Search Results (924)

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

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

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

The human skin microbiota, a complex community of bacterial, fungal, and viral organisms, plays a crucial role in maintaining skin homeostasis and regulating host-pathogen interactions. Dysbiosis within this microbial ecosystem has been implicated in various dermatological conditions, including acne vulgaris, psoriasis, seborrheic dermatitis, and atopic dermatitis. This review, for the first time, provides recent advancements in all four layers of omic technologies—metagenomics, metatranscriptomics, metaproteomics, and metabolomics—offering comprehensive insights into microbial diversity, in the context of functional skin modeling. Thus, this review explores the application of these omic tools to in vitro skin models, providing an integrated framework for understanding the molecular mechanisms underlying skin–microbiota interactions in both healthy and pathological contexts. We highlight the importance of developing advanced in vitro skin models, including the integration of immune components and endothelial cells, to accurately replicate the cutaneous microenvironment. Moreover, we discuss the potential of these models to identify novel therapeutic targets, enabling the design of personalized treatments aimed at restoring microbial balance, reinforcing the skin barrier, and modulating inflammation. As the field progresses, the incorporation of multi-omic approaches into skin-microbiome research will be pivotal in unraveling the complex interactions between host and microbiota, ultimately advancing therapeutic strategies for skin-related diseases. Full article

Breast cancer (BC) is a neoplasm characterized by high heterogeneity and is influenced by intrinsic molecular subtypes and clinical stage, aspects that remain underexplored in the Colombian population. This study aimed to characterize metabolic alterations associated with subtypes and disease progression in a group of newly diagnosed, treatment-naive Colombian women using an untargeted metabolomics approach. To improve metabolite coverage, samples were analyzed using LC-QTOF-MS and GC-QTOF-MS, along with amino acid profiling. The Luminal B subtype exhibited elevated levels of long-chain acylcarnitines and higher free fatty acid concentrations than the other subtypes. It also presented elevated levels of carbohydrates and essential glycolytic intermediates, suggesting that this subtype may adopt a hybrid metabolic phenotype characterized by increased glycolytic flux as well as enhanced fatty acid catabolism. Tumor, Node, and Metastasis (TNM) staging analysis revealed progressive metabolic reprogramming of BC. In advanced stages, a sustained increase in phosphatidylcholines and a decrease in lysophosphatidylcholines were observed, reflecting lipid alterations associated with key roles in tumor progression. In early stages (I-II), plasma metabolites with high discriminatory power were identified, such as glutamic acid, ribose, and glycerol, which are associated with dysfunctions in energy and carbohydrate metabolism. These results highlight metabolomics as a promising tool for the early diagnosis, clinical follow-up, and molecular characterization of BC. Full article

The agro-industrial sector produces large amounts of by-products that have a high environmental impact, so it has become essential to recover food waste at all levels. This is because it often contains bioactive molecules that can be a valuable source of new products such as animal feed, biopolymers, or products for human use, (e.g., cosmetics and nutraceuticals) due to its antioxidant, antimicrobial, and anti-inflammatory properties. Advanced analytical methodologies such as liquid chromatography coupled to mass spectrometry (LC-MS) are crucial for the characterisation of bioactive chemicals in these waste materials. LC-MS enables both targeted and untargeted metabolomic approaches, facilitating the identification and quantification of a wide range of secondary metabolites, including polyphenols, flavonoids, alkaloids, and terpenoids. The choice of extraction methodology is essential for the precise identification and quantification of these metabolites. This study provides an overview of LC-MS as an effective tool for analysing complex extracts derived from plant waste, discussing both methodological aspects and typical bioactive metabolites identified, and offering examples of their potential applications in cosmeceutics. Full article

This study employs a machine learning approach to identify a small-molecule-based signature capable of predicting Alzheimer’s disease (AD). Utilizing metabolomics data from the plasma of a well-characterized cohort of 94 AD patients and 62 healthy controls; metabolite levels were assessed using the Biocrates MxP^® Quant 500 platform. Data preprocessing involved removing low-quality samples, selecting relevant biochemical groups, and normalizing metabolite data based on demographic variables such as age, sex, and fasting time. Linear regression models were used to identify concomitant parameters that consisted of the data for a given metabolite within each of the biochemical families that were considered. Detection of these “concomitant” metabolites facilitates normalization and allows sample comparison. Residual analysis revealed distinct metabolite profiles between AD patients and controls across groups, such as amino acid-related compounds, bile acids, biogenic amines, indoles, carboxylic acids, and fatty acids. Correlation heatmaps illustrated significant interdependencies, highlighting specific molecules like carnosine, 5-aminovaleric acid (5-AVA), cholic acid (CA), and indoxyl sulfate (Ind-SO₄) as promising indicators. Linear Discriminant Analysis (LDA), validated using Leave-One-Out Cross-Validation, demonstrated that combinations of four or five molecules could classify AD with accuracy exceeding 75%, sensitivity up to 80%, and specificity around 79%. Notably, optimal combinations integrated metabolites with both a tendency to increase and a tendency to decrease in AD. A multivariate strategy consistently identified included 5-AVA, carnosine, CA, and hypoxanthine as having predictive potential. Overall, this study supports the utility of combining data of plasma small molecules as predictors for AD, offering a novel diagnostic tool and paving the way for advancements in personalized medicine. Full article

The distinctive nature of ferroptosis is that it is induced chemically and signifies a regulated cell death dependent on iron-dependent lipid peroxidation. The mechanism of ferroptosis involves oxidative damage to the membrane lipids. It differs from apoptosis and necroptosis, triggering metabolic changes in the iron-lipid homeostasis and antioxidant defense, such as glutathione (GSH) and glutathione peroxidase 4 (GPX4). Herein, the molecular mechanisms of ferroptosis and its role in the tumorigenesis process and infection-related diseases are presented. It also discusses metabolic reprogramming as a factor that modifies the levels of cell-sensitizing polyunsaturated fatty acids (PUFAs), iron dysregulation, and oxidative stress in aggressive cancers and inflammatory diseases such as sepsis, tuberculosis, and COVID-19. Particular attention is given to chemical modulators of ferroptosis, including synthetic inducers and inhibitors, as well as bioactive natural compounds. Our focus is on the significance of analytical tools, such as lipidomics and metabolomics, in understanding the phenomenon of ferroptosis. Finally, we explore novel therapeutic approaches targeting ferroptosis in cancer and infectious diseases, while navigating both the opportunities and challenges in drug development. The review then draws on chemical biology and disease pathology to propose promising areas of study for ferroptosis-related therapies. Full article

Heart failure (HF) is a global health burden characterized by high morbidity and mortality, necessitating advancements in diagnostic and therapeutic approaches. Molecular diagnostics, encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenetics, offer unprecedented insights into HF pathogenesis, aiding early diagnosis, risk stratification, and personalized management. This state-of-the-art review critically examines recent developments in molecular diagnostics in HF, evaluates their translational potential, and highlights key challenges in clinical implementation. Emerging tools such as liquid biopsy, multi-omics integration, and artificial intelligence (AI)-driven platforms are explored. We propose strategies to enhance clinical translation, equity in access, and utility in guiding treatment, thereby advancing precision cardiovascular medicine Full article

The need to maximize aquaculture production while addressing environmental and food security challenges posed by climate change has driven research towards the development of functional aquafeeds that enhance performance and immunity in farmed species. However, exposure to dietary and environmental stressors affects marine organisms, altering key metabolic pathways best understood through high-throughput “omics” tools. This study assessed the effects of Asparagopsis taxiformis supplementation on central metabolic pathways by analyzing changes in primary metabolite levels in the liver of farmed Diplodus sargus under optimal and suboptimal temperature conditions. Results showed that seaweed supplementation had a beneficial effect on the fish’s primary metabolome; however, inclusion levels and rearing conditions played a crucial role in determining outcomes. While 1.5% supplementation maintained a balanced primary metabolome under optimal temperature conditions, 3.0% supplementation most effectively mitigated the adverse effects of acute thermal stress during a marine heatwave. These findings highlight the nutritive and functional potential of A. taxiformis supplementation in aquafeeds for marine omnivorous fish species and emphasize the importance of evaluating functional aquafeeds under suboptimal rearing conditions. Overall, our results demonstrate the value of metabolomics in elucidating the molecular basis underlying biological pathways in farmed marine fish and optimizing production through climate-smart dietary strategies. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide. Even though the screening programs have decreased the incidence rates, the prognosis for CRC varies depending on the stage at diagnosis. Thus, early diagnosis is still a big challenge due to screening methods, and subsequent diagnosis is not very sensitive. Methods: In this work, LC-MS-based metabolomics, a powerful and sensitive tool to study complex dynamic changes, was used to analyze 211 human fecal samples from control individuals (CTRL), adenoma (AA), and CRC patients. Results: Multivariate and univariate statistical analysis highlighted cholesteryl esters (CEs) and fecal haemoglobin, quantified by fecal immunochemical test (FIT), as relevant biomarkers that clearly differentiate CRC from AA and CTRL. Predictive models based on random forest and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) demonstrate that CEs, together with FIT measurement, improved the CRC and CTRL classification, but not AA. This study revealed that the AA group is a transitional stage with high heterogeneity. The increased tendency observed in CEs from CTRL to CRC might be related to the imbalance of cholesterol homeostasis due to cancer cells requiring a high cholesterol level for cell development and proliferation. The free cholesterol is probably obtained from CEs, as it is the most cost/effective way to obtain the needed cholesterol. Conclusions: The accumulation of CEs is produced by two possible approaches: (1) dysfunction of cholesterol absorption in the small intestine and/or (2) transported inside exosomes from cell to cell to promote proliferation. Full article

Pharmaceuticals such as fluoxetine, paroxetine, sertraline, and mianserin occur in aquatic environments at low yet persistent concentrations due to their incomplete removal in wastewater treatment plants. Although frequently detected, these neuroactive compounds remain underrepresented in ecotoxicological assessments. Given their pharmacodynamic potency, environmentally relevant concentrations may induce sublethal effects in non-target organisms. In this study, we applied untargeted LC-MS-based metabolomics to investigate the sublethal effects of four widely used antidepressants—paroxetine, sertraline, fluoxetine (SSRIs), and mianserin (TeCA)—on two ecologically relevant freshwater invertebrates: S. ambiguum and D. magna. Organisms were individually exposed to each compound for 48 h at a concentration of 100 µg/L and 25 µg/L, respectively. Untargeted metabolomics captured the sublethal biochemical effects of these antidepressants, revealing both shared disruptions—e.g., in glycerophospholipid metabolism and cysteine and methionine metabolism—and species-specific responses. More pronounced pathway changes observed in D. magna suggest interspecies differences in metabolic capacity or xenobiotic processing mechanisms between taxa. Among the four antidepressants tested, sertraline in D. magna and fluoxetine in S. ambiguum exerted the most extensive metabolomic perturbations, as evidenced by the highest number and pathway impact scores. In D. magna, fluoxetine and mianserin produced similar metabolic profiles, largely overlapping with those of sertraline, whereas paroxetine affected only a single pathway, indicating minimal impact. In S. ambiguum, paroxetine and mianserin elicited comparable responses, also overlapping with those of fluoxetine, while sertraline triggered the fewest changes. These results suggest both compound-specific effects and a conserved metabolic response pattern among the antidepressants used. They also underscore the considerable potential of metabolomics as a powerful and sensitive tool for ecotoxicological risk assessments, particularly when applied across multiple model organisms to capture interspecies variations. However, further research is essential to identify which specific pathway disruptions are most predictive of adverse effects on organismal health. Full article

The escalating impacts of climate change pose significant threats to global agriculture, necessitating a rapid development of climate-resilient crop varieties. The integration of multi-omics technologies—such as genomics, transcriptomics, proteomics, metabolomics, and phenomics—has revolutionized our understanding of the intricate molecular networks that govern plant stress responses. Coupled with advanced predictive modeling approaches such as machine learning, deep learning, and multi-omics-assisted genomic selection, these integrated frameworks enable accurate genotype-to-phenotype predictions that accelerate breeding for augmented stress tolerance. This review comprehensively synthesizes the current strategies for multi-omics data integration, highlighting computational tools, conceptual frameworks, and challenges in harmonizing heterogeneous datasets. We examine the contribution of digital phenotyping platforms and environmental data in dissecting genotype-by-environment interactions critical for climate adaptation resilience. Further, we discuss technical, biological, and ethical challenges, encompassing computational bottlenecks, trait complexity, data standardization, and equitable data sharing. Finally, we outline future directions that prioritize scalable infrastructures, interpretability, and collaborative platforms to facilitate the deployment of multi-omics-guided breeding in diverse agroecological contexts. This integrative approach possesses transformative potential for the development of resilient crops, ensuring agricultural sustainability amidst increasing environmental volatility. Full article

The inhibition of steroid 5-alpha reductase (S5AR), a key mechanism for managing dihydrotestosterone-dependent conditions, has been demonstrated in teak (Tectona grandis L.f.) leaf extracts. Our recent clinical study confirmed the effectiveness of a hair growth formulation containing teak leaf extract in males with androgenic alopecia. However, significant variability in S5AR inhibitory activity among teak leaf samples from different regions underscores the need for quality control of raw materials. This study applied a metabolomics approach to investigate the influence of leaf age, harvesting period, and geographic origin on chemical composition and S5AR inhibitory activity, as well as to identify active S5AR inhibitors. Geographic origin emerged as the primary determinant of variations in chemical profiles and S5AR inhibitory activity. Using orthogonal partial least squares analysis, six diterpenoid S5AR inhibitors were identified, including four compounds reported for the first time as S5AR inhibitors: rhinocerotinoic acid, 7-oxo-8-labden-15-oic acid, 8-hydroxy-labd-13-en-15-oic acid, and a novel diterpene, 7-hydroxy-labd-8,13-dien-15-oic acid. These findings highlight the potential of metabolomics as a powerful tool for discovering bioactive compounds and optimizing raw material selection. By prioritizing proven geographic sources, consistent bioactivity can be achieved, supporting the therapeutic potential of teak leaves in managing S5AR-related conditions. Full article