Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages

Metabolomics applications for plant-based foods origin tracing, cultivars identification and processing: Feasibility and future aspects

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

ModiFinder: Tandem Mass Spectral Alignment Enables Structural Modification Site Localization

Untargeted tandem mass spectrometry (MS/MS) has become a high-throughput method to measure small molecules in complex samples. One key goal is the transformation of these MS/MS spectra into chemical structures. Computational techniques such as MS/MS library search have enabled the reidentification of known compounds. Analog library search and molecular networking extend this identification to unknown compounds. While there have been advancements in metrics for the similarity of MS/MS spectra of structurally similar compounds, there is still a lack of automated methods to provide site specific information about structural modifications. Here we introduce ModiFinder which leverages the alignment of peaks in MS/MS spectra between structurally related known and unknown small molecules. Specifically, ModiFinder focuses on shifted MS/MS fragment peaks in the MS/MS alignment. These shifted peaks putatively represent substructures of the known molecule that contain the site of the modification. ModiFinder synthesizes this information together and scores the likelihood for each atom in the known molecule to be the modification site. We demonstrate in this manuscript how ModiFinder can effectively localize modifications which extends the capabilities of MS/MS analog searching and molecular networking to accelerate the discovery of novel compounds.

Effect of different isolation sources of Lactococcus lactis subsp. lactis on volatile metabolites in fermented milk

Lactococcus lactis subsp. lactis (L. lactis subsp. lactis) is a commonly used starter cultures in fermented dairy products, contributing distinct flavor and texture characteristics with high application value. However, the strains from different isolates have different contributions to milk fermentation. Therefore, this study aimed to investigate the influence of L. lactis subsp. lactis isolated from various sources on the volatile metabolites present in fermented milk. In this study, L. lactis subsp. lactis from different isolation sources (yogurt, koumiss and goat yogurt) was utilized as a starter culture for fermentation. The volatile metabolites of fermented milk were subsequently analyzed by headspace solid phase microextraction gas chromatography-mass spectrography (HS-SPME-GC-MS). The results indicated significant differences in the structure and abundance of volatile metabolites in fermented milk produced with different isolates (R2Y = 0.96, Q2 = 0.88). Notably, the strains isolated from goat yogurt appeared to enhance the accumulation of ketones (goat yogurt vs yogurt milk: 50 %; goat yogur vs koumiss: 27.3 %)and aldehydes (goat yogurt vs yogurt milk: 21.4 %; goat yogurt vs koumiss: 54.5 %) in fermented milk than strains isolated from koumiss and yogurt milk. It significantly promoted the production of 8 flavor substances (1 substance with OAV ≥ 1 and 6 substances with OAV > 0.1) and enhanced the biosynthesis of valine, leucine, and isoleucine. This study provides valuable insights for the application of Lactococcus lactis subsp. lactis isolated from different sources in fermented dairy production and screening of potential starter cultures.

Identification of a β-Carboline Alkaloid from Chemoselectively Derived Vanilla Bean Extract and Its Prevention of Lipid Droplet Accumulation in Human Hepatocytes (HepG2)

Targeting bioactive compounds to prevent lipid droplet accumulation in the liver, we explored an antioxidative extract from vanilla bean (Vainilla planifolia) after chemo-selective derivatization through heating and acid modification. The chemical analysis of vanilla bean extract through chemoselective derivatization resulted in the identification of sixteen compounds (34-50) using LC-MS/MS analysis. A β-carboline alkaloid with a piperidine C-ring and a vanillin moiety at C-1 (34) was identified by molecular networking and diagnostic fragmentation filtering approaches. β-carboline alkaloid 34 exhibited significant inhibitory activity of lipid droplet accumulation (LDAI) in oleic acid-loaded hepatocellular carcinoma HepG2 cells. The LDAI activity was associated with both activation of lipolysis and suppression of lipogenesis in the cells. The study indicates that crude plant extracts, following chemoselective derivatization, may contain bioactive compounds that could be beneficial in preventing hepatosteatosis and could serve as a source of lead compounds for drug development. This approach may be useful to investigate other mixtures of natural products and food resources.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Targeted and Suspect Fatty Acid Profiling of Royal Jelly by Liquid Chromatography—High Resolution Mass Spectrometry

Royal jelly (RJ) is a bee product produced by the mandibular and hypopharyngeal glands of worker honeybees which has attracted special attention because of its numerous pharmacological activities and its applications to dermatology and cosmetics. In 2020, we demonstrated a liquid chromatography–high resolution mass spectrometry (LC–HRMS) method for the determination of seven medium-chain FFAs in RJ samples. The aim of the present work was to extend our studies on FA profiling of RJ, exploring the presence of common long-chain saturated, mono-unsaturated and poly-unsaturated free FAs in RJ samples using this LC–HRMS method. Among twenty common FAs studied by a targeted approach, palmitic acid, stearic acid and oleic acid were found at concentrations higher than the rest of the FAs (the concentrations of these three acids ranged from 37.4 to 48.0, from 17.7 to 24.0 and from 9.4 to 11.1 mg/100 g of fresh RJ, respectively). The high mass accuracy of LC–HRMS allowed the application of a suspect approach, which enabled the exploration of various C9 and C11 FAs, as well as hydroxylated C12 FAs. Nonenoic acid was indicated as the most abundant among these acids. In addition, for the first time, the presence of a variety of regio-isomers of hydroxymyristic, hydroxypalmitic and hydroxystearic acids was demonstrated in RJ samples.

Metabolomics during the spontaneous fermentation in cocoa (Theobroma cacao L.): An exploraty review

Spontaneous fermentation is a process that depends on substrates' physical characteristics, crop variety, and postharvest practices; it induces variations in the metabolites that are responsible for the taste, aroma, and quality. Metabolomics makes it possible to detect key metabolites using chemometrics and makes it possible to establish patterns or identify biomarker behaviors under certain conditions at a given time. Therefore, sensitive and highly efficient analytical techniques allow for studying the metabolomic fingerprint changes during fermentation; which identify and quantify metabolites related to taste and aroma formation of an adequate processing time. This review shows that studying metabolomics in spontaneous fermentation permits the characterization of spontaneous fermentation in different stages. Also, it demonstrates the possibility of modulating the quality of cocoa by improving the spontaneous fermentation time (because of volatile aromatic compounds formation), thus standardizing the process to obtain attributes and quality that will later impact the chocolate quality.

Multi-omics signatures of the human early life exposome

Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations ( https://helixomics.isglobal.org/ ) will serve to guide future investigation into the biological imprints of the early life exposome.

Integrated multi-omics approaches to understand microbiome assembly in Jiuqu, a mixed-culture starter

The use of Jiuqu as a saccharifying and fermenting starter in the production of fermented foods is a very old biotechnological process that can be traced back to ancient times. Jiuqu harbors a hub of microbial communities, in which prokaryotes and eukaryotes cohabit, interact, and communicate. However, the spontaneous fermentation based on empirical processing hardly guarantees the stable assembly of the microbiome and a standardized quality of Jiuqu. This review describes the state of the art, limitations, and challenges towards the application of traditional and omics-based technology to study the Jiuqu microbiome and highlights the need for integrating meta-omics data. In addition, we review the varieties of Jiuqu and their production processes, with particular attention to factors shaping the microbiota of Jiuqu. Then, the potentials of integrated omics approaches used in Jiuqu research are examined in order to understand the assembly of the microbiome and improve the quality of the products. A variety of different approaches, including molecular and mass spectrometry-based techniques, have led to scientific advances in the analysis of the complex ecosystem of Jiuqu. To date, the extensive research on Jiuqu has mainly focused on the microbial community diversity, flavor profiles, and biochemical characteristics. An integrative approach to large-scale omics datasets and cultivated microbiota has great potential for understanding the interrelation of the Jiuqu microbiome. Further research on the Jiuqu microbiome may explain the inherent property of compositional stability and stable performance of a complex microbiota coping with environmental perturbations and provide important insights to reconstruct synthetic microbiota and develop modern intelligent manufacturing procedures for Jiuqu.

Comprehensive investigation on non-volatile and volatile metabolites in four types of green teas obtained from the same tea cultivar of Longjing 43 (Camellia sinensis var. sinensis) using the widely targeted metabolomics

In this study, we produced roasted, baked, steamed, and sun-dried green tea products using the same batch of fresh tea leaves (FTL) of Longjing 43 (Camellia sinensis var. sinensis), and explored processing effects on the metabolic profiles of four types of green teas (FGTs) using the widely targeted metabolomics. Results showed that 146 differential metabolites including flavonoids, amino acids, lipids, and phenolic acids were screened among 1034 non-volatiles. In addition, nineteen differential metabolites were screened among 79 volatiles. Most of non-volatiles and volatiles metabolites changed notably in different manufacturing processes, whereas there were no significant differences (p＞0.05) in the levels of total catechins between FGTs and FTL. The transformation of metabolites was the dominant trend during green tea processing. The results contribute to a better understanding of how the manufacturing process influences green tea quality, and provide useful information for the enrichment of tea biochemistry theory.

LAB Fermentation Improves Production of Bioactive Compounds and Antioxidant Activity of Withania somnifera Extract and Its Metabolic Signatures as Revealed by LC-MS/MS

In this study we investigated the effect of lactic acid bacteria (LAB) fermentation on the ingredients and anti-oxidant activity of Withania somnifera extract. Four strains of LAB could proliferate normally in medium containing W. somnifera extract after the pH reached 3.1~3.5. LAB fermentation increased the content of alcohols and ketones, endowing the extract with the characteristic aroma of fermentation. Compared to the control, the DPPH and ABTS free radical scavenging rates in the fermented samples were significantly improved, ranging from 48.5% to 59.6% and 1.2% to 6.4%. The content of total phenols was significantly increased by 36.1% during the fermentation of mixed bacteria. Moreover, the original composition spectrum of the extract was significantly changed while the differentially accumulated metabolites (DAMs) were closely related to bile secretion, tryptophan metabolism and purine metabolism. Therefore, LAB fermentation can be used as a promising way to improve the flavor and bioactivity of the extracts of W. somnifera, making the ferments more attractive for use as functional food.

Non-targeted analysis for organic components of microplastic leachates

Organic components of microplastic leachates were investigated in an integrated non-targeted analysis study that included statistical analysis on leachates generated under different leaching scenarios. Leaching experiments were undertaken with simulated gastric fluid (SGF), river water, and seawater with common polymer types, including polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, and polyester fabrics comprising both raw and recycled materials. Totals of 111.0 ± 26.7, 98.5 ± 20.3, and 53.5 ± 4.7 different features were tentatively identified as compounds in SGF, freshwater, and seawater leachates, respectively, of which 5 compounds were confirmed by reference standards. The leaching capacities of the media were compared, and the clusters of structurally related features leached in the same medium were studied. For leachates generated from raw and recycled plastics, volcano plots and Pearson's Chi-squared tests were used to identify characteristic features. More characteristic features (3-20) had an average intensity across all recycled plastics that were significantly higher (p < 0.05) than that (1-3) of raw plastics under different conditions. The results indicate that gastric solution is more likely to leach components from microplastics, and there exists the difference of leachate's organic composition between raw and recycled materials, providing new insights into understanding microplastic environmental effects.

Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics

Chia, flax, and sesame seeds are well known for their nutritional quality and are commonly included in bakery products. So far, the development of methods to verify their presence and authenticity in foods is a requisite and a raised need. In this work we applied untargeted metabolomics to propose authenticity markers. Seeds were analyzed by HPLC-MS/MS and 9938 features in negative mode and 9044 in positive mode were obtained by Mzmine. After isotopes grouping, alignment, gap-filling, filtering adducts, and normalization, PCA was applied to explore the dataset and recognize pre-existent classification patterns. OPLS-DA analysis and S-Plots were used as supervised methods. Twenty-five molecules (12 in negative mode and 13 in positive mode) were selected as discriminant for the three seeds, polyphenols and lignans were identified among them. To the best of our knowledge, this is the first approach using non-target HPLC-MS/MS for the authentication of chia, flax and sesame seeds.

Exploring Chemical Markers Related to the Acceptance and Sensory Profiles of Concentrated Liquid Coffees: An Untargeted Metabolomics Approach

In this study, we aimed to apply an untargeted LC/QTOF-MS analysis for the identification of compounds that positively and negatively affect the acceptance of coffee beverages from liquid coffee concentrates (CLCs) before and after storage. The metabolomic results were integrated with physicochemical and sensory parameters, such as color, pH, titratable acidity, and oxygen contents, by a bootstrapped version of partial least squares discriminant analysis (PLS-DA) to select and classify the most relevant variables regarding the rejection or acceptance of CLC beverages. The OPLS-DA models for metabolite selection discriminated between the percent sensory acceptance (the Accepted group) and rejection (the Rejected group). Eighty-two molecular features were considered statistically significant. Our data suggest that coffee sample rejection is associated with chlorogenic acid hydrolysis to produce ferulic and quinic acids, consequently generating methoxybenzaldehydes that impact the perceived acidity and aroma. Furthermore, acceptance was correlated with higher global scores and sweetness, as with lactones such as feruloyl-quinolactone, caffeoyl quinolactone, and 4-caffeoyl-1,5-quinolactone, and significant oxygen levels in the headspace.

TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data

The human microbiome is a complex community of microorganisms, their enzymes, and the molecules they produce or modify. Recent studies show that imbalances in human microbial ecosystems can cause disease. Our microbiome affects our health through the products of biochemical reactions catalyzed by microbial enzymes (microbial biotransformations). Despite their significance, currently, there are no systematic strategies for identifying these chemical reactions, their substrates and molecular products, and their effects on health and disease. We present TransDiscovery, a computational algorithm that integrates molecular networks (connecting related molecules with similar mass spectra), association networks (connecting co-occurring molecules and microbes) and knowledge bases of microbial enzymes to discover microbial biotransformations, their substrates, and their products. After searching the metabolomics and metagenomics data from the American Gut Project and the Global Foodomic Project, TranDiscovery identified 17 potentially novel biotransformations from the human gut microbiome, along with the corresponding microbial species, substrates, and products.

Metabolomics of acid whey derived from Greek yogurt

Acid whey, a byproduct of Greek yogurt production, has little commercial value due to its low protein content and is also environmentally harmful when disposed of as waste. However, as a product of microbial fermentation, acid whey could be a rich source of beneficial metabolites associated with fermented foods. This study increases understanding of acid whey composition by providing a complete metabolomic profile of acid whey. Commercial and laboratory-made Greek yogurts, prepared with 3 different bacterial culture combinations, were evaluated. Samples of uncultured milk and cultured whey from each batch were analyzed. Ultra-high-performance liquid chromatography-tandem mass spectrometry metabolomics were used to separate and identify 477 metabolites. Compared with uncultured controls, acid whey from fermented yogurt showed decreases in some metabolites and increases in others, presumably due to the effects of microbial metabolism. Additional metabolites appeared in yogurt whey but not in the uncultured control. Therefore, the effect of microbial fermentation is complex, leading to increases or decreases in potentially bioactive bovine metabolites while generating new microbial compounds that may be beneficial. Metabolite production was significantly affected by combinations of culturing organisms and production location. Differences between laboratory-made and commercial samples could be caused by different starting ingredients, environmental factors, or both.

Metabolomics: An analytical technique for food processing evaluation

This review aimed to retrieve the most recent research with strong impact concerning the application of metabolomics analysis in food processing. The literature reveals the high capacity of this methodology to evaluate chemical and organoleptic transformations that occur during food production. Current and potential applications of metabolomics analysis will be addressed, focusing on process-composition-function relationships. The use of the metabolomics approach to evaluate transformations in foods submitted to minimal processes, heat or cold treatments, drying, fermentation, chemical and enzymatic treatments and processes using innovative technologies will be discussed. Moreover, the main strategies and advantages of metabolomics-based approaches are reviewed, as well as the most used analytical platforms. Overall, metabolomics can be seen as an important tool to support academia and industry on pursuing knowledge about the transformation of raw animal or plant materials into ready-to-eat products.

Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment

Molecular networking connects mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. As a result, the fragmentation spectra of these ion species often remain unconnected in tandem mass spectrometry-based molecular networks, leading to redundant and disconnected sub-networks of the same compound classes. To overcome this bottleneck, we develop Ion Identity Molecular Networking (IIMN) that integrates chromatographic peak shape correlation analysis into molecular networks to connect and collapse different ion species of the same molecule. The new feature relationships improve network connectivity for structurally related molecules, can be used to reveal unknown ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral reference libraries. IIMN is integrated into various open source feature finding tools and the GNPS environment. Moreover, IIMN-based spectral libraries with a broad coverage of ion species are publicly available.

Application of Interactive and Intelligent Packaging for Fresh Fish Shelf-Life Monitoring

Smart packaging, also known as intelligent packaging, is responsive to external stimuli, moisture, light, oxygen, heat, pH, and bacterial growth. It has evolved from extensive applications in food safety, bacterial response, and medical packaging. Interactive packaging has a scientific basis for additional information about food products because these codes give all required data. This work deals with a combination of frontline food sciences, smart and interactive packaging that are applicable for future production of nutrition packages through smart detection of food spooling. Additionally, it verifies the best degree of food safety and population demands as the third generation of packaging technology. High qualified duplex laminated package with a nano-encapsulated pH monitoring label for fresh fish was printed. The interactive Quick Response code icon was combined in a designed package with important information about cooking, smart packaging, and fish quality. Therefore, particle size, zeta potential, and surface area are measured for a nanoencapsulated indicator which exhibits 74.4 nm, 23.6 mV, and 88.9 m²/g, respectively: overall migration, water vapor, and oxygen permeability. The properties of printing for 11 color spots are evaluated by x-rite before and after the cold storage period without any detectable changes in the rate of color change (ΔE). The bacterial count of the tested sample is examined by counting the microbial colonies in the agar plate media. There is good agreement between microbial count and smart indicator color change as an effective direct detection tool for sustainable food quality and safety.

MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Repository scale classification and decomposition of tandem mass spectral data

Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Dynamic changes in the metabolite profile and taste characteristics of Fu brick tea during the manufacturing process

Fu brick tea is a typical post-fermentation tea known for its special flavor and health benefits. Liquid chromatography-mass spectrometry, and sensory evaluation with multivariate analysis were used to characterize the dynamic changes in metabolite profile and taste characteristics. Seventy-one compounds were identified as critical metabolites, catechins, flavonoids, phenolic acids, terpenoids and others. During the manufacturing process, these compounds exhibited sharp fluctuations in content, the intensities of astringency, bitterness, and sourness of the tea materials reduced greatly, but the mellow intensity increased sharply. Several catechins and phenolic acids were positively related to the 'astringent', 'bitter', and 'sour' tastes attributes. The fungal genera, Aspergillus, Candida, unclassified_o_Hypocreales, unclassified_o_Saccharomycetales and Wallemia and the bacterial genus, Klebsiella, were identified as core functional microorganisms linked to the metabolic variations during the process. Overall, these findings provided a more comprehensive understanding of the formation of the sensory characteristics in Fu brick tea during the manufacturing process.

Strategies for data reduction in non-targeted screening analysis: The impact of sample variability for food safety applications

While analytical methods targeting specific compounds are critical for food safety, analytes excluded from the targeted list will not be identified. Non-targeted analyses (NTA) using LC/HR-MS complement these approaches by producing information-rich data sets where molecular formula can be generated for each detected compound; however, data mining can be labor intensive. Thus, we examined different NTA approaches to reduce the number of compounds needing further investigation, without relying on a suspect list or MS/MS database, both in single ingredient foods (i.e., oats) and more complex, oat-containing samples. We investigated inherent sample variability and utilized this information to build in-house databases for removing food compounds from sample data. While food databases were useful for data reduction, differential analysis was the most promising approach for single ingredient foods because it substantially reduced the number of features while retaining spiked QC compounds; however, a combination of approaches was necessary with greater sample complexity.

Development of a Statistical Workflow for Screening Protein Extracts Based on Their Nutritional Composition and Digestibility: Application to Elderly

The objective of the study is to develop a workflow to screen protein extracts and identify their nutritional potential as high quality nutritional culinary aids for recipes for the elderly. Twenty-seven protein extracts of animal, vegetable, and dairy origin were characterized. We studied their fate by monitoring static in vitro digestion, mimicking the physiological digestion conditions of the elderly. At the end of the gastric and intestinal phase, global measurements of digestibility and antioxidant bioactivities were performed. The statistical analysis workflow developed allowed: (i) synthesizing the compositional and nutritional information of each protein extract by creating latent variables, and (ii) comparing them. The links between variables and similarities between protein extracts were visualized using a heat map. A hierarchical cluster analysis allowed reducing the 48 quantitative variables into 15 qualitative latent variables (clusters). The application of the k-means method on each cluster enable to classify the protein extracts by level. This defined level was used as categorical value. Multiple correspondence analysis revealed groups of protein extracts with varied patterns. This workflow allowed the comparison/hierarchization between protein extracts and the creation of a tool to select the most interesting ones on the basis of their nutritional quality.

Comprehensive investigation on volatile and non-volatile metabolites in broccoli juices fermented by animal- and plant-derived Pediococcus pentosaceus

A metabolomics approach was employed to investigate differences and correlations among key odorants and non-volatile metabolites in broccoli juices fermented by plant- and animal-derived Pediococcus pentosaceus. Forty volatile metabolites were identified by headspace solid-phase microextraction/gas chromatography-mass spectrometry. According to orthogonal projections to latent structures-differential analysis, 24 and 21 differential volatiles were detected after fermentation by plant- and animal-derived P. pentosaceus, respectively. The concentrations of 10 odorants (OAV ≥ 1) detected by gas chromatography-olfactometry changed significantly after fermentation by P. pentosaceus. Using ultrahigh-pressure liquid chromatography/quadrupole time-of-flight mass spectrometry, 49.47% of the non-volatile metabolites were classified as lipids and lipid-like molecules. The relative expressions of five non-volatile metabolites that exhibited significant correlations with odorants using Spearman correlation analysis changed significantly after fermentation. Fermentation with animal- and plant-derived P. pentosaceus can therefore change key odorants and non-volatile metabolites in broccoli juice that contribute to the characteristic organoleptic properties of products.

Reducing background absorbance via a double-lock strategy for detection of alkaline phosphatase and α-fetoprotein

Lowering the background signal for more sensitive analysis of determinands is as important as amplifying the target signal. The photoinduced oxidase of fluorescein has been reported, which can catalyze the oxidization of common substrates in a few minutes. As a metaphor for locks and keys, we designed double locks confining the activity of fluorescein to reduce the background absorbance during colorimetric detection. The first lock inhibits the main activity of fluorescein by phosphating. The second lock almost completely deactivates fluorescein by forming coordination nanoparticles (CNPs) via the self-assembly of cerium chloride and fluorescein diphosphate (FDP). The Ce-FDP CNPs are characterized by scanning electron microscope (SEM), dynamic light scattering (DLS), Fourier transform infrared spectrometer (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive spectrum (EDS), which show electrostatic formation and amorphous character in the morphology. Alkaline phosphatase (ALP), the key to release fluorescein, can destroy Ce-FDP CNPs along with decomposing FDP by degrading phosphate groups. Therefore, a novel colorimetric strategy for sensitive detection of ALP is established. The detection of α-fetoprotein (AFP) is further succeeded by labeling AFP antibody with ALP. By dramatically reducing the background absorbance, the detection limits of ALP and AFP are as low as 0.014 mU/mL and 0.023 ng/mL, respectively. This convenient, brief, sensitive assay provides a promising prospect for clinical diagnosis. Graphical abstract.

PhyteByte: identification of foods containing compounds with specific pharmacological properties

Background

Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds.

Results

PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator.

Conclusions

PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

The exposome and health: Where chemistry meets biology

Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.

Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses.

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

The lipidome of royal jelly (RJ) consists of medium-chained (8-12 carbon atoms) free fatty acids. We present herein a liquid chromatography-high resolution mass spectrometry (HRMS) method that permits the determination of RJ fatty acids and at the same time the detection of suspect fatty acids. The method allows for the direct quantification of seven free fatty acids of RJ, avoiding any derivatization step. It was validated and applied in seven RJ samples, where the major RJ fatty acid trans-10-hydroxy-2-decenoic acid (10-HDA) was found to vary from 0.771 ± 0.08 to 0.928 ± 0.04 g/100 g fresh RJ. Four additional suspect fatty acids were simultaneously detected taking advantage of the HRMS detection.