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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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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
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author Taherkhani, Amir
Kalantari, Shiva
Oskouie, Afsaneh Arefi
Nafar, Mohsen
Taghizadeh, Mohammad
Tabar, Koorosh
author_facet Taherkhani, Amir
Kalantari, Shiva
Oskouie, Afsaneh Arefi
Nafar, Mohsen
Taghizadeh, Mohammad
Tabar, Koorosh
author_sort Taherkhani, Amir
collection PubMed
description 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.
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spelling pubmed-61723902018-10-19 Network analysis of membranous glomerulonephritis based on metabolomics data Taherkhani, Amir Kalantari, Shiva Oskouie, Afsaneh Arefi Nafar, Mohsen Taghizadeh, Mohammad Tabar, Koorosh Mol Med Rep Articles 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. D.A. Spandidos 2018-11 2018-09-12 /pmc/articles/PMC6172390/ /pubmed/30221719 http://dx.doi.org/10.3892/mmr.2018.9477 Text en Copyright: © Taherkhani et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Taherkhani, Amir
Kalantari, Shiva
Oskouie, Afsaneh Arefi
Nafar, Mohsen
Taghizadeh, Mohammad
Tabar, Koorosh
Network analysis of membranous glomerulonephritis based on metabolomics data
title Network analysis of membranous glomerulonephritis based on metabolomics data
title_full Network analysis of membranous glomerulonephritis based on metabolomics data
title_fullStr Network analysis of membranous glomerulonephritis based on metabolomics data
title_full_unstemmed Network analysis of membranous glomerulonephritis based on metabolomics data
title_short Network analysis of membranous glomerulonephritis based on metabolomics data
title_sort network analysis of membranous glomerulonephritis based on metabolomics data
topic Articles
url 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
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