Metabolomics-based systematic prediction of yeast lifespan and its application for semi-rational screening of ageing-related mutants.
Metabolomics - 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 semi-rational 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.
Yoshida
Ryo
R
Department of Biotechnology, Osaka University, Suita, Osaka 565-0871, Japan.
Tamura
Takayuki
T
Takaoka
Chika
C
Harada
Kazuo
K
Kobayashi
Akio
A
Mukai
Yukio
Y
Fukusaki
Eiichiro
E
D005800:Genes, Fungal / Q000235:genetics; D005820:Genetic Testing / Q000379:methods*; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D009154:Mutation / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism*; D013997:Time Factors