Gene-nutrient interaction markedly influences yeast chronological lifespan

Abstract

Purpose Research into the genetic mechanisms of aging has expanded rapidly over the past two decades. This has in part been the result of the use of model organisms (particularly yeast, worms and flies) and high-throughput technologies, combined with a growing interest in aging research. Despite this progress, widespread consensus regarding the pathways that are fundamental to the modulation of cellular aging and lifespan for all organisms has been limited due to discrepancies between different studies. We have compared results from published genome-wide, chronological lifespan (CLS) screens of individual gene deletion strains in S. cerevisiae in order to identify gene deletion strains with consistent influences on longevity as possible indicators of fundamental aging processes from this single-celled, eukaryotic model organism. Methods Three previous reports have described genetic modifiers of chronological aging in the budding yeast (S. cerevisiae) using the yeast gene deletion strain collection. We performed a comparison among the data sets using correlation and decile distribution analysis to describe concordance between screens and identify strains that consistently increased or decreased CLS. We used gene enrichment analysis in an effort to understand the biology underlying genes identified in multiple studies. We attempted to replicate the different experimental conditions employed by the screens to identify potential sources of variability in CLS worth further investigating. Results Among 3209 strains present in all three screens, nine (2.80%) deletions strains were in common in the longest-lived decile and thirteen (4.05%) were in common in the shortest-lived decile for all three screens. Similarly, pairwise overlap between screens was low. When the same comparison was extended to three deciles to include more mutants studied in common between the three screens, enrichment of cellular processes based on gene ontology analysis in the long-lived strains remained very limited. To test the hypothesis that different parental strain auxotrophic requirements or media formulations employed by the respective genome-wide screens might contribute to the lack of concordance, different CLS assay conditions were assessed in combination with strains having different ploidy and auxotrophic requirements (all relevant to differences in the way the three genome-wide CLS screens were performed). This limited but systematic analysis of CLS with respect to auxotrophy, ploidy, and media revealed several instances of gene × nutrient interaction. Conclusions There is surprisingly little overlap between the results of three independently performed genome-wide screens of CLS in S. cerevisiae. However, differences in strain genetic background (ploidy and specific auxotrophic requirements) were present, as well as different media and experimental conditions (e.g., aeration and pooled vs. individual culturing), which, along with stochastic effects such as genetic drift or selection of secondary mutations that suppress the los...

Msc6p is required for mitochondrial translation initiation in the absence of formylated Met‐tRNAfMet

“…Here we showed that msc6 and ifm1 mutants present low viability in two different longevity/survival assays in agreement with some recent data but distinct from others mitochondrial translational factors mutants . At any rate, the best method to measure yeast life span is controversial and depends on several factors, moreover different genome‐wide screens life span studies present little overlap, probably as a consequence of genetic background and experimental conditions .…”

Dietary restriction and lifespan: Lessons from invertebrate models

“…Chronological lifespan, in contrast, is defined as the length of time that an individual yeast cell can maintain viability in a non-dividing quiescent state and yet retain the ability to re-enter the cell cycle upon appropriate stimulation (Fabrizio and Longo, 2003). Studies of both types of aging in yeast have uncovered novel mechanisms of aging (Kaeberlein, 2010; Longo and Fabrizio, 2012; Mirisola et al, 2014), and genome-wide analyses have been carried out for both replicative (McCormick et al, 2015; Smith et al, 2008b) and chronological (Burtner et al, 2011; Fabrizio et al, 2010; Matecic et al, 2010; Powers et al, 2006; Smith et al, 2016) lifespan, which have identified multiple genetic modifiers of aging in yeast that are shared with multicellular eukaryotes (discussed further below).…”

An optimized competitive-aging method reveals gene-drug interactions underlying the chronological lifespan of Saccharomyces cerevisiae

“…Genetic analysis of the CLS of budding yeast has led to the genome-wide identification of genes involved in aging; recent efforts have sought to describe interactions between genetic and environmental modulators of the phenotype 13,[15][16][17] . A previous report from our group has shown some advantages of using a competitive-aging approach, in which fluorescently-labeled strains in co-culture provide high resolution in parallel setups 13 .…”

“…While this may be explained in part by differences in genotypic background, media composition, and subtle environmental variations 17 , the large fraction of false positives and little overlap may also suggest that available large-scale CLS phenotyping approaches are still lacking enough technical replicability. Moreover, changes in specific controlled and uncontrolled environmental conditions are known to be important modifiers of CLS phenotypes and may be confounding causes of aging 12,[17][18][19][20] . Hence, a combination of high throughput and good resolution is needed to correctly determine not only genetic aging factors, but also to quantitatively derive their interactions with nutrimental, chemical, or pharmacological environments.…”

“…350with specific quantitative information on the magnitude and sign of the effects, indicate a 351 functional association between protein glycosylation and metformin treatment, providing 352 novel information to understand the mechanisms of longevity by metformin in yeast.353 Together, results in this section show that competitive-aging yields accurate and 354 replicable large-scale CLS data in a straight-forward manner to shed light on the 355 mechanisms of pharmacological interventions that extend lifespan. 356 DISCUSSION 357 Genetic analysis of the CLS of budding yeast has led to the genome-wide identification 358 of genes involved in aging; recent efforts have sought to describe interactions between 359 genetic and environmental modulators of the phenotype (Garay et al, 2014; Smith et 360 al , 2016;Campos et al, 2018;Jung et al, 2018)…”