Open AccessMetabolomics‐based systematic prediction of yeast lifespan and its application for semi‐rational screening of ageing‐related mutants
Author(s) -
Yoshida Ryo,
Tamura Takayuki,
Takaoka Chika,
Harada Kazuo,
Kobayashi Akio,
Mukai Yukio,
Fukusaki Eiichiro
Publication year - 2010
Publication title -
aging cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.103
H-Index - 140
eISSN - 1474-9726
pISSN - 1474-9718
DOI - 10.1111/j.1474-9726.2010.00590.x
Subject(s) - biology , metabolomics , phenotype , mutant , yeast , longevity , computational biology , metabolome , metabolic pathway , genetics , bioinformatics , gene
Summary 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.