S. cerevisiae has been a useful model of aging for post-mitotic cells. This survival non-dividing, quiescent but metabolically active cells, is termed chronological lifespan (CLS). Several genome-scale CLS screens have reported abundant CLS-determining genes in recent years;thus, there are thought to be hundreds of genetic determinants of CLS. What is needed now is a way to understand how so many genes function together to regulate the aging process. However, there are difficulties in achieving such insight due to lack of consensus about the primary players, since different screens have not reached the same results. It remains obscure why some genes do not give reproducible phenotypes, and often this can be the case even within the same laboratory. Our research group assembles complementary skills and technologies to address this CLS quandary by an approach we call constructing """"""""data-driven networks"""""""". We have developed an enabling technology for data driven analysis: it is called quantitative high throughput cell array phenotyping (Q-HTCP), and it increases the capacity to directly measure CLS phenotypes of clonal cultures by several hundred fold over existing technologies. We wish to apply Q-HTCP to systematically and quantitatively assess CLS in all 6000 mutant knockouts of non-essential genes and knockdowns of essential genes in haploid yeast growing, as well as in an outbred model for CLS, consisting of much more genetically heterogeneous strains. We will perform CLS under variable environmental conditions know to influence CLS. These 'perturbations'will include variable glucose concentration, a well-known effecter of CLS, but also inputs to the sulfur metabolic pathways (SMP). Additionally, we have found aeration to influence CLS and suspect that it interacts with other environmental, as well as genetic factors. SMP are required for production of the essential amino acid methionine, for which dietary restriction has been shown to extend life span even in the absence of caloric restriction. In the first use of Q-HTCP for a genome-wide CLS screen, we identified 363 out of 4750 gene deletion strains to reproducibly (2 of 2 cultures) have longer survival than the reference control strain. Lending confidence to our result were the smooth trends of separation of long survivors from the wild type, and the high correlation in CLS survival curves between replicate cultures. Among our most confident 363 hits, only 69 overlapped with the top 300 of at least one of three other genome screens;14 overlapped with two of the three, and none overlapped with all three. Our result confirms a lack of consensus about CLS determining genes, since our screen was in accord about equally with all of the other three screens. The CLS data quality strongly validated the utility of Q-HTCP for yeast aging research, and so we have assembled an interdisciplinary team including expertise in Q-HTCP, SMP, CLS, transcriptomics, metabolimics and construction of biological networks from large-scale omic data integration. Through interdisciplinary collaboration, we aim to deliver a systems level framework for CLS to help construct gene regulatory networks that can reveal mechanisms of cellular aging.

Public Health Relevance

The budding yeast S. cerevisiae has been instrumental in the study of cellular aging, with multiple, genome-wide, longevity screens reported over the past 5 years. Despite advances in identifying individual genes associated with lifespan changes, a consensus of understanding the underlying causes and regulation aging in cells has been limited. Using the combination of a novel, quantitative, high-throughput, automated yeast lifespan assay;robust metabolite and gene expression profiling;and cutting-edge, high-dimensional data analysis, we will investigate from a genome-wide perspective a new metabolic hypothesis for lifespan determination, using the power of yeast genetics to construct a Systems-Level Aging Network, which we anticipate has the potential to be in the future informative for aging of nearly all cell types.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1-ZIJ-2 (M2))
Program Officer
Velazquez, Jose M
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Alabama Birmingham
Schools of Medicine
United States
Zip Code
Carcamo-Orive, Ivan; Hoffman, Gabriel E; Cundiff, Paige et al. (2017) Analysis of Transcriptional Variability in a Large Human iPSC Library Reveals Genetic and Non-genetic Determinants of Heterogeneity. Cell Stem Cell 20:518-532.e9
Smith Jr, Daniel L; Maharrey, Crystal H; Carey, Christopher R et al. (2016) Gene-nutrient interaction markedly influences yeast chronological lifespan. Exp Gerontol 86:113-123
Hartman 4th, John L; Stisher, Chandler; Outlaw, Darryl A et al. (2015) Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease. Genes (Basel) 6:24-45
Chang, Rui; Karr, Jonathan R; Schadt, Eric E (2015) Causal inference in biology networks with integrated belief propagation. Pac Symp Biocomput :359-70
Wells, J Michael; Jackson, Patricia L; Viera, Liliana et al. (2015) A Randomized, Placebo-controlled Trial of Roflumilast. Effect on Proline-Glycine-Proline and Neutrophilic Inflammation in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 192:934-42
Rodgers, John; Guo, Jingyu; Hartman 4th, John L (2014) Phenomic assessment of genetic buffering by kinetic analysis of cell arrays. Methods Mol Biol 1205:187-208