(1)H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains

Automatic Chemical Profiling of Wine by Proton Nuclear Magnetic Resonance Spectroscopy

We report the development of MagMet-W (magnetic resonance for metabolomics of wine), a software program that can automatically determine the chemical composition of wine via 1H nuclear magnetic resonance (NMR) spectroscopy. MagMet-W is an extension of MagMet developed for the automated metabolomic analysis of human serum by 1H NMR. We identified 70 compounds suitable for inclusion into MagMet-W. We then obtained 1D 1H NMR reference spectra of the pure compounds at 700 MHz and incorporated these spectra into the MagMet-W compound library. The processing of the wine NMR spectra and profiling of the 70 wine compounds were then optimized based on manual 1H NMR analysis. MagMet-W can automatically identify 70 wine compounds in most wine samples and can quantify them to 10-15% of the manually determined concentrations, and it can analyze multiple spectra simultaneously, at 10 min per spectrum. The MagMet-W Web server is available at https://www.magmet.ca.

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

NMR in the Service of Wine Differentiation

NMR is a swift and highly reproducible spectrometric technique that makes it possible to obtain spectra containing a lot of information about the sample analyzed. This approach helps major components be described in complex mixtures such as wine in just one analysis. Analysis of wine metabolites is very often used to understand the impact of geographical origin or variety on wine quality. NMR is often used for tracing the geographical origin of wine. Research on NMR metabolic effects of geographical origin is of great importance as the high added value of wines results from compliance with state legislation on the protected denomination of origin (PDO) and protected geographical indication (PGI) for the administration of the appellation of wines. A review of NMR with emphasis on SNIF-NMR in the analysis of wine authenticity is given. SNIF-NMR remains a method of choice for the detection of wine chaptalization as it is the only approach which provides position-specific information on the origin of sugar in wine. However, the sample preparation step, which lacks major improvements since its conception, is strenuous and expensive, and suffers from drawbacks in terms of low sample throughput. Mainstream 1D and 2D NMR experiments provide a fast and affordable way to authenticate wine based on the geographical origin, vintage, and variety discrimination, and include a simple and non-destructive sample preparation step. With this approach, spectral data processing often represents a crucial step of the analysis. With properly performed NMR experiments good to excellent differentiation of wines from different vintages, regions, and varieties was achieved recently.

An overview of chemical contaminants and other undesirable chemicals in alcoholic beverages and strategies for analysis

The presence of chemical contaminant in alcoholic beverages is a widespread and notable problem with potential implications for human health. With the complexity and wide variation in the raw materials, production processes, and contact materials involved, there are a multitude of opportunities for a diverse host of undesirable compounds to make their way into the final product-some of which may currently remain unidentified and undetected. This review provides an overview of the notable contaminants (including pesticides, environmental contaminants, mycotoxins, process-induced contaminants, residues of food contact material [FCM], and illegal additives) that have been detected in alcoholic products thus far based on prior reviews and findings in the literature, and will additionally consider the potential sources for contamination, and finally discuss and identify gaps in current analytical strategies. The findings of this review highlight a need for further investigation into unwanted substances in alcoholic beverages, particularly concerning chemical migrants from FCMs, as well as a need for comprehensive nontargeted analytical techniques capable of determining unanticipated contaminants.

1H NMR-based metabolomic study of the effects of flavonoids on citrinin production by Monascus

Monascus comprises purple-red molds. Various compounds can be obtained from these species, including statins and food-safe yellow, red, and orange pigments. However, the secondary metabolite citrinin, a mycotoxin, is produced during the late stages of growth. Citrinin biosynthesis should be reduced to apply Monascus pigments safely. Fortunately, this can be achieved by the addition of flavonoids (genistein, daidzein, apigenin, and kaempferol). However, the effects of these flavonoids on other metabolites remain unknown. Here, we report a ¹H NMR-based multivariate metabolomic analysis of the effects of flavonoids on mycotoxin citrinin production by Monascus. Fifteen metabolites involved in lysine and arginine biosynthesis and alanine, aspartate, glutamate, biotin, arginine, proline, and glutathione metabolism were detected. The reduction in glutamate, aspartate, biotin, and 2-phosphoglycerate content suggested their association with the citrinin reduction mechanism. This study identifies the citrinin production pathway in Monascus and will aid in the development of citrinin-control methods.

13C NMR-Based Chemical Fingerprint for the Varietal and Geographical Discrimination of Wines

A fast, economic, and eco-friendly methodology for the wine variety and geographical origin differentiation using ¹³C nuclear magnetic resonance (NMR) data in combination with machine learning was developed. Wine samples of different grape varieties cultivated in different regions in Greece were subjected to ¹³C NMR analysis. The relative integrals of the ¹³C spectral window were processed and extracted to build a chemical fingerprint for the characterization of each specific wine variety, and then subjected to factor analysis, multivariate analysis of variance, and k-nearest neighbors analysis. The statistical analysis results showed that the ¹³C NMR fingerprint could be used as a rapid and accurate indicator of the wine variety differentiation. An almost perfect classification rate based on training (99.8%) and holdout methods (99.9%) was obtained. Results were further tested on the basis of Cronbach's alpha reliability analysis, where a very low random error (0.30) was estimated, indicating the accuracy and strength of the aforementioned methodology for the discrimination of the wine variety. The obtained data were grouped according to the geographical origin of wine samples and further subjected to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The PLS-DA and variable importance in projection (VIP) allowed the determination of a chemical fingerprint characteristic of each geographical group. The statistical analysis revealed the possibility of acquiring useful information on wines, by simply processing the ¹³C NMR raw data, without the need to determine any specific metabolomic profile. In total, the obtained fingerprint can be used for the development of rapid quality-control methodologies concerning wine.

Non-Targeted Metabolite Profiles and Sensory Properties Elucidate Commonalities and Differences of Wines Made with the Same Variety but Different Cultivar Clones

Grapes, one of the oldest agricultural crops, are cultivated to produce table fruits, dried fruits, juice, and wine. Grapevine variety is composed of clones that share common morphological traits. However, they can differ in minor genetic mutations which often result in not only notorious morphological changes but also in other non-visible sensorial distinctive attributes. In the present work, we identified three Vitis vinifera cv. Pinot noir clones grown under identical field conditions that showed different grape cluster types. Here, sensorial analysis together with non-targeted metabolite profiles by Ultra High performance Liquid Chromatography (UPLC) couples to Ultra High Resolution Mass Spectrometry (FT-ICR-MS) of wines elaborated from the three different grape cluster types was studied with the aim of (i) finding sensorial differences among these three types of wines, and, if there were, (ii) determining the molecular features (metabolites) associated with these sensorial attributes by a multivariate statistical approach.

Yeast-Yeast Interactions: Mechanisms, Methodologies and Impact on Composition

During the winemaking process, alcoholic fermentation is carried out by a consortium of yeasts in which interactions occurs. The consequences of these interactions on the wine matrix have been widely described for several years with the aim of controlling the winemaking process as well as possible. In this review, we highlight the wide diversity of methodologies used to study these interactions, and their underlying mechanisms and consequences on the final wine composition and characteristics. The wide variety of matrix parameters, yeast couples, and culture conditions have led to contradictions between the results of the different studies considered. More recent aspects of modifications in the composition of the matrix are addressed through different approaches that have not been synthesized recently. Non-volatile and volatile metabolomics, as well as sensory analysis approaches are developed in this paper. The description of the matrix composition modification does not appear sufficient to explain interaction mechanisms, making it vital to take an integrated approach to draw definite conclusions on them.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Wine and grape marc spirits metabolomics

INTRODUCTION

Mass spectrometry (MS)-based and nuclear magnetic resonance (NMR) spectroscopic analyses play a key role in the field of metabolomics due to their important advantages. The use of metabolomics in wine and grape marc spirits allows a more holistic perspective in monitoring and gaining information on the making processes and thus it can assist on the improvement of their quality.

OBJECTIVES

This review surveys the latest metabolomics approaches for wine and grape marc spirits with a focus on the description of MS-based and NMR spectroscopic analytical techniques.

METHODS

We reviewed the literature to identify metabolomic studies of wine and grape marc spirits that were published until the end of 2017, with the key term combinations of 'metabolomics', 'wine' and 'grape marc spirits'. Through the reference lists from these studies, additional articles were identified.

RESULTS

The results of this review showed that the application of different metabolomics approaches has significantly increased the knowledge of wine metabolome and grape marc spirits; however there is not yet a single analytical platform that can completely separate, detect and identify all metabolites in one analysis.

CONCLUSIONS

The authentication and quality control of wines and grape marc spirits has to be taken with caution, since the product's chemical composition could be affected by many factors. Despite intrinsic limitations, NMR spectroscopy and MS based strategies remain the key analytical methods in metabolomics studies. Authenticity, traceability and health issues related to their consumption are the major research initiatives in wine and grape marc spirits metabolomics analysis.

Metabolic footprint analysis of metabolites that discriminate single and mixed yeast cultures at two key time-points during mixed culture alcoholic fermentations

INTRODUCTION

There has been a growing interest towards creating defined mixed starter cultures for alcoholic fermentations. Previously, metabolite differences between single and mixed cultures have been explored at the endpoint of fermentations rather than during fermentations.

OBJECTIVES

To create metabolic footprints of metabolites that discriminate single and mixed yeast cultures at two key time-points during mixed culture alcoholic fermentations.

METHODS

¹H NMR- and GC-MS-based metabolomics was used to identify metabolites that discriminate single and mixed cultures of Lachancea thermotolerans (LT) and Saccharomyces cerevisiae (SC) during alcoholic fermentations.

RESULTS

Twenty-two metabolites were found when comparing single LT and mixed cultures, including both non-volatiles (carbohydrate, amino acid and acids) and volatiles (higher alcohols, esters, ketones and aldehydes). Fifteen of these compounds were discriminatory only at the death phase initiation (T1) and fifteen were discriminatory only at the death phase termination (T2) of LT in mixed cultures. Eight metabolites were discriminatory at both T1 and T2. These results indicate that specific metabolic changes may be descriptive of different LT growth behaviors. Fifteen discriminatory metabolites were found when comparing single SC and mixed cultures. These metabolites were all volatiles, and twelve metabolites were discriminatory only at T2, indicating that LT-induced changes in volatiles occur during the death phase of LT in mixed cultures and not during their initial growth stage.

CONCLUSIONS

This work provides a detailed insight into yeast metabolites that differ between single and mixed cultures, and these data may be used for understanding and eventually predicting yeast metabolic changes in wine fermentations.

Analysis of Metabolites in Cabernet Sauvignon and Shiraz Dry Red Wines from Shanxi by 1H NMR Spectroscopy Combined with Pattern Recognition Analysis

Metabolomics technology based on proton nuclear magnetic resonance (1H NMR) spectroscopy combined with pattern recognition analysis was used to characterize the Cabernet Sauvignon and Shiraz dry red wines vinified in the Linfen of Shanxi Province, China, in 2016. The results showed that there was a very significant difference between the metabolites of Cabernet Sauvignon and Shiraz dry red wines from the area of Linfen. Compared with Shiraz dry red wines, Cabernet Sauvignon dry red wines contained higher levels of proline, valine, tartaric acid, citric acid, malic acid, gallic acid, β-glucose and ethyl acetate, whereas 2,3-butanediol, lactic acid, choline, glycerol, α-D-glucuronic acid, succinic acid and alanine were present in lower levels. Application of NMR spectroscopy combined with pattern recognition analysis showed the discriminative power between wine varietals from the same production area. The loading plot from partial least squares discriminant analysis (PLs-DA) indicated that the key biomarkers for this differentiation were proline, tartaric acid, glycerol, lactic acid, choline, succinic acid and gallic acid, which was consistent with the result of quantitative analysis.

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.

MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

BACKGROUND

Interpreting non-targeted metabolomics data remains a challenging task. Signals from non-targeted metabolomics studies stem from a combination of biological causes, complex interactions between them and experimental bias/noise. The resulting data matrix usually contain huge number of variables and only few samples, and classical techniques using nonlinear mapping could result in computational complexity and overfitting. Independent Component Analysis (ICA) as a linear method could potentially bring more meaningful results than Principal Component Analysis (PCA). However, a major problem with most ICA algorithms is the output variations between different runs and the result of a single ICA run should be interpreted with reserve.

RESULTS

ICA was applied to simulated and experimental mass spectrometry (MS)-based non-targeted metabolomics data, under the hypothesis that underlying sources are mutually independent. Inspired from the Icasso algorithm, a new ICA method, MetICA was developed to handle the instability of ICA on complex datasets. Like the original Icasso algorithm, MetICA evaluated the algorithmic and statistical reliability of ICA runs. In addition, MetICA suggests two ways to select the optimal number of model components and gives an order of interpretation for the components obtained.

CONCLUSIONS

Correlating the components obtained with prior biological knowledge allows understanding how non-targeted metabolomics data reflect biological nature and technical phenomena. We could also extract mass signals related to this information. This novel approach provides meaningful components due to their independent nature. Furthermore, it provides an innovative concept on which to base model selection: that of optimizing the number of reliable components instead of trying to fit the data. The current version of MetICA is available at https://github.com/daniellyz/MetICA.

LC-MS based metabolomics and chemometrics study of the toxic effects of copper on Saccharomyces cerevisiae

Copper containing fungicides are used to protect vineyards from fungal infections.

Viticultural practice and winemaking effects on metabolic profile of Negroamaro

Metabolic profiles of 32 Negroamaro red wines were analysed using (1)H NMR spectroscopy and multivariate statistical analyses (Principal Component Analysis, PCA, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA). Among winemaking technologies three were compared: ultrasounds (U; 12 samples), cryomaceration using dry ice (C; 12 samples) and traditional (T; 8 samples). Moreover, each vinification technology was used for grapes grown by two different soil management practices, soil tillage (ST; 16 samples) and cover crop (CC; 16 samples), and by two different training systems, monolateral (M; 16 samples) and bilateral Guyot (B; 16 samples). All statistical models applied on NMR data revealed a good separation between ST (soil tillage) and CC (cover crop), showing a higher influence of the soil management practices compared to the winemaking technologies (ultrasound, cryomaceration and traditional). The differentiation among samples, due to soil management practices, was mainly caused by metabolites such as glycerol, 2,3-butanediol, malic acid, α/β-glucose and phenolic compounds, such as tyrosine and caffeic acid.

Analysis of the restorative effect of Bu-zhong-yi-qi-tang in the spleen-qi deficiency rat model using (1)H-NMR-based metabonomics

ETHNOPHARMACOLOGICAL RELEVANCE

Bu-zhong-yi-qi-tang (BT) is a classical formula for the treatment of spleen-qi descending, visceroptosis with hyposplenic qi, uterine prolapse, and rectal prolapse due to chronic diarrhea in traditional Chinese medicine (TCM) and has been identified as an effective drug for the treatment of TCM spleen-qi deficiency in clinical practice. The present study aimed to investigate the restorative effect and the potential mechanisms of Bu-zhong-yi-qi-tang in a rat spleen-qi deficiency model using (1)H-NMR-based metabonomics.

MATERIALS AND METHODS

The rat spleen-qi deficiency model was established as follows: oral administration of Radix Rhei extract (equivalent to 10g/kg body weight of the crude drug), loaded swimming, and starvation for 24h. Each of these treatments was administered consecutively every three days. Sixty male SD rats were randomly divided into five groups, and three of the groups received a different oral dose of the aqueous extract of Bu-zhong-yi-qi-tang during the last seven days of the three-week experimental period. The body weight and motor behavior of the rats were measured and recorded once a week. The endogenous metabolites in the plasma were analyzed using NMR in conjunction with multivariate and statistical techniques. In addition, the liver and spleen were removed and weighed.

RESULTS

All of the rats in the spleen-qi deficiency group presented pasty loose stools, inactiveness, grouping, a decrease in swimming endurance, and lackluster, loose, and disorderly behavior in addition to a significant decrease in body weight, spleen weight, and liver weight. In contrast, the abovementioned demonstrations were reversed to a certain extent in the rats treated with Bu-zhong-yi-qi-tang compared with the model group (p<0.05, p<0.01). A significant separation was determined between the control and model groups in the PCA score plot, which indicates that the spleen-qi deficiency model was successfully duplicated. The changes in the levels of endogenous metabolites in the plasma included lower levels of valine, leucine, and O-acetyl-glycoprotein and a higher concentration of lactate in the spleen-qi deficiency group compared with the control group. Treatment with Bu-zhong-yi-qi-tang at least partially returned the levels of these metabolites to the normal levels.

CONCLUSIONS

The restorative effects of Bu-zhong-yi-qi-tang in rats with spleen-qi deficiency were confirmed, and four endogenous metabolites were identified as potential biomarkers of the symptoms of spleen-qi deficiency and most likely play roles in the changes observed in certain metabolic pathways, such as the energy, protein, and glycolytic metabolisms.

Metabolomic by 1H NMR spectroscopy differentiates "Fiano di Avellino" white wines obtained with different yeast strains

We employed (1)H NMR spectroscopy to examine the molecular profile of a white "Fiano di Avellino" wine obtained through fermentation by either a commercial or a selected autochthonous Saccharomyces cerevisiae yeast starter. The latter was isolated from the same grape variety used in the wine-making process in order to strengthen the relationship between wine molecular quality and its geographical origin. (1)H NMR spectra, where water and ethanol signals were suppressed by a presaturated T1-edited NMR pulse sequence, allowed for definition of the metabolic content of the two differently treated wines. Elaboration of NMR spectral data by multivariate statistical analyses showed that the two different yeasts led to significant diversity in the wine metabolomes. Our results indicate that metabolomics by (1)H NMR spectroscopy combined with multivariate statistical analysis enables wine differentiation as a function of yeast species and other wine-making factors, thereby contributing to objectively relate wine quality to the terroir.

NMR study of histidine metabolism during alcoholic and malolactic fermentations of wine and their influence on histamine production

The metabolic pathways of amino acids play a crucial role in the organoleptic and hygienic quality in wines. In particular, histidine is one of the most studied amino acids of wines due to histamine toxicity in humans, a biogenic amine derived from histidine by enzymatic decarboxylation. The development of new tools to increase knowledge on metabolism that produces histamine in wine is critical. This study investigated by using nuclear magnetic resonance (NMR) spectroscopy the transformation of histidine into histaminol and histamine during alcoholic and malolactic fermentations. The transformations of histidine into histaminol during alcoholic fermentation and into histamine during malolactic fermentation were observed. This paper highlights the importance of selecting lactic acid bacteria for malolactic fermentation to avoid the production of biogenic amines such as histamine.

(1)H NMR-based metabolite profiling of planktonic and biofilm cells in Acinetobacter baumannii 1656-2

Acinetobacter baumannii is an aerobic and gram-negative pathogenic bacterium that is resistant to most antibiotics. Recently, A. baumannii 1656-2 exhibited the ability to form biofilms under clinical conditions. In this study, global metabolite profiling of both planktonic and biofilm forms of A. baumannii 1656-2 was performed using high-resolution nuclear magnetic resonance (NMR) spectroscopy and multivariate statistical analysis to investigate the metabolic patterns leading to biofilm formation. Principal components analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) score plots showed a distinct separation between planktonic and biofilm cells. Metabolites including acetates, pyruvate, succinate, UDP-glucose, AMP, glutamate, and lysine were increasingly involved in the energy metabolism of biofilm formation. In particular, the ratio of N-acetyl-D-glucosamine (GlcNAc) to D-glucosamine (GlcNH₂) was significantly higher during biofilm formation than under the planktonic condition. This study demonstrates that NMR-based global metabolite profiling of bacterial cells can provide valuable insight into the metabolic changes in multidrug resistant and biofilm-forming bacteria such as A. baumannii 1656-2.

Unraveling the concentration-dependent metabolic response of Pseudomonas sp. HF-1 to nicotine stress by ¹H NMR-based metabolomics

Nicotine can cause oxidative damage to organisms; however, some bacteria, for example Pseudomonas sp. HF-1, are resistant to such oxidative stress. In the present study, we analyzed the concentration-dependent metabolic response of Pseudomonas sp. HF-1 to nicotine stress using ¹H NMR spectroscopy coupled with multivariate data analysis. We found that the dominant metabolites in Pseudomonas sp. HF-1 were eight aliphatic organic acids, six amino acids, three sugars and 11 nucleotides. After 18 h of cultivation, 1 g/L nicotine caused significant elevation of sugar (glucose, trehalose and maltose), succinate and nucleic acid metabolites (cytidine, 5'-CMP, guanine 2',3'-cyclic phosphate and adenosine 2',3'-cyclic phosphate), but decrease of glutamate, putrescine, pyrimidine, 2-propanol, diethyl ether and acetamide levels. Similar metabolomic changes were induced by 2 g/L nicotine, except that no significant change in trehalose, 5'-UMP levels and diethyl ether were found. However, 3 g/L nicotine led to a significant elevation in the two sugars (trehalose and maltose) levels and decrease in the levels of glutamate, putrescine, pyrimidine and 2-propanol. Our findings indicated that nicotine resulted in the enhanced nucleotide biosynthesis, decreased glucose catabolism, elevated succinate accumulation, severe disturbance in osmoregulation and complex antioxidant strategy. And a further increase of nicotine level was a critical threshold value that triggered the change of metabolic flow in Pseudomonas sp. HF-1. These findings revealed the comprehensive insights into the metabolic response of nicotine-degrading bacteria to nicotine-induced oxidative toxicity.

A metabolomic approach to the study of wine micro-oxygenation

Wine micro-oxygenation is a globally used treatment and its effects were studied here by analysing by untargeted LC-MS the wine metabolomic fingerprint. Eight different procedural variations, marked by the addition of oxygen (four levels) and iron (two levels) were applied to Sangiovese wine, before and after malolactic fermentation. Data analysis using supervised and unsupervised multivariate methods highlighted some known candidate biomarkers, together with a number of metabolites which had never previously been considered as possible biomarkers for wine micro-oxygenation. Various pigments and tannins were identified among the known candidate biomarkers. Additional new information was obtained suggesting a correlation between oxygen doses and metal contents and changes in the concentration of primary metabolites such as arginine, proline, tryptophan and raffinose, and secondary metabolites such as succinic acid and xanthine. Based on these findings, new hypotheses regarding the formation and reactivity of wine pigment during micro-oxygenation have been proposed. This experiment highlights the feasibility of using unbiased, untargeted metabolomic fingerprinting to improve our understanding of wine chemistry.

Investigations of La Rioja terroir for wine production using 1H NMR metabolomics

In this study, La Rioja wine terroir was investigated by the use of (1)H NMR metabolomics on must and wine samples. Rioja is a small wine region in central northern Spain which can geographically be divided into three subareas (Rioja Alta, Rioja Baja, and Rioja Alavesa). The winemaking process from must, through alcoholic and malolactic fermentation, was followed by NMR metabolomics and chemometrics of nine wineries in the Rioja subareas (terroirs). Application of interval extended canonical variate analysis (iECVA) showed discriminative power between wineries which are geographically very close. Isopentanol and isobutanol compounds were found to be key biomarkers for this differentiation.

Discrimination of three Pegaga (Centella) varieties and determination of growth-lighting effects on metabolites content based on the chemometry of 1H nuclear magnetic resonance spectroscopy

The metabolites of three species of Apiaceae, also known as Pegaga, were analyzed utilizing (1)H NMR spectroscopy and multivariate data analysis. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) resolved the species, Centella asiatica, Hydrocotyle bonariensis, and Hydrocotyle sibthorpioides, into three clusters. The saponins, asiaticoside and madecassoside, along with chlorogenic acids were the metabolites that contributed most to the separation. Furthermore, the effects of growth-lighting condition to metabolite contents were also investigated. The extracts of C. asiatica grown in full-day light exposure exhibited a stronger radical scavenging activity and contained more triterpenes (asiaticoside and madecassoside), flavonoids, and chlorogenic acids as compared to plants grown in 50% shade. This study established the potential of using a combination of (1)H NMR spectroscopy and multivariate data analyses in differentiating three closely related species and the effects of growth lighting, based on their metabolite contents and identification of the markers contributing to their differences.

NMR-based metabolomics in wine science

As metabolomics is becoming an emerging field of 'omics' research, NMR serves as one of the major analytical approaches of the decade in metabolomic study, producing information-rich, highly reliable and reproducible data set in non-targeted or global and multivariate statistical analysis. Recently, NMR is successfully being used to characterize wine and find an association of wine metabolite with environmental and fermentative factors in vineyard and making wine. This review describes important analytical features and recent applications in/of NMR-based metabolomics in wine science.

NMR methods for beer characterization and quality control

The use of high-resolution NMR spectroscopy in the brewing industry is described; most studies having aimed at assessing the composition of beer and its raw materials and correlating it to a variety of quality parameters. First, the application of NMR to the qualitative characterization of beer is reviewed, addressing both targeted and untargeted methods and focusing on both beer extracts and direct beer analysis. A subsequent chapter addresses the NMR studies, which envisage the development of new rapid methods for beer analysis and quality control, such as site-specific natural fractionation-NMR and multivariate data analysis methods for marker search or rapid compound quantification. Finally, possible future perspectives toward a deeper and more complete understanding of beer and its brewing process are discussed.

Metabolic influence of Botrytis cinerea infection in champagne base wine

Botrytis cinerea infection of grape berries leads to changes in the chemical composition of grape and the corresponding wine and, thus, affects wine quality. The metabolic effect of Botrytis infection in Champagne base wine was investigated through a (1)H NMR-based metabolomic approach. Isoleucine, leucine, threonine, valine, arginine, proline, glutamine, γ-aminobutyric acid (GABA), succinate, malate, citrate, tartarate, fructose, glucose, oligosaccharides, amino acid derivatives, 2,3-butanediol, acetate, glycerol, tyrosine, 2-phenylethanol, trigonelline, and phenylpropanoids in a grape must and wine were identified by (1)H NMR spectroscopy and contributed to metabolic differentiations between healthy and botrytized wines by using multivariate statistical analysis such as principal component analysis (PCA). Lowered levels of glycerol, 2,3-butanediol, succinate, tyrosine, valine derivative, and phenylpropanoids but higher levels of oligosaccharides in the botrytized wines were main discriminant metabolites, demonstrating that Botrytis infection of grape caused the fermentative retardation during alcoholic fermentation because the main metabolites responsible for the differentiation are fermentative products. Moreover, higher levels of several oligosaccharides in the botrytized wines also indicated the less fermentative behavior of yeast in the botrytized wines. This study highlights a metabolomic approach for better understanding of the comprehensive metabolic influences of Botrytis infection of grape berries in Champagne wines.

A genome-wide perspective on the response and tolerance to food-relevant stresses in Saccharomyces cerevisiae

The success of food and beverage production processes carried out by Saccharomyces cerevisiae and the thriving of food spoilage fungi are dependent on the ability of a cell to cope with the many environmental insults imposed during food production and preservation processes. Post-genomic approaches, especially transcriptomics, proteomics and chemogenomics, applied to S. cerevisiae made possible the unveiling of general and specific genome-wide adaptive response programs against stress induced by weak acids, ethanol, sulfite, heat and cold shock, osmotic pressure and nutrient limitation. These programs and the underlying signaling pathways are overviewed herein, highlighting the recent identification of genes and pathways found to be involved in stress response and tolerance. These are good candidate targets for genetic engineering aiming at the development of improved strains. The extension of the data gathered in S. cerevisiae to food spoilage fungi is considered. The relevance of the different genome-wide approaches in this context is also discussed.