Different-batch metabolome analysis of Saccharomyces cerevisiae based on gas chromatography/mass spectrometry

Transcriptome and metabolome analyses of Streptococcus gordonii DL1 under acidic conditions

OBJECTIVES

Streptococcus gordonii is associated with the formation of biofilms, especially those that comprise dental plaque. Notably, S. gordonii DL1 causes infective endocarditis (IE). Colonization of this bacterium requires a mechanism that can tolerate a drop in environmental pH by producing acid via its own sugar metabolism. The ability to survive acidic environmental conditions might allow the bacterium to establish vegetative colonization even in the endocardium due to inflammation-induced lowering of pH, increasing the risk of IE. At present, the mechanism by which S. gordonii DL1 survives under acidic conditions is not thoroughly elucidated. The present study was thus conducted to elucidate the mechanism(s) by which S. gordonii DL1 survives under acidic conditions.

METHODS

We analyzed dynamic changes in gene transcription and intracellular metabolites in S. gordonii DL1 exposed to acidic conditions, using transcriptome and metabolome analyses.

RESULTS

Transcriptome analysis revealed upregulation of genes involved in heat shock response and glycolysis, and down regulation of genes involved in phosphotransferase systems and biosynthesis of amino acids. The most upregulated genes were a beta-strand repeat protein of unknown function (SGO_RS06325), followed by copper-translocating P-type ATPase (SGO_RS09470) and malic enzyme (SGO_RS01850). The latter two of these contribute to cytoplasmic alkalinization. S. gordonii mutant strains lacking each of these genes showed significantly reduced survival under acidic conditions. Metabolome analysis revealed that cytoplasmic levels of several amino acids were reduced.

CONCLUSIONS

S. gordonii survives the acidic conditions by recovering the acidic cytoplasm using the various activities, which are regulated at the transcriptional level.

Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation

Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.

Repression of mitochondrial metabolism for cytosolic pyruvate-derived chemical production in Saccharomyces cerevisiae

Background

Saccharomyces cerevisiae is a suitable host for the industrial production of pyruvate-derived chemicals such as ethanol and 2,3-butanediol (23BD). For the improvement of the productivity of these chemicals, it is essential to suppress the unnecessary pyruvate consumption in S. cerevisiae to redirect the metabolic flux toward the target chemical production. In this study, mitochondrial pyruvate transporter gene (MPC1) or the essential gene for mitophagy (ATG32) was knocked-out to repress the mitochondrial metabolism and improve the production of pyruvate-derived chemical in S. cerevisiae.

Results

The growth rates of both aforementioned strains were 1.6-fold higher than that of the control strain. ¹³C-metabolic flux analysis revealed that both strains presented similar flux distributions and successfully decreased the tricarboxylic acid cycle fluxes by 50% compared to the control strain. Nevertheless, the intracellular metabolite pool sizes were completely different, suggesting distinct metabolic effects of gene knockouts in both strains. This difference was also observed in the test-tube culture for 23BD production. Knockout of ATG32 revealed a 23.6-fold increase in 23BD titer (557.0 ± 20.6 mg/L) compared to the control strain (23.5 ± 12.8 mg/L), whereas the knockout of MPC1 revealed only 14.3-fold increase (336.4 ± 113.5 mg/L). Further investigation using the anaerobic high-density fermentation test revealed that the MPC1 knockout was more effective for ethanol production than the 23BD production.

Conclusion

These results suggest that the engineering of the mitochondrial transporters and membrane dynamics were effective in controlling the mitochondrial metabolism to improve the productivities of chemicals in yeast cytosol.

Heterologous expression of bacterial phosphoenol pyruvate carboxylase and Entner-Doudoroff pathway in Saccharomyces cerevisiae for improvement of isobutanol production

Bacterial phosphoenol pyruvate carboxylase (PPC) and enzymes in the Entner-Doudoroff (ED) pathway were heterologously expressed in Saccharomyces cerevisiae to improve the NADPH supply required for the bio-production of chemicals such as isobutanol. The heterologous expression of PPC from Synechocystis sp. PCC6803 increased in the isobutabol titer 1.45-fold (93.2±1.6 mg/L) in metabolically engineered S. cerevisiae strains producing isobutanol. This result suggested that the pyruvate and NADPH supply for isobutanol biosynthesis was activated by PPC overexpression. On the other hand, the expression of two enzymes organizing the ED pathway (6-phosphogluconate dehydratase [6PGD] and 2-dehydro-3-deoxy-phosphogluconate aldolase [KDPGA]) had no effect to isobutabol bio-production. Further analysis, however, revealed that additional expression of 6PGD and KDPGA improved the growth rate of S. cerevisiae strain BY4742 gnd1Δ. A ¹³C-labeling experiment using [1-¹³C] glucose also suggested that metabolic flow levels in the ED pathway increased slightly with the additional expression. These results showed that the ED pathway was successfully constructed in S. cerevisiae, even though activity of the pathway was too weak to improve isobutanol biosynthesis.

Non-invasive real time monitoring of yeast volatilome by PTR-ToF-MS

INTRODUCTION

Producing a wide range of volatile secondary metabolites Saccharomyces cerevisiae influences wine, beer, and bread sensory quality and hence selection of strains based on their volatilome becomes pivotal. A rapid on-line method for volatilome assessing of strains growing on standard solid media is still missing.

OBJECTIVES

Methodologically, the aim of this study was to demonstrate the automatic, real-time, direct, and non-invasive monitoring of yeast volatilome in order to rapidly produce a robust large data set encompassing measurements relative to many strains, replicates and time points. The fundamental scope was to differentiate volatilomes of genetically similar strains of oenological relevance during the whole growing process.

METHOD

Six different S. cerevisiae strains (four meiotic segregants of a natural strain and two laboratory strains) inoculated onto a solid medium have been monitored on-line by Proton Transfer Reaction-Time-of-Flight-Mass Spectrometry for 11 days every 4 h (3540 time points). FastGC PTR-ToF-MS was performed during the stationary phase on the 5th day.

RESULTS

More than 300 peaks have been extracted from the average spectra associated to each time point, 70 have been tentatively identified. Univariate and multivariate analyses have been performed on the data matrix (3640 measurements × 70 peaks) highlighting the volatilome evolution and strain-specific features. Laboratory strains with opposite mating type, and meiotic segregants of the same natural strain showed significantly different profiles.

CONCLUSIONS

The described set-up allows the on-line high-throughput screening of yeast volatilome of S. cerevisiae strains and the identification of strain specific features and new metabolic pathways, discriminating also genetically similar strains, thus revealing a novel method for strain phenotyping, identification, and quality control.

Application of Metabolomics for High Resolution Phenotype Analysis

Metabolome, a total profile of whole metabolites, is placed on downstream of proteome. Metabolome is thought to be results of implementation of genomic information. In other words, metabolome can be called as high resolution phenotype. The easiest operation of metabolomics is the integration to the upstream ome information including transcriptome and/or proteome. Those trials have been reported at a certain scientific level. In addition, metabolomics can be operated in stand-alone mode without any other ome information. Among metabolomics tactics, the author's group is particularly focusing on metabolic fingerprinting, in which metabolome information is employed as explanatory variant to evaluate response variant. Metabolic fingerprinting technique is expected not only for analyzing slight difference depending on genotype difference but also for expressing dynamic variation of living organisms. The author introduces several good examples which he performed. Those are useful for easy understanding of the power of metabolomics. In addition, the author mentions the latest technology for analysis of metabolic dynamism. The author's group developed a facile analytical method for semi-quantitative metabolic dynamism. The author introduces the novel method that uses time dependent variation of isotope distribution based on stable isotope dilution.

Novel strategy for non-targeted isotope-assisted metabolomics by means of metabolic turnover and multivariate analysis

Isotope-labeling is a useful technique for understanding cellular metabolism. Recent advances in metabolomics have extended the capability of isotope-assisted studies to reveal global metabolism. For instance, isotope-assisted metabolomics technology has enabled the mapping of a global metabolic network, estimation of flux at branch points of metabolic pathways, and assignment of elemental formulas to unknown metabolites. Furthermore, some data processing tools have been developed to apply these techniques to a non-targeted approach, which plays an important role in revealing unknown or unexpected metabolism. However, data collection and integration strategies for non-targeted isotope-assisted metabolomics have not been established. Therefore, a systematic approach is proposed to elucidate metabolic dynamics without targeting pathways by means of time-resolved isotope tracking, i.e., "metabolic turnover analysis", as well as multivariate analysis. We applied this approach to study the metabolic dynamics in amino acid perturbation of Saccharomyces cerevisiae. In metabolic turnover analysis, 69 peaks including 35 unidentified peaks were investigated. Multivariate analysis of metabolic turnover successfully detected a pathway known to be inhibited by amino acid perturbation. In addition, our strategy enabled identification of unknown peaks putatively related to the perturbation.