Metabolomics‐based systematic prediction of yeast lifespan and its application for semi‐rational screening of ageing‐related mutants

Abstract

SummaryMetabolomics -the comprehensive analysis of metabolites -was recently used to classify yeast mutants with no overt phenotype using raw data as metabolic fingerprints or footprints. In this study, we demonstrate the estimation of a complicated phenotype, longevity, and semirational screening for relevant mutants using metabolic profiles as strain-specific fingerprints. The fingerprints used in our experiments are profiled data consisting of individually identified and quantified metabolites rather than raw spectrum data. We chose yeast replicative lifespan as a model phenotype. Several yeast mutants that affect lifespan were selected for analysis, and they were subjected to metabolic profiling using mass spectrometry. Fingerprinting based on the profiles revealed a correlation between lifespan and metabolic profile. Amino acids and nucleotide derivatives were the main contributors to this correlation. Furthermore, we established a multivariate model to predict lifespan from a metabolic profile. The model facilitated the identification of putative longevity mutants. This work represents a novel approach to evaluate and screen complicated and quantitative phenotype by means of metabolomics.

Changes in Transcription and Metabolism During the Early Stage of Replicative Cellular Senescence in Budding Yeast

“…Multivariate Analysis-GC-MS data were processed as described previously (20) and judged by principal component analysis (PCA) using the SIMCA-p ϩ program (version 12.0.1, Umetrics, Malmö, Sweden).…”

Transcription factor genes essential for cell proliferation and replicative lifespan in budding yeast

“…In our previous study, metabolic fingerprinting revealed a correlation between lifespan and metabolic profile [12,13]. To investigate a possibility that heterozygous deletion of FHL1, RAP1, REB1, and MCM1 changes cellular metabolisms to shorten lifespan, the whole-cell metabolite levels were surveyed by 1 H-NMR-based metabolomic analysis.…”

“…In a previous study, mass spectrometry-based metabolome analysis of intracellular compounds (fingerprinting) revealed a correlation between the replicative lifespan and metabolic profiles, and the prediction model of yeast lifespan was constructed based on the metabolome data [12]. The metabolic profiles presumed that UGA3 gene encoding a transcription factor (TF) for g-aminobutyric acid metabolic pathway genes was involved in lifespan regulation, followed by identification of the Uga3p-targeted UGA1 and GAD1 metabolic enzyme genes [12,13]. Furthermore, metabolome analysis of aging yeast cells showed metabolic changes at the early stage of Abbreviations: TF, transcription factor; NMR, nuclear magnetic resonance; RTqPCR, reverse transcription-quantitative polymerase chain reaction; OPLS, orthogonal projections to latent structures.…”

Application of Metabolomics for High Resolution Phenotype Analysis

“…14) "By the way, phenotype in the conventional genetics has been de ned as a 'qualitative' feature, but not 'quantitative' one." erefore classical phenotype analysis is similar to a "yes/no" problem about existence of quantitative phenotype.…”