Discrimination of rosé wines using shotgun metabolomics with a genetic algorithm and MS ion intensity ratios

Untargeted metabolomics analyses to identify a new sweet compound released during post-fermentation maceration of wine

The sensory quality of a wine is mainly based on its aroma and flavor. Sweetness contributes in the gustatory balance of red wines. The investigation of compounds involved in this flavor was based on empirical observations, such as the increase in wine sweetness during yeast autolysis, concomitant to post-fermentation maceration in red winemaking. An untargeted metabolomics approach using UHPLC-HRMS has been developed to discover a new sweet molecule released during this stage. Among several markers highlighted, one compound was selected to be isolated by various separative techniques. It was unambiguously identified by NMR as N6-succinyladenosine and is reported for the first time in wine at an average concentration of 3.16 mg/L in 85 red wines. Furthermore, sensory analysis has highlighted its sweetness. In addition to discovering a new sweet compound in wine, this study proposes new tools for studying taste-active compounds in natural matrices.

UHPLC-Q-Orbitrap metabolomics of Syrah red wines during bottle ageing: Molecular markers of evolution and cork permeability

An experiment involving the ageing of Syrah red wine was conducted over a period of 24 months, during which the impact of four different micro-agglomerated corks was examined. An untargeted UHPLC-Q-Orbitrap metabolomics analysis was performed and provided valuable insights into the chemical dynamics of red wine evolution. Forty-three specific discriminating compounds were found for non-aged wines, including various CHO and CHON-types molecules. Thirteen specific discriminating compounds were found for 24-months-aged wines including CHO, CHNOS and CHOS compounds. Among them, sulfonated flavanols and pyranoanthocyanins were identified and emerged as key molecular markers of wine ageing. This metabolomics analysis also enabled us to identify specific chemical markers of cork oxygen transfer rate (OTR) influence. Analysis revealed specific molecules linked to corks with low and high OTR such as anthocyanins and proanthocyanins respectively. This research enhances our comprehension of intricate chemical changes during red wine ageing and underscores the potential impact of cork OTR on wine composition.

Low levels of peripheral blood activated and senescent T cells characterize people with HIV-1-associated neurocognitive disorders

Background

HIV infection induces a 75% increase in the risk of developing neurocognitive impairment (NCI), which has been linked to immune activation. We therefore looked for immune activation markers correlating with NCI.

Method

Sixty-five people aged 55-70 years living with controlled HIV-1 infection were enrolled in the study and their neurocognitive ability was assessed according to the Frascati criteria. Fifty-nine markers of T4 cell, T8 cell, NK cell, and monocyte activation, inflammation and endothelial activation were measured in their peripheral blood. White matter hyperintensities (WMH) were identified by magnetic resonance imaging. Double hierarchical clustering was performed for the activation markers and 240 patients including the 65 whose neurocognitive performance had been evaluated.

Results

Thirty-eight percent of volunteers presented NCI. Twenty-four percent of them were asymptomatic and fourteen percent had a mild disorder. Strikingly, activated (HLA-DR+) as well as senescent (CD57+CD28-CD27±) T4 cells and T8 cells were less prevalent in the peripheral blood of participants with NCI than in participants without the disorder. Accordingly, the percentage of HLA-DR+ T4 cells was lower in volunteers with periventricular and deep WMH. The double hierarchical clustering unveiled six different immune activation profiles. The neurocognitive performances of participants with two of these six profiles were poor. Here again, these two profiles were characterized by a low level of T4 and T8 cell activation and senescence.

Conclusion

Our observation of low circulating levels of activated and senescent T cells in HIV-1 patients with NCI raises the interesting hypothesis that these lymphocytes may be recruited into the central nervous system.

Chemometric-Guided Approaches for Profiling and Authenticating Botanical Materials

Botanical supplements with broad traditional and medicinal uses represent an area of growing importance for American health management; 25% of U.S. adults use dietary supplements daily and collectively spent over $9. 5 billion in 2019 in herbal and botanical supplements alone. To understand how natural products benefit human health and determine potential safety concerns, careful in vitro, in vivo, and clinical studies are required. However, botanicals are innately complex systems, with complicated compositions that defy many standard analytical approaches and fluctuate based upon a plethora of factors, including genetics, growth conditions, and harvesting/processing procedures. Robust studies rely upon accurate identification of the plant material, and botanicals' increasing economic and health importance demand reproducible sourcing, as well as assessment of contamination or adulteration. These quality control needs for botanical products remain a significant problem plaguing researchers in academia as well as the supplement industry, thus posing a risk to consumers and possibly rendering clinical data irreproducible and/or irrelevant. Chemometric approaches that analyze the small molecule composition of materials provide a reliable and high-throughput avenue for botanical authentication. This review emphasizes the need for consistent material and provides insight into the roles of various modern chemometric analyses in evaluating and authenticating botanicals, focusing on advanced methodologies, including targeted and untargeted metabolite analysis, as well as the role of multivariate statistical modeling and machine learning in phytochemical characterization. Furthermore, we will discuss how chemometric approaches can be integrated with orthogonal techniques to provide a more robust approach to authentication, and provide directions for future research.

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification

Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing 'omics' data in 'mgf' data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.

Numerical Study of the Environmental and Economic System through the Computational Heuristic Based on Artificial Neural Networks

In this study, the numerical computation heuristic of the environmental and economic system using the artificial neural networks (ANNs) structure together with the capabilities of the heuristic global search genetic algorithm (GA) and the quick local search interior-point algorithm (IPA), i.e., ANN-GA-IPA. The environmental and economic system is dependent of three categories, execution cost of control standards and new technical diagnostics elimination costs of emergencies values and the competence of the system of industrial elements. These three elements form a nonlinear differential environmental and economic system. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions. The optimization of an error-based objective function is performed using the differential environmental and economic system and its initial conditions.

Insulin resistance is linked to a specific profile of immune activation in human subjects

We tested the hypothesis that a particular immune activation profile might be correlated with insulin resistance in a general population. By measuring 43 markers of immune, endothelial, and coagulation activation, we have previously shown that five different immune activation profiles may be distinguished in 150 volunteers. One of these profiles, Profile 2, characterized by CD4+ T cell senescence, inflammation, monocyte, B cell, and endothelial activation, presented elevated insulinemia, glycemia, triglyceridemia, and γ-glutamyl transferase, a marker of liver injury, in comparison with other profiles. Our data are compatible with a model in which a particular immune activation profile might favor the development of insulin resistance and metabolic syndrome. In this hypothesis, identification of this profile, that is feasible with only 3 markers with an error rate of 5%, might allow to personalize the screening and prevention of metabolic syndrome-driven morbidities as liver steatosis.

Development of a Wine Metabolomics Approach for the Authenticity Assessment of Selected Greek Red Wines

Wine metabolomics constitutes a powerful discipline towards wine authenticity assessment through the simultaneous exploration of multiple classes of compounds in the wine matrix. Over the last decades, wines from autochthonous Greek grape varieties have become increasingly popular among wine connoisseurs, attracting great interest for their authentication and chemical characterization. In this work, 46 red wine samples from Agiorgitiko and Xinomavro grape varieties were collected from wineries in two important winemaking regions of Greece during two consecutive vintages and analyzed using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QToF-MS). A targeted metabolomics methodology was developed, including the determination and quantification of 28 phenolic compounds from different classes (hydroxycinnamic acids, hydroxybenzoic acids, stilbenes and flavonoids). Moreover, 86 compounds were detected and tentatively identified via a robust suspect screening workflow using an in-house database of 420 wine related compounds. Supervised chemometric techniques were employed to build an accurate and robust model to discriminate between two varieties.

Untargeted LC-HRMS profiling followed by targeted fractionation to discover new taste-active compounds in spirits

Taste is a key driver of food and beverage acceptability due to its role in consumers' pleasure. The great interest that natural food and beverages now arouse lies notably in the complexity of their taste, which in turn is related to a wide range of taste-active compounds. Going beyond the classic divide between targeted and untargeted strategies, an integrative methodology to spirits was applied. Untargeted profiling of several cognac spirits was implemented by LC-HRMS to identify compounds of interest among hundreds of ions. A targeted fractionation protocol was then developed. By using HRMS and NMR, dihydrodehydrodiconiferyl alcohol was identified and described for the first time in spirits and oak wood. It was characterized as sweet at 2 mg/L in two matrices and was quantified in spirits up to 4 mg/L. These findings demonstrated how this methodology is relevant and effective to discover new taste-active compounds.

Polyphenols: Natural Antioxidants to Be Used as a Quality Tool in Wine Authenticity

Polyphenols are a diverse group of compounds possessing various health-promoting properties that are of utmost importance for many wine sensory attributes. Apart from genetic and environmental parameters, the implementation of specific oenological practices as well as the subsequent storage conditions deeply affect the content and nature of the polyphenols present in wine. However, polyphenols are effectively employed in authenticity studies. Provision of authentic wines to the market has always been a prerequisite meaning that the declarations on the wine label should mirror the composition and provenance of this intriguing product. Nonetheless, multiple cases of intentional or unintentional wine mislabeling have been recorded alarming wine consumers who demand for strict controls safeguarding wine authenticity. The emergence of novel platforms employing instrumentation of exceptional selectivity and sensitivity along with the use of advanced chemometrics such as NMR (nuclear magnetic resonance)- and MS (mass spectrometry)-based metabolomics is considered as a powerful asset towards wine authentication.

Red Wine Oxidation: Accelerated Ageing Tests, Possible Reaction Mechanisms and Application to Syrah Red Wines

Wine oxidation and ageing involve many complex chemical pathways and reaction mechanisms. The purpose of this study is to set up new and reproducible accelerated red wine ageing tests and identify chemical oxidation or ageing molecular markers. Three accelerated and reproducible ageing tests were developed: a heat test (60 °C); an enzymatic test (laccase test; a chemical test (hydrogen peroxide test). Depending on the test, oxygen consumption was significantly different. For a young wine (2018), the oxygen consumption rate moved from 2.40 ppm.h-1 for the heat test to 3.33 ppm.h-1 for the enzymatic test and 2.86 ppm.h-1 for the chemical test. Once applied to two other vintages (2010 and 2014) from the same winery, the tests revealed different comportments corresponding to wine natural evolution. High resolution UPLC-MS was performed on forced ageing samples and compared to naturally aged red wines. Specific oxidation or ageing ion markers were found with significant differences between tests, revealing the specificity of each test and different possible molecular pathways involved. The hydrogen peroxide test seems to be closer to natural oxidation with an important decrease in absorbance at 520 nm and similar molecular ion variations for [M+H]+ = 291, 331, 347, 493, 535, 581, 639 Da.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.