Metabolic footprinting as a tool for discriminating between brewing yeasts

Abstract

The characterization of industrial yeast strains by examining their metabolic footprints (exometabolomes) was investigated and compared to genome-based discriminatory methods. A group of nine industrial brewing yeasts was studied by comparing their metabolic footprints, genetic fingerprints and comparative genomic hybridization profiles. Metabolic footprinting was carried out by both direct injection mass spectrometry (DIMS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS), with data analysed by principal components analysis (PCA) and canonical variates analysis (CVA). The genomic profiles of the nine yeasts were compared by PCR-restriction fragment length polymorphism (PCR-RFLP) analysis, genetic fingerprinting using amplified fragment length polymorphism (AFLP) analysis and microarray comparative genome hybridizations (CGH). Metabolomic and genomic analysis comparison of the nine brewing yeasts identified metabolomics as a powerful tool in separating genotypically and phenotypically similar strains. For some strains discrimination not achieved genomically was observed metabolomically.

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

“…This extends to natural populations that have multiple uncharacterized genetic changes, such as an accumulation of mutations, as well as sometimes extensive genetic differences such as pathogenicity islands (21), which may interact to give complex phenotypes. Molecular phylogenetic methods based on gene sequences have proved successful in classifying bacteria into taxonomic groupings, but these may not always correspond to easily identifiable phenotypes or ecotypes (29,33,48). Hence, additional methods for strain assessment that could be related to function would still be valuable.…”

“…In contrast, exometabolome or supernatant profiling ("metabolic footprinting") is simple, and extracellular metabolites can exhibit very large changes in pool size (1,27,40,45). These multiple advantages mean that exometabolome analysis has already been used for a number of diverse applications, such as phenotyping of both single-gene deletion mutants and isolates from natural populations, although thus far mostly for fungi rather than bacteria (1,2,9,25,40,48).…”

Yeast: World’s Finest Chef

“…In fact, a genotype analysis of 651 S. cerevisiae strains revealed that ale strains were more closely related to wine and bread strains (above referred as Baker's yeast), than to lager brewer's yeast strains [36]. Reports of hybrids in ale yeasts showed that strains traditionally classified as S. cerevisiae may indeed be the result of hybridization events [37]. Ale beer has less representation in worldwide markets, and as a consequence less studies and information are available on the corresponding yeasts.…”

Near-Infrared Spectroscopy-Derived Tissue Oxygen Saturation in Battlefield Injuries: A Case Series Report

“…Highthroughput technologies such as Nuclear Magnetic Resonance (NMR), Infrared (IR) and Raman spectroscopy, chromatographic methods and mass spectrometry, generate complex data (''fingerprints'') based on chemical composition and metabolite profiles of micro-organisms (metabolome) [9][10][11][12][13]. Such metabolomic methods can detect genotypic and phenotypic differences even in closely related microorganisms and also in genetically modified strains [13][14][15][16][17], with greater discriminatory power than transcriptomics and proteomics [17,18]. Spectroscopic techniques characterise rapidly and simultaneously multiple chemical compounds in biological fluids [10,[19][20][21][22][23][24][25][26][27][28].…”