Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy

Abedi, Maryam; Gheisari, Yousof

doi:10.7717/peerj.1284

Cited by 32 publications

(25 citation statements)

References 25 publications

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“…Firstly, the small sample size will affect the accuracy of the present results. Secondly, it is believed that DN is the result of combinations among multiple genetic and environmental factors, but not a single factor [30][31][32]. Therefore, other factors should be considered in future studies.…”

Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Association of the polymorphisms in FOXO1 gene and diabetic nephropathy risk

Pei

Xue

et al. 2019

Artificial Cells, Nanomedicine, and Biotechnology

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Purpose: This study was aimed to study the hypothesis that forkhead box O1 (FOXO1) gene rs17446614 and rs17592236 single nucleotide polymorphisms (SNPs) influenced the development of diabetic nephropathy (DN). Methods: This study included 138 DN patients and 149 healthy controls. Controls were matched with the patients in age and gender. The method of polymerase chain reaction-restriction fragment length polymorphisms (PCR-RFLP) was used to detect FOXO1 gene polymorphisms. Haploview software was conducted to analyze the linkage disequilibrium and haplotypes of FOXO1 gene polymorphisms. Relative risk of DN was expressed by odds ratios (ORs) and 95% confidence intervals (95% CIs), then the results were adjusted by clinical characteristics of the study subjects using logistic regression analysis. Subgroup analysis was performed according to gender. Results: AA genotype of rs17446614 SNPs was significantly associated with the risk of DN (P ¼ .037, adjusted OR ¼ 5.412, 95% CI ¼ 1.103-26.559), especially in female (OR ¼ 8.700, 95% CI ¼ 1.008-75.062, P ¼ .021). FOXO1 rs17446614 A allele positively associated with the development of DN (P ¼ .027, adjusted OR ¼ 1.680, 95% CI ¼ 1.060-2.662), particularly in women (OR ¼ 2.003, 95% CI ¼ 1.070-3.749, P ¼ .028). A-C haplotype formed by FOXO1 gene rs17446614 and rs17592236 SNPs was significantly associated with the increased risk of DN (P ¼ .011, OR ¼ 1.850, 95% CI ¼ 1.146-2.986). Conclusion: FOXO1 gene rs17446614 SNP, and the A-C haplotype of rs17446614 and rs17592236 polymorphisms were risk factors for the development of DN.

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Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Association of the polymorphisms in FOXO1 gene and diabetic nephropathy risk

Pei

Xue

et al. 2019

Artificial Cells, Nanomedicine, and Biotechnology

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“…Systems biology gives meaning to large amounts of data derived from ‘-omics-’ technologies ( 15 ). The ‘-omics-’ technologies refer to high-throughput techniques, which may simultaneously detect a large number of molecules (including genes, transcriptomes, proteins and metabolites) in complex bio-samples ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Network analysis of membranous glomerulonephritis based on metabolomics data

et al. 2018

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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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“…Taken together, these correlations reveal the success of artificially constructed biological process networks in identifying promising biomarkers for each stage of DN. Many studies have supported the use of PPI as a basis for determining novel connections between proteins involved in the progression of diabetes (Abedi and Gheisari, 2015[ 1 ]; Saito et al, 2016[ 68 ]; Varemo et al, 2015[ 79 ]).…”

Section: Utilization Of Bioinformatics In Diabetes Researchmentioning

confidence: 99%