The Renal Gene Ontology Annotation Initiative

Alam-Faruque, Yasmin; Dimmer, Emily; Huntley, Rachael P.; O′Donovan, Claire; Scambler, Peter J.; Apweiler, Rolf

doi:10.4161/org.6.2.11294

Cited by 13 publications

(16 citation statements)

References 26 publications

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“…For the purposes of this publication we built two prognostic models, one in which the features were equally weighted during EN fitting (“concentration-only model”) and a second one in which features were unequally weighted according to the number of kidney-relevant genes the corresponding microRNAs are predicted to bind to (“concentration-binding model”). Such genes were identified from the Renal Gene Ontology (RGO), a public manually annotated resource of genes implicated in renal disease and development [ 32 ]. Previous work has shown that RGO-based annotation may improve [ 33 ] the interpretation of gene expression data from kidney biopsies in patients with diabetic nephropathy [ 34 ], but to our knowledge this is the first application of the RGO to enhance microRNA analyses.…”

Section: Methodsmentioning

confidence: 99%

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes

Argyropoulos

Wang

Bernardo

et al. 2015

JCM

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Microalbuminuria provides the earliest clinical marker of diabetic nephropathy among patients with Type 1 diabetes, yet it lacks sensitivity and specificity for early histological manifestations of disease. In recent years microRNAs have emerged as potential mediators in the pathogenesis of diabetes complications, suggesting a possible role in the diagnosis of early stage disease. We used quantiative polymerase chain reaction (qPCR) to evaluate the expression profile of 723 unique microRNAs in the normoalbuminuric urine of patients who did not develop nephropathy (n = 10) relative to patients who subsequently developed microalbuminuria (n = 17). Eighteen microRNAs were strongly associated with the subsequent development of microalbuminuria, while 15 microRNAs exhibited gender-related differences in expression. The predicted targets of these microRNAs map to biological pathways known to be involved in the pathogenesis and progression of diabetic renal disease. A microRNA signature (miR-105-3p, miR-1972, miR-28-3p, miR-30b-3p, miR-363-3p, miR-424-5p, miR-486-5p, miR-495, miR-548o-3p and for women miR-192-5p, miR-720) achieved high internal validity (cross-validated misclassification rate of 11.1%) for the future development of microalbuminuria in this dataset. Weighting microRNA measurements by their number of kidney-relevant targets improved the prognostic performance of the miRNA signature (cross-validated misclassification rate of 7.4%). Future studies are needed to corroborate these early observations in larger cohorts.

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Section: Methodsmentioning

confidence: 99%

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes

Argyropoulos

Wang

Bernardo

et al. 2015

JCM

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“…Consequently, the Renal and Cardiovascular Initiatives [10], [11] were instigated to support the interpretation of these datasets by providing a comprehensive public resource of GO annotations for targeted protein sets. The annotation focus of these two initiatives is proteins implicated in renal and cardiovascular development, function and disease.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Focused Gene Ontology Curation of Specific Mammalian Systems

et al. 2011

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The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome.AvailabilityGO annotation data is freely available from: ftp://ftp.geneontology.org/pub/go/gene-associations/

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“…Here, we applied constraint-based modeling method, a novel computational approach developed by Shlomi et al [24], to predict metabolic biomarkers for kidney-related diseases in this kidney stoichiometric metabolic model. Firstly, the genes set associated with renal development and function were downloaded from the public resource database, European Bioinformatics Institute (EBI) (http://www.ebi.ac.uk/GOA/kidney) [26]. Then, we performed gene functional annotation for these genes by using DAVID bioinformatics enrichment tools [27, 28] to identify kidney-related disease genes, following the classification criteria of Online Mendelian Inheritance in Man (OMIM database).…”

Section: Methodsmentioning

confidence: 99%

Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

Zhang

Dai

Huang

2013

BioMed Research International

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With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases.

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The Renal Gene Ontology Annotation Initiative

Cited by 13 publications

References 26 publications

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes

Urinary MicroRNA Profiling Predicts the Development of Microalbuminuria in Patients with Type 1 Diabetes

The Impact of Focused Gene Ontology Curation of Specific Mammalian Systems

Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

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