In the post-genomic era, increasing efforts have been made to describe the relationship between the genome and the phenotype in cells and organisms. It has become clear that even a complete understanding of the state of the genes, messages, and proteins in a living system does not reveal its phenotype. Therefore, researchers have started to study the metabolome (or the metabolic complement of functional genomics). Within this context, mass spectrometry (MS) has increasingly occupied a central position in the methodologies developed for determination of the metabolic state. This review is mainly focused on the status of MS in the metabolome field, trying to direct the reader to the main approaches for analysis of metabolites, reviewing basic methodologies in sample preparation, and the most recent MS techniques introduced. Apart from the description of the different methods, this review will try to state a general comparison between the several different techniques that involve MS and metabolite analysis, and will highlight their limitations and preferred applicability.

Sample preparation is considered one of the limiting steps in microbial metabolome analysis. Eukaryotes and prokaryotes behave very differently during the several steps of classical sample preparation methods for analysis of metabolites. Even within the eukaryote kingdom there is a vast diversity of cell structures that make it imprudent to blindly adopt protocols that were designed for a specific group of microorganisms. We have therefore reviewed and evaluated the whole sample preparation procedures for analysis of yeast metabolites. Our focus has been on the current needs in metabolome analysis, which is the analysis of a large number of metabolites with very diverse chemical and physical properties. This work reports the leakage of intracellular metabolites observed during quenching yeast cells with cold methanol solution, the efficacy of six different methods for the extraction of intracellular metabolites, and the losses noticed during sample concentration by lyophilization and solvent evaporation. A more reliable procedure is suggested for quenching yeast cells with cold methanol solution, followed by extraction of intracellular metabolites by pure methanol. The method can be combined with reduced pressure solvent evaporation and therefore represents an attractive sample preparation procedure for high-throughput metabolome analysis of yeasts.

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Analysis of S. cerevisiae cultures with generation times varying between 2 and 35 hours shows that the expression of half of all yeast genes is affected by the specific growth rate.

The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription-metabolism studies of yeasts.

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An automated glucose feeding strategy that avoids acetate accumulation in cultivations of Escherichia coli is discussed. We have previously described how a probing technique makes it possible to detect and avoid overflow metabolism using a dissolved oxygen sensor. In this article these ideas are extended with a safety net that guarantees that aerobic conditions are maintained. The method is generally applicable, as no strain-specific information is needed and the only sensor required is a standard dissolved oxygen probe. It also gives the highest feed rate possible with respect to limitations from overflow metabolism and oxygen transfer, thus maximizing bioreactor productivity. The strategy was implemented on three different laboratory-scale platforms and fed-batch cultivations under different operating conditions were performed with three recombinant strains, E. coli K-12 UL635, E. coli BL21(DE3), and E. coli K-12 UL634. In spite of disturbances from antifoam and induction of recombinant protein production, the method reproducibly gave low concentrations of acetate and glucose. The ability to obtain favorable cultivation conditions independently of strain and operating conditions makes the presented strategy a useful tool, especially in situations where it is important to get good results on the first attempt.