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Network analysis of membranous glomerulonephritis based on metabolomics data

  • Authors:
    • Amir Taherkhani
    • Shiva Kalantari
    • Afsaneh Arefi Oskouie
    • Mohsen Nafar
    • Mohammad Taghizadeh
    • Koorosh Tabar
  • View Affiliations / Copyright

    Affiliations: Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, Iran, Chronic Kidney Disease Research Center, Shahid Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1666663111, Iran, Department of Basic Science, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 1971653313, Iran, Urology Nephrology Research Center, Shahid Labbafinejad Hospital, Shahid Beheshti University of Medical Sciences, Tehran 1666663111, Iran, Bioinformatics Department, Institute of Biochemistry and Biophysics, Tehran University, Tehran 1417614411, Iran, Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
    Copyright: © Taherkhani et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 4197-4212
    |
    Published online on: September 12, 2018
       https://doi.org/10.3892/mmr.2018.9477
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Abstract

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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Copy and paste a formatted citation
Spandidos Publications style
Taherkhani A, Kalantari S, Arefi Oskouie A, Nafar M, Taghizadeh M and Tabar K: Network analysis of membranous glomerulonephritis based on metabolomics data. Mol Med Rep 18: 4197-4212, 2018.
APA
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., & Tabar, K. (2018). Network analysis of membranous glomerulonephritis based on metabolomics data. Molecular Medicine Reports, 18, 4197-4212. https://doi.org/10.3892/mmr.2018.9477
MLA
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., Tabar, K."Network analysis of membranous glomerulonephritis based on metabolomics data". Molecular Medicine Reports 18.5 (2018): 4197-4212.
Chicago
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., Tabar, K."Network analysis of membranous glomerulonephritis based on metabolomics data". Molecular Medicine Reports 18, no. 5 (2018): 4197-4212. https://doi.org/10.3892/mmr.2018.9477
Copy and paste a formatted citation
x
Spandidos Publications style
Taherkhani A, Kalantari S, Arefi Oskouie A, Nafar M, Taghizadeh M and Tabar K: Network analysis of membranous glomerulonephritis based on metabolomics data. Mol Med Rep 18: 4197-4212, 2018.
APA
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., & Tabar, K. (2018). Network analysis of membranous glomerulonephritis based on metabolomics data. Molecular Medicine Reports, 18, 4197-4212. https://doi.org/10.3892/mmr.2018.9477
MLA
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., Tabar, K."Network analysis of membranous glomerulonephritis based on metabolomics data". Molecular Medicine Reports 18.5 (2018): 4197-4212.
Chicago
Taherkhani, A., Kalantari, S., Arefi Oskouie, A., Nafar, M., Taghizadeh, M., Tabar, K."Network analysis of membranous glomerulonephritis based on metabolomics data". Molecular Medicine Reports 18, no. 5 (2018): 4197-4212. https://doi.org/10.3892/mmr.2018.9477
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