Natural variation of chronological aging in the Saccharomyces cerevisiae species reveals diet-dependent mechanisms of life span control

Abstract

Aging is a complex trait of broad scientific interest, especially because of its intrinsic link with common human diseases. Pioneering work on aging-related mechanisms has been made in Saccharomyces cerevisiae, mainly through the use of deletion collections isogenic to the S288c reference strain. In this study, using a recently published high-throughput approach, we quantified chronological life span (CLS) within a collection of 58 natural strains across seven different conditions. We observed a broad aging variability suggesting the implication of diverse genetic and environmental factors in chronological aging control. Two major Quantitative Trait Loci (QTLs) were identified within a biparental population obtained by crossing two natural isolates with contrasting aging behavior. Detection of these QTLs was dependent upon the nature and concentration of the carbon sources available for growth. In the first QTL, the RIM15 gene was identified as major regulator of aging under low glucose condition, lending further support to the importance of nutrient-sensing pathways in longevity control under calorie restriction. In the second QTL, we could show that the SER1 gene, encoding a conserved aminotransferase of the serine synthesis pathway not previously linked to aging, is causally associated with CLS regulation, especially under high glucose condition. These findings hint toward a new mechanism of life span control involving a trade-off between serine synthesis and aging, most likely through modulation of acetate and trehalose metabolism. More generally it shows that genetic linkage studies across natural strains represent a promising strategy to further unravel the molecular basis of aging.

Fig. 1

Chronological life span variation in a natural yeast strain collection. a Representative chronological life span (CLS) assay, based on determination of the Survival Integral (SI), for the FY4 strain in our standard SC medium. b CLS determination for our natural strain collection in SC condition. Arrows indicate the parental strains of the “sake × tecc cross”. c Heatmap and boxplot representation of CLS variation within the natural strain collection in seven different conditions. The color gradient represents SI variation with dark and light green corresponding to long and short life span, respectively. Gray rectangles in the heatmap indicate no growth of the corresponding strains in the respective conditions. Strains depicted in red correspond to the parental strains of the “sake × tecc cross”

Fig. 2

CLS variation across the “sake × tecc cross” reveals different aging QTLs depending on growth conditions. a Comparison of SIs between the parental strains of the “sake × tecc cross” shows significant differences; Asterisk, significantly different by Student’s t-test, p-value < 0.05. Values are mean ± SDs. b Violin plot representation of survival integrals determined for the “sake × tecc cross” segregants in the indicated conditions. c QTL mapping using Individual Segregant Analysis (ISA, left column) and Bulk Segregant Analysis (BSA, right column) strategies for CLS data obtained when strains were aged in SC medium containing 2% glucose (SC), 0.5% glucose (CR), 10% glucose (Glu10), and 2% galactose (Gal). For ISA, the dotted red line represents significance thresholds corresponding to p-values of 0.05. The black dashed line in the BSA panels, indicating the G′ statistic significance threshold, corresponds to a false discovery rate of 0.05

Fig. 3

Heritability analyses of CLS in a segregating yeast population. a Broad-sense heritability H² was estimated as described in Supplementary Information. Distributions of SIs determined for the entire “sake × tecc cross” are shown for the four tested growth conditions. Average of SIs for parental strains and the segregating population are depicted in red and green, respectively. b Narrow-sense heritability h² was estimated by comparing the averages of the CLS data obtained for the parental strains and the segregants in the indicated conditions. Average colony sizes obtained for parental and segregant strains in 12 growth conditions were also compared. For a better chart clarity, CLS data are represented at a 1/5 ratio for SC, Glu10, and Gal conditions and a 1/10 ratio for the CR condition. The h² was estimated based on the parent-offspring regression taking into account all the data points

Fig. 4

Validation of RIM15 as an aging gene by a non-complementation approach. Survival curves and survival integrals were determined using an outgrowth kinetics assay for the indicated hybrid strains in SC medium containing 2% glucose (a) and 0.5% glucose (calorie restriction) (b). The results correspond to mean ± SDs for three biological replicates. Double asterisk, significantly different from control strain by Student’s t-test, p-value < 0.005

Fig. 5

Validation of SER1 as an aging gene. a Survival integrals for allele-swapped strains were determined using an outgrowth kinetics assay for the indicated strains in SC medium containing 2% glucose, 2% galactose, and 10% glucose. b Survival integrals for ser1Δ and ser3Δser33Δ deletion mutants and the isogenic BY4741 strain. Results correspond to mean ± SDs for three biological replicates. n.s. not significantly different; *p < 0.05; **p < 0.005 according to Student’s t-test

Fig. 6

Metabolome variation during aging for the parental strains of the “sake × tecc cross”. a CLS profiles obtained for YO486 (violet) and YO502 (orange) from flask cultivations in SC medium supplemented with 2% glucose. The aging cultivations were sampled at different time points for endo-metabolome and exo-metabolome analyses. b Acetic acid was quantified by GC-MS in the extracellular medium of the YO486 (in red) and YO502 (in blue) strains. c Intracellular trehalose, serine, glycine, and cysteine concentrations were also determined during aging in the two strains using LC-MS. d Schematic overview showing the metabolites quantified in this study within the known metabolic network of S. cerevisiae. The endo-metabolome data is represented by heatmaps where red and blue squares correspond to high and low amounts of each metabolite, respectively, at the different time points. The main serine synthesis pathway, starting from 3-phosphoglycerate, is also shown. The first and second reactions in this pathway are catalyzed by the Ser3 (or its paralog Ser33) and Ser1 enzymes, respectively. It is the latter enzyme that is deficient in the YO486 strain (as indicated by the red cross). 3-PHP, 3-phosphohydroxypyruvate