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Network analysis of membranous glomerulonephritis based on metabolomics data

Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podo...

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Detalles Bibliográficos
Autores principales: Taherkhani, Amir, Kalantari, Shiva, Oskouie, Afsaneh Arefi, Nafar, Mohsen, Taghizadeh, Mohammad, Tabar, Koorosh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6172390/
https://www.ncbi.nlm.nih.gov/pubmed/30221719
http://dx.doi.org/10.3892/mmr.2018.9477
Descripción
Sumario:Membranous glomerulonephritis (MGN) is one of the most frequent causes of nephrotic syndrome in adults. It is characterized by the thickening of the glomerular basement membrane in the renal tissue. The current diagnosis of MGN is based on renal biopsy and the detection of antibodies to the few podocyte antigens. Due to the limitations of the current diagnostic methods, including invasiveness and the lack of sensitivity of the current biomarkers, there is a requirement to identify more applicable biomarkers. The present study aimed to identify diagnostic metabolites that are involved in the development of the disease using topological features in the component-reaction-enzyme-gene (CREG) network for MGN. Significant differential metabolites in MGN compared with healthy controls were identified using proton nuclear magnetic resonance and gas chromatography-mass spectrometry techniques, and multivariate analysis. The CREG network for MGN was constructed, and metabolites with a high centrality and a striking fold-change in patients, compared with healthy controls, were introduced as putative diagnostic biomarkers. In addition, a protein-protein interaction (PPI) network, which was based on proteins associated with MGN, was built and analyzed using PPI analysis methods, including molecular complex detection and ClueGene Ontology. A total of 26 metabolites were identified as hub nodes in the CREG network, 13 of which had salient centrality and fold-changes: Dopamine, carnosine, fumarate, nicotinamide D-ribonucleotide, adenosine monophosphate, pyridoxal, deoxyguanosine triphosphate, L-citrulline, nicotinamide, phenylalanine, deoxyuridine, tryptamine and succinate. A total of 13 subnetworks were identified using PPI analysis. In total, two of the clusters contained seed proteins (phenylalanine-4-hydroxlylase and cystathionine γ-lyase) that were associated with MGN based on the CREG network. The following biological processes associated with MGN were identified using gene ontology analysis: ‘Pyrimidine-containing compound biosynthetic process’, ‘purine ribonucleoside metabolic process’, ‘nucleoside catabolic process’, ‘ribonucleoside metabolic process’ and ‘aromatic amino acid family metabolic process’. The results of the present study may be helpful in the diagnostic and therapeutic procedures of MGN. However, validation is required in the future.