GC-FID-MS Based Metabolomics to Access Plum Brandy Quality.

Plum brandy (Slivovitz (en); Sljivovica(sr)) is an alcoholic beverage that is increasingly consumed all over the world. Its quality assessment has become of great importance. In our study, the main volatiles and aroma compounds of 108 non-aged plum brandies originating from three plum cultivars, and fermented using different conditions, were investigated. The chemical profiles obtained after two-step GC-FID-MS analysis were subjected to multivariate data analysis to reveal the peculiarity in different cultivars and fermentation process. Correlation of plum brandy chemical composition with its sensory characteristics obtained by expert commission was also performed. The utilization of PCA and OPLS-DA multivariate analysis methods on GC-FID-MS, enabled discrimination of brandy samples based on differences in plum varieties, pH of plum mash, and addition of selected yeast or enzymes during fermentation. The correlation of brandy GC-FID-MS profiles with their sensory properties was achieved by OPLS multivariate analysis. Proposed workflow confirmed the potential of GC-FID-MS in combination with multivariate data analysis that can be applied to assess the plum brandy quality.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics.

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

Comment on Metabolomics for a Millenniums-Old Crop: Tea Plant (Camellia sinensis).

Metabolomics is the study of metabolite profiles at the system level. Since its introduction in the early 2000s, metabolomics has greatly contributed to the understanding of the distribution of metabolites in organisms under various physiological conditions. In this comment, we show our research on the temporal development of metabolomics in general and in agricultural, food, and nutritional sciences. According to our investigation, metabolomics develops in a sigmoid kinetics. On the basis of the analysis, we made a prediction on the future of the metabolomics study, which may benefit the research community in the field.

Diet-Related Metabolomic Signature of Long-Term Breast Cancer Risk Using Penalized Regression: An Exploratory Study in the SU.VI.MAX Cohort.

BACKGROUND: Diet has been recognized as a modifiable risk factor for breast cancer. Highlighting predictive diet-related biomarkers would be of great public health relevance to identify at-risk subjects. The aim of this exploratory study was to select diet-related metabolites discriminating women at higher risk of breast cancer using untargeted metabolomics. METHODS: Baseline plasma samples of 200 incident breast cancer cases and matched controls, from a nested case-control study within the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, were analyzed by untargeted LC-MS. Diet-related metabolites were identified by partial correlation with dietary exposures, and best predictors of breast cancer risk were then selected by Elastic Net penalized regression. The selection stability was assessed using bootstrap resampling. RESULTS: 595 ions were selected as candidate diet-related metabolites. Fourteen of them were selected by Elastic Net regression as breast cancer risk discriminant ions. A lower level of piperine (a compound from pepper) and higher levels of acetyltributylcitrate (an alternative plasticizer to phthalates), pregnene-triol sulfate (a steroid sulfate), and 2-amino-4-cyano butanoic acid (a metabolite linked to microbiota metabolism) were observed in plasma from women who subsequently developed breast cancer. This metabolomic signature was related to several dietary exposures such as a "Western" dietary pattern and higher alcohol and coffee intakes. CONCLUSIONS: Our study suggested a diet-related plasma metabolic signature involving exogenous, steroid metabolites, and microbiota-related compounds associated with long-term breast cancer risk that should be confirmed in large-scale independent studies. IMPACT: These results could help to identify healthy women at higher risk of breast cancer and improve the understanding of nutrition and health relationship.

Integration of targeted metabolite profiling and sequential optimization method for discovery of chemical marker combination to identify the closely-related plant species.

BACKGROUND: Quality control of herbal medicines based on characteristic components is an important trend. Although the plant metabolomics provide a powerful tool for species classification, the discovered marker is usually limited in practical application. For rapid discovery of efficient marker combination, we proposed a strategy integrating targeted metabolite profiling and sequential optimization method. METHODS: This strategy included: (1) directional enrichment and chemical profiling of targeted metabolites by matrix solid phase dispersion (MSPD) combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). (2) Partial least squares discrimination analysis (PLS-DA)-based sequential screening of efficient marker combination was constructed for various species predictions. Five Lonicera species and their characteristic metabolites, sponins, were taken as a case study. RESULTS: A total of 19 saponins were identified, and 12 major and available saponins were enriched based on MSPD and quantified by LC-MS/MS in 5 Lonicera species flower buds. Followed by 3 runs of PLS-DA-based screening, a combination consisting of macranthoidin B, dipsacoside B and α-hederin was discovered as the effective chemical marker for 5 analogous Lonicera flower classification. CONCLUSION: Our study provides an effective and applicable approach to select the practical marker combination for the assessment of analogical herb medicines.

A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process.

In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to a high-resolution mass spectrometer has been developed to provide the characterization of coffee beans roasted at three different levels (light, medium, and dark). In this way, it was possible to investigate how metabolites change during the roasting process in order to identify those than can be considered as relevant markers. Twenty-five percent methanol was selected as extracting solvent since it provided the highest number of molecular features. In addition, the effect of chromatographic and MS parameters was evaluated in order to obtain the most adequate separation and detection conditions. Data were analyzed using both non-supervised and supervised multivariate statistical methods to point out the most significant markers that allow group discrimination. A total of 24 and 33 compounds in positive and negative ionization modes, respectively, demonstrated to be relevant markers; most of them were from the hydroxycinnamic acids family. Graphical abstract ᅟ.

Systems Biology of Metabolism.

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Metabolomics based on liquid chromatography with mass spectrometry reveals the chemical difference in the stems and roots derived from Ephedra sinica.

To better understand different traditional uses of the stems (known as Mahuang) and roots (known as Mahuanggen) of Ephedra sinica, their chemical difference should be investigated. In this study, an ultra-fast liquid chromatography coupled with ion trap time-of-flight mass spectrometry untargeted metabolomics approach was established to reveal global chemical difference between Mahuang and Mahuanggen. Clear separation was observed in scores plots of principal component analysis and orthogonal partial least squares-discriminant analysis. Twenty two chemical markers responsible for such separation were screened out and unambiguously/tentatively characterized. Then chemical markers of pharmacologically important ephedrine and pseudoephedrine were absolutely quantified using liquid chromatography coupled with tandem mass spectrometry under multiple reaction monitoring mode. The results showed that Mahuang was rich in ephedrine-type alkaloids, while Mahuanggen was rich in macrocyclic spermine alkaloids. Additionally, different types of flavan-3-ols and flavones exist in Mahuang and Mahuanggen extracts. This research facilitates a better understanding of different traditional uses of Mahuang and Mahuanggen and provides references for chemical analysis of other medicinal plants.

Use of the local false discovery rate for identification of metabolic biomarkers in rat urine following Genkwa Flos-induced hepatotoxicity.

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.

Metabolomics of medicinal plants: the importance of multivariate analysis of analytical chemistry data.

Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.