Background
Insulin resistance is associated with prediabetes and further progression to type 2 diabetes mellitus (T2DM). This study aims to investigate novel hepatic lncRNAs associated with key genes in insulin resistance in prediabetes.
Methods
In the bioinformatics phase, we have collected screened a pool of lncRNAs and mRNAs according to their potential association to prediabetic condition. We performed pathway analysis of mRNAs, using DAVID tool based on KEGG repository data. Then, we used Python programming language to get a subset of lncRNAs located in 50 kb proximity with high-fat (HF)-responsive mRNAs. In the experimental phase, prediabetic mice model was established by the treatment of HF diets for 12 weeks. After this treatment, HF-fed animals were divided into two groups of endurance exercised or sedentary, both continuing on the HF diet for 8 weeks. Besides, a group of diabetic mice was treated using a HF diet for 8 weeks followed by injection with STZ solution and then a HF diet for another 4 weeks.
Results
We found three genes having paired lncRNAs annotated in insulin resistance pathway. Their hepatic expression levels were altered in prediabetic condition as upregulation of Srebf1 was associated with GM38501, upregulation of Pck1 was associated with Ctcflos and GM36691, downregulation of Cpt1b was associated with GM44502. All of these expression patterns were replicated in diabetic mice, correlated positively with their predicted lncRNAs. Interestingly, exercise reversed their expression patterns.
Conclusions
We suggest that the expression pattern of the hepatic mRNA-lncRNA (HML) network in prediabetic state undergoes similar modification to that of diabetes.
Background
Metabolic associated fatty liver disease (MAFLD) is a complex disease that results from the accumulation of fat in the liver. MAFLD is directly associated with obesity, insulin resistance, diabetes, and metabolic syndrome. PPAR
γ
ligands, including pioglitazone, are also used in the management of this disease. Noncoding RNAs play a critical role in various diseases such as diabetes, obesity, and liver diseases including MAFLD. However, there is no adequate knowledge about the translation of using these ncRNAs to the clinics, particularly in MAFLD conditions. The aim of this study was to identify ncRNAs in the etiology of MAFLD as a novel approach to the therapeutic targets.
Methods
We collected human and mouse MAFLD gene expression datasets available in GEO. We performed pathway enrichment analysis of total mRNAs based on KEGG repository data to screen the most potential pathways in the liver of MAFLD human subjects and mice model, and analyzed pathway interconnections via ClueGO. Finally, we screened disease causality of the MAFLD ncRNAs, which were associated with PPARs, and then discussed the role of revealed ncRNAs in PPAR signaling and MAFLD.
Results
We found 127 ncRNAs in MAFLD which 25 out of them were strongly validated before for regulation of PPARs. With a polypharmacology approach, we screened 51 ncRNAs which were causal to a subset of diseases related to MAFLD.
Conclusion
This study revealed a subset of ncRNAs that could help in more clear and guided designation of preclinical and clinical studies to verify the therapeutic application of the revealed ncRNAs by manipulating the PPARs molecular mechanism in MAFLD.
In order to achieve a sufficient population of cardiac-committed progenitor cells, it is crucial to know the mechanisms of cardiac progenitor formation. Previous studies suggested ROS effect on cardiac commitment events to play a key role in the cell signaling and activate cardiac differentiation of pluripotent stem cells. We previously reported that PPARγ activity is essential for cardiac progenitor cell commitment. Although several studies have conducted the involvement of PPARγ-related signaling pathways in cardiac differentiation, so far, the regulatory mechanisms of these signaling pathways have not been discussed and cleared. In this study, we focus on the role of PPARγ agonist in ROS generation and its further effects on the differentiation of cardiac cells from mESCs. The results of this study show that the presence of ROS is necessary for heart differentiation in the precursor stage of cardiac cells, and the coenzyme Q10 antioxidant precludes proper cardiac differentiation. In addition, this antioxidant prevents the action of pioglitazone in increasing oxygen radicals as well as beating cardiomyocyte differentiation properties. In this case, it can be concluded that PPARγ is required to modulate ROS levels during cardiac differentiation.
Background
Dyslexia is one of the most common learning disabilities, especially among children. Type 2 diabetes is a metabolic disorder that affects a large population globally, with metabolic disorders. There have been several genes that are identified as causes of Dyslexia, and in recent studies, it has been found out that some of those genes are also involved in several metabolic pathways. For several years, it has been known that type 2 diabetes causes several neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, in several studies, it was suggested that type 2 diabetes also has some associations with learning disabilities. This raises the question of whether “Is there a connection between type 2 diabetes and dyslexia?”. In this study, this question is elaborated by linking their developmental processes via bioinformatics analysis about these two diseases individually and collectively.
Result
The literature review for dyslexia and type two diabetes was completed. As the result of this literature review, the genes that are associated to type 2 diabetes and dyslexia were identified. The biological pathways of dyslexia, and dyslexia associated genes, type 2 diabetes, and type 2 diabetes associated genes were identified. The association of these genes, regarding to their association with pathways were analysed, and using STRING database the gene associations were analysed and identified.
Conclusion
The findings of this research included the interaction analysis via gene association, co-expression and protein–protein interaction. These findings clarified the interconnection between dyslexia and type 2 diabetes in molecular level and it will be the beginning of an answer regarding to the relationship between T2D and dyslexia. Finally, by improving the understanding this paper aims to open the way for the possible future approach to examine this hypothesis.
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