In Silico Analysis to Link Insulin Resistance, Obesity and Ageing with Alzheimer's Disease.
Priyanka SarkarPremkumar JayarajKetaki PatwardhanSamiksha YeoleSourajit DasYash SomaiyaRajagopal DesikanKavitha ThirumuruganPublished in: Journal of molecular neuroscience : MN (2021)
The process of ageing accompanies several metabolic diseases. With ageing, fats accumulate to increase the visceral and abdominal adiposity leading to hyperinsulinemia, insulin resistance, obesity and several other diseases. Drosophila melanogaster is often used to study the ageing process and its related disorders. Therefore, in this study, we performed an in silico analysis to relate the process of ageing and insulin resistance. We analysed the data of insulin-resistant Drosophila from the GEO database and compared it with the data from the literature survey. We observed that 98 genes were common in both the models, and they showed gene modulations related to metabolic pathways, fatty acid metabolism, insulin resistance and neural receptor-ligand binding pathways. Analysis of the REACTOME database against human data revealed that the TRKB signalling pathway is commonly affected. The TRKB-mediated BDNF pathway is a major regulator of memory loss. We further analysed the common genes in Alzheimer's disease and compared the fly data with human data to identify the diseases related to these common genes. Then, we performed a literature survey to provide protective mechanisms for the TRKB signalling pathway activation, mediated through polyphenols. We treated the flies with sesamol-conjugated lipoic acid derivative (a phenolic compound) at hormetic doses to evaluate its effect on the memory of flies.
Keyphrases
- insulin resistance
- type diabetes
- adipose tissue
- metabolic syndrome
- high fat diet induced
- high fat diet
- drosophila melanogaster
- skeletal muscle
- electronic health record
- polycystic ovary syndrome
- big data
- genome wide
- endothelial cells
- glycemic control
- systematic review
- fatty acid
- data analysis
- photodynamic therapy
- gene expression
- weight loss
- single cell
- machine learning
- bioinformatics analysis
- weight gain
- stress induced
- genome wide analysis