Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

Fechete, Raul; Heinzel, Andreas; Perco, Paul; Mönks, Konrad; Söllner, Johannes; Stelzer, Gil; Eder, Susanne; Lancet, Doron; Oberbauer, Rainer; Mayer, Gert; Mayer, Bernd

doi:10.1002/prca.201000136

Cited by 36 publications

(29 citation statements)

References 63 publications

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“…Migration of immune cells into the renal is a feature of early DN, and it is implicated in the development of this complication [34–37]. Previous studies also have found C1S and C1R to be differentially expressed in DN [38, 39]. In our study, C1S and C1R were regulated by LEF1 and hsa-miR-33a.…”

Section: Discussionsupporting

confidence: 67%

Crucial genes associated with diabetic nephropathy explored by microarray analysis

Wang

Zhang

et al. 2016

BMC Nephrol

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BackgroundThis study sought to investigate crucial genes correlated with diabetic nephropathy (DN), and their potential functions, which might contribute to a better understanding of DN pathogenesis.MethodsThe microarray dataset GSE1009 was downloaded from Gene Expression Omnibus, including 3 diabetic glomeruli samples and 3 healthy glomeruli samples. The differentially expressed genes (DEGs) were identified by LIMMA package. Their potential functions were then analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, miRNAs and transcription factors (TFs) regulating DEGs were predicted by the GeneCoDis tool, and miRNA-DEG-TF regulatory network was visualized by Cytoscape. Additionally, the expression of DEGs was validated using another microarray dataset GSE30528.ResultsTotally, 14 up-regulated DEGs and 430 down-regulated ones were identified. Some DEGs (e.g. MTSS1, CALD1 and ACTN4) were markedly relative to cytoskeleton organization. Besides, some other ones were correlated with arrhythmogenic right ventricular cardiomyopathy (e.g. ACTN4, CTNNA1 and ITGB5), as well as complement and coagulation cascades (e.g. C1R and C1S). Furthermore, a series of miRNAs and TFs modulating DEGs were identified. The transcription factor LEF1 regulated the majority of DEGs, such as ITGB5, CALD1 and C1S. Hsa-miR-33a modulated 28 genes, such as C1S. Additionally, 143 DEGs (one upregulated gene and 142 downregulated genes) were also differentially expressed in another dataset GSE30528.ConclusionsThe genes involved in cytoskeleton organization, cardiomyopathy, as well as complement and coagulation cascades may be closely implicated in the progression of DN, via the regulation of miRNAs and TFs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12882-016-0343-2) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 67%

Crucial genes associated with diabetic nephropathy explored by microarray analysis

Wang

Zhang

et al. 2016

BMC Nephrol

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“…This approach also allowed the association of biomarker candidates with relevant pathophysiologic pathways in diabetic nephropathy (for example, oxidative stress responses, extracellular matrix-receptor interactions or chemokine signalling). 131 Similarly, an analysis of indepth proteomic data from a mouse model of folic acidinduced AKI involved the assessment of 6,564 nonredundant proteins from kidney. 132 The investigators verified the upregulation of several proteins previously associated with AKI, and identi fied novel biomarker candidates suggesting the involvement of the glutamatergic signalling system in AKI.…”

Section: Systems Medicine Approachesmentioning

confidence: 99%

Proteomic biomarkers in kidney disease: issues in development and implementation

et al. 2015

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Proteomic biomarkers offer the hope of improving the management of patients with kidney diseases by enabling more accurate and earlier detection of renal pathology than is possible with currently available biomarkers, serum creatinine and urinary albumin. In addition, proteomic biomarkers could also be useful to define the most suitable therapeutic targets in a given patient or disease setting. This Review describes the current status of proteomic and protein biomarkers in the context of kidney diseases. The valuable lessons learned from early clinical studies of potential proteomic biomarkers in kidney disease are presented to give context to the newly identified biomarkers, which have potential for actual clinical implementation. This article also includes an overview of protein-based biomarker candidates that are undergoing development for use in nephrology, focusing on those with the greatest potential for clinical implementation. Relevant issues and problems associated with the discovery, validation and clinical application of proteomic biomarkers are discussed, along with suggestions for solutions that might help to guide the design of future proteomic studies. These improvements might remove some of the current obstacles to the utilization of proteomic biomarkers, with potentially beneficial results.

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“…In a pilot study, six metabolites suggested by consortium members as strong candidate CKD biomarkers were analysed. This resulted in the finding of shared descriptors between the genes for each metabolite, thus ranking the relevance of the metabolites for the kidney disease [29]. This capacity is now being augmented by a weighting algorithm to prioritise the metabolite-related gene sets.…”

Section: Omics Integrationmentioning

confidence: 99%

“…A literature mining of papers of 17 omics studies related to CKD[29] assisted in benchmarking the GeneKid pipeline (Figure 2). Additional benchmarking was performed based on a list of 26 initial biomarkers (22 proteins, two peptides, one autoantibody and one nucleotide), prioritised by the extent of relevant gene annotations, using the GeneCards database for obtaining diseases, compounds and pathway relationships.…”

Section: Omics Integrationmentioning

confidence: 99%

In-silico human genomics with GeneCards

et al. 2011

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Since 1998, the bioinformatics, systems biology, genomics and medical communities have enjoyed a synergistic relationship with the GeneCards database of human genes (http://www.genecards.org). This human gene compendium was created to help to introduce order into the increasing chaos of information flow. As a consequence of viewing details and deep links related to specific genes, users have often requested enhanced capabilities, such that, over time, GeneCards has blossomed into a suite of tools (including GeneDecks, GeneALaCart, GeneLoc, GeneNote and GeneAnnot) for a variety of analyses of both single human genes and sets thereof. In this paper, we focus on inhouse and external research activities which have been enabled, enhanced, complemented and, in some cases, motivated by GeneCards. In turn, such interactions have often inspired and propelled improvements in GeneCards. We describe here the evolution and architecture of this project, including examples of synergistic applications in diverse areas such as synthetic lethality in cancer, the annotation of genetic variations in disease, omics integration in a systems biology approach to kidney disease, and bioinformatics tools.

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Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

Cited by 36 publications

References 63 publications

Crucial genes associated with diabetic nephropathy explored by microarray analysis

Crucial genes associated with diabetic nephropathy explored by microarray analysis

Proteomic biomarkers in kidney disease: issues in development and implementation

In-silico human genomics with GeneCards

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