Ames dwarf mice (df/df) display delayed aging relative to their normal siblings, living approximately 40-60% longer. As such, investigating the mechanisms that enable these organisms to have superior life expectancies may prove useful in developing therapies to slow aging and deter age-related disease. Nonalcoholic fatty liver disease (NAFLD) is a condition that is characterized by the accumulation of excess adipose tissue in the liver. Interestingly, its prevalence tends to increase with age with an incidence rate of greater than 40% in individuals over the age of 60. Previous studies highlight the potential of calorie restriction (CR) in promoting longevity, but little is known about its effects on the biomolecular processes that govern NAFLD. In this study, we explored the livers of Ames dwarf mice following 6 months of continuous calorie-restriction and discovered significant down regulation of candidate miR-34a, an established biomarker for fatty liver. Using qPCR and western blot, we found miR-34a to be correlated with the expression of age-related and lipid trafficking mRNA and protein. Specifically, CR upregulated Sirt1 in the Ames dwarf liver and subsequently induced gluconeogenesis and lipid catabolism. To verify the role of miR-34a in influencing fatty acid metabolism, we transfected the human liver cancer (HepG2) cell line with miR-34a mimic and observed its effect on direct targets SIRT1, AMPKa, and PPARa which are believed to influence downstream lipid transport genes. In all, our findings suggest that CR is a robust driver of the SIRT1 signaling pathway which reverses the pathology associated with age-related diseases by influencing insulin signaling in the Ames dwarf liver thereby improving fatty acid oxidation and cholesterol efflux and maintaining lipid turnover via autophagy.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1641 |
| Date | 01 January 2021 |
| Creators | Ashiqueali, Sarah |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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