High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts

Abstract

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.

Global metabolite analysis of yeast: evaluation of sample preparation methods

“…Similar conclusions were also achieved for filamentous fungi (Hajjaj et al, 1998) and bacterial cells (Maharjan and Ferenci, 2003). The recent methodologies developed to acquire accurate information on microbial metabolite levels (Buchholz et al, 2002;Soga et al, 2002;van Dam et al, 2002;Mashego et al, 2004;Villas-Bôas et al, 2005; and others) urgently need an extraction protocol that is able to trap a higher diversity of intracellular metabolites of yeasts. However, the yeast metabolome comprises a variety of compounds with a wide range of physical and chemical properties that make this task virtually impossible.…”

“…The amino and non-amino organic acids and the nucleotides were determined simultaneously by GC-MS after MCF derivatization, according to the method described by Villas-Bôas et al (2005). Sugars, sugar phosphates, sugar alcohols and peptides were determined simultaneously by GC-MS after methoxymation and silylation derivatization, following the protocol described by Roessner et al (2000).…”

“…The metabolite levels in the supernatant of quenched samples confirmed that the Values expressed in percentage of the total intracellular level. The total intracellular level is calculated from intracellular concentration (Villas-Bôas et al, 2005) plus leaked levels (for details, see Materials and methods). Experiments: I, 5 ml sample to 20 ml 60% (v/v) cold methanol solution; II, 10 ml sample to 20 ml 75% (v/v) cold methanol solution; III, 5 ml sample to 20 ml 60% (v/v) buffered cold methanol solution; IV, 10 ml sample to 20 ml 75% (v/v) buffered cold methanol solution; V, 5 ml sample to 20 ml 60% (v/v) cold methanol solution and biomass separation under fast centrifugation (5 min at 1540 × g).…”

“…Therefore, it is important to determine the intracellular levels of metabolites accurately, which requires efficient and reliable methods for sample preparation. There have been considerable efforts to develop sensitive and accurate methods for detection of metabolites (Buchholz et al, 2002;Soga et al, 2002;van Dam et al, 2002;Mashego et al, 2004;Villas-Bôas et al, 2005;and others) and to improve the efficiency of data mining and data analysis (Goodacre et al, 2004;Kell, 2004;van der Greef et al, 2004) but relative little attention has been given to sample preparation procedures. Figure 1 summarizes the main steps in sample preparation for metabolome analysis.…”

The ORF YNL274c (GOR1) codes for glyoxylate reductase in Saccharomyces cerevisiae

“…Glyoxylate is formed by isocitrate lyase from isocitrate and by a deaminase from glycine (Villas-Bõas et al, 2005a, 2005bDevantier et al, 2005). It is consumed by malate synthase, alanine : glyoxylate aminotransferase and glyoxylate reductase (Figure 3).…”