Data Mining of Two Large Transcriptomic Data Sets that Utilize Drosophila as a Model System for the Study of Neurodegeneration due to Aging and Traum

Intermittent fasting (IF) has been shown to have a positive impact on aging Drosophila, which included promoting longevity, lowering neural aggregates and improving behavior and autophagy responses in middle-aged flies. In a separate traumatic brain injury (TBI) model, we determined that repetitive bouts of injury had long-term negative consequences to longevity and neural function. Understanding the impact that aging, diet and TBI exposure has on the cellular and molecular response of the CNS could identify the conserved mechanisms that influence aging and trauma responses in human and mammalian systems and the development of potential treatments or therapies. Using the recent advances in RNA sequencing, tissues from IF and TBI Drosophila models were used for the transcriptional analysis of genes and functional pathways influenced by aging, diet and injury. The work outlined in this proposal used bioinformatics techniques to analyze RNAsequencing data generated from adult tissues taken at different ages or exposed to IF and TBI treatment conditions. For aged and IF-treated mRNA profiles, we developed an analytical pipeline that examined fold and variance changes from replicates within individual tissue datasets. Tissue-specific expression changes to metabolic, behavioral and proteolytic pathways were identified and correlated with the progressive dysregulation of key phenotypes. Much of this work centered on the novel concept that age-dependent increase "transcriptional drift" variance was suppressed following IF-treatment, thus partly restoring more youthful expression and phenotypic profiles. Preliminary analysis of TBI RNA-seq datasets has identified both acute and delayed changes (fold) to molecular pathways involved with inflammatory responses, wound healing, DNA repair, histone modification, and chromatin remodeling responses following TBI exposure. Through this analysis, both studies have useful insights into some of the complex molecular mechanism that are impacted with age and following IF-treatment and TBI exposure. A consistent finding from both RNA-seq studies was the fold change in epigenetic pathway components. The implications are that data mining of multiple RNA-seq datasets can identify profound in vivo changes to key molecular mechanisms that are impacted by aging, diet and trauma.