The transcriptome, or all Ribonucleic Acid (RNA) molecules within a tissue or cell, is altered during metabolic diseases and aging. Understanding how specific foods and biological processes alter the transcriptome will enable identification of interventions to attenuate aging-associated or metabolic disease processes. As described throughout this dissertation, we examined how aging and specific dietary patterns affect the transcriptome across multiple tissues in rats, Drosophila, and humans to achieve the following objectives: 1) Identify if short-term dietary whole egg consumption modifies microRNAs in the blood or in tissues; 2) Examine if long-term dietary whole egg consumption alters the transcriptome during type 2 diabetes mellitus (T2DM); and 3) Determine normal transcriptomic signatures of aging and examine if these profiles can predict age in humans and Drosophila in order to identify conserved aging genes.

In our first study, we examined the role that nutrition plays in regulating the transcriptome by examining how whole egg consumption affects the blood, liver, kidney, prefrontal cortex, and adipose tissue. We identified that short-term consumption of whole egg does not alter the circulating microRNA status in the blood, but modifies the transcriptomes across the liver, kidney, brain, and adipose tissues. In the second study, we examined if long-term consumption of whole egg alters gene expression or microRNA profiles during T2DM and determined that the gene-diet interactions from consuming whole egg may yield therapeutic benefit during diabetes by improving glutathione metabolism. Collectively, we demonstrated aging-associated transcriptomic profiles across multiple species can be used to model aging, consuming whole egg doesn’t alter microRNA status in the blood but may serve as a beneficial dietary approach to improve glutathione metabolism during T2DM.

In the final study, we processed publicly available data to identify how the transcriptome in the prefrontal cortex is altered during aging between humans and Drosophila. Then we examined if gene expression in the prefrontal cortex can be used to predict age or classify subjects into age groups. We discovered 41 conserved genes across humans and Drosophila that predict aging across species and demonstrated that using genes that are highly correlated with age in Drosophila, we can predict aging in humans using machine learning algorithms. Overall, these research studies have expanded the knowledge of how aging and diet impact gene expression across tissues, while uncovering novel genes and microRNAs that might be altered during aging or while consuming diets rich in whole egg.