Mats Åkesson

Global metabolite analysis of yeast: evaluation of sample preparation methods

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. Copyright

Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae

BackgroundGrowth rate is central to the development of cells in all organisms. However, little is known about the impact of changing growth rates. We used continuous cultures to control growth rate and studied the transcriptional program of the model eukaryote Saccharomyces cerevisiae, with generation times varying between 2 and 35 hours.ResultsA total of 5930 transcripts were identified at the different growth rates studied. Consensus clustering of these revealed that half of all yeast genes are affected by the specific growth rate, and that the changes are similar to those found when cells are exposed to different types of stress (>80% overlap). Genes with decreased transcript levels in response to faster growth are largely of unknown function (>50%) whereas genes with increased transcript levels are involved in macromolecular biosynthesis such as those that encode ribosomal proteins. This group also covers most targets of the transcriptional activator RAP1, which is also known to be involved in replication. A positive correlation between the location of replication origins and the location of growth-regulated genes suggests a role for replication in growth rate regulation.ConclusionOur data show that the cellular growth rate has great influence on transcriptional regulation. This, in turn, implies that one should be cautious when comparing mutants with different growth rates. Our findings also indicate that much of the regulation is coordinated via the chromosomal location of the affected genes, which may be valuable information for the control of heterologous gene expression in metabolic engineering.

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

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.

Probing control of fed-batch cultivations: analysis and tuning

Production of various proteins can today be made using genetically modified Escherichia coli bacteria. In cultivations of E. coli it is important to avoid accumulation of the by-product acetate. Formation of acetate occurs when the specific glucose uptake exceeds a critical value and can be avoided by a proper feeding strategy. A difficulty is that the critical glucose uptake often is poorly known and even time varying. We here analyze an approach for control of glucose feeding that enables feeding at the critical glucose uptake without prior information. The key idea is to superimpose a probing signal to the feed rate in order to obtain information used to determine if the feed rate should be increased or decreased. The main contribution of this paper is to derive guidelines for tuning of the probing controller. A sufficient condition for stability is presented. By introducing proportional and proportional¯integral control action it is possible to improve performance with an unchanged stability guarantee. This also gives a possibility to maintain a minimum specified distance to the critical glucose uptake where acetate formation starts. (Less)

An Improved Probing Controller for Substrate Feeding in Fed-Batch Cultures of E. Coli: Simulations and Experiments

A strategy for feedback control of the glucose feed rate in cultivations of E.coli is discussed. By making probing pulses in the feedrate it is possible to detect and avoid undesirable by-productformation using a standard dissolved oxygen sensor. The feasibility of an improved and simplified probing algorithm is demonstrated by simulations as well as laboratory-scale experiments.

En Route for Systems Biology: In Silico Pathway Analysis and Metabolite Profiling

With the developments in genomics there has been an increasing focus on the behaviour of complete biological systems and this has resulted in the development of Systems Biology as a new research field in biology [23]. Biological data from all levels of metabolism, such as genome, transcriptome, proteome, metabolome, any of the interactomes (protein-protein, protein-DNA, protein-mRNA, etc.) as well as the fluxome [25] are to be integrated in order to view a cell, an organism or even a population as a whole rather than investigating the single components of the system (Fig. 1). In order to integrate the wealth of information at the different levels of the metabolism, mathematical models play an important role, and Systems Biology is therefore often associated with quantitative investigation of the biological system under study. Much information on individual components, or sub-systems, in living cells has been obtained during the 20th century, but with tools such as DNA arrays, proteomics, metabolite profiling, it is now possible to analyse all the components in the system at the same time, thereby enabling a move from reductionist approaches to a global or a system approach [28].

Acetate Formation and Dissolved Oxygen Responses to Feed Transients in Escherichia Coli Fermentations: Modeling and Experiments

Two hypotheses for acetate formation in Escherichia coli are used to obtain relations between glucose uptake, oxygen uptake and acetate production. Based on these relations, models for dissolved oxygen responses to feed rate transients are derived. Simulations of the models are compared with experimental data. It is shown that information about acetate production can be obtained from the dissolved oxygen measurement by making short pulses in the feed rate.