Evaluation of miRNAs regulation of BDNF and IGF1 genes in T2DM insulin resistance in experimental models: bioinformatics based approach.
R M FreitasS M S FelipeJannison Karly Cavalcante RibeiroValdevane Rocha AraújoC P S MartinM A F OliveiraS D MartinsJefferson Pacheco Amaral FortesJ O AlvesPaula Matias SoaresVânia Marilande CeccattoPublished in: Brazilian journal of biology = Revista brasleira de biologia (2022)
microRNAs (miRNAs) are recognized as diabetes mellitus type 2 (T2DM) biomarkers useful for disease metabolism comprehension and have great potential as therapeutics targets. BDNF and IGF1 increased expression are highly involved in the benefits of insulin and glucose paths, however, they are down-regulated in insulin resistance conditions, while their expression increase is correlated to the improvement of glucose and insulin metabolism. Studies suggest the microRNA regulation of these genes in several different contexts, providing a novel investigation approach for comprehending T2DM metabolism and revealing potential therapeutic targets. In the present study, we investigate in different animal models (human, rat, and mouse) miRNAs that target BDNF and IGF1 in skeletal muscle tissue with T2DM physiological conditions. Bioinformatics tools and databases were used to miRNA prediction, molecular homology, experimental validation of interactions, expression in the studied physiological condition, and network interaction. The findings showed three miRNAs candidates for IGF1(miR-29a, miR-29b, and miR-29c) and one for BDNF (miR-206). The experimental evaluations and the search for the expression in skeletal muscle from T2DM subjects confirmed the predicted interaction between miRNA-mRNA for miR-29b and miR-206 through human, rat, and mouse models. This interaction was reaffirmed in multiple network analyses. In conclusion, our results show the regulation relationship between miR-29b and miR-206 with the investigated genes, in several tissues, suggesting an inhibition pattern. Nevertheless, these data show a large number of possible interaction physiological processes, for future biotechnological prospects.
Keyphrases
- long non coding rna
- poor prognosis
- cell proliferation
- binding protein
- skeletal muscle
- insulin resistance
- glycemic control
- long noncoding rna
- type diabetes
- endothelial cells
- genome wide
- pi k akt
- gene expression
- oxidative stress
- adipose tissue
- blood glucose
- mouse model
- transcription factor
- induced pluripotent stem cells
- current status
- blood pressure
- climate change
- big data
- risk assessment
- dna methylation
- artificial intelligence
- pluripotent stem cells
- genome wide analysis