We are using phenomic analysis of the S. cerevisiae yeast gene deletion strain (YGDS) library to systematically investigate genes, pathways, and environmental factors that influence aging, using the chronological lifespan (CLS) model of aging for post-mitotic cells. Phenomic analysis consists of quantitative high-throughput cell array phenotyping (Q-HTCP) of the YGDS, which yields growth curve parameters associated with every gene that are used to estimate their contribution to CLS. The met17-Δ0 or MET17 allele status can influence CLS, and methionine restriction is known to extend lifespan in higher eukaryotes. The genomic collection of deletion strains libraries exists for both genetic backgrounds and we recently completed genome-wide CLS analysis using Q-HTCP for both libraries. Growth curve parameters, from Q-HTCP analysis, were plotted for each gene deletion strain against the parameters for the reference (background) strain. CLS phenotypes were then assigned based on the shift in growth curve parameters for the deletion strains with respect to the reference strain. We identified 1,379 long-lived strains and 277 short-lived strains out of 5,664 total strains in the met17-Δ0 deletion library. In the MET17 deletion library, we identified 1,285 long-lived and 367 short-lived strains out of 5,464 total. In depth analysis of these results is ongoing to confirm CLS phenotypes assigned from preliminary data and to gain insight about effects of methionine pathway interactions on CLS.
To further investigate the met17-Δ0 effect on CLS, metabolite profiling is being used to gain additional insight about possible physiological correlates. Preliminary analysis reveled 234 metabolites with significant 3-term (time, media, genetic background) interactions. When only two-terms are considered, time and media or genetic background, we identified 335 and 157 differentially expressed metabolites, respectively. We are developing a method using isotopic labeling to predict chemical formulas for the observed metabolite masses, which will aid the effort to integrate data from the genome-wide CLS screens and metabolite profiling experiments. The ultimate goal is to understand how genetic and metabolic pathways are connected with respect to influencing cellular aging, to better explain how mitochondrial function, nutrient sensing, calorie restriction and other phenomena constitute what appear to be aging mechanisms conserved broadly across eukaryotic evolution.