Quantitative analysis of sulfur-related metabolites during cadmium stress response in yeast by capillary electrophoresis–mass spectrometry

Integrating transcriptomics and metabolomics for the analysis of the aroma profiles of Saccharomyces cerevisiae strains from diverse origins

Background

During must fermentation thousands of volatile aroma compounds are formed, with higher alcohols, acetate esters and ethyl esters being the main aromatic compounds contributing to floral and fruity aromas. The action of yeast, in particular Saccharomyces cerevisiae, on the must components will build the architecture of the wine flavour and its fermentation bouquet. The objective of the present work was to better understand the molecular and metabolic bases of aroma production during a fermentation process. For such, comparative transcriptomic and metabolic analysis was performed at two time points (5 and 50 g/L of CO₂ released) in fermentations conducted by four yeast strains from different origins and/or technological applications (cachaça, sake, wine, and laboratory), and multivariate factorial analyses were used to rationally identify new targets for improving aroma production.

Results

Results showed that strains from cachaça, sake and wine produced higher amounts of acetate esters, ethyl esters, acids and higher alcohols, in comparison with the laboratory strain. At fermentation time T1 (5 g/L CO₂ released), comparative transcriptomics of the three S. cerevisiae strains from different fermentative environments in comparison with the laboratory yeast S288c, showed an increased expression of genes related with tetracyclic and pentacyclic triterpenes metabolism, involved in sterol synthesis. Sake strain also showed upregulation of genes ADH7 and AAD6, involved in the formation of higher alcohols in the Ehrlich pathway. For fermentation time point T2 (50 g/L CO₂ released), again sake strain, but also VL1 strain, showed an increased expression of genes involved in formation of higher alcohols in the Ehrlich pathway, namely ADH7, ADH6 and AAD6, which is in accordance with the higher levels of methionol, isobutanol, isoamyl alcohol and phenylethanol observed.

Conclusions

Our approach revealed successful to integrate data from several technologies (HPLC, GC-MS, microarrays) and using different data analysis methods (PCA, MFA). The results obtained increased our knowledge on the production of wine aroma and flavour, identifying new gene in association to the formation of flavour active compounds, mainly in the production of fatty acids, and ethyl and acetate esters.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3816-1) contains supplementary material, which is available to authorized users.

Capillary Electrophoresis in Metabolomics

Metabolomics is an analytical toolbox to describe (all) low-molecular-weight compounds in a biological system, as cells, tissues, urine, and feces, as well as in serum and plasma. To analyze such complex biological samples, high requirements on the analytical technique are needed due to the high variation in compound physico-chemistry (cholesterol derivatives, amino acids, fatty acids as SCFA, MCFA, or LCFA, or pathway-related metabolites belonging to each individual organism) and concentration dynamic range. All main separation techniques (LC-MS, GC-MS) are applied in routine to metabolomics hyphenated or not to mass spectrometry, and capillary electrophoresis is a powerful high-resolving technique but still underused in this field of complex samples. Metabolomics can be performed in the non-targeted way to gain an overview on metabolite profiles in biological samples. Targeted metabolomics is applied to analyze quantitatively pre-selected metabolites. This chapter reviews the use of capillary electrophoresis in the field of metabolomics and exemplifies solutions in metabolite profiling and analysis in urine and plasma.

Rapid determination of 4-aminobutyric acid and L-glutamic acid in biological decarboxylation process by capillary electrophoresis-mass spectrometry

4-Aminobutylic acid (GABA) is a monomer of plastic polyamide 4. Bio-based polyamide 4 can be produced by using GABA obtained from biomass. The production of L-glutamic acid (Glu) from biomass has been established. GABA is produced by decarboxylation of Glu in biological process. High-performance liquid chromatography (HPLC) with derivatization is generally used to determine the concentration of GABA and Glu in reacted solution samples for the efficient production of GABA. In this study, we have investigated the rapid determination of GABA and Glu by capillary electrophoresis-mass spectrometry (CE-MS) without derivatization. The determination was achieved with the use of a shortened capillary, a new internal standard for GABA, and optimization of sheath liquid composition. Determined concentrations of GABA and Glu by CE-MS were compared with those by pre-column derivatization HPLC with phenylisothiocyanate. The determined values by CE-MS were close to those by HPLC with pre-column derivatization. These results suggest that the determination of GABA and Glu in reacted solution is rapid and simplified by the use of CE-MS.

Advances in separation science applied to metabonomics

Metabonomics focuses on metabolite profile changes in diverse living systems caused by a biological perturbation. These metabolite signatures can be achieved with techniques such as gas chromatography, high-performance liquid chromatography (ultra-high-performance/pressure liquid chromatography and capHPLC), capillary electrophoresis, and capillary electrochromatography normally hyphenated with MS. In this review we present the latest developments of the abovementioned techniques applied in the field of metabonomics, with applications covering phytochemistry, toxicology and clinical research.

Development of a capillary electrophoresis-mass spectrometry method using polymer capillaries for metabolomic analysis of yeast

Metabolomics is an emerging field in analytical biochemistry, and the development of such a method for comprehensive and quantitative analysis of organic acids, carbohydrates, and nucleotides is a necessity in the era of functional genomics. When a concentrated yeast extract was analyzed by CE-MS using a successive multiple ionic-polymer layer (SMIL)-coated capillary, the adsorption of the contaminants on the capillary wall caused severe problems such as no elution, band-broadening, and asymmetric peaks. Therefore, an analytical method for the analysis of anionic metabolites in yeast was developed by pressure-assisted CE using an inert polymer capillary made from poly(ether etherketone) (PEEK) and PTFE. We preferred to use the PEEK over the PTFE capillary in CE-MS due to the easy-to-use PEEK capillary and its high durability. The separation of anionic metabolites was successfully achieved with ammonium hydrogencarbonate/formate buffer (pH 6.0) as the electrolyte solution. The use of 2-propanol washing after every electrophoresis run not only eliminated wall-adsorption phenomena, but allowed for good repeatability to be obtained for migration times in the metabolomic analysis.

Integrated transcriptomics, proteomics, and metabolomics analyses to survey ozone responses in the leaves of rice seedling

Ozone (O(3)), a serious air pollutant, is known to significantly reduce photosynthesis, growth, and yield and to cause foliar injury and senescence. Here, integrated transcriptomics, proteomics, and metabolomics approaches were applied to investigate the molecular responses of O(3) in the leaves of 2-week-old rice (cv. Nipponbare) seedlings exposed to 0.2 ppm O(3) for a period of 24 h. On the basis of the morphological alteration of O(3)-exposed rice leaves, transcript profiling of rice genes was performed in leaves exposed for 1, 12, and 24 h using rice DNA microarray chip. A total of 1535 nonredundant genes showed altered expression of more than 5-fold over the control, representing 8 main functional categories. Genes involved in information storage and processing (10%) and cellular processing and signaling categories (24%) were highly represented within 1 h of O(3) treatment; transcriptional factor and signal transduction, respectively, were the main subcategories. Genes categorized into information storage and processing (17, 23%), cellular processing and signaling (20, 16%) and metabolism (18, 19%) were mainly regulated at 12 and 24 h; their main subcategories were ribosomal protein, post-translational modification, and signal transduction and secondary metabolites biosynthesis, respectively. Two-dimensional gel electrophoresis-based proteomics analyses in combination with tandem mass spectrometer identified 23 differentially expressed protein spots (21 nonredundant proteins) in leaves exposed to O(3) for 24 h compared to respective control. Identified proteins were found to be involved in cellular processing and signaling (32%), photosynthesis (19%), and defense (14%). Capillary electrophoresis-mass spectrometry-based metabolomic profiling revealed accumulation of amino acids, gamma-aminobutyric acid, and glutathione in O(3) exposed leaves until 24 h over control. This systematic survey showed that O(3) triggers a chain reaction of altered gene, protein and metabolite expressions involved in multiple cellular processes in rice.

High-throughput tissue extraction protocol for NMR- and MS-based metabolomics

In metabolomics, tissues typically are extracted by grinding in liquid nitrogen followed by the stepwise addition of solvents. This is time-consuming and difficult to automate, and the multiple steps can introduce variability. Here we optimize tissue extraction methods compatible with high-throughput, reproducible nuclear magnetic resonance (NMR) spectroscopy- and mass spectrometry (MS)-based metabolomics. Previously, we concluded that methanol/chloroform/water extraction is preferable for metabolomics, and we further optimized this here using fish liver and an automated Precellys 24 bead-based homogenizer, allowing rapid extraction of multiple samples without carryover. We compared three solvent addition strategies: stepwise, two-step, and all solvents simultaneously. Then we evaluated strategies for improved partitioning of metabolites between solvent phases, including the addition of extra water and different partition times. Polar extracts were analyzed by NMR and principal components analysis, and the two-step approach was preferable based on lipid partitioning, reproducibility, yield, and throughput. Longer partitioning or extra water increased yield and decreased lipids in the polar phase but caused metabolic decay in these extracts. Overall, we conclude that the two-step method with extra water provides good quality data but that the two-step method with 10 min partitioning provides a more accurate snapshot of the metabolome. Finally, when validating the two-step strategy using NMR and MS metabolomics, we showed that technical variability was considerably smaller than biological variability.