Search Results (5,672)

A comprehensive metabolic profiling of Catharanthus roseus (L.) G. Don was performed using tandem mass spectrometry, along with an evaluation of the biological activities of its various solvent extracts. Among these, the methanolic leaf extract exhibited mild radical scavenging activity, low to moderate antimicrobial activity, and limited cytotoxicity in both the brine shrimp lethality assay and MTT assay against HeLa and A549 cell lines. High-performance liquid chromatography–electrospray ionization–high-resolution tandem mass spectrometry (HPLC-ESI-HRMS/MS) analysis led to the annotation of 34 metabolites, primarily alkaloids. These included 23 indole alkaloids, two fatty acids, two pentacyclic triterpenoids, one amino acid, four porphyrin derivatives, one glyceride, and one chlorin derivative. Notably, two metabolites—2,3-dihydroxypropyl 9,12,15-octadecatrienoate and (10S)-hydroxypheophorbide A—were identified for the first time in C. roseus. Furthermore, Global Natural Products Social Molecular Networking (GNPS) analysis revealed 18 additional metabolites, including epoxypheophorbide A, 11,12-dehydroursolic acid lactone, and 20-isocatharanthine. These findings highlight the diverse secondary metabolite profile of C. roseus and support its potential as a source of bioactive compounds for therapeutic development. Full article

Lotus (Nelumbo nucifera Gaertn.) is a quintessential medicinal and edible plant, exhibiting marked differences in therapeutic effects among its various parts. The lotus seed constitutes a key component of this plant. Notably, the entire seed and the plumule display distinct medicinal properties. To investigate the “homologous plants with different effects” phenomenon in traditional Chinese medicine, this study established a Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) method. This study employed immature lotus seeds as the experimental material, diverging from the mature seeds conventionally used. Conductive double-sided tape was employed for sample preparation, and complete longitudinal sections of the seeds were obtained, followed by MALDI-MSI analysis to identify and visualize the spatial distribution of characteristic secondary metabolites within the entire seeds. The results unveiled the diversity of metabolites in lotus seeds and their differential distribution across tissues, with pronounced distinctions in the plumule. A total of 152 metabolites spanning 13 categories were identified in lotus seeds, with 134, 89, 51, and 98 metabolites discerned in the pericarp, seed coat, cotyledon, and plumule, respectively. Strikingly, young lotus seeds were devoid of liensinine/isoliensinine and neferine, the dominant alkaloids of mature lotus seed plumule, revealing an early-stage alkaloid profile that sharply contrasts with the well-documented abundance found in mature seeds and has rarely been reported. We further propose a biosynthetic pathway to explain the presence of the detected benzylisoquinoline and the absence of the undetected bisbenzylisoquinoline alkaloids in this study. These findings present the first comprehensive metabolic atlas of immature lotus seeds, systematically exposing the pronounced chemical divergence from their mature counterparts, and thus lays a metabolomic foundation for dissecting the spatiotemporal mechanisms underlying the nutritional and medicinal value of lotus seeds. Full article

Background: Type 2 diabetes (T2D) is a global health epidemic with rising prevalence within Asian populations, particularly amongst individuals with high visceral adiposity and ectopic organ fat, the so-called Thin-Outside, Fat-Inside phenotype. Metabolomic and microbiome shifts may herald T2D onset, presenting potential biomarkers and mechanistic insight into metabolic dysregulation. However, multi-omics datasets across ethnicities remain limited. Methods: We performed cross-sectional multi-omics analyses on 171 adults (99 Asian Chinese, 72 European Caucasian) from the New Zealand-based TOFI_Asia cohort at 4-years follow-up. Paired plasma and faecal samples were analysed using untargeted metabolomic profiling (polar/lipid fractions) and shotgun metagenomic sequencing, respectively. Sparse multi-block partial least squares regression and discriminant analysis (DIABLO) unveiled signatures associated with ethnicity, glycaemic status, and sex. Results: Ethnicity-based DIABLO modelling achieved a balanced error rate of 0.22, correctly classifying 76.54% of test samples. Polar metabolites had the highest discriminatory power (AUC = 0.96), with trigonelline enriched in European Caucasians and carnitine in Asian Chinese. Lipid profiles highlighted ethnicity-specific signatures: Asian Chinese showed enrichment of polyunsaturated triglycerides (TG.16:0_18:2_22:6, TG.18:1_18:2_22:6) and ether-linked phospholipids, while European Caucasians exhibited higher levels of saturated species (TG.16:0_16:0_14:1, TG.15:0_15:0_17:1). The bacteria Bifidobacterium pseudocatenulatum, Erysipelatoclostridium ramosum, and Enterocloster bolteae characterised Asian Chinese participants, while Oscillibacter sp. and Clostridium innocuum characterised European Caucasians. Cross-omic correlations highlighted negative correlations of Phocaeicola vulgatus with amino acids (r = −0.84 to −0.76), while E. ramosum and C. innocuum positively correlated with long-chain triglycerides (r = 0.55–0.62). Conclusions: Ethnicity drove robust multi-omic differentiation, revealing distinctive metabolic and microbial profiles potentially underlying the differential T2D risk between Asian Chinese and European Caucasians. Full article

Powdery mildew (PM), caused by Sphaerotheca phaseoli, severely threatens mungbean (Vigna radiata) productivity and quality, yet the molecular basis of resistance remains poorly defined. This study employed transcriptome profiling to compare defense responses in a resistant genotype, SUPER5, and a susceptible variety, CN84-1, following pathogen infection. A total of 1755 differentially expressed genes (DEGs) were identified, with SUPER5 exhibiting strong upregulation of genes encoding pathogenesis-related (PR) proteins, disease resistance proteins, and key transcription factors. Notably, genes involved in phenylpropanoid and flavonoid biosynthesis, pathways associated with antimicrobial compound and lignin production, were markedly induced in SUPER5. In contrast, CN84-1 showed limited activation of defense genes and downregulation of essential regulators such as MYB14. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted the involvement of plant–pathogen interaction pathways, MAPK signaling, and reactive oxygen species (ROS) detoxification in the resistant response. Quantitative real-time PCR validated 11 candidate genes, including PAL3, PR2, GSO1, MLO12, and P21, which function in pathogen recognition, signaling, the biosynthesis of antimicrobial metabolites, the production of defense proteins, defense regulation, and the reinforcement of the cell wall. Co-expression network analysis revealed three major gene modules linked to flavonoid metabolism, chitinase activity, and responses to both abiotic and biotic stresses. These findings offer valuable molecular insights for breeding PM-resistant mungbean varieties. Full article

Background: Charcot–Marie–Tooth (CMT) is a group of inherited diseases impairing the peripheral nervous system. CMT originates from genetic variants that affect proteins fundamental for the myelination of peripheral nerves and survival. Moreover, environmental and humoral factors can impact disease development and evolution. Currently, no therapy is available. Metabolomics is an emerging field of biomedical research that enables the development of novel biomarkers for neurodegenerative diseases by targeting metabolic pathways or metabolites. This study aimed to evaluate the metabolomics profile of CMT disease by comparing patients with healthy individuals. Methods: A total of 22 CMT patients (CMT) were included in this study and were demographically matched with 26 healthy individuals (C). Serum samples were analyzed through Nuclear Magnetic Resonance spectroscopy, and multivariate and univariate statistical analyses were subsequently applied. Results: A supervised model showed a clear separation (R²X = 0.3; R²Y = 0.7; Q² = 0.4; p-value = 0.0004) between the two classes of subjects, and nine metabolites were found to be significantly different (2-hydroxybutyrate, 3-hydroxybutyrate, 3-methyl-2-oxovalerate, choline, citrate, glutamate, isoleucine, lysine, and methyl succinate). The combined ROC curve showed an AUC of 0.94 (CI: 0.9–1). Additional altered metabolic pathways were also identified within the disease context. Conclusion: This study represents a promising starting point, demonstrating the efficacy of metabolomics in evaluating CMT patients and identifying novel potential disease biomarkers. Full article

Background/Objectives: Nitrogen (N) is an essential macronutrient that strongly influences maize growth and metabolism. While many studies have focused on nitrogen responses during later developmental stages, early-stage physiological and metabolic responses remain less explored. This study investigated the effect of different nitrogen-deficient levels on maize seedling growth and primary metabolite profiles. Methods: Seedlings were treated with N-modified nutrient solution, which contained 0% to 120% of the standard nitrogen level (8.5 mM). Results: Nitrogen starvation (N0) significantly reduced plant height (by 11–14%), shoot fresh weight (over 30%) compared to the optimal N supply (N100). Total leaf nitrogen content under N0–N20 was less than half of that in N100, whereas moderate N deficiency resulted in moderate reductions in growth and nitrogen content. Metabolite analysis revealed that N deficiency induced the accumulation of soluble sugars and organic acids (up to threefold), while sufficient N promoted the synthesis of amino acids related to nitrogen assimilation and protein biosynthesis. Statistical analyses (PCA and ANOVA) showed that both genotypes (MB and TYC) and tissue type (upper vs. lower leaves) influenced the metabolic response to nitrogen, with MB displaying more consistent shifts and TYC exhibiting greater variability under moderate stress. Conclusions: These findings highlight the sensitivity of maize seedlings to early nitrogen deficiency, with severity influenced by nitrogen level, tissue-specific position, and genotype; thus underscore the close coordination between physiological growth and primary metabolic pathways in response to nitrogen availability. These findings expand current knowledge of nitrogen response mechanisms and offer practical insights for improving nitrogen use efficiency in maize cultivation. Full article

Plant genetics and chemotaxonomic analysis are considered key parameters in understanding evolution, plant diversity and adaptation. Korean Peninsula has a unique biogeographical landscape that supports various aromatic plant species, each with considerable ecological, ethnobotanical, and pharmacological significance. This review aims to provide a comprehensive overview of the chemotaxonomic traits, biological activities, phylogenetic relationships and potential applications of Korean aromatic plants, highlighting their significance in more accurate identification. Chemotaxonomic investigations employing techniques such as gas chromatography mass spectrometry, high-performance liquid chromatography, and nuclear magnetic resonance spectroscopy have enabled the identification of essential oils and specialized metabolites that serve as valuable taxonomic and diagnostic markers. These chemical traits play essential roles in species delimitation and in clarifying interspecific variation. The biological activities of selected taxa are reviewed, with emphasis on antimicrobial, antioxidant, anti-inflammatory, and cytotoxic effects, supported by bioassay-guided fractionation and compound isolation. In parallel, recent advances in phylogenetic reconstruction employing DNA barcoding, internal transcribed spacer regions, and chloroplast genes such as rbcL and matK are examined for their role in clarifying taxonomic uncertainties and inferring evolutionary lineages. Overall, the search period was from year 2001 to 2025 and total of 268 records were included in the study. By integrating phytochemical profiling, pharmacological evidence, and molecular systematics, this review highlights the multifaceted significance of Korean endemic aromatic plants. The conclusion highlights the importance of multidisciplinary approaches including metabolomics and phylogenomics in advancing our understanding of species diversity, evolutionary adaptation, and potential applications. Future research directions are proposed to support conservation efforts. 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

Rhubarb, a medicinal herb in Gansu Province, China, undergoes significant quality changes during sun-drying. This study investigated color changes, drying kinetics, anthraquinone (AQ) content, metabolic profiles, and enzyme activity during the process. Results showed that drying induced enzymatic browning, with the browning index (BI) progressively increasing over extended drying periods (4–16 h) and with greater slice thickness (2–8 mm). Catalase (CAT) activity first decreased and then increased, while polyphenol oxidase (PPO) activity decreased throughout drying. Slice thickness significantly affected AQ content, with the highest in 2 mm slices and the lowest in 4 mm slices. The drying process followed a logarithmic model (R² = 0.99418, RMSE = 0.02310, and χ² = 0.0005). Metabolomics analysis identified 631 differential metabolites, with 8 key metabolites linked to flavonoid biosynthesis, phenylalanine biosynthesis, and tyrosine metabolism. Fifteen enzymes were involved in metabolite synthesis and decomposition, though some enzyme activity trends contradicted metabolite changes. This study provides insight into rhubarb drying mechanisms and a basis for optimizing the drying process. Full article

Mitochondria, pivotal organelles in cellular metabolism and energy production, have emerged as critical players in the pathogenesis of cancer. This review outlines the progress in mitochondrial profiling through mass spectrometry-based metabolomics and its applications in cancer research. We provide unprecedented insights into the mitochondrial metabolic rewiring that fuels tumorigenesis, metastasis, and therapeutic resistance. The purpose of this review is to provide a comprehensive guide for the implementation of mitochondrial metabolomics, integrating advanced methodologies—including isolation, detection, and data integration—with insights into cancer-specific metabolic rewiring. We first summarize current methodologies for mitochondrial sample collection and pretreatment. Furthermore, we then discuss the recent advancements in mass spectrometry-based methodologies that facilitate the detailed profiling of mitochondrial metabolites, unveiling significant metabolic reprogramming associated with tumorigenesis. We emphasize how recent technological advancements have addressed longstanding challenges in the field and explore the role of mitochondrial metabolism-driven cancer development and progression for novel drug discovery and translational research applications in cancer. Collectively, this review delineates emerging opportunities for therapeutic discovery and aims to establish a foundation for future investigations into the therapeutic modulation of mitochondrial pathways in cancer, thereby paving the way for innovative diagnostic and therapeutic approaches targeting mitochondrial pathways. Full article

Background: Biostimulants naturally improve plant growth, stress tolerance, and nutrient use efficiency and activate defenses by increasing protective metabolites (phenols, anthocyanins) in grapes. In viticulture, especially when using inactive yeasts, they modulate genetic expression and improve the skin resistance, color, and aroma profile of wine grapes in line with sustainable practices. Methods: Two wine grape cultivars, Merlot and Cabernet Sauvignon, were sprayed with the inactive yeast Saccharomyces cerevisiae in a single treatment in pre-veraison or in a double treatment in pre-veraison and veraison. Berry weight, must, total polyphenols, anthocyanins, and mechanical and colorimetric properties were measured on fresh grapes. Results: Two-way ANOVA revealed that titratable acidity (TA), pH, and total polyphenol content (TPC) were not affected, while mean berry weight and anthocyanin content varied by cultivar, treatment, and interaction; total soluble solids (TSS) differed only by cultivar. Inactive yeasts reduced weight in the single-treatment thesis but stabilized it in the double-treatment one; anthocyanins decreased in Cabernet Sauvignon but increased in Merlot. Mechanical and colorimetric analyses showed cultivar-dependent responses, with significant improvements in elasticity, skin thickness, and hue of berries, especially in Merlot when the treatment was applied twice. Conclusions: Inactive yeasts (IYs) showed an effect on the weight of the berries, the anthocyanins, the mechanics, and the color; Merlot significantly improved skin thickness, elasticity, and hue; and Cabernet remained less reactive to treatments. 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

Background: Citrinin (CIT) is a mycotoxin produced by various fungi contaminating stored cereals and fruits. While biomonitoring and food occurrence data indicate widespread exposure, its public health risks remain unclear due to the lack of human toxicokinetic (TK) data. Methods: A UHPLC-MS/MS method was validated for CIT quantification in capillary blood (VAMS Mitra^® tips), feces, and urine obtaining LLOQs ≤ 0.05 ng/mL. A human TK study was conducted following a single oral bolus of 200 ng/kg bw CIT. Individual capillary blood (VAMS Mitra^® tips), feces, and urine samples were collected for 48 h after exposure. Samples were analyzed to determine CIT’s TK profile. Results: TK modeling was performed using a multi-compartmental structure with a hierarchical Bayesian population approach, allowing robust parameter estimation despite the lack of standards for CIT metabolites. Conclusions: The derived TK parameters align with preliminary human data and significantly advance CIT exposure assessment via biomonitoring. A human inter-individual toxicokinetic variability (HK_AF) of 1.92 was calculated based on the derived AUC, indicating that EFSA’s current default uncertainty factor for TK variability is adequately protective for at least 95% of the population. Full article

Background/Objectives: Hepatic cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are major global health concerns due to rising incidence and limited therapeutic success. While traditional risk factors include chronic liver disease and environmental exposures, recent evidence underscores the significance of genetic alterations and gut microbiota in liver cancer development and progression. This review aims to integrate emerging knowledge on the interplay between host genomic changes and gut microbial dynamics in the pathogenesis and treatment of hepatic cancers. Methods: We conducted a comprehensive review of current literature on genetic and epigenetic drivers of HCC and CCA, focusing on commonly mutated genes such as TP53, CTNNB1, TERT, IDH1/2, and FGFR2. In parallel, we evaluated studies addressing the gut–liver axis, including the roles of dysbiosis, microbial metabolites, and immune modulation. Key clinical and preclinical findings were synthesized to explore how host–microbe interactions influence tumorigenesis and therapeutic response. Results: HCC and CCA exhibit distinct but overlapping genomic landscapes marked by recurrent mutations and epigenetic reprogramming. Alterations in the gut microbiota contribute to hepatic inflammation, genomic instability, and immune evasion, potentially enhancing oncogenic signaling pathways. Furthermore, microbiota composition appears to affect responses to immune checkpoint inhibitors. Emerging therapeutic strategies such as probiotics, fecal microbiota transplantation, and precision oncology based on mutational profiling demonstrate potential for personalized interventions. Conclusions: The integration of host genomics with microbial ecology provides a promising paradigm for advancing diagnostics and therapies in liver cancer. Targeting the gut–liver axis may complement genome-informed strategies to improve outcomes for patients with HCC and CCA. 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