All ETDs from UAB

Advisory Committee Chair

John L Hartman Iv

Advisory Committee Members

Stephen Barnes

Kasturi Mitra

Douglas Moellering

Keshav Singh

Daniel Smith

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) Heersink School of Medicine


Systematic investigation of chronological lifespan (CLS) in the Saccharomyces cerevisiae yeast gene knockout and knockdown (YKO/KD) strains reveals genes, path-ways, and interactions of potential relevance for aging of all eukaryotic cell types. Growth curves obtained by quantitative high-throughput cell array phenotyping (Q-HTCP) of the entire YKO/KD strain collection provide comprehensive snapshots of the influence of each individual gene on CLS. Yeast CLS is also influenced by media com-position, auxotrophic background, and media buffering. We used Q-HTCP to analyze CLS of the YKO/KD libraries, focusing on the interaction between gene networks and media buffering, with respect to their influence on CLS. We also examined the depend-ence of CLS gene networks on glucose exposure (media-buffered context) or auxotrophy. Aging of each YKO/KD strain culture was determined by quantifying the rightward shifts in growth curves, which indicate loss of viability over time. The influence of each gene on chronological aging was thus established by comparing CLS of the corresponding YKO/KD strain with the parental (non-mutant) reference strain. Metabolism is a major factor for cellular aging in all eukaryotes, thus we developed a metabolite isolation meth-od in order to integrate it with phenomic data. Targeted analysis of TCA cycle metabolite pools, as well as non-targeted analysis of all ions was performed to ascertain differential expression of metabolic pathways during aging, and with respect to media buffering. The same media conditions were analyzed by phenomic analysis of CLS in the YKO/KD li-braries. Integration of the phenomic and metabolomics data provided systems level in-sight into the effect of pH homeostasis on chronological aging in yeast. In summary, ex-perimental and computational approaches to understand how genetic and metabolic net-works are integrated with respect to aging, and how these functional networks change dynamically as a function of nutrient and environmental context will be presented.



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