Endocrine Abstracts

Context: Metabolic diseases affect millions of people in both developed and transition countries. In addition to genetic inheritance of risk alleles, emerging evidence has shown that these diseases are also linked to lifestyle and inherited epigenetic pattern interactions. The strong link between epigenetics and metabolism may offer attractive clinical applications to counteract and manage the escalating prevalence of metabolic diseases, such as obesity, type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), among others. Regarding the epigenetic factors, microRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression. Moreover, evidences suggested a role for miRNAs in the pathogenesis of metabolic disorders, supporting that they may represent potential biomarkers or targets for prevalent chronic diseases. However, current results are often controversial.

Objective: To feature the associations between miRNAs-mRNA, miRNA-lncRNAs, and miRNAs-small molecules in human metabolic diseases, including obesity, T2DM, and NAFLD.

Design: The metabolic-related miRNAs were obtained from the Human MicroRNA Disease Database (HMDD) and miR2Disease database. Search on the databases Matrix Decomposition and Heterogeneous Graph Inference (MDHGI) and DisGeNET were also performed. MiRNAs target genes were obtained from three independent sources: Microcosm v5.0, TargetScan v7.0, and miRTarBase v4.4. The functional enrichment analysis of miRNA-target genes was performed to retrieve Gene Ontology (GO) and KEGG pathways using the plug-ins BiNGO v3.0.3 and ClueGO/Cluepedia v2.3.5. The interactions between miRNAs-lncRNA and miRNA-small molecules were performed using the miRNet web tool. The associations were corrected for multiple hypotheses using the Benjamini & Hochberg False Discovery Rate test and interactions with a q-value <0.05 were considered strongly enriched. All network analyses were performed using Cytoscape software v3.7.0.

Results: A total of 20 miRNAs were found associated with metabolic disorders in our study. Interestingly, 6 miRNAs (miR-17-5p, miR-29c-3p, miR-34a-5p, miR-103a-3p, miR-107, and miR-132-3p) were found in the four databases (HMDD, miR2Disease, MDHGI, and DisGeNET) used for these analyses, presenting a stronger association with the selected diseases. The functional enrichment analysis of miRNAs target genes reflected the complex biological behavior of metabolic diseases, being associated with multiple signaling pathways. Moreover, interactions between miRNA-lncRNA and miRNA-small molecules were also originally evidenced, suggesting that some molecules can modulate gene expression by such indirect way; although others studies are required to understand these outcomes.

Conclusion: The construction of miRNA-mRNA, miRNA-lncRNA, and miRNA-small molecules networks provides a novel approach to investigate the metabolic diseases pathogenesis and for the personalized treatment in the future.

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