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Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy.

ABSTRACT: BACKGROUND: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ?5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). METHODOLOGY/PRINCIPAL FINDINGS: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. CONCLUSIONS: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.

SUBMITTER: Alkhalaf A

PROVIDER: S-EPMC2958112 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy.

Alkhalaf Alaa A Zürbig Petra P Bakker Stephan J L SJ Bilo Henk J G HJ Cerna Marie M Fischer Christine C Fuchs Sebastian S Janssen Bart B Medek Karel K Mischak Harald H Roob Johannes M JM Rossing Kasper K Rossing Peter P Rychlík Ivan I Sourij Harald H Tiran Beate B Winklhofer-Roob Brigitte M BM Navis Gerjan J GJ

PloS one 20101020 10

<h4>Background</h4>Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with ...[more]

PMID: 20975990

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Project description:ObjectiveThe cause of preeclampsia remains unknown and the diagnosis can be uncertain. We used proteomic-based analysis of urine to improve disease classification and extend the pathophysiologic understanding of preeclampsia.Study designUrine samples from 284 women were analyzed by surface-enhanced laser desorption/ionization. In the exploratory phase, 59 samples were used to extract the proteomic fingerprint characteristic of severe preeclampsia requiring mandated delivery and to develop a diagnostic algorithm. In the challenge phase, we sought to prospectively validate the algorithm in 225 women screened for a variety of high- and low-risk conditions, including preeclampsia. Of these, 19 women were followed longitudinally throughout pregnancy. The presence of biomarkers was interpreted relative to clinical classification, need for delivery, and other urine laboratory measures (ratios of protein to creatinine and soluble fms-like tyrosine kinase-1 to placental growth factor). In the translational phase, biomarker identification by tandem mass spectrometry and validation experiments in urine, serum, and placenta were used to identify, quantify, and localize the biomarkers or related proteins.ResultsWe report that women with preeclampsia appear to present a unique urine proteomic fingerprint that predicts preeclampsia in need of mandated delivery with highest accuracy. This characteristic proteomic profile also has the ability to distinguish preeclampsia from other hypertensive or proteinuric disorders in pregnancy. Pregnant women followed longitudinally who developed preeclampsia displayed abnormal urinary profiles more than 10 weeks before clinical manifestation. Tandem mass spectrometry and de novo sequencing identified the biomarkers as nonrandom cleavage products of SERPINA1 and albumin. Of these, the 21 amino acid C-terminus fragment of SERPINA1 was highly associated with severe forms of preeclampsia requiring early delivery. In preeclampsia, increased and aberrant SERPINA1 immunoreactivity was found in urine, serum, and placenta, in which it localized predominantly to placental villi and placental vascular spaces adherent to the endothelium. In addition, significant perivascular deposits of misfolded SERPINA1 aggregates were exclusively identified in preeclamptic placentae.ConclusionProteomics-based characterization of urine in preeclampsia identified a proteomic fingerprint composed of SERPINA1 and albumin fragments, which can accurately diagnose preeclampsia and shows promise to discriminate it from other hypertensive proteinuric diseases. These findings provide insight into a novel pathophysiological mechanism of preeclampsia related to SERPINA1 misfolding, which may offer new therapeutic opportunities in the future.

Project description:Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

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Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy.

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Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy.

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