary proteome analysis differentiates PCA from CP and may serve as PCA screening tool.

Introduction La calcification vasculaire associee a la maladie renale chronique (MRC) est un facteur de risque decisif de complications cardiovasculaires (CCV). Pourtant, les options therapeutiques ciblant ce processus restent limitees. La calprotectine est une alarmine pouvant activer les recepteurs TLR4 et RAGE et jouant un role important dans diverses maladies auto-immunes et inflammatoires. Description L’objectif de ce travail etait d’etudier le role de la calprotectine en tant que nouvel acteur de la calcification vasculaire associee a la MRC et d’evaluer le potentiel therapeutique du paquinimod, un medicament inhibant la calprotectine. Methodes La concentration de calprotectine circulante a ete mesuree par ELISA dans : (i) le serum de 63 patients MRC (stades 3 et 4) ayant developpe (n = 34) ou non (n = 29) des CCV au cours du suivi sur 4 ans et (ii) dans un modele animal de MRC (nephrectomie 5/6) avec calcifications vasculaires induites par un regime riche en phosphate. Le contenu en calcium (marqueur de calcification) a ete mesure dans des cellules primaires musculaires lisses vasculaires (CMLV) humaines et dans des anneaux d’aorte murins traites par la calprotectine avec ou sans pre-traitement par le paquinimod. Resultats Les taux de calprotectine serique sont augmentes chez les patients MRC avec CCV par comparaison aux patients sans CCV. De plus, la concentration plasmatique de calprotectine est augmentee chez la souris en cas d’atteinte renale avec calcifications vasculaires. Le traitement par la calprotectine de CMLV et d’anneaux d’aorte induit une augmentation significative de la calcification et cet effet est significativement inhibe par le paquinimod. Conclusion Ces resultats suggerent que la calprotectine est associee aux CCV chez le patient MRC avec une implication en particulier dans le processus de calcification vasculaire. Ainsi, le ciblage de la calprotectine par le paquinimod pourrait etre une approche therapeutique innovante et efficace afin d’ameliorer la survie de ces patients.

Introduction L’analyse omique de l’urine est consideree comme une biopsie liquide du rein qui a revolutionne la comprehension des maladies renales. Les malformations congenitales du rein et du tractus urinaire (CAKUT) sont des maladies genetiquement complexes dont la physiopathologie est encore meconnue. Dans la mesure ou la production de liquide amniotique (LA) est principalement due a l’excretion de l’urine fœtale, l’exploration de son contenu devrait faciliter la comprehension des mecanismes lies au CAKUT. Description L’objectif de cette etude est donc d’identifier dans le LA de nouveaux acteurs associes au developpement renal pathologique des CAKUT. Methodes En comparant par LC-MS/MS le proteome du LA de 22 fœtus sains, 19 fœtus atteints d’une forme peu severe de CAKUT et 14 fœtus porteurs d’une forme severe, nous avons identifie la Plastine-3 (PLS3) dont l’abondance augmente graduellement des fœtus controles aux fœtus avec un CAKUT severe. Resultats La presence renale de PLS3 a ete validee par immunohistochimie chez les fœtus sains/CAKUT et dans le modele murin. L’invalidation de PLS3 chez la souris entraine une augmentation de l’aire glomerulaire, au prorata de la surface corticale, et un epaississement des pedicelles et de la membrane basale glomerulaire. Ces anomalies sont associees a la diminution de l’expression des proteines podocytaires nephrine et podocalyxine, et conduisent a une augmentation significative de l’albuminurie. L’invalidation de PLS3 chez le poisson-zebre entraine egalement a 48 h post-fecondation, une alteration de fusion glomerulaire et une augmentation tubulaire proximale ressemblant a un retard de developpement. Conclusion Ainsi, ce travail demontre pour la premiere fois le role de PLS3 dans le developpement renal pathologique des fœtus CAKUT. Des etudes complementaires d’invalidation in vitro de PLS3 au sein de lignees podocytaires (AB8-13) et tubulaire (HK-2) permettront de preciser les mecanismes moleculaires en aval de PLS3.

International audience

IntroductionCertain subject behaviours and characteristics increase the risk of some cancer types (e.g., obesity, alcohol intake) while others reduce cancer risk (e.g., physical activity). In 2007, the World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) published recommendations to reduce cancer risk related to these behaviours. Objectives and ApproachThe objective is to examine the association between self-reported behaviour consistent with WCRF/AICR recommendations for body fatness, physical activity, vegetable/fruit consumption, and alcohol intake and the risk of all cancers combined and specific cancer types. The study cohort, comprised of the Canadian Community Health Survey (CCHS) Ontario sample, will be linked with health administrative databases, including the Ontario Cancer Registry to determine cancer outcomes. Individuals will be assessed for behaviours consistent with WCRF/AICR recommendations based on their responses to CCHS questions and the association of these behaviours with cancer risk will be explored using multivariable Cox proportional hazard regression models. ResultsTo detect a log hazard ratio of 1.10 (where a=0.05, power=0.80, proportion of the sample assigned to the exposure group=0.25 and R2=0.20), a sample size of 4,538 is required. Based on the number of records in the CCHS data frame (159,474) and an assumption that the CCHS sample experiences cancer incidence at a similar rate to the rest of the Ontario population, we expect to have 5,000 cancer cases for these analyses. Upon completion of the analysis, we will report hazard ratios that estimate the difference in cancer risk between individuals reporting behaviour consistent with the WCRF/AICR recommendations and those reporting behaviour not consistent with the recommendations. Conclusion/ImplicationsWCRF/AICR recommendations were developed as the basis for primary cancer prevention, both for individuals and population-wide policies and programs. The current study will quantify the difference in overall cancer risk between individuals who do and do not adhere to selected WCRF/AICR recommendations for the first time in a Canadian population.

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.

Most of biomarker panels, extracted from single omics traits, still need improvement since they display a gray zone where prediction is uncertain. Here we verified whether a combination of omics traits, fetal urinary metabolites and peptides analyzed in the same sample, improved prediction of postnatal renal function in fetuses with posterior urethral valves (PUV) compared to individual omics traits. Using CE-MS, we explored the urinary metabolome of 13 PUV fetuses with end stage renal disease (ESRD) and 12 PUV fetuses without postnatal ESRD at 2 years postnatally. This allowed the selection of 24 differentially abundant metabolite features which were modelled into predictive classifiers, alone or in combination with 12 peptides previously identified as predictive of ESRD. Validation in 35 new fetuses showed that the combination of peptides and metabolites significantly outperformed the 24 metabolite features with increased AUC (0.987 vs 0.905), net reclassification improvement (36%) and better sensitivity accuracy (86% vs 60%). In addition, the two trait combination tended to improve, but without reaching statistical significance, the already high performances of the 12 peptide biomarkers (AUC 0.967, accuracy 80%). In conclusion, this study demonstrates the potential of cumulating different omics traits in biomarker research where single omics traits fall short. Significance Although increasingly proposed in disease-diagnosis and -prognosis because of their improved efficacy over single markers, panels of body fluid biomarkers based on single omics analysis still fail to display perfect accuracy, probably due to biological variability. Here, we hypothesized that combination of different omics traits allowed to better capture this biological variability. As proof of concept, we studied the added value of fetal urine metabolites and peptides using CE-MS, starting from the same urine sample, to predict postnatal renal outcome in fetuses with posterior urethral valves. We observed that the prognostic power of combined metabolite and peptide markers was clearly higher than that of metabolites alone and slightly, but non-significantly, improved compared to the peptides alone. To our knowledge, this report is the first to demonstrate that combining multiomics traits extracted from (fetal) urine samples displays clear promise for kidney disease stratification.

Background\ud Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CVDO), capture domain knowledge in a computational form and can provide context for gene/protein names as written in the literature. This study investigates: 1) if word embeddings from Deep Learning algorithms can provide a list of term variants for a given gene/protein of interest; and 2) if biological knowledge from the CVDO can improve such a list without modifying the word embeddings created.\ud \ud Methods\ud We have manually annotated 105 gene/protein names from 25 PubMed titles/abstracts and mapped them to 79 unique UniProtKB entries corresponding to gene and protein classes from the CVDO. Using more than 14m PubMed articles (titles and available abstracts), word embeddings were generated with CBOW and Skip-gram. We setup two experiments for a synonym detection task, each with four raters, and 3,672 pairs of terms (target term and candidate term) from the word embeddings created. For Experiment I, the target terms for 64 UniProtKB entries were those that appear in the titles/abstracts; Experiment II involves 63 UniProtKB entries and the target terms are a combination of terms from PubMed titles/abstracts with terms (i.e. increased context) from the CVDO protein class expressions and labels.\ud \ud Results\ud In Experiment I, Skip-gram finds term variants (full and/or partial) for 89% of the 64 UniProtKB entries, while CBOW finds term variants for 67%. In Experiment II (with the aid of the CVDO), Skip-gram finds term variants for 95% of the 63 UniProtKB entries, while CBOW finds term variants for 78%. Combining the results of both experiments, Skip-gram finds term variants for 97% of the 79 UniProtKB entries, while CBOW finds term variants for 81%.\ud \ud Conclusions\ud This study shows performance improvements for both CBOW and Skip-gram on a gene/protein synonym detection task by adding knowledge formalised in the CVDO and without modifying the word embeddings created. Hence, the CVDO supplies context that is effective in inducing term variability for both CBOW and Skip-gram while reducing ambiguity. Skip-gram outperforms CBOW and finds more pertinent term variants for gene/protein names annotated from the scientific literature.\ud \ud Keywords: Semantic deep learningOntologyDeep learningCBOWSkip-gramCardiovascular disease ontologyPubMed

Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD.A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoEIn human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with LdlrThe human cIMT signature was partially mimicked by Ldlr

In the quest to fully comprehend the proteolytic events leading to the generation of the salivary peptidome, we have developed a method for the sequential elution of salivary peptides throughout progressive endogenous proteolysis. By screening the time-dependent changes in the salivary peptidome we can predict the activity pattern of salivary proteases responsible for such peptide fingerprint and identify susceptible protein targets. Herein, we describe a step-by-step tutorial based on a filter-aided sample preparation (FASP) method, taking advantage of the endogenous salivary proteases armamentarium (endoProteoFASP), to produce new peptides from the salivary proteins, adding to those present in the sample at the time of collection. In this protocol, the different sets of peptides retrieved after sample elution are identified following a liquid chromatography-tandem mass spectrometry approach. The likelihood of a large set of endogenous proteases (collected from several public sources) to be responsible for the generation of such peptides can be predicted by the analysis of the cleavage site specificity by Proteasix ( http://proteasix.cs.man.ac.uk /) algorithm. The attained peptidome-protease profile can be useful to elucidate the peptidome dynamics and the proteolytic events underpinning pathophysiological phenomena taking place locally within the oral cavity. This may help clinicians to diagnose oral pathologies and develop preventive therapeutic plans.

In the quest to fully comprehend the proteolytic events leading to the generation of the salivary peptidome, we have developed a method for the sequential elution of salivary peptides throughout progressive endogenous proteolysis. By screening the time-dependent changes in the salivary peptidome we can predict the activity pattern of salivary proteases responsible for such peptide fingerprint and identify susceptible protein targets. Herein, we describe a step-by-step tutorial based on a filter-aided sample preparation (FASP) method, taking advantage of the endogenous salivary proteases armamentarium (endoProteoFASP), to produce new peptides from the salivary proteins, adding to those present in the sample at the time of collection. In this protocol, the different sets of peptides retrieved after sample elution are identified following a liquid chromatography-tandem mass spectrometry approach. The likelihood of a large set of endogenous proteases (collected from several public sources) to be responsible for the generation of such peptides can be predicted by the analysis of the cleavage site specificity by Proteasix ( http://proteasix.cs.man.ac.uk /) algorithm. The attained peptidome-protease profile can be useful to elucidate the peptidome dynamics and the proteolytic events underpinning pathophysiological phenomena taking place locally within the oral cavity. This may help clinicians to diagnose oral pathologies and develop preventive therapeutic plans.

International audience; Background and aims: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD. Methods: A targeted mass spectrometry assay was used to quantify and compare a series of blood me-tabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE À/À , Ldlr À/À , and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates. Results: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcar-nitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr À/À and ApoE À/À mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr À/À mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE À/À mice shared 10. Conclusions: The human cIMT signature was partially mimicked by Ldlr À/À and ApoE À/À mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD.

BACKGROUND: \ud \ud 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.\ud \ud METHODS: \ud \ud 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.\ud \ud RESULTS: \ud \ud 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.\ud \ud CONCLUSIONS: \ud \ud 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.

OncoTarget 6(33), 34106-34117 (2015). doi:10.18632/oncotarget.5896, Published by Impact Journals LLC, [S.l.]

The aim of this study is to determine the best fragmentation method for sequence identification of naturally occurring urinary peptides in the field of clinical proteomics.We used LC-MS/MS analysis of urine samples to determine the analytical performance of higher energy collisional dissociation (HCD), CID with high and low resolution MS/MS for the identification of naturally occurring peptides in the low molecular weight urinary proteome.HCD and CID high-resolution generated a 22% error rate in peptide sequence identifications. CID low-resolution showed significantly higher error rates (37%). Excluding the error rate (i.e rejection of cysteine-containing peptides), we observed a higher degree of overlap between HCD and CID high resolution for identification of peptide sequences of rank 1 and cross-correlation ≥ 1.9 (262 peptide sequences) compared to CID low (208 peptide sequences with HCD and 192 peptide sequences with CID high). Reproducibility of detected peptides in three out of the five replicates was also higher in HCD and CID high in relation to CID low resolution.Our data demonstrated that HCD and CID high-resolution performed with better accuracy and reproducibility than CID low resolution in respect to the identification of naturally occurring urinary peptide sequences.

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.

The aim of this analysis is to examine long-term trends in alcohol consumption and associations with lagged data on specific types of cancer mortality, and indicate policy implications. Data on per capita annual sales of pure alcohol; mortality for three alcohol-related cancers - larynx, esophageal, and lip, oral cavity, and pharynx; and per capita consumption of tobacco products were extracted at the country level. The Unobservable Components Model was used for this time-series analysis to examine the temporal association between alcohol consumption and cancer mortality, using lagged data, from 17 countries. Statistically significant associations were observed between alcohol sales and cancer mortality, in the majority of countries examined, which remained after controlling for tobacco use (P

Clinical proteomics has led to the identification of a substantial number of disease-associated peptides and protein fragments in several conditions such as cancer, kidney, or cardiovascular diseases. In silico prediction tools that can facilitate linking of identified peptide biomarkers to predicted protease activity might therefore significantly contribute to the understanding of pathophysiological mechanisms of these diseases. Proteasix is an open-source, peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. From an input peptide list, Proteasix allows for automatic cleavage site reconstruction and protease associations.

Clinical proteomics has led to the identification of a substantial number of disease-associated peptides and protein fragments in several conditions such as cancer, kidney, or cardiovascular diseases. In silico prediction tools that can facilitate linking of identified peptide biomarkers to predicted protease activity might therefore significantly contribute to the understanding of pathophysiological mechanisms of these diseases. Proteasix is an open-source, peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. From an input peptide list, Proteasix allows for automatic cleavage site reconstruction and protease associations.

Extracting information from peptidomics data is a major current challenge, as endogenous peptides can result from the activity of multiple enzymes. Proteolytic enzymes can display overlapping or complementary specificity. The activity spectrum of human endogenous peptide-generating proteases is not fully known. Hence, the indirect study of proteolytic enzymes through the analysis of its substrates is largely hampered. Antimicrobial peptides (AMPs) represent a primordial set of immune defense molecules generated by proteolytic cleavage of precursor proteins. These peptides can be modulated by host and microorganismal stimuli, which both dictate proteolytic enzymes' expression and activity. Peptidomics is an attractive approach to identify peptides with a biological role and to assess proteolytic activity. However, bioinformatics tools to deal with peptidomics data are lacking. PROTEASIX is an excellent choice for the prediction of AMPs-generating proteases based on the reconstitution of a substrate's cleavage sites and the crossing of such information with known proteases' specificity retrieved by several publicly available databases. Therefore, the focus of the present tutorial is to explore the potential of PROTEASIX when gather information concerning proteases involved in the generation of human AMPs and to teach the user how to make the most out of peptidomics results using PROTEASIX. Significance This tutorial provides a step-by-step guide on to use PROTEASIX for making sense and unveiling the biological implications of peptidomics data. In addition to its educational focus, this work also reveals which proteases contribute the most to the formation of human antimicrobial peptides distributed throughout body fluids in human defense barriers.

Chronic kidney disease (CKD) is the gradual decrease in renal function. Currently available biomarkers are effective only in detecting late stage CKD. Biomarkers of early stage CKD and prognostic biomarkers are required. We review the major findings in urinary proteomics in CKD during the last five years. Significant progress has been made and today urinary proteomics is applied in large randomized trials, and in patient management. Many of the biomarkers indicate altered protease activity. We therefore also review the literature on proteases associated with renal function loss. We anticipate in silico prediction tools of protease activity and additional system biology studies may contribute to biomarker discovery and elucidate the role of proteases in CKD development and progression. These approaches will enable the deciphering of the molecular pathophysiology of CKD, and hence definition of the most appropriate therapeutic targets in the future. Together with stable biomarker panels available today, this will significantly improve patient management.

Clinical proteomics has been applied to the identification of biomarkers of obstetric and neonatal disease. We will discuss a number of encouraging studies that have led to potentially valid biomarkers in the context of Down's syndrome, preterm birth, amniotic infections, preeclampsia, intrauterine growth restriction and obstructive uropathies. Obtaining noninvasive biomarkers (e.g., from the maternal circulation, urine or cervicovaginal fluid) may be more feasible for obstetric diseases than for diseases of the fetus, for which invasive methods are required (e.g., amniotic fluid, fetal urine). However, studies providing validated proteomics-identified biomarkers are limited. Efforts should be made to save well-characterized samples of these invasive body fluids so that many valid biomarkers of pregnancy-related diseases will be identified in the coming years using proteomics based analysis upon adoption of 'clinical proteomics guidelines'.

The role of fluid shear stress is well established in vascular pathophysiology. However, urinary shear stress now also appears as a key mechanism in the regulation of renal function. In addition, there is a growing body of evidence showing that modified urinary shear stress is involved in the development of nephropathies. Therefore we review here the state-of-the-art on the pathophysiological roles of urinary shear stress.

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.

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.

Objective: The aim of the study is to explore school nurses’ experience of health education. Design: A qualitative approach, phenomenology was used to answer the question. Method: Sixteen participants were recruited through purposeful and snowball sampling. Participants undertook an audio-recorded interview which was transcribed and analysed. Results: Five themes represent school nurses’ experience of health education. Within these five themes, three issues were identified by the participants as having a negative impact on their experience of health education. These were: (1) feeling unwanted by the school; (2) not supported by the school hierarchy; and (3) a lack of role definition. Conclusion: These three issues provide important insight into school nurses’ experience of health education and have implications for other school nurses and professionals in the school environment.

The incidence of chronic kidney disease leading to end-stage renal disease has significantly increased and may reach epidemic proportions over the next decade. Regardless of the initial insult, the progression of most forms of renal disease results in tubulo-interstitial fibrosis. This has been closely correlated to the future appearance of renal failure and has therefore been associated with poor long-term prognosis. New molecules and agents to limit the development of tubulo-interstitial fibrosis and slow down the progression towards end-stage renal disease are needed. In the past twenty years, many efforts have been made to understand the mechanisms of tubulo-intersititial fibrosis with the final goal to develop new therapeutic strategies. In this context, this review will focus on the mechanisms and factors involved in the development and the progression of renal fibrosis and will discuss the new promising therapeutic strategies in animal and humans.

Use of capillary electrophoresis coupled to mass spectrometry (CE-MS) technology in proteome analysis has increased, with a focus on the identification of biomarker peptides in clinical proteomics. Among the reported applications, the main focus is on the urinary biomarkers for kidney disease. In this review, we discuss the principal, theoretical, and practical obstacles that are encountered when using CE-MS for the analysis of body fluids for biomarker discovery. We present several examples of a successful application of CE-MS for biomarker discovery in kidney disease, implications for disease diagnosis, prognosis, and therapy evaluation, and will also discuss current challenges and possible future improvements.

Introduction Les malformations congenitales bilaterales des reins et des voies urinaires (CAKUT) representent la premiere cause d’insuffisance renale (IR) chez les enfants. La prise en charge des fœtus CAKUT est malheureusement extremement difficile du fait de notre incapacite a evaluer en periode prenatale la progression des atteintes renales. Le but de notre etude est donc de developper un outil pronostique, tire de l’exploration du peptidome amniotique, pour prevoir in utero le devenir fonctionnel des reins apres la naissance des patients CAKUT. Patients/Materiels et methodes Cent-soixante-dix-huit fœtus avec CAKUT ont ete inclus dans une etude prospective multicentrique (NCT02675686). Le critere de jugement etait la fonction renale a 2 ans de vie. Trente-huit fœtus ont finalement ete exclus pour des raisons cliniques ou techniques. Le groupe controle comprenait 69 fœtus avec un DFG postnatal normal ou moderement reduit (> 60 mL/min). Le groupe cas regroupait 35 fœtus atteints d’une IR postnatale severe, ou decedes d’une IR terminale ou sujets a une interruption medicale de grossesse (IMG) avec un phenotype renal severe confirme par fœtopathologie. Huit fœtus ont subi une IMG alors que la fœtopathologie montrait des lesions renales compatibles avec la vie. Vingt-huit autres IMG ont ete pratiquees sans que les anatomopathologistes ne puissent se prononcer sur la severite des anomalies. Le contenu peptidique du liquide amniotique a ete analyse par electrophorese capillaire couplee a la spectrometrie de masse. Observation/Resultats La comparaison du peptidome amniotique de 35 controles et 18 cas a permis d’identifier 98 peptides ayant une abondance significativement differente. Ces 98 marqueurs ont ete combines dans un modele mathematique pour generer l’outil pronostique nomme « bCAKUTPep ». Base sur l’attribution d’un score, bCAKUTPep a predit en aveugle la fonction renale postnatale de 51 nouveaux patients (34 controles, 17 cas) avec une sensibilite de 88 % [64 %–98 %], une specificite de 97 % [85 %–100 %] et une aire sous la courbe de ROC (AUC) egale a 0,96 [0,87–1,00]. Ces excellentes performances depassaient celles des parametres echographiques habituellement utilises en clinique comme le volume de liquide amniotique (AUC 0,84 [0,70–0,92], p = 0,03). De maniere tout a fait interessante, bCAKUTPep a egalement predit une bonne fonctionnalite renale pour 6/8 des grossesses interrompues cliniquement a tort, confirmant de ce fait l’analyse fœtopathologique. Discussion/Conclusion Ainsi, en permettant une evaluation objective et quantifiable de la severite des anomalies renales, l’outil predictif bCAKUTPep constituera un veritable soutien au clinicien, ameliorant de ce fait le conseil prenatal et la prise en charge des grossesses CAKUT.

Ureteropelvic junction (UPJ) obstruction is the most frequently observed cause of obstructive nephropathy in children. Neonatal and foetal animal models have been developed that mimic closely what is observed in human disease. The purpose of this review is to discuss how obstructive nephropathy alters kidney histology and function and describe the molecular mechanisms involved in the progression of the lesions, including inflammation, proliferation/apoptosis, renin-angiotensin system activation and fibrosis, based on both human and animal data. Also we propose that during obstructive nephropathy, hydrodynamic modifications are early inducers of the tubular lesions, which are potentially at the origin of the pathology. Finally, an important observation in animal models is that relief of obstruction during kidney development has important effects on renal function later in adult life. A major short-coming is the absence of data on the impact of UPJ obstruction on long-term adult renal function to elucidate whether these animal data are also valid in humans.

International audience; Severe inflammation characterizes rapidly progressive glomerulonephritides, and expression of the kinin B1 receptor (B1R) associates with inflammation. Delayed B1R blockade reduces renal inflammation in a model of unilateral ureteral obstruction, but whether B1R modulates the pathophysiology of glomerulonephritides is unknown. Here, we observed an association of B1R protein expression and inflammation, in both glomeruli and the renal interstitium, in biopsies of patients with glomerulonephritides, Henoch-Schönlein purpura nephropathy, and ANCA-associated vasculitis. In the nephrotoxic serum-induced glomerulonephritis model, we observed upregulation of the B1R receptor; treatment with a B1R antagonist beginning 2 weeks after the onset of disease reduced both glomerular and tubular lesions and improved renal function. B1R blockade reduced renal chemokine expression and macrophage accumulation. Collectively, our data demonstrate that blockade of the kinin B1R has significant potential for the treatment of glomerulonephritis.

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Dans l'Iliade, le chant VI presente une scene tres touchante et celebre, la derniere entrevue entre Hector et sa femme Andromaque, qui lui montre leur bebe Astyanax (v. 344-500). Seules les femmes troyennes sont mentionnees par le poete, et surtout quand elles parlent a Hector (VI 251-285, Hecube et Helene, qui a un statut ambigu, ΧΧΠ 79-91, Hecube). Ces scenes ont un interet litteraire et aussi sociologique, c'est pourquoi il vaut la peine d'etudier comment les scholiastes ont analyse ces passages. En fait les scholiastes ont fait des remarques sur la phonetique, la morphologie, la syntaxe et la lexicologie : les scholiastes, qui sont parfois anonymes et de date indeterminee, montrent que la poesie homerique etait devenue partiellement difficile a comprendre. C'est vrai pour des raisons grammaticales, la langue homerique produisant beaucoup de formes artificielles, et aussi parce que les structures sociales avaient change (il est necessaire d'expliquer la signification des edna homeriques). Malgre le reel interet de leurs travaux, ecrits dans un style lourd, le lecteur moderne est surpris du point de vue etroit sous lequel les scholiastes etudient la poesie homerique. Ils ignorent les scenes tragiques, ne mentionnent pas les passages emouvants et pathetiques (quand ils les etudient, c'est toujours brievement). Ils ne savent pas apprecier la beaute du texte homerique.

High resolution proteomics approaches have been successfully utilized for the comprehensive characterization of the cell proteome. However, in the case of quantitative proteomics an open question still remains, which quantification strategy is best suited for identification of biologically relevant changes, especially in clinical specimens. In this study, a thorough comparison of a label-free approach (intensity-based) and 8-plex iTRAQ was conducted as applied to the analysis of tumor tissue samples from non-muscle invasive and muscle-invasive bladder cancer. For the latter, two acquisition strategies were tested including analysis of unfractionated and fractioned iTRAQ-labeled peptides. To reduce variability, aliquots of the same protein extract were used as starting material, whereas to obtain representative results per method further sample processing and MS analysis were conducted according to routinely applied protocols. Considering only multiple-peptide identifications, LC-MS/MS analysis resulted in the identification of 910, 1092 and 332 proteins by label-free, fractionated and unfractionated iTRAQ, respectively. The label-free strategy provided higher protein sequence coverage compared to both iTRAQ experiments. Even though pre-fraction of the iTRAQ labeled peptides allowed for a higher number of identifications, this was not accompanied by a respective increase in the number of differentially expressed changes detected. Validity of the proteomics output related to protein identification and differential expression was determined by comparison to existing data in the field (Protein Atlas and published data on the disease). All methods predicted changes which to a large extent agreed with published data, with label-free providing a higher number of significant changes than iTRAQ. Conclusively, both label-free and iTRAQ (when combined to peptide fractionation) provide high proteome coverage and apparently valid predictions in terms of differential expression, nevertheless label-free provides higher sequence coverage and ultimately detects a higher number of differentially expressed proteins. The risk for receiving false associations still exists, particularly when analyzing highly heterogeneous biological samples, raising the need for the analysis of higher sample numbers and/or application of adjustment for multiple testing.

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.