Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

David J. Alfego

doi:10.17918/eb8m-3k32

Cellular aging describes the buildup of changes over time that affect normal mechanisms of cells, tissues and organisms throughout their lifespan, which can lead to any number of potential health risks, diseases or other disorders. One of the major causes of these changes is declining mitochondrial function, though the cause of this energy stress is still debated. The prevailing experimental model for aging studies examines cells in a senescent state as the hallmark of aging. Yet this permanent, post-mitotic phase is more commonly observed in vitro. Aged cells in vivo often retain their mitotic potential, indicative of a paused, quiescent state. This thesis proposes a new platform to study aging through perturbations of mitochondrial function via an experimental energy restriction in quiescence (ERiQ) model that may be more relevant to aging in tissues. This model causes adaptive changes in major stress response pathways for AKT, NF-[kappa]B, p53 and mTOR as a reaction to reduced ATP, NAD+ and NADP levels. The construction of a theoretical computational model, complementary to the experimental model, is based on feedback motifs that investigate the interplay between those key stress response pathways. The in silico model demonstrates adaptations to sudden energetic perturbations, promoting pro-survival phenotypes and recovery. This thesis hypothesizes that the very same survival mechanisms are chronically activated during aging, but also cause conflicting responses that actively suppress mitochondrial function to contribute to a lockstep progression of decline. The model makes predictions consistent with inhibitory and gain-of-function experiments in aging. The relevance of ERiQ as a model to study aging 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 majority of in vivo aged tissues correlated with the ERiQ profile instead of senescence, confirming its relevance as a new experimental model. Fitting motifs in a minimalistic protein-protein interaction (PPI) network model allowed us to probe for connectivity to distinct stress sensors, as well as identify novel targets of study in transcription factors that significantly switch enrichment between ERiQ and senescence. In the PPI, 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, this thesis exemplifies the combined use of the predictive power of the computational model with experimental investigation in vitro. Preliminary experiments show how the model can be refined to reflect how certain conditions may alter metabolic output and offer intriguing insights into the future of cellular aging studies.

Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

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Characterization and Modeling of Metabolic Stress Responses in Cellular Aging

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