Search Results (2,490)

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article

Male infertility is a multifactorial condition often associated with disruptions in sperm metabolism and mitochondrial function, yet traditional semen analysis provides limited insight into these molecular mechanisms. Understanding sperm bioenergetics and metabolic dysfunctions is crucial for improving the diagnosis and treatment of conditions such as asthenozoospermia and azoospermia. This systematic review synthesizes recent literature, focusing on advanced tools and techniques—including omics technologies, advanced imaging, spectroscopy, and functional assays—that enable comprehensive molecular assessment of sperm metabolism and development. The reviewed studies highlight the effectiveness of metabolomics, proteomics, and transcriptomics in identifying metabolic biomarkers linked to male infertility. Non-invasive imaging modalities such as Raman and magnetic resonance spectroscopy offer real-time metabolic profiling, while the seminal microbiome is increasingly recognized for its role in modulating sperm metabolic health. Despite these advances, challenges remain in clinical validation and implementation of these techniques in routine infertility diagnostics. Integrating molecular metabolic assessments with conventional semen analysis promises enhanced diagnostic precision and personalized therapeutic approaches, ultimately improving reproductive outcomes. Continued research is needed to standardize biomarkers and validate clinical utility. Furthermore, these metabolic tools hold significant potential to elucidate the underlying causes of previously misunderstood and unexplained infertility cases, offering new avenues for diagnosis and treatment. Full article

Cancer is a systemic disease rather than a localized pathology and is characterized by widespread effects, including whole-body exhaustion and chronic inflammation. A thorough understanding of cancer pathophysiology requires a systemic approach that accounts for the complex interactions between cancer cells and host tissues. To explore these dynamics, we employed a comprehensive metabolomic analysis of plasma samples from patients with either esophageal or head and neck squamous cell carcinoma (SCC). Plasma samples from 149 patients were metabolically profiled and correlated with clinical data. Among the metabolites identified, lysophosphatidylcholine (LPC) emerged as the sole biomarker strongly correlated with prognosis. A significant reduction in plasma LPC levels was linked to poorer overall survival. Plasma LPC levels demonstrated minimal correlation with patient-specific factors, such as tumor size and general condition, but showed significant association with the response to immune checkpoint inhibitor therapy. Proteomic and cytokine analyses revealed that low plasma LPC levels reflected systemic chronic inflammation, characterized by high levels of inflammatory proteins, the cytokines interleukin-6 and tumor necrosis factor-α, and coagulation-related proteins. These findings indicate that plasma LPC levels may be used as reliable biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in patients with SCC. Full article

Background/Objective: Pharmacological interventions often exert systemic effects beyond their primary targets, underscoring the need for a comprehensive evaluation of their metabolic impact. Etodolac is a nonsteroidal anti-inflammatory drug (NSAID) that alleviates pain, fever, and inflammation by inhibiting cyclooxygenase-2 (COX-2), thereby reducing prostaglandin synthesis. While its pharmacological effects are well known, the broader metabolic impact and potential mechanisms underlying improved clinical outcomes remain underexplored. Untargeted metabolomics, which profiles the metabolome without prior selection, is an emerging tool in clinical pharmacology for elucidating drug-induced metabolic changes. In this study, untargeted metabolomics was applied to investigate metabolic changes following a single oral dose of etodolac in healthy male volunteers. By analyzing serial blood samples over time, we identified endogenous metabolites whose concentrations were positively or inversely associated with the drug’s plasma levels. This approach provides a window into both therapeutic pathways and potential off-target effects, offering a promising strategy for early-stage drug evaluation and multi-target discovery using minimal human exposure. Methods: Thirty healthy participants received a 400 mg dose of Etodolac. Plasma samples were collected at five time points: pre-dose, before Cmax, at Cmax, after Cmax, and 36 h post-dose (n = 150). Samples underwent LC/MS-based untargeted metabolomics profiling and pharmacokinetic analysis. A total of 997 metabolites were significantly dysregulated between the pre-dose and Cmax time points, with 875 upregulated and 122 downregulated. Among these, 80 human endogenous metabolites were identified as being influenced by Etodolac. Results: A total of 17 metabolites exhibited time-dependent changes closely aligned with Etodolac’s pharmacokinetic profile, while 27 displayed inverse trends. Conclusions: Etodolac influences various metabolic pathways, including arachidonic acid metabolism, sphingolipid metabolism, and the biosynthesis of unsaturated fatty acids. These selective metabolic alterations complement its COX-2 inhibition and may contribute to its anti-inflammatory effects. This study provides new insights into Etodolac’s metabolic impact under healthy conditions and may inform future therapeutic strategies targeting inflammation. Full article

Background: Phoebe zhennan and Phoebe chekiangensis are valuable evergreen trees recognized for their unique aromas and ecological significance, yet the organ-related distribution and functional implications of aroma-active volatiles remain insufficiently characterized. Methods: In this study, we applied an integrated GC-MS-based volatile metabolomics approach combined with a relative odor activity value (rOAV) analysis to comprehensively profile and compare the volatile metabolite landscape in the seeds and leaves of both species. Results: In total, 1666 volatile compounds were putatively identified, of which 540 were inferred as key aroma-active contributors based on the rOAV analysis. A multivariate statistical analysis revealed clear tissue-related separation: the seeds were enriched in sweet, floral, and fruity volatiles, whereas the leaves contained higher levels of green leaf volatiles and terpenoids associated with ecological defense. KEGG pathway enrichment indicated that terpenoid backbone and phenylpropanoid biosynthesis pathways played major roles in shaping these divergent profiles. A Venn diagram analysis further uncovered core and unique volatiles underlying species and tissue specificity. Conclusions: These insights provide an integrated reference for understanding tissue-divergent volatile profiles in Phoebe species and offer a basis for fragrance-oriented selection, ecological trait evaluation, and the sustainable utilization of organ-related metabolic characteristics in breeding and conservation programs. Full article

Polyvinyl chloride (PVC) is commonly employed as mulch in agriculture to boost crop yields. However, its toxicity is often overlooked. Due to its chemical stability, resistance to degradation, and the inadequacy of the recycling system, PVC tends to persist in farm environments, where it can decompose into microplastics (MPs) or nanoplastics (NPs). The radish (Raphanus sativus L.) was chosen as the model plant for this study to evaluate the underlying toxic mechanisms of PVC NPs on seedling growth through the integration of multi-omics approaches with oxidative stress evaluations. The results indicated that, compared with the control group, the shoot lengths in the 5 mg/L and 150 mg/L treatment groups decreased by 33.7% and 18.0%, respectively, and the root lengths decreased by 28.3% and 11.3%, respectively. However, there was no observable effect on seed germination rates. Except for the peroxidase (POD) activity in the 150 mg/L group, all antioxidant enzyme activities and malondialdehyde (MDA) levels were higher in the treated root tips than in the control group. Both transcriptome and metabolomic analysis profiles showed 2075 and 4635 differentially expressed genes (DEGs) in the high- and low-concentration groups, respectively, and 1961 metabolites under each treatment. PVC NPs predominantly influenced seedling growth by interfering with plant hormone signaling pathways and phenylpropanoid production. Notably, the reported toxicity was more evident at lower concentrations. This can be accounted for by the plant’s “growth-defense trade-off” strategy and the manner in which nanoparticles aggregate. By clarifying how PVC NPs coordinately regulate plant stress responses via hormone signaling and phenylpropanoid biosynthesis pathways, this research offers a scientific basis for assessing environmental concerns related to nanoplastics in agricultural systems. Full article

Background: Nutrition is of paramount importance during early development, since suboptimal growth in this period of life is linked to adverse long- and mid-term outcomes. This is particularly relevant for preterm infants, who fail to thrive during the first weeks of life and develop extrauterine growth restriction (EUGR). This group of premature babies represents an interesting population to investigate using a metabolomic approach to optimize nutritional intake. Aims: To analyse and compare the urinary metabolomic pattern at birth of preterm infants with and without growth restriction at 36 weeks of postmenstrual age or at discharge, searching for putative markers of growth failure. Methods: We enrolled preterm infants between 23 and 32 weeks of gestational age (GA) and/or with a birth weight <1500 g, admitted to the Neonatal Intensive Care Unit (NICU) at the Department of Women’s and Children’s Health of Padova University Hospital. We collected urinary samples within 48 h of life and performed untargeted metabolomic analysis using mass spectrometry. Results: Sixteen EUGR infants were matched with sixteen non-EUGR controls. The EUGR group showed lower levels of L-cystathionine, kynurenic acid, L-carnosine, N-acetylglutamine, xanthurenic acid, aspartylglucosamine, DL5-hydroxylysine-hydrocloride, homocitrulline, and L-aminoadipic acid, suggesting a lower anti-inflammatory and antioxidant status with respect to the non-EUGR group. Conclusions: Metabolomic analysis suggests a basal predisposition to growth restriction, the identification of which could be useful for tailoring nutritional approaches. 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

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

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

Traditional ecotoxicology primarily investigates pollutant toxicity through physiological, biochemical, and single-molecular indicators. However, the limited data obtained through this approach constrain its application in the mechanistic analysis of pollutant toxicity. Omics technologies have emerged as a major research focus in recent years, enabling the comprehensive and accurate analysis of biomolecular-level changes. The integration of multi-omics technologies can holistically reveal the molecular toxicity mechanisms of pollutants, representing a primary emphasis in current toxicological research. This paper introduces the fundamental concepts of genomics, transcriptomics, proteomics, and metabolomics, while reviewing recent advancements in integrated omics approaches within aquatic toxicology. Furthermore, it provides a reference framework for the implementation of multi-omics strategies in ecotoxicological investigations. While multi-omics integration enables the unprecedented reconstruction of pollutant-induced molecular cascades and earlier biomarker discovery (notably through microbiome–metabolome linkages), its full potential requires experimental designs, machine learning-enhanced data integration, and validation against traditional toxicological endpoints within environmentally relevant models. Full article

Background: Population persistence for organisms to survive in a world with a rapidly changing climate will require either dispersal to suitable areas, evolutionary adaptation to altered conditions and/or sufficient phenotypic plasticity to withstand it. Given the slow growth and geographically isolated populations of many tree species, there is a high likelihood of local adaption or the acclimation of functional traits in these populations across the UK. Objectives: Given the slow growth and often isolated populations of Tilia cordata (lime tree), we hypothesised that there is a high likelihood of local adaptation or the acclimation of metabolic traits in these populations across the UK. Our aim was to test if the functional metabolomic traits of Tilia cordata (lime tree), collected in situ from natural populations, varied within and between populations and to compare this to neutral allele variation in the population. Methods: We used a metabolic fingerprinting approach to obtain a snapshot of the metabolic status of leaves collected from T. cordata from six populations across the UK. Environmental metadata, longer-term functional traits (specific leaf area) and neutral allelic variation in the population were also measured to assess the plastic capacity and local adaptation of the species. Results: The metabolic fingerprints derived from leaf material collected and fixed in situ from individuals in six populations of T. cordata across its UK range were similar, despite contrasting environmental conditions during sampling. Neutral allele frequencies showed almost no significant group structure, indicating low differentiation between populations. The specific leaf area did vary between sites. Conclusions: The low metabolic variation between UK populations of T. cordata despite contrasting environmental conditions during sampling indicates high levels of phenotypic plasticity. Full article

Legumes are vital sources of protein, dietary fibre and nutrients, making them crucial for global food security and sustainable agriculture. However, their widespread acceptance and consumption are often limited by undesirable sensory characteristics, such as “a beany flavour”, bitterness or variable textures. Addressing these challenges requires a comprehensive understanding of the complex molecular mechanisms governing appearance, aroma, taste, flavour, texture and palatability in legumes, aiming to enhance their sensory appeal. This review highlights the transformative power of multi-omics approaches in dissecting these intricate biological pathways and facilitating the targeted enhancement of legume sensory qualities. By integrating data from genomics, transcriptomics, proteomics and metabolomics, the genetic and biochemical networks that directly dictate sensory perception can be comprehensively unveiled. The insights gained from these integrated multi-omics studies are proving instrumental in developing strategies for sensory enhancement. They enable the identification of key biomarkers for desirable traits, facilitating more efficient marker-assisted selection (MAS) and genomic selection (GS) in breeding programs. Furthermore, a molecular understanding of sensory pathways opens avenues for precise gene editing (e.g., using CRISPR-Cas9) to modify specific genes, reduce off-flavour compounds or optimise texture. Beyond genetic improvements, multi-omics data also inform the optimisation of post-harvest handling and processing methods (e.g., germination and fermentation) to enhance desirable sensory profiles and mitigate undesirable ones. This holistic approach, spanning from the genetic blueprint to the final sensory experience, will accelerate the development of new legume cultivars and products with enhanced palatability, thereby fostering increased consumption and ultimately contributing to healthier diets and more resilient food systems worldwide. 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