Search Results (343)

Saponins are valuable health-promoting metabolites. The genus Sechium spp. is a valuable source of such metabolites. Unfortunately, there is no established method for the extraction of saponins from the fruits of this species. Therefore, this research aimed to compare three gravimetric extraction methods for saponins in two Sechium genotypes. The analysis included FT-MIR and HPTLC fingerprinting, as well as spectrophotometric quantification. Independent of the extraction method, bagasse produced higher extraction yields than juice. Among the gravimetric methods, M3 produced the highest yields, while M1 captured the most remarkable diversity and abundance of saponins. The spectrophotometric quantification corroborated the higher total saponin content in bagasse extracts. This data highlights the use of fruit bagasse as the primary source of saponin extraction in Sechium. In addition, we recommend extracting bagasse through M3 for scalable pre-enrichment, while M1 extraction must be used when preserving chemical diversity is critical. Full article

Background: Distinguishing between osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PsA) remains challenging despite different underlying mechanisms. Synovial fluid reflects metabolic changes within affected joints, yet comprehensive metabolomic comparisons across these conditions are limited. We aimed to identify disease-specific metabolic signatures in synovial fluid that could improve differential diagnosis and reveal therapeutic targets. Methods: We collected synovial fluid from 39 patients (20 OA, 5 RA, and 14 PsA) during routine knee arthrocentesis between January 2023 and February 2024. Following metabolite extraction, we performed untargeted metabolomic profiling using quadrupole time-of-flight liquid chromatography–mass spectrometry (Q-TOF LC/MS). Data underwent multivariate statistical analysis, including principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA), to identify discriminatory metabolites. Results: While unsupervised analysis showed overlap between groups, supervised PLS-DA achieved clear metabolic separation. RA samples showed elevated itaconic acid, indicating inflammatory macrophage activation, and increased O-acetylserine, suggesting altered one-carbon metabolism. Hypoxanthine was decreased, which reflected severe metabolic stress. PsA exhibited the unique elevation of 4,4-dimethylcholestane and 2-oxoarginine. These metabolites have previously been unreported in this disease. OA demonstrated increased hippuric acid and indoleacetic acid, which are both gut microbiota products, supporting the gut–joint axis hypothesis. Conclusions: Each arthritis type displayed distinct metabolic fingerprints in synovial fluid. Candidate discriminatory metabolites, including gut-derived metabolites in OA and specific lipid alterations in PsA, open new diagnostic and therapeutic avenues. Given the limited RA sample size (n = 5), RA-related results should be viewed as exploratory and requiring validation in larger independent cohorts. These metabolites may, after rigorous validation in larger and independent cohorts, contribute to multi-metabolite biomarker panels for earlier diagnosis and to the rational design of targeted therapeutics addressing disease-specific metabolic disruptions. Full article

Fungal endophytes have emerged as a promising source of bioactive compounds with potent antifungal properties for plant disease management. This study aimed to isolate and characterize fungal endophytes from Antillean avocado (Persea americana var. americana) trees in the Colombian Caribbean, capable of producing bio-fungicide metabolites against Fusarium solani and Fusarium equiseti. For this, dual culture assays, liquid-state fermentation of endophytic isolates, and metabolite extractions were conducted. From 88 isolates recovered from leaves and roots, those classified within the Diaporthe genus exhibited the most significant antifungal activity. Some of their organic extracts displayed median inhibitory concentrations (IC₅₀) approaching 200 μg/mL. To investigate the mechanism of action, in silico studies targeting chitin synthase (CS) were performed, including homology models of the pathogens’ CS generated using Robetta, followed by molecular docking with Vina and interaction fingerprint similarity analysis of 15 antifungal metabolites produced by Diaporthe species using PROLIF. A consensus scoring strategy identified diaporxanthone A (12) and diaporxanthone B (13) as the most promising candidates, achieving scores up to 0.73 against F. equiseti, comparable to the control Nikkomycin Z (0.82). These results suggest that Antillean avocado endophytes produce bioactive metabolites that may inhibit fungal cell wall synthesis, offering a sustainable alternative for disease management. Full article

NMR spectroscopy enables the study of complex mixtures, including plant extracts. The interpretation of specific ranges of ¹H NMR spectra allows for the determination of polyphenolic compound, sugar, amino acid, and fatty acid profiles. The main goal of ¹H NMR analyses of plant extracts is to identify the unique “fingerprint” of the material being studied. The aim of this study was to determine the metabolomic profile and antioxidant activity of various Lavandula angustifolia (Betty’s Blue, Elizabeth, Hidcote, and Blue Mountain White) and Lavandula × intermedia cultivars (Alba, Grosso, and Gros Bleu) grown in Poland. Modern green chemistry extraction methods (supercritical fluid extraction (SFE) and ultrasound-assisted extraction (UAE)) were used to prepare the lipophilic and hydrophilic extracts, respectively. The secondary metabolite profiles were determined using the diagnostic signals from ¹H NMR and HPLC-DAD analyses. These metabolomic profiles were used to illustrate the differences between the different lavender and lavandin cultivars. The HPLC-DAD analysis revealed that both lavender species have similar polyphenolic profiles but different levels of individual compounds. The extracts from L. angustifolia were characterized by higher phenolic acid and flavonoid contents, while the extracts from L. × intermedia had a higher coumarin content. Diagnostic ¹H NMR signals can be used to verify the authenticity and origin of plant extracts, and identify directions for further research, providing a basis for applications such as in cosmetics. Full article

Background and Rationale: Tinospora crispa (L.) Hook.f. & Thomson (T. crispa) is a climbing medicinal plant with long-standing ethnopharmacological use, particularly in inflammatory and hepatic disorders and cancer-related conditions. There is a knowledge gap regarding how wild versus cultivated ecotypes differ in chemotype, bioactivity, and safety, and how this might support or refine traditional use. Study Objectives: This study aimed to compare wild and cultivated ecotypes of T. crispa from the Nile Delta (Egypt) in terms of quantitative and qualitative phytochemical profiles; selected in vitro biological activities (especially antioxidant and cytotoxic actions); genetic markers potentially associated with metabolic variation; and short-term oral safety in an animal model. Core Methodology: Standardized extraction of plant material from wild and cultivated ecotypes. Determination of total phenolics, total flavonoids, and major phytochemical classes (alkaloids, tannins, terpenoids). Metabolomic characterization using UHPLC-ESI-QTOF-MS, supported by NMR, to confirm key compounds such as berberine, palmatine, chlorogenic acid, rutin, and borapetoside C. In vitro bioassays including: Antioxidant activity (e.g., radical-scavenging assay with EC₅₀ determination). Cytotoxicity against human cancer cell lines, with emphasis on HepG2 hepatoma cells and calculation of IC₅₀ values. Targeted genetic analysis to detect single-nucleotide polymorphisms (SNPs) in the gen1 locus that differentiate ecotypes. A 14-day oral toxicity study in rats, assessing liver and kidney function markers and performing histopathology of liver and kidney tissues. Principal Results: The wild ecotype showed a 43–65% increase in total flavonoid and polyphenol content compared with the cultivated ecotype, as well as substantially higher levels of key alkaloids, particularly berberine (around 12.5 ± 0.8 mg/g), along with elevated chlorogenic acid and borapetoside C. UHPLC-MS and NMR analyses confirmed the identity of the main bioactive constituents and defined a distinct chemical fingerprint for the wild chemotype. Bioassays demonstrated stronger antioxidant activity of the wild extract than the cultivated one and selective cytotoxicity of the wild extract against HepG2 cells (IC₅₀ ≈ 85 µg/mL), being clearly more potent than extracts from cultivated plants. Genetic profiling detected a C → T SNP within the gen1 region that differentiates the wild ecotype and may be linked to altered biosynthetic regulation. The 14-day oral toxicity study (up to 600 mg/kg) revealed no evidence of hepatic or renal toxicity, with biochemical markers remaining within physiological limits and normal liver and kidney histology. Conclusions and Future Perspectives: The wild Nile-Delta ecotype of T. crispa appears to be a stress-adapted chemotype characterized by enriched levels of multiple bioactive metabolites, superior in vitro bioactivity, and an encouraging preliminary safety margin. These findings support further evaluation of wild T. crispa as a candidate source for standardized botanical preparations targeting oxidative stress-related and hepatic pathologies, while emphasizing the need for: More comprehensive in vivo efficacy studies. Cultivation strategies that deliberately maintain or mimic beneficial stress conditions to preserve phytochemical richness. Broader geographical and genetic sampling to assess how generalizable the present chemotypic and bioactivity patterns are across the species. Full article

Background/Objectives: Coleus aromaticus (Lamiaceae), also known as Cuban oregano or Indian borage, is a semi-succulent perennial species widely used in traditional medicine for its therapeutic and nutritional properties. While its essential oils and aromatic fraction have been extensively investigated, the characterization of its non-volatile metabolites remains limited. The aim of this study was to explore the chemical composition of fresh leaves with a focus on the non-volatile fraction. Methods: Fresh leaves of C. aromaticus were cryogenically treated with liquid nitrogen, ground, and subjected to three different extraction procedures: hydroalcoholic maceration, ethyl acetate maceration, and liquid–liquid partitioning to obtain a dichloromethane organic phase and a hydroalcoholic phase. Extracts and fractions were analyzed by HPTLC and HPLC for metabolic profiling. In addition, the Bligh–Dyer method was applied to separate polar and non-polar metabolites, which were subsequently characterized using NMR spectroscopy. Results: Chromatographic analyses highlighted the occurrence and distribution of organic acids, polyphenols (notably flavonoids), and proteinogenic amino acids. Spectroscopic data confirmed the presence of diverse polar and non-polar metabolites, providing a more detailed chemical fingerprint of C. aromaticus. This integrated approach broadened the phytochemical profile of the species beyond the well-documented essential oils. Conclusions: The results contribute to a better understanding of the non-volatile metabolites of C. aromaticus, offering novel insights into its chemical diversity. These findings highlight the potential of this plant as a valuable source of bioactive compounds, supporting its future application in nutraceutical and pharmaceutical research. Full article

Perilla frutescens Britton var. acuta Kudo leaves are widely consumed in East Asia due to their culinary and medicinal properties, which are largely attributed to their high levels of bioactive metabolites such as rosmarinic acid. In this study, we investigated the variation in rosmarinic acid content and overall metabolite profile of P. frutescens leaves collected from six different provinces in Republic of Korea. Quantitative analysis of the rosmarinic acid content was performed using HPLC, revealing significant regional differences, with the highest concentration observed in the leaves collected in Gyeongsangbuk-do and the lowest concentration in those from Jeollanam-do. HRESIMS and ¹H-NMR spectrometry were used to determine the chemical structure of the isolated rosmarinic acid. LC-Q-TOF/MS analysis identified ten major metabolites, including phenolic acids, flavonoids, and triterpenoids. Multivariate statistics (OPLS-DA) revealed distinct clustering of populations, indicating a strong relationship between metabolites and environmental parameters. The distribution of the metabolite fingerprints and rosmarinic acid contents in P. frutescens leaves were also found to differ according to the cultivation region, suggesting that secondary metabolite expression is influenced by environmental and geographic factors. This work shows that metabolome profiles can be used in quality control and the development of high-quality products derived from P. frutescens. Full article

Background/Objectives: Land management practices strongly influence soil biochemical processes, yet conventional soil measurements often overlook dynamic small-molecule variation underlying nutrient cycling and microbial activity. This study aimed to evaluate whether MS¹-based untargeted metabolomics can resolve meaningful biochemical differences among soil systems under distinct land management practices. Methods: Soils from six land-use types—conventional cultivation, organic cultivation, pasture, white pine, tulip poplar, and hardwood forest—were analyzed using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). Multivariate analyses, including PLS-DA, were performed to evaluate metabolic variation across systems. Both identified metabolites and unknown spectral features (MSI Level 4) were assessed, and biosynthetic class assignment of unknown features was performed using NPClassifier. Results: Metabolic features revealed clear separation between land management systems, demonstrating distinct chemical fingerprints across ecosystems. While conventional elemental ratios (e.g., C/N) showed minimal differentiation, phosphorus-related stoichiometric ratios (C/P and N/P) displayed strong land-use-dependent differences. NPClassifier superclasses highlighted unique chemical patterns, with forest soils enriched in diverse secondary metabolites, cultivated soils characterized by simplified profiles, and pasture soils dominated by microbial membrane lipids and alkaloids. Conclusions: Untargeted MS¹-based metabolomics effectively distinguished soil systems under different land-use practices and revealed ecologically meaningful variation even without complete structural identification. This study demonstrates that an MS¹-only workflow leveraging unknown spectral features can robustly distinguish soil systems, underscoring their value in untargeted metabolomics analyses. Full article

Background/Objectives: Natural products containing hydroxyanthracene derivatives (HADs) such as Cascara (Rhamnus purshiana), Frangula (Rhamnus frangula), Rhubarb (Rheum palmatum), and Senna (Cassia angustifolia) have long been used for their laxative properties, but also raise safety concerns due to reported genotoxic and carcinogenic potential. Most studies have focused on quantifying HADs, whereas the broader secondary metabolite landscape of these herbal drugs remains underexplored. We aimed to generate an untargeted metabolomic fingerprint of these four species and to explore their chemical diversity using AI-based structural classification. Methods: Four commercial botanical raw materials were extracted with 60% methanol and analysed by UPLC–HRMS/MS in positive and negative ion modes. Features were processed in Compound Discoverer and annotated by accurate mass and MS/MS matching against spectral databases, then assigned to structural classes using a graph neural network classifier. Multivariate analyses (PCA, HCA) were used to compare metabolic patterns across species. Results: In total, 93, 83, 83 and 51 metabolites were annotated in cascara, frangula, rhubarb, and senna, respectively, spanning flavonoids, anthraquinones, phenylpropanoids and other classes. Only four flavonoids were shared by all species, indicating marked biochemical divergence. Several putatively species-enriched features were observed, including pavine in cascara and frangula, vicenin-2 in senna, and piceatannol in rhubarb. Senna displayed the most distinct metabolic profile, whereas cascara and frangula clustered closely. Conclusions: This work provides a chemistry-centred metabolomic fingerprint of four HAD-containing herbal drugs using graph-based neural networks for natural product classification, supporting future studies on the pharmacological potential, bioavailability and safety of their metabolites. Full article

In the context of the ten-year fishing ban on the Yangtze River, illegal poaching for profit persists. To support the enforcement of this ban and protect the river’s ecosystem, an efficient and precise method for distinguishing between wild and farmed common carp is essential. This study utilized ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) combined with metabolomics technology to analyze and compare the metabolic differences between wild and farmed common carp. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) revealed a clear separation between the two groups, which was further verified by metabolic fingerprint profiles. Moreover, 16 metabolites with high discriminatory potential were identified from 491 differentially metabolites, such as phytosphingosine, succinic acid and threonine. In addition, a cluster analysis of the differential metabolites classified them into four classes: peptides, fatty acyls, steroids and steroid derivatives, and glycerophospholipids. Furthermore, candidate biomarkers, including 3-hydroxybutyrylcarnitine, 3-hydroxyhexanoylcarnitine and jasminoside were identified to potential distinguish wild populations. To our knowledge, this is the first study to apply metabolomics technology to differentiate wild from farmed common carp, providing a new theoretical basis for ecological restoration efforts in the context of the Yangtze River fishing ban. Full article

The Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the co-crystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structure-based modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors. Full article

Cancer metabolomics has become a powerful way of understanding tumor biology, identifying biomarkers and metabolites, and helping precision oncology. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), among many other analytical platforms, has gained popularity over the past two and a half decades due to its unique ability of directly analyzing metabolites in tissue with spatial resolution. This review will study 2000–2025 MALDI-based strategies for cancer metabolite detection, spanning from early proof-of-concept protein profiling to the development of high-resolution MALDI-MS imaging (MALDI-MSI), which is capable of mapping thousands of metabolites at near single-cell resolution. Its applications include the differentiation of tumor versus normal tissue, discovery of stage and subtype specific biomarkers, mapping of metabolic heterogeneity, and the visualization of drug metabolism in situ. Breakthrough technological milestones, such as the advanced matrices, on-tissue derivatization, MALDI-2 post-ionization, and the integration with Orbitrap or Fourier-transform ion cyclotron resonance (FT-ICR) platforms, have significantly improved the overall sensitivity, metabolite coverage, and spatial fidelity. Clinically, MALDI-MS has shown its purpose in breast, prostate, colorectal, lung, and liver cancers by providing metabolic fingerprints that are linked to tumor microenvironments, hypoxia, and therapeutic response. However, challenges such as the inclusion of matrix interface with low-mass metabolites, limited quantitation, ion suppression, and the lack of standardized procedures do not yet allow for the transition from translation to routine diagnostics. Even with these hurdles, the future of MALDI-MS in oncology remains in a good position with major advancements in multimodal imaging, machine learning-based data integration, portable sampling devices, and clinical validation studies that are pushing the field towards precision treatment. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

Hypoxis hemerocallidea (Hypoxidaece) is thoroughly researched and well documented for its plethora of anecdotal and scientifically backed pharmacological potentials. Its anecdotal uses and pharmacological activities are attributed to its extract’s inherent bioactive compounds like hypoxoside, rooperol, and β-sitosterol. This study aimed at conducting a targeted and holistic phytochemical profiling of variations in Hypoxis hemerocallidea (H. hemerocallidea) and related species. The chemotaxonomic classifications of H. hemerocallidea and seven other related species were also carried out to avert the possibility of over harvesting H. hemerocallidea and the encouragement of species inter-change. The plant extracts were analysed with reverse phase ultra-pure liquid chromatography quadrupole time-of-flight mass spectrometry and gas chromatography, as well as high-performance thin-layer chromatography. The generated chromatographic data were made compatible for chemometric computation using Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) models. The results obtained unveil orcinol glycoside, curculigoside C, hypoxoside, dehydroxyhypoxoside, bisdehydroxy hypoxoside, hemerocalloside, galpinoside, cholchicoside, geraniol glycoside, β-sitosterol, oleic acid, and 2-hydroxyethyl linoleate as target phytochemicals that define the profiles of the Hypoxis species. In addition, three distinct chemotypes defined by hemerocalloside, galpinoside, and colchicoside, respectively, were observed, as well as holistic variations in all secondary metabolites. Due to similarities in the phytochemical constituents of selected species, species inter-change seems imminent if further research confirms the findings of this study. Full article

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths—particularly the 923 nm band—showed strong theoretical and statistical relevance to quercetin’s molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Full article