PubMed

Metabolites. 2025 Apr 10;15(4):263. doi: 10.3390/metabo15040263.ABSTRACTBackground: Despite the well-established liver-protective efficacy of monoammonium glycyrrhizinate (MONO), diammonium glycyrrhizinate (DIAM), and magnesium isoglycyrrhizinate (MAGN), which has been translated into clinical practice, their clinical differentiation remains elusive owing to their structural similarities and overlapping therapeutic effects. Methods: The present study delves into the pharmacokinetics, cellular-level liver-protective potencies, and underlying mechanisms of action of these three compounds through a comprehensive analysis. Results: The findings reveal that both DIAM and MAGN exhibit superior bioavailability and hepatoprotective profiles compared to MONO. Notably, an investigation of the metabolic pathways mediating liver protection in normal human liver cells (LO2), utilizing an ultra-performance liquid chromatography-time of flight tandem mass spectrometry (UPLC-TOF-MS/MSe) platform, demonstrated that MAGN augments antioxidant components, thereby favoring its application in drug-induced liver injury (DILI). Conversely, DIAM appears to be a more suitable candidate for addressing non-alcoholic fatty liver disease (NAFLD) and viral hepatitis. Conclusion: This study contributes novel perspectives on the mechanisms of action and potential clinical utilities of DIAM and MAGN in liver disease prevention and management.PMID:40278392 | DOI:10.3390/metabo15040263

Metabolites. 2025 Apr 5;15(4):250. doi: 10.3390/metabo15040250.ABSTRACTBackground/Objectives: Millions of new diagnoses of breast cancer are made each year, with many cases having poor prognoses and limited treatment options, particularly for some subtypes such as triple-negative breast cancer. Resveratrol, a naturally occurring polyphenol, has demonstrated many anticancer properties in breast cancer studies. However, the mechanism of action of this compound remains elusive, although prior evidence suggests that this compound may work through altering cancer cell metabolism. Our objective for the current study was to perform untargeted metabolomics analysis on resveratrol-treated breast cancer cells to identify key metabolic targets of this compound. Methods: MCF-7 and MDA-MB-231 breast cancer cells were treated with varying doses of resveratrol and extracted for mass spectrometry-based untargeted metabolomics. Data preprocessing and filtering of metabolomics data from MCF-7 samples yielded 4751 peaks, with 312 peaks matched to an in-house standards library and 3459 peaks matched to public databases. Results: Pathway analysis in MetaboAnalyst identified significant (p < 0.05) metabolic pathways affected by resveratrol treatment, particularly those involving steroid, fatty acid, amino acid, and nucleotide metabolism. Evaluation of standard-matched peaks revealed acylcarnitines as a major target of resveratrol treatment, with long-chain acylcarnitines exhibiting a 2-5-fold increase in MCF-7 cells and a 5-13-fold increase in MDA-MB-231 cells when comparing the 100 µM treated cells to vehicle-treated cells (p < 0.05, VIP > 1). Notably, doses below 10 µM showed an opposite effect, possibly indicating a biphasic effect of resveratrol due to a switch from anti-oxidant to pro-oxidant effects as dose levels increase. Conclusions: These findings suggest that resveratrol induces mitochondrial metabolic reprogramming in breast cancer cells in a dose-dependent manner. The biphasic response indicates a potential optimal dosage for therapeutic effectiveness. Further research is warranted to explore the mechanisms underlying these metabolic alterations and their implications for precision nutrition strategies in cancer treatment.PMID:40278380 | DOI:10.3390/metabo15040250

Metabolites. 2025 Apr 5;15(4):251. doi: 10.3390/metabo15040251.ABSTRACTBackground: The World Health Organization has indicated that airport noise is strongly associated with cardiovascular disease, with vascular inflammation identified as the primary mechanism. Therefore, long-term exposure to airport noise is considered far more harmful than other types of noise. However, there remains a lack of research into the mechanisms underlying long-term exposure to airport noise and harm to the human body. Methods: A mouse model was established and exposed to airport noise at a maximum sound pressure level of 95 dB(A) and an equivalent continuous sound pressure level of 72 dB(A) for 12 h per day over a period of 100 days. Quantitative polymerase chain reaction (qPCR) was used to detect the mRNA expression levels of pro-inflammatory and anti-inflammatory factors. Enzyme-linked immunosorbent assay (ELISA) was used to detect LPS, LTA, TMA, and TMAO levels. Intestinal flora composition was analyzed by 16S rDNA sequencing, and targeted metabolomics was employed to determine the levels of serum short-chain fatty acids. Results: Long-term airport noise exposure significantly increased systolic blood pressure, diastolic blood pressure, and mean blood pressure (p < 0.05); significantly increased the mRNA expression levels of oxidative stress parameters (nuclear matrix protein 2, 3-nitrotyrosine, and monocyte chemoattractant protein-1) (p < 0.05); significantly increased pro-inflammatory factors (interleukin 6 and tumor necrosis factor alpha) (p < 0.05); significantly decreased the mRNA expression level of anti-inflammatory factor interleukin 10 (p < 0.05); and significantly increased the content of LPS and LTA (p < 0.05). The composition of the main flora in the intestinal tract was structurally disordered, and there were significant differences between the noise-exposed and control groups at the levels of the phylum, family, and genus of bacteria. β-diversity of the principal component analysis diagrams was clearly distinguished. Compared with those of the control group, TMA-producing bacteria and levels of TMA and TMAO were significantly reduced, and the serum ethanoic acid and propanoic acid levels of the noise-exposed group were significantly decreased (p < 0.05). Conclusions: Long-term airport noise exposure causes significant elevation of blood pressure and structural disruption in the composition of the intestinal flora in mice, leading to elevated levels of oxidative stress and inflammation, resulting in metabolic disorders that lead to significant changes in the production of metabolites.PMID:40278379 | DOI:10.3390/metabo15040251

Metabolites. 2025 Apr 4;15(4):249. doi: 10.3390/metabo15040249.ABSTRACTBackground: Quantification of metabolites from nuclear magnetic resonance (NMR) spectra in an accurate, high-throughput manner requires effective data processing tools. Neural networks are relatively underexplored in quantitative NMR metabolomics despite impressive speed and throughput compared to more conventional peak-fitting metabolomics software. Methods: This work investigates practices for dataset and model development in the task of metabolite quantification directly from simulated NMR spectra for three neural network models: the multi-layered perceptron, the convolutional neural network, and the transformer. Model architectures, training parameters, and training datasets are optimized before comparing each model on simulated 400-MHz 1H-NMR spectra of complex mixtures with 8, 44, or 86 metabolites to quantify in spectra ranging from simple to highly complex and overlapping peaks. The optimized models were further validated on spectra at 100- and 800-MHz. Results: The transformer was the most effective network for NMR metabolite quantification, especially as the number of metabolites per spectra increased or target concentrations were low or had a large dynamic range. Further, the transformer was able to accurately quantify metabolites in simulated spectra from 100-MHz up to 800-MHz. Conclusions: The methods developed in this work reveal that transformers have the potential to accurately perform fully automated metabolite quantification in real-time and, with further development with experimental data, could be the basis for automated quantitative NMR metabolomics software.PMID:40278378 | DOI:10.3390/metabo15040249

Metabolites. 2025 Apr 4;15(4):248. doi: 10.3390/metabo15040248.ABSTRACTBackground/Objectives: The gut microbiota plays a pivotal role in host physiology through metabolite production, with lipids serving as essential biomolecules for cellular structure, metabolism, and signaling. This review aims to elucidate the interactions between gut microbiota and lipid metabolism and their implications for enhancing swine production. Methods: We systematically analyzed current literature on microbial lipid metabolism, focusing on mechanistic studies on microbiota-lipid interactions, key regulatory pathways in microbial lipid metabolism, and multi-omics evidence (metagenomic/metabolomic) from swine models. Results: This review outlines the structural and functional roles of lipids in bacterial membranes and examines the influence of gut microbiota on the metabolism of key lipid classes, including cholesterol, bile acids, choline, sphingolipids, and fatty acids. Additionally, we explore the potential applications of microbial lipid metabolism in enhancing swine production performance. Conclusions: Our analysis establishes a scientific framework for microbiota-based strategies to optimize lipid metabolism. The findings highlight potential interventions to improve livestock productivity through targeted manipulation of gut microbial communities.PMID:40278377 | DOI:10.3390/metabo15040248

Metabolites. 2025 Apr 3;15(4):247. doi: 10.3390/metabo15040247.ABSTRACTBACKGROUND: Severe acute asthma (SAA) in children can be life-threatening. There has been a significant rise in paediatric intensive care unit (PICU) admissions due to SAA over the past two decades. While asthma is a heterogeneous disease, its underlying pathophysiological pathways remain underexplored. This study aimed to assess the value of non-invasive targeted exhaled breath metabolomics analysis to better characterise SAA.METHODS: Breath samples from 17 children admitted to the PICU with SAA (cases) and 27 children with controlled severe asthma (controls) were analysed using thermal desorption gas chromatography-mass spectrometry (TD-GC-MS).RESULTS: A targeted volatile organic compound (VOC) analysis identified 25 compounds, of which 16 were shared between groups. Four VOCs were significantly more often present in SAA, and nine VOCs exhibited higher concentrations in SAA. Longitudinal analysis of VOCs from follow-up samples of 10 cases showed no significant temporal differences, reinforcing the reproducibility of identified biomarkers.CONCLUSIONS: This study exemplifies the potential of exhaled breath analysis to provide insights into the molecular background of SAA. Breath metabolomics may enable early recognition of severe asthma attacks and preventive therapeutic interventions in children with severe asthma.PMID:40278376 | DOI:10.3390/metabo15040247

Metabolites. 2025 Apr 2;15(4):241. doi: 10.3390/metabo15040241.ABSTRACTBackground: The natural differences in running capacities among rats remain poorly understood, and the mechanisms driving these differences need further investigation. Methods: Twenty male Sprague-Dawley (SD) rats were selected. High and low running capacity rats were identified using Treadmill Exhaustion Tests. Peripheral blood was collected for serum isolation, followed by a metabolomics analysis using LC-MS/MS. Data were preprocessed, and a principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were applied to identify metabolic profile differences. Significant metabolites were screened, and a pathway enrichment analysis was conducted using the KEGG database to determine key metabolic pathways. Forty SD rats (equal male and female) were randomly divided into an inosine triphosphate (ITP) group (24.29 mg/kg.bw daily) and a control group. Running capacity was assessed after one week of continuous treatment. Results: Three independent measurements showed consistent differences in running capacity. A total of 519 differential metabolites were identified, with 255 up-regulated and 264 down-regulated. The KEGG pathway analysis revealed a significant enrichment of the Purine Metabolism pathway (ITP-ATP) in the high running capacity group (p < 0.05). The ITP-treated group exhibited a significantly higher running capacity than the controls (p < 0.05), confirming the efficacy of dietary ITP supplementation. Conclusions: The running capacity of rats is influenced by the ITP-ATP pathway, and exogenous ITP administration through dietary intervention significantly improves running ability.PMID:40278370 | DOI:10.3390/metabo15040241

Metabolites. 2025 Mar 28;15(4):232. doi: 10.3390/metabo15040232.ABSTRACTBACKGROUND: Best-corrected visual acuity (BCVA) is the current gold standard of retinal function measurement but is not affected in early and intermediate forms of age-related macular degeneration (AMD). Increasing evidence suggests that microperimetry is a sensitive measure of visual function. This study sought to analyze the associations between plasma and urine metabolites and microperimetry in AMD.METHODS: We included data on 363 eyes (95 controls, 268 AMD). Microperimetry was performed in patients with or without AMD using the Macular Integrity Assessment (MAIA) microperimetry system, employing a 37-point full-threshold protocol. Plasma and urine samples were analyzed via ultra-high-performance liquid chromatography-mass spectrometry. Multilevel mixed-effects linear models were used to assess associations between the metabolites and retinal sensitivity. Statistical significance was determined by considering the number of independent tests that accounted for 80% of the variance (ENT80).RESULTS: We identified two plasma and seven urine metabolites, which were significantly associated with mean retinal sensitivity in AMD, and the key results include metabolites in the lysine metabolism pathway.CONCLUSIONS: To our knowledge, we present the first assessment of the associations between plasma and urinary metabolites and retinal microperimetry sensitivity in AMD. This work can reveal more insight into the pathogenesis of AMD.PMID:40278361 | DOI:10.3390/metabo15040232

Metabolites. 2025 Mar 27;15(4):229. doi: 10.3390/metabo15040229.ABSTRACTBACKGROUND: Global warming poses significant challenges to aquaculture, as elevated water temperatures adversely affect fish health and survival. This study investigated the effects and potential mechanisms of dietary tea polyphenols (TPs) on acute heat stress and survival in hybrid crucian carp HCC2.METHODS: The fish in the control (CON) group and heat stress group (HS group, three replicates, each containing 20 fish, n = 60 per group) were fed diets with 0 mg/kg TPs, and the three experimental groups (HSLTP, HSMTP, and HSHTP, n = 20 × 3 replicates) were fed the diets with 100, 200, or 400 mg/kg TPs for 60 days. Further, fish in the experimental groups (HS, HSLTP, HSMTP, and HSHTP) were exposed at 38 °C for 24 h to induce acute heat stress. Survival data and serum and tissue samples were collected for the analysis. Metabolomics using UPLC-Q-TOF/MS was employed to evaluate the metabolite changes in the fish livers.RESULTS: Notably, dietary TPs significantly improved survival rates and antioxidant enzyme levels and reduced serum ALT, AST, cortisol, glucose, MDA, and liver HSP-70 levels in the heat-stressed fish. Metabolomic analysis revealed that TPs modulated lipid metabolism, particularly glycerophospholipid and arachidonic acid pathways, which may contribute to a higher tolerance to acute heat stress.CONCLUSIONS: These findings suggest that TPs are a promising, eco-friendly feed additive for protecting fish from heat stress and optimizing aquaculture practices.PMID:40278359 | DOI:10.3390/metabo15040229

Metabolites. 2025 Mar 27;15(4):228. doi: 10.3390/metabo15040228.ABSTRACTBackground/Objectives: Severe fever with thrombocytopenia syndrome (SFTS) is a viral infection primarily found in Asia, with a case fatality rate of about 10%. Despite its increasing prevalence, the underlying pathogenic mechanisms remain poorly understood, limiting the development of effective therapeutic interventions. Methods: We employed an untargeted metabolomics approach using liquid chromatography-mass spectrometry (LC-MS) to analyze serum samples from 78 SFTS patients during the acute phase of their illness. Differential metabolic features between survival and fatal cases were identified through multivariate statistical analysis. Furthermore, we constructed a metabolic prognostic model based on these biomarkers to predict disease severity. Results: Significant alterations were observed in four key metabolic pathways: sphingolipid metabolism, biosynthesis of phenylalanine, tyrosine, and tryptophan, primary bile acid biosynthesis, and phenylalanine metabolism. Elevated levels of phenyllactic acid and isocitric acid were strongly associated with adverse outcomes and demonstrated high discriminatory power in distinguishing fatal cases from survivors. The metabolic prognostic model incorporating these biomarkers achieved a sensitivity of 75% and a specificity of 90% in predicting disease severity. Conclusions: Our findings highlight the pivotal role of metabolic dysregulation in the pathogenesis of SFTS and suggest that targeting specific metabolic pathways could open new avenues for therapeutic development. The identification of prognostic biomarkers provides a valuable tool for early risk stratification and timely clinical intervention, potentially improving patient outcomes.PMID:40278357 | DOI:10.3390/metabo15040228

Metabolites. 2025 Mar 26;15(4):226. doi: 10.3390/metabo15040226.ABSTRACTBackground: The dark green coloration of bunching onion leaf blades is a key determinant of market value, nutritional quality, and visual appeal. This trait is regulated by a complex network of pigment interactions, which not only determine coloration but also serve as critical indicators of plant growth dynamics and stress responses. This study aimed to elucidate the mechanisms regulating the dark green trait and develop a predictive model for accurately assessing pigment composition. These advancements enable the efficient selection of dark green varieties and facilitate the establishment of optimal growth environments through plant growth monitoring. Methods: Seven varieties and lines of heat-tolerant bunching onions were analyzed, including two commercial F1 cultivars, along with two purebred varieties and three F1 hybrid lines bred in Yamaguchi Prefecture. The analysis was conducted on visible spectral reflectance data (400-700 nm at 20 nm intervals) and pigment compounds (chlorophyll a, chlorophyll b and pheophytin a, lutein, and β-carotene), whereas primary and secondary metabolites were assessed by using widely targeted metabolomics. In addition, a random forest regression model was constructed by using spectral reflectance data and pigment compound contents. Results: Principal component analysis based on spectral reflectance data and the comparative profiling of 186 metabolites revealed characteristic metabolite accumulation associated with each green color pattern. The "green" group showed greater accumulation of sugars, the "gray green" group was characterized by the accumulation of phenolic compounds, and the "dark green" group exhibited accumulation of cyanidins. These metabolites are suggested to accumulate in response to environmental stress, and these differences are likely to influence green coloration traits. Furthermore, among the regression models for estimating pigment compound contents, the one for chlorophyll a content achieved high accuracy, with an R2 value of 0.88 in the test dataset and 0.78 in Leave-One-Out Cross-Validation, demonstrating its potential for practical application in trait evaluation. However, since the regression model developed in this study is based on data obtained from greenhouse conditions, it is necessary to incorporate field trial results and reconstruct the model to enhance its adaptability. Conclusions: This study revealed that cyanidin is involved in the characteristics of dark green varieties. Additionally, it was demonstrated that chlorophyll a can be predicted using visible spectral reflectance. These findings suggest the potential for developing markers for the dark green trait, selecting high-pigment-accumulating varieties, and facilitating the simple real-time diagnosis of plant growth conditions and stress status, thereby enabling the establishment of optimal environmental conditions. Future studies will aim to elucidate the genetic factors regulating pigment accumulation, facilitating the breeding of dark green varieties with enhanced coloration traits for summer cultivation.PMID:40278355 | DOI:10.3390/metabo15040226

Metabolites. 2025 Mar 25;15(4):222. doi: 10.3390/metabo15040222.ABSTRACTBACKGROUND/OBJECTIVES: Gastric cancer (GC) is a prevalent malignant tumor worldwide, with its pathological mechanisms largely unknown. Understanding the metabolic reprogramming associated with GC is crucial for the prevention and treatment of this disease. This study aims to identify significant alterations in metabolites and pathways related to the development of GC.METHODS: A liquid chromatography-mass spectrometry-based non-targeted metabolomics data acquisition was performed on paired tissues from 80 GC patients. Differences in metabolic profiles between tumor and adjacent normal tissues were first investigated through univariate and multivariate statistical analyses. Additionally, differential correlation network analysis and a newly proposed network analysis method (NAM) were employed to explore significant metabolite pathways and subnetworks related to tumorigenesis and various TNM stages of GC.RESULTS: Over half of the annotated metabolites exhibited significant alterations. Phosphatidylcholine (PC)_30_0 and fatty acid C20_3 demonstrated strong diagnostic performance for GC, with AUCs of 0.911 and 0.934 in the discovery and validation sets, respectively. Differential correlation network analysis revealed significant fatty acid-related metabolic reprogramming in GC with elevated levels of medium-chain acylcarnitines and increased activity of medium-chain acyl-CoA dehydrogenase, firstly observed in clinical GC tissues. Of note, using NAM, two correlation subnetworks were identified as having significant alterations across different TNM stages, centered with choline and carnitine C4_0-OH, respectively.CONCLUSIONS: The identified significant alterations in fatty acid metabolism and TNM-related metabolic subnetworks in GC tissues will facilitate future investigations into the metabolic reprogramming associated with gastric cancer.PMID:40278351 | DOI:10.3390/metabo15040222

Metabolites. 2025 Mar 24;15(4):221. doi: 10.3390/metabo15040221.ABSTRACTBackground/Objectives: Endoplasmic reticulum (ER) stress occurs when ER homeostasis is disrupted, leading to the accumulation of misfolded or unfolded proteins. This condition activates the unfolded protein response (UPR), which aims to restore balance or trigger cell death if homeostasis cannot be achieved. In cancer, ER stress plays a key role due to the heightened metabolic demands of tumor cells. This review explores how metabolomics can provide insights into ER stress-related metabolic alterations and their implications for cancer therapy. Methods: A comprehensive literature review was conducted to analyze recent findings on ER stress, metabolomics, and cancer metabolism. Studies examining metabolic profiling of cancer cells under ER stress conditions were selected, with a focus on identifying potential biomarkers and therapeutic targets. Results: Metabolomic studies highlight significant shifts in lipid metabolism, protein synthesis, and oxidative stress management in response to ER stress. These metabolic alterations are crucial for tumor adaptation and survival. Additionally, targeting ER stress-related metabolic pathways has shown potential in preclinical models, suggesting new therapeutic strategies. Conclusions: Understanding the metabolic impact of ER stress in cancer provides valuable opportunities for drug development. Metabolomics-based approaches may help identify novel biomarkers and therapeutic targets, enhancing the effectiveness of antitumor therapies.PMID:40278350 | DOI:10.3390/metabo15040221

Metabolites. 2025 Mar 24;15(4):217. doi: 10.3390/metabo15040217.ABSTRACTBackground: One of the greatest challenges to biologists is to understand the adaptive mechanisms of how plants will respond to climate at all levels from individual physiology to whole populations. For example, variation (plasticity) in the composition and concentration of metabolites will determine productivity, reproduction, and ultimately survival and distribution of plants, especially those subjected to rapid climate change. Objectives: Our aim was to study how interspecific and intraspecific metabolic variation in plant species within a single community can be elucidated. Methods: We used a metabolomics approach to study metabolic acclimation (by measuring the metabolome between plants under "common garden" controlled environment conditions) and metabolic plasticity (using field based reciprocal transplant studies) in a set of Atlantic sand dune annual communities along a latitudinal gradient from Portugal to England. Results: In the common garden study, metabolically phenotyping (using a fingerprinting direct injection mass spectrometry approach) five species of annual plants showed that species living together in a community have distinct metabolic phenotypes (high inter-specific metabolic variation). There was low intra-specific metabolic variation between populations growing under standard environmental conditions. The metabolic variation in one species Veronica arvensis was measured in the reciprocal transplant study. Metabolic phenotypes obtained from all samples were similar across all sites regardless of where the plants originated from. Conclusions: This implies that the metabolome is highly plastic and the measurable metabolome in this study was influenced more by local environmental factors than inherent genetic factors. This work highlights that species are fulfilling different niches within this community. Furthermore, the measurable metabolome was highly plastic to environmental variation.PMID:40278346 | DOI:10.3390/metabo15040217

Metabolites. 2025 Mar 21;15(4):214. doi: 10.3390/metabo15040214.ABSTRACTBACKGROUND/OBJECTIVES: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. This discipline, which involves the comprehensive analysis of metabolites in a biological system, provides valuable insights into complex biological processes and disease states. As metabolomics assumes an increasingly prominent role in the diagnosis of human diseases and in precision medicine, there is a pressing need for more robust artificial intelligence tools that can offer enhanced reliability and accuracy in medical applications. The proposed DW-EML model addresses this by integrating multiple classifiers within a double-weighted voting scheme, which assigns weights based on the cross-validation accuracy and classification confidence, ensuring a more reliable prediction framework.METHODS: The model was applied to publicly available datasets derived from studies on critical illness in children, chronic typhoid carriage, and early detection of ovarian cancer.RESULTS: The results demonstrate that the DW-EML approach outperformed methods traditionally used in metabolomics, such as the Partial Least Squares Discriminant Analysis in terms of accuracy and predictive power.CONCLUSIONS: The DW-EML model is a promising tool for metabolomic data analysis, offering enhanced robustness and reliability for diagnostic and prognostic applications and potentially contributing to the advancement of personalized and precision medicine.PMID:40278343 | DOI:10.3390/metabo15040214

Metabolites. 2025 Mar 21;15(4):213. doi: 10.3390/metabo15040213.ABSTRACTBackground/Objectives: Currently, the diagnosis of autism spectrum disorder (ASD) relies on behavioral observations, frequently causing delays in early identification. Prognostic markers are essential for customizing therapy and monitoring progress. However, there are currently no recognized biomarkers for ASD. The current systematic review aims to analyze studies on the intestinal metabolome in children (both autistic and non-autistic) to identify potential metabolites for diagnostic and prognostic purposes. Methods: We searched Medline, Scopus, Embase, and Web of Science for relevant publications. Results: We identified 11 studies examining the gut metabolome that distinguished between autistic and non-autistic children. These studies also revealed connections between gut metabolites, developmental scores, and symptoms. The substances identified were associated with metabolic pathways such as amino acids, vitamins, lipids, oxidative stress, glycans, xenobiotics, and nucleotides. Conclusions: These findings suggest metabolic changes that may be linked to the causes or development of autism. Although these observations came from a few reports, only high-quality studies were included in this review. Further research is essential to confirm the identified substances as biomarkers.PMID:40278342 | DOI:10.3390/metabo15040213

J Pers Med. 2025 Mar 24;15(4):124. doi: 10.3390/jpm15040124.ABSTRACTBackground: Hepatobiliary liver cancers (HBLCs) represent the sixth most common neoplasm in the world. Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) constitute the main HBLC types, with alarming epidemiological projections. Methods: In recent decades, alterations in gut microbiota, with mutual implications on the gut-liver axis and gut-biliary axis permeability status, have been massively investigated and proposed as HBLC pathogenetic deus ex machina. Results: In the HCC setting, elevated intestinal levels of Escherichia coli and other Gram-negative bacteria have been demonstrated, resulting in a close association with increased lipopolysaccharide (LPS) serum levels and, consequently, chronic systemic inflammation. In contrast, the intestinal microbiota of HCC individuals feature reduced levels of Lactobacillus spp., Bifidobacterium spp., and Enterococcus spp. In the CC setting, evidence has revealed an increased expression of Lactobacillus spp., with enhanced levels of Actynomices spp. and Alloscardovia spp. Besides impaired strains/species representation, gut-derived metabolites, including bile acids (BAs), short-chain fatty acids (SCFAs), and oxidative-stress-derived products, configure a network severely impacting the progression of HBLC. Conclusions: In the era of Precision Medicine, the clarification of microbiota composition and functioning in HCC and CC settings can contribute to the identification of individual signatures, potentially providing novel diagnostic markers, therapeutic approaches, and prognostic/predictive tools.PMID:40278303 | DOI:10.3390/jpm15040124

J Xenobiot. 2025 Mar 26;15(2):48. doi: 10.3390/jox15020048.ABSTRACTDuckweeds are a family of small floating macrophytes (the Lemnaceae) that inhabit quiet freshwaters worldwide. They have long been employed to determine toxicity to higher plants in the aquatic environment, and standardized national and international protocols have been developed for this purpose using two representative species. While these protocols, which assess the growth of the leaf-like fronds of the tested duckweed, are indeed suitable and still frequently used for detecting the toxicity of water-borne substances to aquatic higher plant life, they are cumbersome and lengthy, determine endpoints rather than depict toxicity timelines, and provide no information as to the mechanisms involved in the indicated toxicity. Progress has been made in downscaling, shortening and improving the standardized assay procedures, and the use of alternative duckweed species, protocols and endpoints for detecting toxicity has been explored. Biomarkers of toxic effect have long been determined concomitantly with testing for toxicity itself, and their potential for the assessment of toxicity has recently been greatly expanded by transcriptomic, proteomic and metabolomic techniques complemented by FITR spectroscopy, transformation and genotoxicity and timescale toxicity testing. Improved modern biomarker analysis can help to both better understand the mechanisms underlying toxicity and facilitate the identification of unknown toxins.PMID:40278153 | DOI:10.3390/jox15020048

J Fungi (Basel). 2025 Apr 19;11(4):325. doi: 10.3390/jof11040325.ABSTRACTRecent studies highlight the potential of Saccharomyces species as probiotics due to their ability to modulate microbial interactions and reduce cariogenic activity, yet the underlying metabolic mechanisms remain unclear. This study investigates the cross-kingdom metabolic effects of Saccharomyces boulardii and Saccharomyces cerevisiae on the metabolic processes of Streptococcus mutans and Candida albicans using a metabolomics-based approach. Untargeted LC-MS/MS analysis was conducted to assess metabolites in a planktonic model, followed by metabolomic profiling and pathway analysis to identify key metabolic alterations. The results revealed that S. boulardii and S. cerevisiae demonstrated metabolic regulatory effects on S. mutans and C. albicans. Specifically, S. boulardii down-regulated 262 metabolites and up-regulated 168, while S. cerevisiae down-regulated 265 metabolites and up-regulated 168. Both yeast species down-regulated carbohydrate and amino acid metabolism in S. mutans and C. albicans, resulting in reduced biomolecule synthesis and a less acidic environment. S. boulardii and S. cerevisiae also up-regulated certain metabolic processes, including purine metabolism, suggesting a compensatory mechanism for nucleotide synthesis. Notably, dual regulatory effects were observed, where specific metabolites were simultaneously up-regulated and down-regulated, indicating complex metabolic crosstalk. These findings suggest that both S. boulardii and S. cerevisiae modulate microbial metabolism through a shared mechanism, offering potentials for dental caries prevention.PMID:40278145 | DOI:10.3390/jof11040325

J Fungi (Basel). 2025 Apr 18;11(4):323. doi: 10.3390/jof11040323.ABSTRACTPhlebopus portentosus is an edible and medicinal ectomycorrhizal mushroom with delicious and high nutritional value. However, the mechanism of secondary metabolite biosynthesis in P. portentosus is still unclear. In this study, the genomics, metabolomics, and transcriptomics were integrated to reveal the biosynthesis mechanism of secondary metabolites in P. portentosus under different cultivation conditions. The 31.4 Mb genome of P. portentosus YAF023 with 15 scaffolds was assembled by Illumina and Nanopore sequencing and annotated, and 206 cytochrome P450s, 201 carbohydrate-active enzymes, 186 transcription factors, 18 terpene synthases (TPSs), and 5 polyketide synthases (PKSs) were identified. Multi-omics analysis showed that PpPKS1 is probably involved in the biosynthesis of Ethyl orsellinate; PpPKS2 and PpPKS5 are probably involved in the synthesis of 6-Methylsalicylic acid and Cytochalasin Z5, respectively; PpTRI5 was involved in the tetracyclic sesquiterpene β-type trichodiene compounds; and PpSTCs was involved in the synthesis of β-copaene analogs or derivatives. Co-expression network analysis and binding site prediction of the promoter regions suggested that PpHOX4 and PpHSF1 regulated the gene expression of PpPKS1, and Ppzf-C2H2 32 and PpHSF5 regulated the gene expression of PpSTCs 8, and PpSTCs 3, respectively. This study will provide an important foundation for further development and utilization of secondary metabolites of P. portentosus.PMID:40278143 | DOI:10.3390/jof11040323