Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review

Chemical Compositions and Antibacterial Activities of Litsea cubeba Essential Oil and Its Distillates Prepared by Vacuum Fractional Distillation and Molecular Distillation

Litsea cubeba essential oil (LCEO) has various bioactivities and wide applications. However, most reported LCEOs are directly extracted from plants, and studies on further processing of LCEO to enrich bioactive components using modern separation techniques are scarce. In this study, LCEO was extracted by hydrodistillation and further processed via vacuum fractional distillation (VFD) and molecular distillation (MD). The chemical compositions of LCEO and seven distillates were analyzed, and the activities of the EOs and eight individual constituents against seven bacteria were tested. Distillates VFD3 and MDH2 showed the best activity against Escherichia coli, Staphylococcus aureus, and Agrobacterium rhizogenes. VFD3 exerted antibacterial action against A. rhizogenes by inhibiting biofilms, damaging the cell membrane and cell wall, and perturbing metabolic pathways. VFD and MD are effective processing methods for changing the chemical composition and enhancing the bioactivity of LCEO, which might be used to improve the quality and extend the applications of LCEO and other EOs.

Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by 1H NMR-Based Metabolic Profiling

Olive trees are a cornerstone of Mediterranean agriculture but face significant threats from diseases such as Verticillium wilt and olive anthracnose. These diseases, caused by Verticillium dahliae and Colletotrichum spp., respectively, result in significant economic losses and degrade olive oil quality. While traditional chemical treatments present environmental risk, sustainable alternatives such as biological control agents (BCAs) are gaining attention. Epicoccum nigrum, an antagonistic fungus, has shown potential as a BCA due to its production of antimicrobial secondary metabolites. This study aimed to observe whether E. nigrum has an antagonistic ability against V. dahliae and C. acutatum, and to elucidate the metabolic interactions between these fungi using NMR-based metabolomics. E. nigrum showed inhibitory effects on the growth of C. acutatum and V. dahlia of 44.97% and 38.73% respectively. Metabolomic profiling revealed distinct biochemical responses in E. nigrum, V. dahliae, and C. acutatum under mono- and dual-culture. Multivariate statistical analysis highlighted the metabolic shifts in mycelia and identified the primary metabolites, such as glutamine, 4-aminobutyrate, and phenylalanine that are involved in adaption for survival in stress conditions such as the presence of a competitor. The results could be important for a better understanding of the primary fungal metabolism, which is still poorly characterized. Further investigation is needed, but these results suggest that E. nigrum could serve as a BCA, offering a more sustainable approach to managing olive diseases.

Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

Differentiation of Escherichia coli and Shigella flexneri by Metabolite Profiles Obtained Using Gold Nanoparticles-Based Surface-Assisted Laser Desorption/Ionization Mass Spectrometry

Escherichia coli and Shigella flexneri are challenging to differentiate using methods such as phenotyping, 16S rRNA sequencing, or protein profiling through matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) due to their close relatedness. This study explores the potential for identifying E. coli and S. flexneri by incorporating reference spectra of metabolite profiles, obtained via surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) employing gold nanoparticles (AuNPs), into the Bruker Biotyper database. Metabolite extracts from E. coli and S. flexneri cells were prepared using liquid-liquid extraction in a chloroform-methanol-water system. The extracts were analyzed using Au-SALDI MS in positive ion mode, and reference spectra, compiled from 30 spectra for each bacterium, were added to the database. Identification of bacteria based on metabolite fingerprints in the Biotyper database produced correct results with scores exceeding 2.75. The results of Partial Least Squares-Discriminant Analysis (PLS-DA) demonstrated that the metabolomic approach could accurately differentiate the microorganisms under study. A panel of nine m/z values was also identified, each with an area under the ROC curve of above 0.8, enabling accurate identification of E. coli and S. flexneri. A search of metabolite databases allowed the following compounds to be assigned to the selected m/z values: N-acetylputrescine, arginine, 2-maleylacetate, benzoyl phosphate, N8-acetylspermidine, alanyl-glutamate, 4-hydroxy-2,3,4,5-tetrahydrodipicolinate, and sucrose. The analyses showed that identification of bacteria based on metabolite profiles obtained by the Au-SALDI MS method is feasible and can be useful for distinguishing closely related microorganisms that are difficult to differentiate by other techniques.

Application of Electrospun Nanofiber-Based Electrochemical Sensors in Food Safety

Food safety significantly impacts public health and social welfare. Recently, issues such as heavy metal ions, drug residues, food additives, and microbial contamination in food have become increasingly prominent. Electrochemical sensing technology, known for its low cost, simplicity, rapid response, high sensitivity, and excellent selectivity, has been crucial in food safety detection. Electrospun nanofibers, with their high specific surface area, superior mechanical properties, and design flexibility, offer new insights and technical platforms for developing electrochemical sensors. This study introduces the fundamental principles, classifications, and detection mechanisms of electrochemical sensors, along with the principles and classifications of electrospinning technology. The applications of electrospun nanofiber-based electrochemical sensors in food safety detection over the past five years are detailed, and the limitations and future research prospects are discussed. Continuous innovation and optimization are expected to make electrospun nanofiber-based electrochemical sensors a key technology in rapid food safety detection, providing valuable references for expanding their application and advancing food safety detection methods.

Metabonomic analysis to identify exometabolome changes underlying antifungal and growth promotion mechanisms of endophytic Actinobacterium Streptomyces albidoflavus for sustainable agriculture practice

In recent years, there has been an increasing focus on microbial ecology and its possible impact on agricultural production, owing to its eco-friendly nature and sustainable use. The current study employs metabolomics technologies and bioinformatics approaches to identify changes in the exometabolome of Streptomyces albidoflavus B24. This research aims to shed light on the mechanisms and metabolites responsible for the antifungal and growth promotion strategies, with potential applications in sustainable agriculture. Metabolomic analysis was conducted using Q Exactive UPLC-MS/MS. Our findings indicate that a total of 3,840 metabolites were identified, with 137 metabolites exhibiting significant differences divided into 61 up and 75 downregulated metabolites based on VIP >1, |FC| >1, and p < 0.01. The interaction of S. albidoflavus B24 monoculture with the co-culture demonstrated a stronger correlation coefficient. The Principal Component Analysis (PCA) demonstrates that PCA1 accounted for 23.36%, while PCA2 accounted for 20.28% distinction. OPLS-DA score plots indicate significant separation among different groups representing (t1) 24% as the predicted component (to1) depicts 14% as the orthogonal component. According to the findings of this comprehensive study, crude extracts from S. albidoflavus demonstrated varying abilities to impede phytopathogen growth and enhance root and shoot length in tested plants. Through untargeted metabolomics, we discovered numerous potential molecules with antagonistic activity against fungal phytopathogens among the top 10 significant metabolites with the highest absolute log2FC values. These include Tetrangulol, 4-Hydroxybenzaldehyde, and Cyclohexane. Additionally, we identified plant growth-regulating metabolites such as N-Succinyl-L-glutamate, Nicotinic acid, L-Aspartate, and Indole-3-acetamide. The KEGG pathway analysis has highlighted these compounds as potential sources of antimicrobial properties. The inhibitory effect of S. albidoflavus crude extracts on pathogen growth is primarily attributed to the presence of specific gene clusters responsible for producing cyclic peptides such as ansamycins, porphyrin, alkaloid derivatives, and neomycin. Overall, it is apparent that crude extracts from S. albidoflavus exhibited varying abilities to inhibit the growth of three phytopathogens and enhancement in both root and shoot length of tested plants. This research enhances our understanding of how secondary metabolites contribute to growth promotion and biocontrol, supporting ecosystem sustainability and resilience while boosting productivity in sustainable agriculture.

The Metabolite Profiling of Aspergillus fumigatus KMM4631 and Its Co-Cultures with Other Marine Fungi

An Aspergillus fumigatus KMM 4631 strain was previously isolated from a Pacific soft coral Sinularia sp. sample and was found to be a source of a number of bioactive secondary metabolites. The aims of this work are the confirmation of this strain' identification based on ITS, BenA, CaM, and RPB2 regions/gene sequences and the investigation of secondary metabolite profiles of Aspergillus fumigatus KMM 4631 culture and its co-cultures with Penicillium hispanicum KMM 4689, Amphichorda sp. KMM 4639, Penicillium sp. KMM 4672, and Asteromyces cruciatus KMM 4696 from the Collection of Marine Microorganisms (PIBOC FEB RAS, Vladivostok, Russia). Moreover, the DPPH-radical scavenging activity, urease inhibition, and cytotoxicity of joint fungal cultures' extracts on HepG2 cells were tested. The detailed UPLC MS qTOF investigation resulted in the identification and annotation of indolediketopiperazine, quinazoline, and tryptoquivaline-related alkaloids as well as a number of polyketides (totally 20 compounds) in the extract of Aspergillus fumigatus KMM 4631. The metabolite profiles of the co-cultures of A. fumigatus with Penicillium hispanicum, Penicillium sp., and Amphichorda sp. were similar to those of Penicillium hispanicum, Penicillium sp., and Amphichorda sp. monocultures. The metabolite profile of the co-culture of A. fumigatus with Asteromyces cruciatus differed from that of each monoculture and may be more promising for the isolation of new compounds.

Automatization of metabolite extraction for high-throughput metabolomics: case study on transgenic isoprene-emitting birch

Metabolomics studies are becoming increasingly common for understanding how plant metabolism responds to changes in environmental conditions, genetic manipulations and treatments. Despite the recent advances in metabolomics workflow, the sample preparation process still limits the high-throughput analysis in large-scale studies. Here, we present a highly flexible robotic system that integrates liquid handling, sonication, centrifugation, solvent evaporation and sample transfer processed in 96-well plates to automatize the metabolite extraction from leaf samples. We transferred an established manual extraction protocol performed to a robotic system, and with this, we show the optimization steps required to improve reproducibility and obtain comparable results in terms of extraction efficiency and accuracy. We then tested the robotic system to analyze the metabolomes of wild-type and four transgenic silver birch (Betula pendula Roth) lines under unstressed conditions. Birch trees were engineered to overexpress the poplar (Populus × canescens) isoprene synthase and to emit various amounts of isoprene. By fitting the different isoprene emission capacities of the transgenic trees with their leaf metabolomes, we observed an isoprene-dependent upregulation of some flavonoids and other secondary metabolites as well as carbohydrates, amino acid and lipid metabolites. By contrast, the disaccharide sucrose was found to be strongly negatively correlated to isoprene emission. The presented study illustrates the power of integrating robotics to increase the sample throughput, reduce human errors and labor time, and to ensure a fully controlled, monitored and standardized sample preparation procedure. Due to its modular and flexible structure, the robotic system can be easily adapted to other extraction protocols for the analysis of various tissues or plant species to achieve high-throughput metabolomics in plant research.

An untargeted exometabolomics approach to characterize dissolved organic matter in groundwater of the Samail Ophiolite

The process of serpentinization supports life on Earth and gives rise to the habitability of other worlds in our Solar System. While numerous studies have provided clues to the survival strategies of microbial communities in serpentinizing environments on the modern Earth, characterizing microbial activity in such environments remains challenging due to low biomass and extreme conditions. Here, we used an untargeted metabolomics approach to characterize dissolved organic matter in groundwater in the Samail Ophiolite, the largest and best characterized example of actively serpentinizing uplifted ocean crust and mantle. We found that dissolved organic matter composition is strongly correlated with both fluid type and microbial community composition, and that the fluids that were most influenced by serpentinization contained the greatest number of unique compounds, none of which could be identified using the current metabolite databases. Using metabolomics in conjunction with metagenomic data, we detected numerous products and intermediates of microbial metabolic processes and identified potential biosignatures of microbial activity, including pigments, porphyrins, quinones, fatty acids, and metabolites involved in methanogenesis. Metabolomics techniques like the ones used in this study may be used to further our understanding of life in serpentinizing environments, and aid in the identification of biosignatures that can be used to search for life in serpentinizing systems on other worlds.

Untargeted metabolomics analysis based on LC-IM-QTOF-MS for discriminating geographical origin and vintage of Chinese red wine

Identifying wine geographical origin and vintage is vital due to the abundance of fraudulent activity associated with wine mislabeling of region and vintage. In this study, an untargeted metabolomic approach based on liquid chromatography/ion mobility quadrupole time-of-flight mass spectrometry (LC-IM-QTOF-MS) was used to discriminate wine geographical origin and vintage. Wines were well discriminated according to region and vintage with orthogonal partial least squares-discriminant analysis (OPLS-DA). The differential metabolites subsequently were screened by OPLS-DA with pairwise modeling. 42 and 48 compounds in positive and negative ionization modes were screened as differential metabolitesfor the discrimination of different wine regions, and 37 and 35 compounds were screened for wine vintage. Furthermore, new OPLS-DA models were performed using these compounds, and the external verification trial showed excellent practicality with an accuracy over 84.2%. This study indicated that LC-IM-QTOF-MS-based untargeted metabolomics was a feasible tool for wine geographical origin and vintage discrimination.

Fluorescent Probe Combined with Photoelectric Analysis Technology for Detection of Escherichia coli

Food safety is facing great challenges in preventing foodborne diseases caused by pathogenic pollution, especially in resource-limited areas. The rapid detection technique of microorganisms, such as immunological methods and molecular biological methods, plays a crucial key in timely bioanalysis and disease treatment strategies. However, it is difficult for these methods to simultaneously meet the criteria of simple operation, high specificity, and sensitivity, as well as low cost. Coconut water is known as the "water of life" in Hainan. It is a refreshing and nutritious beverage which is widely consumed due to its beneficial properties to health. Coconut water processing is an important pillar industry in Hainan. The detection of pathogenic microorganisms, such as Escherichia coli, in coconut water has become an important factor which has restricted the upgrading and development of this industry. Based on the needs of industrial development, we developed a microbial photoelectric detection system which was composed of a fluorescent probe detection reagent and a photoelectric sensor detection device. This system combined microbial enzyme targets, selective fluorescent substrate metabolism characteristics, and a photoelectric sensor signal transduction mechanism, which produce a strong signal with a high signal-to-noise ratio. The microbial detection system developed here has a simple structure, simple and convenient operation, short detecting time (≥2 h), and high sensitivity (1 CFU/mL). This system may also enable early warning and monitoring programs for other pathogenic microorganisms in order to promote the overall competitiveness of the Hainan coconut water industry.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

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.

Unveiling Microbial Chemical Interactions Based on Metabolomics Approaches

Microorganisms are ubiquitous in diverse habitats and studying their chemical interactions with the environment and comprehend its complex relations with both hosts and environment, are crucial for the development of strategies to control microbial diseases. This chapter discusses the importance of studying microorganisms with agricultural benefits, using specialized metabolites as examples. Herein we highlight the challenges and opportunities in utilizing microorganisms as alternatives to synthetic pesticides and fertilizers in agriculture. Genome-guided investigations and improved analytical methodologies are necessary to characterize diverse and complex biomolecules produced by microorganisms. Predicting and isolating bioproducts based on genetic information have become a focus for researchers, aided by tools like antiSMASH, BiG-SCAPE, PRISM, and others. However, translating genomic data into practical applications can be complex. Therefore, integrating genomics, transcriptomics, and metabolomics enhances chemical characterization, aiding in discovering new metabolic pathways and specialized metabolites. Additionally, elicitation is one promising strategy to enhance beneficial metabolite production. Finally, identify and characterize microbial secondary metabolites remain challenging due to their low production, complex chemical structure characterization and different environmental factors necessary for metabolite in vitro production.

Metabolomics insights into the potential of encapsulated essential oils as multifunctional food additives

Growing consumer concern about foodborne disease outbreaks and health risks associated with chemical additives has propelled the usage of essential oils (EOs) as novel food additives, but are limited by instability. In this regard, a series of EOs nano/micro-capsules have been widely used to enhance their stability and improve food quality. However, classical food quality assessment methods are insufficient to fully characterize the effects of encapsulated EOs on food properties, including physical, biochemical, organoleptic, and microbial changes. Recently, the rapid development of high-throughput sequencing is accelerating the application of metabolomics in food safety and quality analysis. This review seeks to present the most recent achievements in the application of non-targeted metabolomics to identify and quantify the overall metabolite profile associated with food quality, which can guide the development of emerging food preservation technologies. The scientific findings confirm that metabolomics opens up exciting prospects for biomarker screening in food preservation and contributes to an in-depth understanding of the mechanisms of action (MoA) of EOs. Future research should focus on constructing food quality assessment criteria based on multi-omics technologies, which will drive the standardization and commercialization of EOs for food industry applications.

Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing

The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

Use of MALDI-TOF MS to Discriminate between Aflatoxin B1-Producing and Non-Producing Strains of Aspergillus flavus

Aflatoxin B₁ (AFB₁) is one of the most toxic mycotoxins. One of the producers of AFB₁ is Aspergillus flavus. Therefore, its rapid identification plays a key role in various sectors of the food and feed industry. MALDI-TOF mass spectrometry is one of the fastest and most accurate methods today. Therefore, the aim of this research was to develop the rapid identification of producing and non-producing strains of A. flavus based on the entire mass spectrum. To accomplish the main goal a different confirmatory MALDI-TOF MS and TLC procedures such as direct AFB₁ identification by scraping from TLC plates, A. flavus mycelium, nutrient media around A. flavus growth, and finally direct AFB₁ identification from infected wheat and barley grains had to be conducted. In this experiment, MALDI-TOF mass spectrometry with various modifications was the main supporting technology. All confirmatory methods confirmed the presence of AFB₁ in the samples of aflatoxin-producing strains of A. flavus and vice versa; AFB₁ was not detected in the case of non-producing strains. Entire mass spectra (from 2 to 20 kDa) of aflatoxin-producing and non-producing A. flavus strains were collected, statistically analyzed and clustered. An in-depth analysis of the obtained entire mass spectra showed differences between AFB₁-producing and non-producing strains of A. flavus. Statistical and cluster analysis divided AFB₁-producing and non-producing strains of A. flavus into two monasteries. The results indicate that it is possible to distinguish between AFB₁ producers and non-producers by comparing the entire mass spectra using MALDI-TOF MS. Finally, we demonstrated that if there are established local AFB₁-producing and non-producing strains of A. flavus, the entire mass spectrum database identification of aflatoxigenic A. flavus strains can be even faster and cheaper, without the need to identify the toxin itself.

Effect of Thymus vulgaris L. essential oil and thymol on the microbiological properties of meat and meat products: A review

Since foodborne diseases are often considered as one of the biggest public health threats worldwide, effective preservation strategies are needed to inhibit the growth of undesirable microorganisms in food commodities. Up to now, several techniques have been adopted for the production of safe and high-quality products. Although the traditional methods can improve the reliability, safety, and shelf-life of food, some of them cannot be applied without rising health concerns. Thereby, the addition of various phytochemicals has gained much attention during the last decades, especially for meat products that may be contaminated with pathogenic and spoilage organisms. Thyme (Thymus vulgaris L.), as an important medicinal and culinary herb, is a promising source of bioactive compounds that have a great impact on the microbiological stability of meat by suppressing the undesirable microflora. However, the use of these antimicrobials is still facing difficulties due to their aromatic properties and variable efficacy against targeted species. In this paper, we provide an overview on the potential effects of thyme essential oil (EO) and thymol as bio-preservative agents in meat products. Furthermore, this paper provides insights into the limitations and current challenges of the addition of EOs and their constituents to meat commodities and suggests viable solutions that can improve the applicability of these phytochemicals.

Synergistic effect of co-culture rhizosphere Streptomyces: A promising strategy to enhance antimicrobial activity and plant growth-promoting function

Rhizosphere Streptomyces is one of the important types of rhizosphere microorganisms that plays an important role in promoting plant growth and controlling plant diseases to maintain agricultural ecosystem balance and green ecological agriculture development as beneficial bacteria. Microbial co-culture simulates the complex biocommunity in nature, which has more advantages than the monoculture with a synergistic effect. As the key signal mediums of microorganisms, plants, and their interactions, microbial metabolites are of great significance in revealing their functional mechanism. In this study, two potential plant growth-promoting rhizobacteria, Streptomyces albireticuli MDJK11, and Streptomyces alboflavus MDJK44, were selected to explore the effects of co-culture and monoculture on plant growth promotion and disease prevention, and the metabolic material basis was analyzed by metabonomics. Results showed that Streptomyces MDJK11, MDJK44 monoculture, and co-culture condition all showed good growth promoting and antimicrobial effects. Moreover, as compared to the monoculture, the co-culture showed the advantage of a synergistic enhancement effect. LC-MS-based metabonomics analysis showed the metabolic material bases of Streptomyces for plant growth promotion and disease prevention were mainly plant hormone and antibiotics and the co-culture condition could significantly stimulate the production of plant hormone promoters and macrolide, cyclic peptide, and aminoglycoside antibiotics. The study proved that the co-cultures of S. albireticuli MDJK11 and S. alboflavus MDJK44 have great potential in crop growth promotion and disease prevention.

Fungi Occurrence in Ready-to-Eat Hazelnuts (Corylus avellana) From Different Boreal Hemisphere Areas

The present study evaluated the fungal contamination of ready-to-eat dried hazelnuts considering for the first time the application of the same condition drying process of several hazelnut cultivars from different boreal hemisphere areas. Fifty lots of hazelnuts (Corylus avellana), belonging to eight cultivars from seven regions in four countries, were analyzed for fungal microbiota, describing both load levels and species diversity. For this purpose, a polyphasic approach consisting of morphological examination (optical and scanning electron microscope observation) and molecular characterization [PCR-DGGE analysis and sequence analyses of the internal transcribed spacer (ITS)] was performed. The results show that different fungal populations occur in dried hazelnuts regardless of their geographical area of production. Although some varieties appear to be relatively less susceptible, species related to Aspergillus, such as A. commune and A. ochraceus, Penicillium, including P. commune, P. solitum, and P. expansum, and Rhizopus, for instance, R. stolonifer and R. oryzae, have generally been found. A related character "hazelnut cultivar-fungi" was found for species related to the genera Trichoderma and Fusarium, including F. oxyxporum, F. solani, and F. falciforme. All 14 species found are known to host pathogenic strains. Therefore, their presence in a ready-to-eat product, such as dried hazelnuts, can pose a real danger to the consumer. Based on these considerations, the development of new protective strategies seems highly desirable. The species-level description of the contaminating fungal community acquired through this study is the starting point for the development of tailor-made protective biotechnologies.

Bacterial Metabolomics: Sample Preparation Methods

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important "omics" tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Rapid photothermal detection of foodborne pathogens based on the aggregation of MPBA-AuNPs induced by MPBA using a thermometer as a readout

Rapid and portable detection of foodborne pathogens is of great significance for food safety and public health. The colorimetric methods based on naked-eye have been demonstrated to be a suitable qualitative method for point-of-care testing (POCT). However, analytical instruments like a microplate reader must be needed for the quantitative assay. To overcome its limitation, we herein report a novel photothermal method for foodborne pathogens based on the photothermal effect of aggregated mercaptophenylboronic acid-functionalized AuNPs (MPBA-AuNPs) induced by MPBA to translate the colorimetric detection into a simple temperature measurement using thermometers as the readout. The aggregated AuNPs show higher photothermal conversion efficiency than well-separated AuNPs under 660 nm laser irradiation. In the presence of bacteria, MPBA-AuNPs will attach to the surface of bacteria and keep separated from aggregation induced by excess MPBA, resulting in a lower temperature increase under 660 nm laser irradiation. Using E. coli O157:H7 as a model target, a good linear relationship is observed between temperature increase and bacteria concentration from 1.00 × 10⁵-1.00 × 10⁹ cfu mL^-1 (R² = 0.9877) with a detection limit of 1.97 × 10⁴ cfu mL^-1, which is three orders of magnitude lower than of the MPBA-AuNPs-based colorimetric assays. The proposed photothermal method provided a universal platform for rapid and portable detection of broad-spectrum bacteria strains in real samples.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

A New Strategy for Microbial Taxonomic Identification through Micro-Biosynthetic Gold Nanoparticles and Machine Learning

Microorganisms can serve as biological factories for the synthesis of inorganic nanomaterials that can become useful as nanocatalysts, energy-harvesting-storage components, antibacterial agents, and biomedical materials. Herein, the development of biosynthesis of inorganic nanomaterials into a simple, stable, and accurate strategy for distinguishing microorganisms from multiple classification levels (i.e., kingdom, order, genus, and species) without gene amplification, biochemical testing, or target recognition is reported. Gold nanoparticles (AuNPs) biosynthesized by different microorganisms differ in color of the solution, and their features can be characterized, including the particle size, the surface plasmon resonance (SPR) spectrum, and the surface potential. The inter-relation between the features of micro-biosynthetic AuNPs and the classification of microorganisms are exploited at different levels through machine learning to establish a taxonomic model. This model agrees well with traditional classification methods that offers a new strategy for microbial taxonomic identification. The underlying mechanism of this strategy is related to the biomolecules produced by different microorganisms including glucose, glutathione, and nicotinamide adenine dinucleotide phosphate-dependent reductase that regulate the features of micro-biosynthetic AuNPs. This work broadens the application of biosynthesis of inorganic materials through micro-biosynthetic AuNPs and machine learning, which holds great promise as a tool for biomedical research.

Comparative Evaluation of Plasma Metabolomic Data from Multiple Laboratories

In mass spectrometry-based metabolomics, the differences in the analytical results from different laboratories/machines are an issue to be considered because various types of machines are used in each laboratory. Moreover, the analytical methods are unique to each laboratory. It is important to understand the reality of inter-laboratory differences in metabolomics. Therefore, we have evaluated whether the differences in analytical methods, with the exception sample pretreatment and including metabolite extraction, are involved in the inter-laboratory differences or not. In this study, nine facilities are evaluated for inter-laboratory comparisons of metabolomic analysis. Identical dried samples prepared from human and mouse plasma are distributed to each laboratory, and the metabolites are measured without the pretreatment that is unique to each laboratory. In these measurements, hydrophilic and hydrophobic metabolites are analyzed using 11 and 7 analytical methods, respectively. The metabolomic data acquired at each laboratory are integrated, and the differences in the metabolomic data from the laboratories are evaluated. No substantial difference in the relative quantitative data (human/mouse) for a little less than 50% of the detected metabolites is observed, and the hydrophilic metabolites have fewer differences between the laboratories compared with hydrophobic metabolites. From evaluating selected quantitatively guaranteed metabolites, the proportion of metabolites without the inter-laboratory differences is observed to be slightly high. It is difficult to resolve the inter-laboratory differences in metabolomics because all laboratories cannot prepare the same analytical environments. However, the results from this study indicate that the inter-laboratory differences in metabolomic data are due to measurement and data analysis rather than sample preparation, which will facilitate the understanding of the problems in metabolomics studies involving multiple laboratories.

Metabolomics Provides Novel Insights into Epilepsy Diagnosis and Treatment: A Review

Epilepsy is one of the most common diseases of the central nervous system. The diagnosis of epilepsy mainly depends on electroencephalograms and symptomatology, while diagnostic biofluid markers are still lacking. In addition, approximately 30% of patients with epilepsy (PWE) show a poor response to the currently available anti-seizure medicines. An increasing number of studies have reported alterations in the blood, brain tissue, cerebrospinal fluid and urine metabolome in PWE and animal models of epilepsy. The aim of this review was to identify potential metabolic biomarkers and pathways that might facilitate diagnostic, therapeutic and prognostic determination in PWE and the understanding of the pathogenesis of the disease. The PubMed and Embase databases were searched for metabolomic studies of PWE and epileptic models published before December 2020. The study objectives, types of models and reported differentially altered metabolites were examined and compared. Pathway analyses were performed using MetaboAnalyst 5.0 online software. Thirty-five studies were included in this review. Metabolites such as glutamate, lactate and citrate were disturbed in both PWE and epileptic models, which might be potential biomarkers of epilepsy. Metabolic pathways including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; glycerophospholipid metabolism; glyoxylate and dicarboxylate metabolism; and arginine and proline metabolism were involved in epilepsy. These pathways might play important roles in the pathogenesis of the disease. This review summarizes metabolites and metabolic pathways related to epilepsy and provides a novel perspective for the identification of potential biomarkers and therapeutic targets for epilepsy.

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.

Metabolomics as a Tool to Elucidate the Sensory, Nutritional and Safety Quality of Wheat Bread-A Review

Wheat (Triticum aestivum) is one of the most extensively cultivated and used staple crops in human nutrition, while wheat bread is annually consumed in more than nine billion kilograms over the world. Consumers’ purchase decisions on wheat bread are largely influenced by its nutritional and sensorial characteristics. In the last decades, metabolomics is considered an effective tool for elucidating the information on metabolites; however, the deep investigations on metabolites still remain a difficult and longtime action. This review gives emphasis on the achievements in wheat bread metabolomics by highlighting targeted and untargeted analyses used in this field. The metabolomics approaches are discussed in terms of quality, processing and safety of wheat and bread, while the molecular mechanisms involved in the sensorial and nutritional characteristics of wheat bread are pointed out. These aspects are of crucial importance in the context of new consumers’ demands on healthy bakery products rich in bioactive compounds but, equally, with good sensorial acceptance. Moreover, metabolomics is a potential tool for assessing the changes in nutrient composition from breeding to processing, while monitoring and understanding the transformations of metabolites with bioactive properties, as well as the formation of compounds like toxins during wheat storage.

Recent Advances in Mycotoxin Analysis and Detection of Mycotoxigenic Fungi in Grapes and Derived Products

Mycotoxins are secondary metabolites of filamentous fungi that can cause toxic effects in human and animal health. Most of the filamentous fungi that produce these mycotoxins belong to four genera, namely, Aspergillus, Penicillium, Fusarium, and Alternaria. Mycotoxigenic fungi, along with mycotoxins, create a constant and serious economic threat for agriculture in many terms, counting product losses due to crop contamination and food spoilage, as well malnutrition when considering nutritional quality degradation. Given the importance of robust and precise diagnostics of mycotoxins and the related producing fungi in the grape food chain, one of the most important agricultural sectors worldwide, the present review initially delivers a comprehensive presentation of mycotoxin reports on grape and derived products, including a wide range of commodities such as fresh grapes, raisins, wine, juices, and other processed products. Next, based on worldwide regulations’ requirements for mycotoxins, and referring to the relative literature, this work presents methodological approaches for mycotoxin determination, and stresses major methods for the detection of fungal species responsible for mycotoxin production. The principle of function and basic technical background on the available analytical and molecular biology techniques developed—including chromatography, mass spectrometry, immunochemical-based assays, biosensors, and molecular assays—is briefly given, and references for their application to grape and derived product testing are highlighted.