“…7) may offer new pathobiological pathways to explain the asthma-COPD comorbidity as well. 50–54 Fig. 7Network-based identification of miRNA-mediated pathobiological pathways between COPD and asthma.…”
Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways.
“…7) may offer new pathobiological pathways to explain the asthma-COPD comorbidity as well. 50–54 Fig. 7Network-based identification of miRNA-mediated pathobiological pathways between COPD and asthma.…”
Disease–disease relationships (e.g., disease comorbidities) play crucial roles in pathobiological manifestations of diseases and personalized approaches to managing those conditions. In this study, we develop a network-based methodology, termed meta-path-based Disease Network (mpDisNet) capturing algorithm, to infer disease–disease relationships by assembling four biological networks: disease–miRNA, miRNA–gene, disease–gene, and the human protein–protein interactome. mpDisNet is a meta-path-based random walk to reconstruct the heterogeneous neighbors of a given node. mpDisNet uses a heterogeneous skip-gram model to solve the network representation of the nodes. We find that mpDisNet reveals high performance in inferring clinically reported disease–disease relationships, outperforming that of traditional gene/miRNA-overlap approaches. In addition, mpDisNet identifies network-based comorbidities for pulmonary diseases driven by underlying miRNA-mediated pathobiological pathways (i.e., hsa-let-7a- or hsa-let-7b-mediated airway epithelial apoptosis and pro-inflammatory cytokine pathways) as derived from the human interactome network analysis. The mpDisNet offers a powerful tool for network-based identification of disease–disease relationships with miRNA-mediated pathobiological pathways.
“…DUSP family genes, including DUSP1, DUSP2, DUSP5 and DUSP16, are the major regulators of the MAPK signaling pathway and they are implicated in the control of anti-inflammatory responses, mainly in References [33][34][35]. A study developed by Kozmus et al demonstrated that MAP3K2 was significantly decreased in asthmatic patients after ICS treatment [36]. This fact could be indicate that the alteration in gene expression could occur via miRNA regulation, although more studies must be developed to elucidate it.…”
Severe eosinophilic asthma poses a serious health and economic problem, so new therapy approaches have been developed to control it, including biological drugs such as benralizumab, which is a monoclonal antibody that binds to IL-5 receptor alpha subunit and depletes peripheral blood eosinophils rapidly. Biomarkers that predict the response to this drug are needed so that microRNAs (miRNAs) can be useful tools. This study was performed with fifteen severe eosinophilic asthmatic patients treated with benralizumab, and serum miRNAs were evaluated before and after treatment by semi-quantitative PCR (qPCR). Patients showed a clinical improvement after benralizumab administration. Additionally, deregulation of miR-1246, miR-5100 and miR-338-3p was observed in severe asthmatic patients after eight weeks of therapy, and a correlation was found between miR-1246 and eosinophil counts, including a number of exacerbations per year in these severe asthmatics. In silico pathway analysis revealed that these three miRNAs are regulators of the MAPK signaling pathway, regulating target genes implicated in asthma such as NFKB2, NFATC3, DUSP1, DUSP2, DUSP5 and DUSP16. In this study, we observed an altered expression of miR-1246, miR-5100 and miR-338-3p after eight weeks of benralizumab administration, which could be used as early response markers.
“…Potential reference genes (RGs) were chosen according to the data presented in published papers [ 3 , 40 , 41 , 49 , 50 , 51 , 52 , 53 , 54 ]. The genes tested in this study are listed in Table 1 .…”
The quantitative polymerase chain reaction (qRT-PCR) technique gives promising opportunities to detect and quantify RNA targets and is commonly used in many research fields. This study aimed to identify suitable reference genes for physical exercise and omega-3 fatty acids supplementation intervention. Forty healthy, physically active men were exposed to a 12-week eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) supplementation and standardized endurance training protocol. Blood samples were collected before and after the intervention and mRNA levels of six potential reference genes were tested in the leukocytes of 18 eligible participants using the qRT-PCR method: GAPDH (Glyceraldehyde-3-phosphate dehydrogenase), ACTB (Beta actin), TUBB (Tubulin Beta Class I), RPS18 (Ribosomal Protein S18), UBE2D2 (Ubiquitin-conjugating enzyme E2 D2), and HPRT1 (Hypoxanthine Phosphoribosyltransferase 1). The raw quantification cycle (Cq) values were then analyzed using RefFinder, an online tool that incorporates four different algorithms: NormFinder, geNorm, BestKeeper, and the comparative delta-Ct method. Delta-Ct, NormFinder, BestKeeper, and RefFinder comprehensive ranking have found GAPDH to be the most stably expressed gene. geNorm has identified TUBB and HPRT as the most stable genes. All algorithms have found ACTB to be the least stably expressed gene. A combination of the three most stably expressed genes, namely GAPDH, TUBB, and HPRT, is suggested for obtaining the most reliable results.
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