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Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

Friday, December 8, 2017

11:00 AM-1:00 PM

BIOMED PhD Thesis Defense

Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

David Alfego, PhD Candidate, School of Biomedical Engineering, Science and Health Systems, Drexel University

Andres Kriete, PhD, Associate Professor School of Biomedical Engineering, Science and Health Systems, Drexel University

Aging affects normal functions and mechanisms of cells, tissues and organisms throughout their lifespan. These changes can lead to any number of potential health risks, diseases and other disorders. With cellular aging activating stress response pathways as the center of this study, the causes leading to these cell stress responses still need to be fully explored, though evidence points towards the involvement of mitochondrial dysfunction and decreased ATP production. Experimental energy restriction in quiescent cells (ERiQ) through perturbations of mitochondrial function causes adaptive changes in response to reduced ATP, NAD+ and NADP levels in AKT, NF-κB, p53 and mTOR pathways. The construction of a theoretical computational model, complementary to the experimental model, is based on feedback modules enabling investigations of the interplay of key signaling nodes and cellular adaptations in response to energy stress can help visualize this. The in-silico model demonstrates adaptations to sudden energetic perturbations, promoting cellular survival and recovery. This thesis hypothesizes that the very same mechanisms are chronically activated during aging, revealing conflicting responses with respect to mitochondrial function contributing to a lockstep progression of decline. The model makes predictions consistent with inhibitory and gain-of-function experiments in aging.

The relevance of ERiQ is further emphasized by a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissues from individuals at different biological ages, which were compared to 7 different experimental platforms. Experimental datasets included replicative senescence and ERiQ, in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC motifs and TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the ERiQ profile was shared with the majority of in-vivo aged tissues. Fitting motifs in a minimalistic protein-protein network model allowed us to probe for connectivity to distinct stress sensors. DNA damage sensors ATM and ATR linked to one subnetwork associated with senescence. By contrast, energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that energy deprivation may be linked to transcriptional patterns characteristic of many aged tissues distinct from cumulative DNA damage associated with senescence. Finally, we exemplify the combined use of the predictive power of the computational model with experimental investigation in-vitro using inhibitors, receptor agonists and antioxidants, which may improve the energetic situation in aged cells.

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