Stress adaptation of Saccharomyces cerevisiae as monitored via metabolites using two-dimensional NMR spectroscopy

Functional groups matter: metabolomics analysis of Escherichia coli exposed to trans-cinnamic acid and its derivatives unveils common and unique targets

Phenolic acids are derivatives of benzoic and cinnamic acids, which possess important biological activities at certain concentrations. Trans-cinnamic acid (t-CA) and its derivatives, such as p-coumaric acid (p-CA) and ferulic acid (FA) have been shown to have antibacterial activity against various Gram-positive and -negative bacteria. However, there is limited information available concerning the antibacterial mode of action of these phenolic acids. In this study, we aimed to ascertain metabolic alterations associated with exposure to t-CA, p-CA, and FA in Escherichia coli BW25113 using a nuclear magnetic resonance (NMR)-based metabolomics approach. The results showed that t-CA, p-CA, and FA treatments led to significant changes (p < 0.05) in the concentration of 42, 55, and 74% of the identified metabolites in E. coli, respectively. Partial least-squares discriminant analysis (PLS-DA) revealed a clear separation between control and phenolic acid groups with regard to metabolic response. Moreover, it was found that FA and p-CA treatment groups were clustered closely together but separated from the t-CA treatment group. Arginine, putrescine, cadaverine, galactose, and sucrose had the greatest impact on group differentiation. Quantitative pathway analysis demonstrated that arginine and proline, pyrimidine, glutathione, and galactose metabolisms, as well as aminoacyl-tRNA and arginine biosyntheses, were markedly affected by all phenolic acids. Finally, the H2O2 content of E. coli cells was significantly increased in response to t-CA and p-CA whereas all phenolic acids caused a dramatic increase in the number of apurinic/apyrimidinic sites. Overall, this study suggests that the metabolic response of E. coli cells to t-CA is relatively different from that to p-CA and FA. However, all phenolic acids had a certain impact on oxidative/antioxidant status, genomic stability, arginine-related pathways, and nucleic acid metabolism.

Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D ¹H) NMR, whereas the analysis of other nuclei, such as ¹³C, or other NMR experiments are still underrepresented. The preference of 1D ¹H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast ( Saccharomyces cerevisiae) was analyzed by 1D ¹H NMR and by two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two data sets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS DA chemometric methods, and similar results were obtained regardless of the data type used. However, data-analysis time for the 2D NMR data set was substantially reduced when compared with the data analysis of the corresponding ¹H NMR data set because, for the 2D NMR data, signal overlap was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide the posterior data analysis of biomarker selection.

Oxidative stress, metabolomics profiling, and mechanism of local anesthetic induced cell death in yeast

The World Health Organization designates lidocaine as an essential medicine in healthcare, greatly increasing the probability of human exposure. Its use has been associated with ROS generation and neurotoxicity. Physiological and metabolomic alterations, and genetics leading to the clinically observed adverse effects have not been temporally characterized. To study alterations that may lead to these undesirable effects, Saccharomyces cerevisiae grown on aerobic carbon sources to stationary phase was assessed over 6h. Exposure of an LC50 dose of lidocaine, increased mitochondrial depolarization and ROS/RNS generation assessed using JC-1, ROS/RNS specific probes, and FACS. Intracellular calcium also increased, assessed by ICP-MS. Measurement of the relative ATP and ADP concentrations indicates an initial 3-fold depletion of ATP suggesting an alteration in the ATP:ADP ratio. At the 6h time point the lidocaine exposed population contained ATP concentrations roughly 85% that of the negative control suggesting the surviving population adapted its metabolic pathways to, at least partially restore cellular bioenergetics. Metabolite analysis indicates an increase of intermediates in the pentose phosphate pathway, the preparatory phase of glycolysis, and NADPH. Oxidative stress produced by lidocaine exposure targets aconitase decreasing its activity with an observed decrease in isocitrate and an increase citrate. Similarly, increases in α-ketoglutarate, malate, and oxaloacetate imply activation of anaplerotic reactions. Antioxidant molecule glutathione and its precursor amino acids, cysteine and glutamate were greatly increased at later time points. Phosphatidylserine externalization suggestive of early phase apoptosis was also observed. Genetic studies using metacaspase null strains showed resistance to lidocaine induced cell death. These data suggest lidocaine induces perpetual mitochondrial depolarization, ROS/RNS generation along with increased glutathione to combat the oxidative cellular environment, glycolytic to PPP cycling of carbon generating NADPH, obstruction of carbon flow through the TCA cycle, decreased ATP generation, and metacaspase dependent apoptotic cell death.

Relationship between recombinant protein expression and host metabolome as determined by two-dimensional NMR spectroscopy

Escherichia coli has been the most widely used host to produce large amounts of heterologous proteins. However, given an input plasmid DNA, E. coli may produce soluble protein, produce only inclusion bodies, or yield little or no protein at all. Many efforts have been made to surmount these problems, but most of them have involved time-consuming and labor-intensive trial-and-error. We hypothesized that different metabolomic fingerprints might be associated with different protein production outcomes. If so, then it might be possible to change the expression pattern by manipulating the metabolite environment. As a first step in testing this hypothesis, we probed a subset of the intracellular metabolites by partially labeling it with ¹³C-glucose. We tested 71 genes and identified 17 metabolites by employing the two-dimensional NMR spectroscopy. The statistical analysis showed that there existed the metabolite compositions favoring protein production. We hope that this work would help devise a systematic and predictive approach to the recombinant protein production.

Differential Off-line LC-NMR (DOLC-NMR) Metabolomics To Monitor Tyrosine-Induced Metabolome Alterations in Saccharomyces cerevisiae

A novel differential off-line LC-NMR approach (DOLC-NMR) was developed to capture and quantify nutrient-induced metabolome alterations in Saccharomyces cerevisiae. Off-line coupling of HPLC separation and ¹H NMR spectroscopy supported by automated comparative bucket analyses, followed by quantitative ¹H NMR using ERETIC 2 (electronic reference to access in vivo concentrations), has been successfully used to quantitatively record changes in the metabolome of S. cerevisiae upon intervention with the aromatic amino acid l-tyrosine. Among the 33 metabolites identified, glyceryl succinate, tyrosol acetate, tyrosol lactate, tyrosol succinate, and N-acyl-tyrosine derivatives such as N-(1-oxooctyl)-tyrosine are reported for the first time as yeast metabolites. Depending on the chain length, N-(1-oxooctyl)-, N-(1-oxodecanyl)-, N-(1-oxododecanyl)-, N-(1-oxomyristinyl)-, N-(1-oxopalmityl)-, and N-(1-oxooleoyl)-l-tyrosine imparted a kokumi taste enhancement above their recognition thresholds ranging between 145 and 1432 μmol/L (model broth). Finally, carbon module labeling (CAMOLA) and carbon bond labeling (CABOLA) experiments with ¹³C₆-glucose as the carbon source confirmed the biosynthetic pathway leading to the key metabolites; for example, the aliphatic side chain of N-(1-oxooctyl)-tyrosine could be shown to be generated via de novo fatty acid biosynthesis from four C₂-carbon modules (acetyl-CoA) originating from glucose.

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.

Application of NMR Spectroscopy in the Assessment of Radiation Dose in Human Primary Cells

We employed the primary cell model system as a first step toward establishing a method to assess the influence of ionizing radiation by using a combination of common and abundant metabolites. We applied X-ray irradiation amounts of 0, 1, and 5 Gy to the cells that were harvested 24, 48, or 72 h later, and profiled metabolites by 2D-NMR spectroscopy to sort out candidate molecules that could be used to distinguish the samples under different irradiation conditions. We traced metabolites stemming from the input ¹³C-glucose, identified twelve of them from the cell extracts, and applied statistical analysis to find out that all the metabolites, including glycine, alanine, and gluatamic acid, increased upon irradiation. The combinatorial use of the selected metabolites showed promising results where the product of signal intensities of alanine and lactate could differentiate samples according to the dose of X-ray irradiation. We hope that this work can form a base for treating radiation-poisoned patients in the future.

Probing Metabolite Space of Escherichia coli via Growth Medium Composition as Monitored by Two-Dimensional NMR Spectroscopy

As for recombinant protein production, Escherichia coli is one of the most frequently employed hosts because it offers a simple and inexpensive, but rapid and high-yield system in addition to the vast information on its molecular genetics and biology. However, due to its prokaryotic nature, it often fails to produce eukaryotic proteins in a desired form. To devise a systematic way leading to a condition that produces a large amount of usable proteins, we attempted to monitor intracellular metabolites under various conditions, and to link them to recombinant protein production. With such an intention, we identified 31 metabolites from cells grown in different media by using two-dimensional (2D) NMR spectroscopy. Our results revealed that 1) the level of betaine was low, while that of glutamic acid was high when grown in minimal media; 2) the level of glycerol was constantly high in all cases; 3) the level of oxidized glutathione was lower in Luria broth (LB); and 4) the level of leucine was low in minimal media. We hope this work might shed light onto how to improve production of the target proteins by metabolite profiling.

NMR analysis of budding yeast metabolomics: a rapid method for sample preparation

Here we propose the optimization of a rapid and reproducible protocol for intracellular metabolite extraction from yeast cells and their metabolic profiling by (1)H-NMR spectroscopy. The protocol reliability has been validated through comparison between the metabolome of cells in different phases of growth or with different genetic backgrounds.

Dosage Effects of Salt and pH Stresses on Saccharomyces cerevisiae as Monitored via Metabolites by Using Two Dimensional NMR Spectroscopy

Saccharomyces cerevisiae, which is a common species of yeast, is by far the most extensively studied model of a eukaryote because although it is one of the simplest eukaryotes, its basic cellular processes resemble those of higher organisms. In addition, yeast is a commercially valuable organism for ethanol production. Since the yeast data can be extrapolated to the important aspects of higher organisms, many researchers have studied yeast metabolism under various conditions. In this report, we analyzed and compared metabolites of Saccharomyces cerevisiae under salt and pH stresses of various strengths by using two-dimensional NMR spectroscopy. A total of 31 metabolites were identified for most of the samples. The levels of many identified metabolites showed gradual or drastic increases or decreases depending on the severity of the stresses involved. The statistical analysis produced a holistic outline: pH stresses were clustered together, but salt stresses were spread out depending on the severity. This work could provide a link between the metabolite profiles and mRNA or protein profiles under representative and well studied stress conditions.

Global LC/MS Metabolomics Profiling of Calcium Stressed and Immunosuppressant Drug Treated Saccharomyces cerevisiae

Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for transcriptomics analysis. Thus, broad discovery-based data mining combined with targeted pathway projections can be an important asset for rapidly distilling, testing and evaluating a large amount of information for further investigation.

Optimised protocols for the metabolic profiling of S. cerevisiae by 1H-NMR and HRMAS spectroscopy

An optimised extraction protocol for the analysis of Saccharomyces cerevisiae aqueous and organic metabolites by nuclear magnetic resonance spectroscopy that allows the identification and quantification of up to 50 different compounds is presented. The method was compared with other metabolic profiling protocols for S. cerevisiae, where generally different analytical techniques are applied for metabolite quantification. In addition, the analysis of intact S. cerevisiae cells by HRMAS was implemented for the first time as a complementary method. The optimised protocols were applied to study the metabolic effect of glucose and galactose on S. cerevisiae growth. Furthermore, the metabolic reaction of S. cerevisiae to osmotic stress has been studied.