Silas G. Villas-Bôas

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

Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography–mass spectrometry

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography–mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).

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.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein–protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental 13C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional 13C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.

Highly Sensitive GC/MS/MS Method for Quantitation of Amino and Nonamino Organic Acids

Metabolite profiling methods are important tools for measurement of metabolite pools in biological systems. While most metabolite profiling methods report relative intensities or depend on a few internal standards representing all metabolites, the ultimate requirement for a quantitative description of the metabolite pool in biological cells and fluids is absolute concentration determination. We report here a high-throughput and sensitive gas chromatography/tandem mass spectrometry (GC/MS/MS) targeted metabolite profiling method enabling absolute quantification of all detected metabolites. The method is based on methyl chloroformate derivatization and quantification by spiking samples with metabolite standards separately derivatized with deuterated derivatization reagents. The traditional electron impact ionization is replaced with positive chemical ionization since the latter to a much larger extent preserve the molecular ion and other high molecular weight fragments. This made it easier to select unique MS/MS transitions among the many coeluting metabolites. Currently, the novel GC/MS/MS method comprises 67 common primary metabolites of which most belong to the groups of amino and nonamino organic acids. We show the applicability of the method on urine and serum samples. The method is a significant improvement of present methodology for quantitative GC/MS metabolite profiling of amino acids and nonamino organic acids.

The metabolic basis of Candida albicans morphogenesis and quorum sensing

Candida albicans is a polymorphic fungus that has the ability to rapidly switch between yeast and filamentous forms. The morphological transition appears to be a critical virulence factor of this fungus. Recent studies have elucidated the signal transduction pathways and quorum sensing molecules that affect the morphological transition of C. albicans. The metabolic mechanisms that recognize, and respond to, such signaling molecules and promote the morphological changes at a system level, however, remain unknown. Here we review the metabolic basis of C. albicans morphogenesis and we discuss the role of primary metabolic pathways and quorum sensing molecules in the morphogenetic process. We have reconstructed, in silico, the central carbon metabolism and sterol biosynthesis of C. albicans based on its genome sequence, highlighting the metabolic pathways associated with the dimorphic transition and virulence as well as pathways involved in the biosynthesis of important quorum sensing molecules.

Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS

MOTIVATION The Automated Mass Spectral Deconvolution and Identification System (AMDIS) is freeware extensively applied in metabolomics. However, datasets processed by AMDIS require extensive data correction, filtering and reshaping to create reliable datasets for further downstream analysis. Performed manually, these processes are laborious and extremely time consuming. Furthermore, manual corrections increase the chance of human error and can introduce additional technical variability to the data. Thus, an automated pipeline for curating GC-MS data is urgently needed. RESULTS We present the Metab R package designed to automate the pipeline for analysis of metabolomics GC-MS datasets processed by AMDIS. AVAILABILITY The Metab package, the AMDIS library and the reference ion library are available at www.metabolomics.auckland.ac.nz/index.php/downloads. CONTACT k.ruggiero@auckland.ac.nz.

Fish oil supplements in New Zealand are highly oxidised and do not meet label content of n-3 PUFA

We evaluated the quality and content of fish oil supplements in New Zealand. All encapsulated fish oil supplements marketed in New Zealand were eligible for inclusion. Fatty acid content was measured by gas chromatography. Peroxide values (PV) and anisidine values (AV) were measured, and total oxidation values (Totox) calculated. Only 3 of 32 fish oil supplements contained quantities of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) that were equal or higher than labelled content, with most products tested (69%) containing <67%. The vast majority of supplements exceeded recommended levels of oxidation markers. 83% products exceeded the recommended PV levels, 25% exceeded AV thresholds, and 50% exceeded recommended Totox levels. Only 8% met the international recommendations, not exceeding any of these indices. Almost all fish oil supplements available in the New Zealand market contain concentrations of EPA and DHA considerably lower than claimed by labels. Importantly, the majority of supplements tested exceeded the recommended indices of oxidative markers. Surprisingly, best-before date, cost, country of origin, and exclusivity were all poor markers of supplement quality.

Alkylation or Silylation for Analysis of Amino and Non-Amino Organic Acids by GC-MS?

Gas chromatography–mass spectrometry (GC-MS) is a widely used analytical technique in metabolomics. GC provides the highest resolution of any standard chromatographic separation method, and with modern instrumentation, retention times are very consistent between analyses. Electron impact ionization and fragmentation is generally reproducible between instruments and extensive libraries of spectra are available that enhance the identification of analytes. The major limitation is the restriction to volatile analytes, and hence the requirement to convert many metabolites to volatile derivatives through chemical derivatization. Here we compared the analytical performance of two derivatization techniques, silylation (TMS) and alkylation (MCF), used for the analysis of amino and non-amino organic acids as well as nucleotides in microbial-derived samples. The widely used TMS derivatization method showed poorer reproducibility and instability during chromatographic runs while the MCF derivatives presented better analytical performance. Therefore, alkylation (MCF) derivatization seems to be preferable for the analysis of polyfunctional amines, nucleotides and organic acids in microbial metabolomics studies.

Stable isotope coded derivatizing reagents as internal standards in metabolite profiling.

Gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometric (MS) detection have become the two main techniques for the analysis of metabolite pools (i.e. Metabolomics). These technologies are especially suited for Metabolite Profiling analysis of various metabolite groups due to high separation capabilities of the chromatographs and high sensitivity of the mass analysers. The trend in quantitative Metabolite Profiling is to add more metabolites and metabolite groups in a single method. This should not be done by compromising the analytical precision. Mass spectrometric detection comes with certain limitations, especially in the quantitative aspects as standards are needed for conversion of ion abundance to concentration and ionization efficiencies are directly dependent on eluent conditions. This calls for novel strategies to counteract all variables that can influence the quantitative precision. Usually, internal standards are used to correct any technical variation. For quantitation of single or just a few analytes this can be executed with spiking isotopically labeled standards. However, for more comprehensive analytical tasks, e.g. profiling tens or hundreds of analytes simultaneously, this strategy becomes expensive and in many cases isotopically labeled standards are not available. An alternative is to introduce a derivatizing step where the sample is derivatized with naturally labeled reagent, while a standard solution is separately derivatized with isotopically labeled reagent and spiked into the sample solution prior to analysis. This strategy, named isotope coded derivatization - ICD, is attractive in the emerging field of quantitative Metabolite Profiling where current protocols can easily comprise over hundred metabolites. This review provides an overview of isotopically labeled derivatizing reagents that have been developed for important metabolite groups with the aim to improve analytical performance and precision.