Search Results (1,141)

This protocol paper outlines a robust and reproducible framework for a ¹H nuclear magnetic resonance (NMR) metabolomics analysis of rodent plasma, designed to facilitate preclinical biomarker discovery. The protocol details optimised steps for plasma collection in a preclinical rodent model, sample preparation, and NMR data acquisition using presaturation Carr–Purcell–Meiboom–Gill (PRESAT-CPMG) pulse sequences, ensuring high-quality spectral data and effective suppression of macromolecule signals. Comprehensive spectral processing and metabolite assignment are described, with guidance on multivariate and univariate statistical analyses to identify metabolic changes and potential biomarkers. The framework emphasises methodological rigour and reproducibility, enabling accurate quantification and interpretation of metabolites relevant to disease mechanisms or therapeutic interventions. By providing a standardised approach, this protocol supports longitudinal and translational studies, bridging findings from rodent models to clinical applications and advancing the reliability of metabolomics-based biomarker discovery in preclinical research. Full article

In the coming decades, the agricultural system will predictably rely on organic material to produce crops and maintain food security. Currently, the use of inorganic fertilizers to grow crops and vegetables, such as Swiss chard, spinach, and lettuce, is on the rise and has been proven to be detrimental to the soil in the long run. Hence, there is a growing need to use organic waste material, such as spent coffee grounds (SCGs), to grow crops. Spent coffee grounds are made of depleted coffee beans that contain important soluble compounds. This study aimed to determine the influence of different levels (0.32 g, 0.63 g, 0.92 g, and 1.20 g) of spent coffee grounds on the metabolomic profile of Swiss chard. The 1H-nuclear magnetic resonance (NMR) results showed that Swiss chard grown with different levels of SCGs contains a total of 10 metabolites, which included growth-promoting metabolites (trehalose; betaine), defense mechanism metabolites (alanine; cartinine), energy-reserve metabolites (sucrose; 1,6 Anhydro-β-D-glucose), root metabolites (thymine), stress-related metabolites (2-deoxyadenosine), caffeine metabo-lites (1,3 Dimethylurate), and body-odor metabolites (trimethylamine). Interestingly, caprate, with the abovementioned metabolites, was detected in Swiss chard grown without the application of SCGs. The findings of the current study suggest that SCGs are an ideal organic material for growing Swiss chard for its healthy metabolites. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

During the course of genome mining initiatives, we identified a marine-derived Micromonospora, assigned here as strain WMMD956; the genome of WMMD956 appeared to contain a number of features associated with everninomicins, well-known antimicrobial orthosomycins. In addition, LCMS-based hierarchical clustering analysis and principal component analysis (hcapca) revealed that WMMD956 displayed an extreme degree of metabolomic and genomic novelty. Dereplication of high-resolution tandem mass spectrometry (HRMS/MS) and Global Natural Product Social molecular networking platform (GNPS) analysis of WMMD956 resulted in the identification of several analogs of the previously known everninomicin. Chemical structures were unambiguously confirmed by HR-ESI-MS, 1D and 2D NMR experiments, and the use of MS/MS data. The isolated metabolites, 1–3, were evaluated for their antibacterial activity against methicillin-resistant Staphalococcus aureus (MRSA). Full article

This study investigates the metabolic responses of the model diatom Phaeodactylum tricornutum under different growth conditions, comparing benthic (adherent) and planktonic states. Using a multiblock metabolomics approach combining LC-HRMS², NMR, and GC-MS techniques, we compared the metabolome of P. tricornutum cultivated on three laboratory substrates (glass, polystyrene, and polydimethylsiloxane) and under planktonic conditions. Our results revealed metabolic differences between adherent and planktonic cultures, particularly concerning the lipid and carbohydrate contents. Adherent cultures showed a metabolic profile with an increase in betaine lipids (DGTA/S), fatty acids (tetradecanoic and octadecenoic acids), and sugars (myo-inositol and ribose), suggesting modifications in membrane composition and lipid remodeling, which play a potential role in adhesion. In contrast, planktonic cultures displayed a higher content of cellobiose, specialized metabolites such as dihydroactinidiolide, quinic acid, catechol, and terpenes like phytol, confirming different membrane composition, energy storage capacity, osmoregulation, and stress adaptation. The adaptative strategies do not only concern adherent and planktonic states, but also different adherent culture conditions, with variations in lipid, amino acid, terpene, and carbohydrate contents depending on the physical properties of the support. Our results highlight the importance of metabolic adaptation in adhesion, which could explain the fouling process. Full article

Geographical origin authentication of agrifood products is essential for ensuring their quality, preventing fraud, and maintaining consumers’ trust. In this study, we used proton nuclear magnetic resonance (¹H NMR) and excitation–emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the geographical origin characterization of olive drupes and leaves from different Tuscany subregions, where olive oil production is relevant. Single-block approaches were implemented for individual datasets, using principal component analysis (PCA) for data visualization and Soft Independent Modeling of Class Analogy (SIMCA) for sample classification. ¹H NMR spectroscopy provided detailed metabolomic profiles, identifying key compounds such as polyphenols and organic acids that contribute to geographical differentiation. EEM fluorescence spectroscopy, in combination with Parallel Factor Analysis (PARAFAC), revealed distinctive fluorescence signatures associated with polyphenolic content. A mid-level data fusion strategy, integrating the common dimensions (ComDim) method, was explored to improve the models’ performance. The results demonstrated that both spectroscopic techniques independently provided valuable insights in terms of geographical characterization, while data fusion further improved the model performances, particularly for olive drupes. Notably, this study represents the first attempt to apply EEM fluorescence for the geographical classification of olive drupes and leaves, highlighting its potential as a complementary tool in geographic origin authentication. The integration of advanced spectroscopic and chemometric methods offers a reliable approach for the differentiation of samples from closely related areas at a subregional level. Full article

Background: There is a high demand for long-term postharvest storage of valuable perishables with high-quality preservation and minimal product loss due to decay and physiological disorders. Postharvest low-pressure storage (LPS) provides a viable option for many fruits. While recent studies have presented the details of technology, this pilot study presents the metabolomics changes due to the hypobaric storage of pomegranates as a model system. Methods: Nuclear magnetic resonance (NMR)-based metabolomics studies were performed on pomegranate fruit tissues, comparing fruit stored under LPS conditions versus the traditional storage system, with modified atmosphere packaging (MAP) as the control. The metabolomic changes in the exocarp, mesocarp, and arils were measured using ¹H NMR spectroscopy, and the results were analyzed using multivariate statistics. Results: Distinguishable differences were noted between the MAP and LPS conditions in fruit quality attributes and metabolite profiles. Sucrose levels in the aril, mesocarp, and exocarp samples were higher under LPS, while sucrose levels were reduced in MAP. In addition, alanine levels were more abundant in the mesocarp and exocarp samples, and ethanol concentration decreased in the exocarp samples, albeit less significantly. Conclusions: This pilot investigation shows the potential for using NMR as a valuable assessment tool for monitoring the performance of viable long-term storage conditions in horticultural commodities. Full article

Background/Objective: The hepatitis B virus (HBV) can cause chronic hepatitis B (CHB), which can rapidly progress into fatal liver cirrhosis (CHB-LC) and hepatocellular carcinoma (CHB-HCC). Methods: In this study, we investigated metabolites associated with distinct clinical stages of HBV infection for the identification of stage-specific serum metabolite biomarkers using ¹H-NMR-based metabolomics. Results: A total of 64 serum metabolites were identified, among which six core discriminatory metabolites, namely isoleucine, tryptophan, histamine (for CHB), and pyruvate, TMAO, lactate (for CHB-HCC), were consistently significant across univariate and multivariate statistical analyses, including ANOVA with FDR, OPLS-DA, and VIP scoring. These metabolites were closely linked to key metabolic pathways, such as propanoate metabolism, pyruvate metabolism, and the Warburg effect. Conclusions: The findings suggest that these six core metabolites serve as potential stage-specific biomarkers for CHB, CHB-LC, and CHB-HCC, respectively, and offer a foundation for the future development of metabolomics-based diagnostic and therapeutic strategies. Full article

This study investigated quality changes in seaweed–yak patties before and after freeze–thaw by varying seaweed addition levels (10–70%). Macroscopically, the effects on water-holding capacity, textural properties, and oxidative indices of restructured yak patties were evaluated. Microscopically, the impact of seaweed-derived bioactive ingredients on patty microstructure and myofibrillar protein characteristics was examined. LF-NMR and MRI showed that 40% seaweed addition most effectively restricted water migration, reduced thawing loss, and preserved immobilized water content. Texture profile analysis (TPA) revealed that moderate seaweed levels (30–40%) enhanced springiness and minimized post-thaw hardness increases. SEM confirmed that algal polysaccharides formed a denser protective network around the muscle fibers. Lipid oxidation (MDA), free-radical measurements, and non-targeted metabolomics revealed a significant reduction in oxidative damage at 40% seaweed addition, correlating with increased total phenolic content. Protein analyses (particle size, zeta potential, hydrophobicity, and SDS-PAGE) demonstrated a cryoprotective effect of seaweed on myofibrillar proteins, reducing aggregation and denaturation. These findings suggest that approximately 40% seaweed addition can improve the physicochemical stability and antioxidant capacity of frozen seaweed–yak meat products. This work thus identifies the optimal seaweed addition level for enhancing freeze–thaw stability and functional quality, offering practical guidance for the development of healthier, high-value restructured meat products. Full article

Wild ales represent a diverse category of spontaneously fermented beers, influenced by complex microbial populations and variable ingredients. This study employed an integrated metabolomic profiling approach combining proton nuclear magnetic resonance (¹H NMR) spectroscopy, liquid chromatography–mass spectrometry (LC-MS), and spectrophotometric assays (DPPH and FRAP) to characterize the molecular composition and antioxidant potential of 22 wild ales from six countries. A total of 53 compounds were identified and quantified using NMR, while 62 compounds were identified by using LC-MS. The compounds in question included organic acids, amino acids, sugars, alcohols, bitter acids, phenolic compounds, and others. Ingredient-based clustering revealed that the addition of dark fruits resulted in a significant increase in the polyphenolic content and antioxidant activity. Concurrently, herb-infused and light-fruit beers exhibited divergent phytochemical profiles. Prolonged aging (>18 months) has been demonstrated to be associated with increased levels of certain amino acids, fermentation-derived aldehydes, and phenolic degradation products. However, the influence of maturation duration on the antioxidant capacity was found to be less significant than that of the type of fruit. Country-specific metabolite trends were revealed, indicating the influence of regional brewing practices on beer composition. Correlation analysis was employed to identify the major contributors to antioxidant activity, with salicylic, dihydroxybenzoic, and 4-hydroxybenzoic acids being identified as the most significant. These findings underscore the biochemical intricacy of wild ales and exemplify metabolomics’ capacity to correlate compositional variation with functionality and authenticity in spontaneously fermented beverages. Full article

Background: Fish escape events from aquaculture facilities are increasing and pose significant ecological, economic, and traceability concerns. Accurate methods to differentiate between wild, cultured, and escaped fish are essential for fishery management and seafood authentication. Methods: This study analyzed muscle tissue from Sparus aurata, Dicentrarchus labrax, and Argyrosomus regius using a multiomics approach. Heavy metals were quantified by ICP-MS, fatty acid profiles were assessed via GC-MS, and metabolomic and lipidomic signatures were identified using 1H NMR spectroscopy. Multivariate statistical models (MDS and PLS-LDA) were applied to classify fish origins. Results: Wild seabream showed significantly higher levels of arsenic (9.5-fold), selenium (3.5-fold), and DHA and ARA fatty acids (3.2-fold), while cultured fish exhibited increased linoleic and linolenic acids (6.5-fold). TMAO concentrations were up to 5.3-fold higher in wild fish, serving as a robust metabolic biomarker. Escaped fish displayed intermediate biochemical profiles. Multivariate models achieved a 100% classification accuracy across species and analytical techniques. Conclusions: The integration of heavy metal analysis, fatty acid profiling, and NMR-based metabolomics enables the accurate differentiation of fish origin. While muscle tissue provides reliable biomarkers relevant to human exposure, future studies should explore additional tissues such as liver and gills to improve the resolution of traceability. These methods support seafood authentication, enhance aquaculture traceability, and aid in managing the ecological impacts of escape events. Full article

Ovarian cancer (OC), the third most common gynecologic malignancy, exhibits distinct metabolic alterations that could enable early detection via liquid biopsy. We developed an advanced machine learning pipeline integrating lipidomics (HPLC-MS, positive/negative ion modes) and NMR-based metabolomics to analyze plasma samples from 229 subjects, including 103 serous OC patients, 107 benign cases, and 19 healthy controls. By systematically evaluating feature selection methods and machine learning architectures, we identified optimal biomarker combinations for OC detection. Convolutional Neural Network (CNN) model based on Mann–Whitney-selected features demonstrated strong discriminatory power (81% accuracy) in distinguishing malignant from benign cases, while Extreme Gradient Boosting (XGBoost) combined with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) achieved exceptional performance (96% accuracy) in differentiating benign from control samples. For multiclass classification, XGBoost with Kruskal–Wallis-selected features achieved 77% accuracy, while one-versus-one CNN models utilizing Mann–Whitney-selected features attained 78% accuracy, demonstrating optimal performance among tested approaches. The complementary strengths of deep learning and ensemble methods underscore their potential for tailored diagnostic applications. While clinical implementation requires further standardization, these findings provide both a methodological framework for metabolic biomarker discovery and biological insights into OC pathophysiology, paving the way for integrated multi-omics approaches in gynecologic oncology. Full article

Background: Exfoliation glaucoma (XFG) represents a form of deleterious ocular aging of unclear etiology. We evaluated prediagnostic nuclear magnetic resonance (NMR)-based metabolites in relation to XFG risk, expanding on our prior findings of XFG-related metabotypes using liquid chromatography-mass spectrometry (LC-MS). Methods: We identified 217 XFG cases and 217 matched controls nested within three prospective health professional cohorts with plasma collected a mean 11.8 years before case identification. Plasma metabolites were analyzed using the targeted NMR Nightingale platform. Conditional logistic models and Metabolite Set Enrichment Analysis were performed. Multiple comparison issues were addressed using the number of effective tests (NEF) and false discovery rate (FDR). Results: Among 235 profiled metabolites, higher glucose was significantly associated with a lower risk of XFG (odds ratio (95%CI) = 0.42 (0.26, 0.7); NEF = 0.03). Among metabolite classes, lipoprotein subclasses and branched-chain amino acids were inversely associated, while relative lipoprotein lipid concentrations were adversely associated (FDR < 0.05). Conclusion: NMR profiling revealed that glucose, branched-chain amino acids, lipoprotein subclasses, and relative lipoprotein lipid concentrations may play important roles in XFG etiology. Full article

Background/Objectives: Thousands of nephrectomies are performed annually in the United States, but the short-term metabolic effects of surgically induced renal ischemia remain unclear. The conventional metabolic markers used to characterize post-surgical renal function, such as creatinine and GFR, are measured in the serum but do not provide metabolic information about the renal parenchyma itself. We aimed to characterize the immediate metabolic effects of surgical ischemia on renal parenchyma within a temporal framework. Methods: Timed renal parenchyma biopsies were collected from eight patients undergoing nephrectomy for renal cell carcinoma both prior to and after ligation of the renal hilum. These samples were ground, extracted, and analyzed using nuclear magnetic resonance (NMR) spectroscopy to measure changes in lactate, succinate, glucose, alanine, and glycine levels. Results: Due to experimental limitations, we were only able to draw limited conclusions from three patients. Of the five remaining patients, all had significant increases in lactate and succinate levels as a function of time, though the degree to which these increases occurred varied between each patient. Glucose levels generally decreased in the renal parenchyma but did not necessarily correlate with lactate production, assuming all glucose underwent fermentation to lactate in a hypoxic environment. Alanine and glycine levels did not change in a predictable pattern across patients. Conclusions: There are significant changes in lactate, glucose and succinate levels within minutes of the onset of renal ischemia in human patients. The degree of change in the metabolites analyzed varied significantly between patients. The length of surgical ischemia must be considered during surgical procurement of tumor specimens for metabolomic analysis. Full article

Leukoplakia is a condition marked by white patches on the inner surfaces of the oral cavity. Its potential to progress to oral squamous cell carcinoma underscores the need for effective screening and early diagnosis procedures. We employed NMR-based salivary and tissue metabolomics to identify potential biomarkers for leukoplakia and dysplastic leukoplakia. Univariate and multivariate methods were used to evaluate the NMR-derived metabolite concentrations. The salivary metabolite profile of leukoplakia exhibited specific alterations compared to healthy controls. These metabolic changes were more pronounced in cases of dysplastic lesions. Multivariate ROC curve analysis, based on a selection of salivary metabolites, ascribed high diagnostic accuracy to the models that discriminate between dysplastic and healthy cases. However, NMR analysis of tissue biopsies was ineffective in extracting metabolic signatures to differentiate between lesional, peri-lesional, and healthy tissues. Our pilot study employing a metabolomics-based approach led to the development of salivary models that represent a complementary strategy for clinically detecting leukoplakia. However, larger-scale validation is required to fully evaluate their diagnostic potential and to effectively stratify leukoplakia patients according to dysplasia status. Full article