Multiomics of GCN4-Dependent Replicative Lifespan Extension Models Reveals Gcn4 as a Regulator of Protein Turnover in Yeast.

We have shown that multiple tRNA synthetase inhibitors can increase lifespan in both the nematode C. elegans and the budding yeast S. cerevisiae by acting through the conserved transcription factor Gcn4 (yeast)/ATF-4 (worms). To further understand the biology downstream from this conserved transcription factor in the yeast model system, we looked at two different yeast models known to have upregulated Gcn4 and GCN4-dependent increased replicative lifespan. These two models were rpl31aΔ yeast and yeast treated with the tRNA synthetase inhibitor borrelidin. We used both proteomic and RNAseq analysis of a block experimental design that included both of these models to identify GCN4-dependent changes in these two long-lived strains of yeast. Proteomic analysis of these yeast indicate that the long-lived yeast have increased abundances of proteins involved in amino acid biosynthesis. The RNAseq of these same yeast uncovered further regulation of protein degradation, identifying the differential expression of genes associated with autophagy and the ubiquitin-proteasome system (UPS). The data presented here further underscore the important role that GCN4 plays in the maintenance of protein homeostasis, which itself is an important hallmark of aging. In particular, the changes in autophagy and UPS-related gene expression that we have observed could also have wide-ranging implications for the understanding and treatment of diseases of aging that are associated with protein aggregation.

Automation of C. elegans lifespan measurement.

Aging is a fundamental biological process that is still not fully understood. As many of the most significant human diseases have aging as their greatest risk factor, a better understanding of aging potentially has enormous practical implications in treating these diseases. The nematode C. elegans is an exceptionally useful genetic model organism that had been used with great success to shed light on many genes and pathways that are involved in aging. Many of these pathways and mechanisms have been shown to be conserved through mammals. The standard methods for assaying survival in C. elegans to measure changes in lifespan are tedious and time consuming. This limits the throughput and productivity of C. elegans aging researchers. In recent years, many inroads have been made into automating various facets of the collection and analysis of C. elegans lifespan experimental data. The advances described in this review all work to ameliorate some of the hurdles that come with manual worm lifespan scoring, by automating or eliminating some of the most time consuming aspects of the assay. By greatly increasing the throughput of lifespan assays, these methods will enable types of experiments (e.g., drug library screens) whose scale is currently impractical. These methods have already proved exceptionally useful, and some of them are likely to be the predecessors of even more refined methods that could lead to breakthroughs in the ability to study lifespan in C. elegans. This could in turn potentially revolutionize our understanding of the basic biology of aging, and one day lead to treatments that could offset or delay age-related diseases in humans.

SPOCK, an R based package for high-throughput analysis of growth rate, survival, and chronological lifespan in yeast.

Plate reader-based methods for high-throughput measurement of growth rate, cellular survival, and chronological lifespan are a compelling addition to the already powerful toolbox of budding yeast Saccharomyces cerevisiae genetics. These methods have overcome many of the limits of traditional yeast biology techniques, but also present a new bottleneck at the point of data-analysis. Herein, we describe SPOCK (Survival Percentage and Outgrowth Collection Kit), an R-based package for the analysis of data created by high-throughput plate reader based methods. This package allows for the determination of chronological lifespan, cellular growth rate, and survival in an efficient, robust, and reproducible fashion.