PubMed

J Pharm Biomed Anal. 2025 Jan 21;257:116668. doi: 10.1016/j.jpba.2025.116668. Online ahead of print.ABSTRACTBile acids (BAs) are essential signaling molecules that engage in host and gut microbial metabolism, playing a crucial role in maintaining organismal stability. Liquid chromatography-mass spectrometry (LC-MS) is a widely employed technique for metabolite analysis in biological samples due to its high sensitivity, excellent specificity, and low detection limits. This method has emerged as the mainstream approach for the detection and analysis of BAs. Pseudo-targeted analysis combines the advantages of both untargeted and targeted metabolomics methodologies. In this study, we developed a comprehensive and rapid method for detecting and analyzing BAs using LC-MS technology, applied to liver samples from bile duct-ligated (BDL) mice exhibiting liver fibrosis. A self-constructed database containing 488 BAs was established, and raw data from universal metabolome standard (UMS) were acquired using UHPLC-Q/TOF-MS. A total of 172 BA compounds were characterized, including 74 free BAs and 158 BAs were successfully detected using the high-coverage assay established with UHPLC-QQQ-MS. This assay was employed in the BDL liver fibrosis mouse model, where statistical analysis tools identified 20 differential BAs in the livers of affected mice. The development of this rapid method signifies a substantial advancement in the field, illustrating its utility in identifying differential BAs and enhancing our understanding of liver fibrosis. Furthermore, the high-coverage assay's ability to accurately analyze a diverse range of BAs could substantially aid in diagnosing and treating liver diseases.PMID:39879819 | DOI:10.1016/j.jpba.2025.116668

J Pharm Biomed Anal. 2025 Jan 27;257:116700. doi: 10.1016/j.jpba.2025.116700. Online ahead of print.ABSTRACTTrametes robiniophila Murr. (Huaier) is a traditional medicinal fungus known for its pharmacological properties, including heat-clearing, detoxifying, anti-inflammatory, and antitumor effects. Our previous research has demonstrated its antiviral activity, but the exact therapeutic mechanisms remain unclear. This study aims to explore the mechanisms of 50 % methanol extract of Huaier (HME) in treating influenza using 16S rRNA high-throughput sequencing and metabolomics techniques. The results showed that the HME significantly reduced the lung index and viral load in the lungs of influenza-infected mice, alleviated pathological damage in lung tissues, and downregulated the expression levels of inflammatory cytokines Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α) and Interferon-γ (IFN-γ) in lung tissues. Furthermore, the HME enhanced the diversity of gut microbiota in infected mice, significantly increasing the relative abundance of beneficial bacteria, such as Alistipes and Alloprevotella. Through non-targeted metabolomic analysis of mouse feces, 45 potential biomarkers were identified. Meanwhile, the low-dose of HME was able to restore the disrupted metabolic levels. Analysis of gut microbiota and biomarker pathways revealed that HME primarily affects nicotinate and nicotinamide metabolism, which may be the key mechanism for its intervention in influenza. In addition, Spearman correlation analysis showed that most biomarkers were significantly associated with pharmacodynamics and the Alloprevotella.PMID:39879816 | DOI:10.1016/j.jpba.2025.116700

Food Chem. 2025 Jan 27;473:143069. doi: 10.1016/j.foodchem.2025.143069. Online ahead of print.ABSTRACTAn attempt was made here to a complemental analytical tool for classical MSI approach. OLE-LC-MS/MS imaging was proposed to plot the spatial-resolved metabolome through deploying mint leaf as a proof-of-concept. A dried leaf underwent chemical composition characterization using OLE-LC-Qtof-MS. Another dried leaf was cut into small pieces, and all pieces were successively packed into a suitable cartridge to undergo OLE-LC-SRM measurements. Fifty-two compounds were observed and identified. Special attention was paid onto isomeric identification using fragment ion intensity ranking style, e.g., 3-O-caffeoylquinic acid vs. 4-O-caffeoylquinic acid. Thereof, 23 abundant ones were involved for relatively quantitative analysis. Quantitative settings were optimized using online ER-MS program. Following spatial metabolome imaging, regioselective distributions were observed for most concerned metabolites. Particularly, isomer-specific occurrences were observed for luteolin-7-O-glucuronide and luteolin-3'-O-glucuronide. Together, OLE-LC-MS/MS is alternative for spatial metabolome imaging due to the advantages at isomeric separation, identification confidence, and quantitative accuracy.PMID:39879757 | DOI:10.1016/j.foodchem.2025.143069

Food Chem. 2025 Jan 27;473:143055. doi: 10.1016/j.foodchem.2025.143055. Online ahead of print.ABSTRACTApple polyphenols (APP) can reduce obesity. However, the effects of APP on abdominal subcutaneous adipose tissue (aSAT) at metabolic level were unclear. In this study, 5-week APP intervenes were conducted on 10-week high-fat diet (HFD) feeding mice with doses of 200 and 500 mg/kg b.w./day, followed by ultra-high-performance liquid chromatography-mass spectrometry based untargeted metabolomics analysis. As expected, APP obviously reversed aSAT weight and index, as well as activities of myeloperoxidase, glutathione peroxidase, superoxide dismutase and catalase. Euclidean distance between HFD and normal chow diet (NCD) group was shortened. 64 and 127 differential metabolites were found in 200 and 500 mg/kg b.w./day group, with 12 and 13 changed pathways, respectively. Specifically, APP restored glycolysis, tricarboxylic acid cycle, amino acid metabolism, and lipid metabolism as dose-dependent manner. Finally, glucose-6-phosphate, xanthine and tyrosine were selected as critical junctures. Collectively, these findings underscore the potential of APP in reversing molecular alterations in aSAT.PMID:39879748 | DOI:10.1016/j.foodchem.2025.143055

J Nat Prod. 2025 Jan 29. doi: 10.1021/acs.jnatprod.4c01051. Online ahead of print.ABSTRACTA structurally novel metabolite, fatuamide A (1), was discovered from a laboratory cultured strain of the marine cyanobacterium Leptolyngbya sp., collected from Faga'itua Bay, American Samoa. A bioassay-guided approach using NCI-H460 human lung cancer cells directed the isolation of fatuamide A, which was obtained from the most cytotoxic fraction. The planar structure of fatuamide A was elucidated by integrated NMR and MS/MS analysis, and a combination of bioinformatic and computational approaches was used to deduce the absolute configuration at its eight stereocenters. A putative hybrid PKS/NRPS biosynthetic gene cluster responsible for fatuamide A production was identified from the sequenced genomic DNA of the cultured cyanobacterium. The biosynthetic gene cluster possessed elements that suggested fatuamide A binds metals, and this metallophore property was demonstrated by native metabolomics and indicated a preference for binding copper. The producing strain was found to be highly resistant to toxicity from elevated copper concentrations in culture media.PMID:39879528 | DOI:10.1021/acs.jnatprod.4c01051

Sci Adv. 2025 Jan 31;11(5):eadr8837. doi: 10.1126/sciadv.adr8837. Epub 2025 Jan 29.ABSTRACTThe Epstein-Barr virus (EBV) infects nearly 90% of adults globally and is linked to over 200,000 annual cancer cases. Immunocompromised individuals from conditions such as primary immune disorders, HIV, or posttransplant immunosuppressive therapies are particularly vulnerable because of EBV's transformative capability. EBV remodels B cell metabolism to support energy, biosynthetic precursors, and redox equivalents necessary for transformation. Most EBV-driven metabolic pathways center on mitochondria. However, how EBV regulates B cell mitochondrial function and metabolic fluxes remains unclear. Here, we show that EBV boosts cardiolipin (CL) biosynthesis, essential for mitochondrial cristae biogenesis, via EBV nuclear antigen 2/MYC-induced CL enzyme transactivation. Pharmacological and CRISPR genetic analyses underscore the essentiality of CL biosynthesis in EBV-transformed B cells. Metabolomic and isotopic tracing highlight CL's role in sustaining respiration, one-carbon metabolism, and aspartate synthesis. Disrupting CL biosynthesis destabilizes mitochondrial matrix enzymes pivotal to these pathways. We demonstrate EBV-induced CL metabolism as a therapeutic target, offering synthetic lethal strategies against EBV-associated B cell malignancies.PMID:39879311 | DOI:10.1126/sciadv.adr8837

Sci Adv. 2025 Jan 31;11(5):eads0535. doi: 10.1126/sciadv.ads0535. Epub 2025 Jan 29.ABSTRACTKetogenesis is a dynamic metabolic conduit supporting hepatic fat oxidation particularly when carbohydrates are in short supply. Ketone bodies may be recycled into anabolic substrates, but a physiological role for this process has not been identified. Here, we use mass spectrometry-based 13C-isotope tracing and shotgun lipidomics to establish a link between hepatic ketogenesis and lipid anabolism. Unexpectedly, mouse liver and primary hepatocytes consumed ketone bodies to support fatty acid biosynthesis via both de novo lipogenesis (DNL) and polyunsaturated fatty acid (PUFA) elongation. While an acetoacetate intermediate was not absolutely required for ketone bodies to source DNL, PUFA elongation required activation of acetoacetate by cytosolic acetoacetyl-coenzyme A synthetase (AACS). Moreover, AACS deficiency diminished free and esterified PUFAs in hepatocytes, while ketogenic insufficiency depleted PUFAs and increased liver triacylglycerols. These findings suggest that hepatic ketogenesis influences PUFA metabolism, representing a molecular mechanism through which ketone bodies could influence systemic physiology and chronic diseases.PMID:39879309 | DOI:10.1126/sciadv.ads0535

PLoS Biol. 2025 Jan 29;23(1):e3002998. doi: 10.1371/journal.pbio.3002998. eCollection 2025 Jan.ABSTRACTUbiquitin-conjugating enzymes (E2s) are key for protein turnover and quality control via ubiquitination. Some E2s also physically interact with the proteasome, but it remains undetermined which E2s maintain proteostasis during aging. Here, we find that E2s have diverse roles in handling a model aggregation-prone protein (huntingtin-polyQ) in the Drosophila retina: while some E2s mediate aggregate assembly, UBE2D/effete (eff) and other E2s are required for huntingtin-polyQ degradation. UBE2D/eff is key for proteostasis also in skeletal muscle: eff protein levels decline with aging, and muscle-specific eff knockdown causes an accelerated buildup in insoluble poly-ubiquitinated proteins (which progressively accumulate with aging) and shortens lifespan. Mechanistically, UBE2D/eff is necessary to maintain optimal proteasome function: UBE2D/eff knockdown reduces the proteolytic activity of the proteasome, and this is rescued by transgenic expression of human UBE2D2, an eff homolog. Likewise, human UBE2D2 partially rescues the lifespan and proteostasis deficits caused by muscle-specific effRNAi and re-establishes the physiological levels of effRNAi-regulated proteins. Interestingly, UBE2D/eff knockdown in young age reproduces part of the proteomic changes that normally occur in old muscles, suggesting that the decrease in UBE2D/eff protein levels that occurs with aging contributes to reshaping the composition of the muscle proteome. However, some of the proteins that are concertedly up-regulated by aging and effRNAi are proteostasis regulators (e.g., chaperones and Pomp) that are transcriptionally induced presumably as part of an adaptive stress response to the loss of proteostasis. Altogether, these findings indicate that UBE2D/eff is a key E2 ubiquitin-conjugating enzyme that ensures protein quality control and helps maintain a youthful proteome composition during aging.PMID:39879147 | DOI:10.1371/journal.pbio.3002998

Eur J Nucl Med Mol Imaging. 2025 Jan 29. doi: 10.1007/s00259-025-07091-8. Online ahead of print.ABSTRACTPURPOSE: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.METHODS: We trained a generative model on 99mTc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis. A blinded reader study was performed to assess the clinical validity and quality of the generated data. We investigated the added value of the generated data by augmenting an independent small single-center dataset with synthetic data and by training a deep learning model to detect abnormal uptake in a downstream classification task. We tested this model on 7,472 scans from 6,448 patients across four external sites in a cross-tracer and cross-scanner setting and associated the resulting model predictions with clinical outcomes.RESULTS: The clinical value and high quality of the synthetic imaging data were confirmed by four readers, who were unable to distinguish synthetic scans from real scans (average accuracy: 0.48% [95% CI 0.46-0.51]), disagreeing in 239 (60%) of 400 cases (Fleiss' kappa: 0.18). Adding synthetic data to the training set improved model performance by a mean (± SD) of 33(± 10)% AUC (p < 0.0001) for detecting abnormal uptake indicative of bone metastases and by 5(± 4)% AUC (p < 0.0001) for detecting uptake indicative of cardiac amyloidosis across both internal and external testing cohorts, compared to models without synthetic training data. Patients with predicted abnormal uptake had adverse clinical outcomes (log-rank: p < 0.0001).CONCLUSIONS: Generative AI enables the targeted generation of bone scintigraphy images representing different clinical conditions. Our findings point to the potential of synthetic data to overcome challenges in data sharing and in developing reliable and prognostic deep learning models in data-limited environments.PMID:39878897 | DOI:10.1007/s00259-025-07091-8

Biofactors. 2025 Jan-Feb;51(1):e2156. doi: 10.1002/biof.2156.ABSTRACTAtherosclerosis is a major cause of morbidity and mortality worldwide; in Israel, ischemic heart disease is the second leading cause of death for both genders aged 45 and above. Atherosclerosis involves stiffening of the arteries due to the accumulation of lipids and oxidized lipids on the blood vessel walls, triggering the development of artery plaque. Coronary artery disease (CAD) is the most common manifestation of atherosclerosis. The prevalence of CAD in the general population remains high, despite efforts to improve the identification of risk factors and preventive treatments. The discovery of new biomarkers is vital to improving the diagnosis of CAD and its risk factors. We aimed to identify novel biomarkers that could provide an early diagnosis of coronary artery atherosclerotic plaques, their type, and the percentage of stenosis. We used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAD detection. The study consisted of 109 patients who underwent cardiac computed tomography angiography at the Cardiology Department of Ziv Medical Center. Fifty-four patients were diagnosed with coronary atherosclerotic plaques (CAD group), and 55 without plaques used control. Untargeted metabolomics using LC-MS/MS revealed 2560 metabolites in the patients' serum: 106 showed statistically significant upregulation in the serum of the CAD group compared with the healthy control group (p < 0.05). These metabolites belonged to the following chemical families: acyl-carnitines, cyclodipeptides, lysophosphatidylcholine, and primary bile acids. In contrast, 98 metabolites displayed statistically significant downregulation in the serum of the CAD group compared with the control group, belonging to the following chemical families: GABA amino acids and derivatives (inhibitory neurotransmitters), lipids, and secondary bile acids. Our comprehensive untargeted serum metabolomic analysis revealed biomarkers that can be used for the diagnosis of patients with CAD. Further cohort studies with a larger number of participants are needed to validate the detected biomarkers.PMID:39878362 | DOI:10.1002/biof.2156

Nat Prod Res. 2025 Jan 29:1-9. doi: 10.1080/14786419.2025.2457123. Online ahead of print.ABSTRACTLeonotis nepetifolia (L.) R. Br., a plant used in traditional medicine, has underexplored phytochemical and toxicological profiles. This study investigates the metabolite profile of L. nepetifolia flowers and assesses their toxicity using zebrafish (ZF) embryos. The main active compounds were characterised using metabolomic approaches. ZF embryos were exposed to methanol extract (CEF), n-hexane (FHF) and ethyl acetate (FAF) fractions at different concentrations for 96 h. Toxicological effects were assessed including acetylcholinesterase activity, lipid peroxidation, cardiotoxicity, as well as hatching delay, developmental defects and morphological malformations. Phytochemical analysis revealed diverse metabolites, including phytosterols, terpenoids, flavonoids and phenylpropanoids. Verbascoside, a major compound, was isolated from the flowers for the first time. Toxicological assessments showed that CEF and FAF caused various toxic effects, with FAF showing pronounced embryotoxic and teratogenic effects. This study highlights the chemical diversity and potential toxicological risks of L. nepetifolia, emphasising the need for thorough evaluations of herbal medicines.PMID:39878299 | DOI:10.1080/14786419.2025.2457123

Int J Surg. 2025 Jan 29. doi: 10.1097/JS9.0000000000002288. Online ahead of print.ABSTRACTBACKGROUND: High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients.METHODS: Nine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app.RESULTS: The random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750-0.780) and a Brier score of 0.183 (95% CI: 0.175-0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/.CONCLUSION: An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.PMID:39878156 | DOI:10.1097/JS9.0000000000002288

Curr Alzheimer Res. 2025 Jan 28. doi: 10.2174/0115672050350196250110092338. Online ahead of print.ABSTRACTBACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative condition with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect metabolic disturbances in biofluids, which may be advantageous for early detection of some ADrelated changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.METHODS: The data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal cohort study with three assessment time points to investigate the predictive ability of baseline metabolomic data for modeling longitudinal fluorodeoxyglucose-positron emission tomography (FDG-PET) trajectory changes in AD patients. A total number of 44 participants with AD were included. The cognitive abilities of participants were evaluated using the Alzheimer's Disease Assessment Scale (ADAS) and the Mini-Mental State Examination (MMSE), while the overall severity of dementia was measured by the Clinical Dementia Rating-Sum of Boxes (CDR-SB). We employed the ADNI's FDG MetaROIs (Meta Regions of Interest) dataset to identify AD-associated hypometabolism in the brain. These MetaROIs were selected based on areas frequently mentioned in FDG-PET studies of AD and MCI subjects.RESULTS: Across models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).CONCLUSION: This research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.PMID:39878109 | DOI:10.2174/0115672050350196250110092338

Food Funct. 2025 Jan 29. doi: 10.1039/d4fo06100d. Online ahead of print.ABSTRACTThis study aimed to investigate the effects of heat-killed Lactiplantibacillus plantarum N1 (HK-N1) and lipoteichoic acid (LTA) derived from it on alleviating insulin resistance by modulating the gut microbiota and amino acid metabolism. High-fat diet (HFD)-fed mice were administered live bacteria or HK-N1, and the results demonstrated that HK-N1 significantly reduced epididymal adipocyte size and serum low density lipoprotein-cholesterol, and improved insulin resistance by increasing the YY peptide and glucagon-like peptide levels. HK-N1 also modulated the gut microbiome composition, enhancing microbiota uniformity and reducing the abundance of Ruminococcus, Oscillospira and norank_f_Mogibacteriaceae. Three main active substances obtained from HK-N1 (membrane protein, peptidoglycan, and lipoteichoic acid) were also used to investigate their potential effects in hyperglycemic zebrafish. Only LTA reduced blood sugar and altered the gut microbiome, particularly reducing Aeromonas, which is positively related to hyperglycemia. Untargeted metabolomics revealed that LTA improved vitamin and amino acid metabolism, thereby alleviating metabolic disorders in zebrafish. Collectively, our findings indicate that HK-N1, primarily through LTA, modulated insulin sensitivity by regulating the gut microbiota and amino acid metabolism, offering a potential therapeutic strategy for insulin resistance and type 2 diabetes mellitus.PMID:39877991 | DOI:10.1039/d4fo06100d

Curr Res Microb Sci. 2025 Jan 7;8:100338. doi: 10.1016/j.crmicr.2024.100338. eCollection 2025.ABSTRACTLegionella pneumophila, the causative agent of Legionnaires' disease, interacts in the environment with free-living amoebae that serve as replicative niches for the bacteria. Among these amoebae, Acanthamoeba castellanii is a natural host in water networks and a model commonly used to study the interaction between L. pneumophila and its host. However, certain crucial aspects of this interaction remain unclear. One such aspect is the role of oxidative stress, with studies focusing on reactive oxygen species (ROS) production by the host and putting less emphasis on the involvement of the host's antioxidant defenses during the infectious process. In this study, we propose to examine the consequences of infection with L. pneumophila wild-type or with an isogenic ΔdotA mutant strain, which is unable to replicate intracellularly, on A. castellanii. For this purpose, we looked at the host ROS levels, host antioxidant defense transcripts, and metabolites linked to the amoeba's antioxidant defenses. It is known that L. pneumophila WT can block the activation of NADPH oxidase as soon as it enters the macrophage and suppress ROS production compared to ΔdotA mutant strain. In addition, it has been shown in macrophages that L. pneumophila WT decreases ROS at 24 h p.i.; here we confirm this result in amoebae and suggest that this decrease could be partly explained by L. pneumophila differentially regulated host antioxidant defense transcripts at 6 h p.i.. We also explored the metabolome of A. castellanii infected or not with L. pneumophila. Among the 617 metabolites identified, four with reduced abundances during infection may be involved in antioxidant responses. This study suggests that L. pneumophila could hijack the host's antioxidant defenses during its replication to maintain a reduced level of ROS.PMID:39877885 | PMC:PMC11772960 | DOI:10.1016/j.crmicr.2024.100338

Gastro Hep Adv. 2024 Sep 12;4(2):100551. doi: 10.1016/j.gastha.2024.09.006. eCollection 2025.ABSTRACTBACKGROUND AND AIMS: Steatotic liver disease (SLD) is the most common chronic liver disease strongly associated with metabolic dysfunction, but its pathogenesis remains incompletely understood. Exploring plasma circulating metabolites may help in elucidating underlying mechanisms and identifying new biomarkers for SLD.METHODS: We examined cross-sectionally the association between plasma metabolites and SLD as well as liver enzymes using data from 4 population-based cohort studies (Rotterdam study, Avon Longitudinal Study of Parents and Children, The Insulin Resistance Atherosclerosis Family Study, and Study of Latinos). Metabolites were assessed in the Nightingale platform (n = 225 metabolites) by nuclear magnetic resonance spectroscopy and in the Metabolon platform (n = 991 metabolites) by ultra-high-performance liquid chromatography-mass spectrometry. Serum levels of liver enzymes (alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transpeptidase) were measured and SLD was diagnosed by ultrasound or computed tomography scan. Logistic and linear regression models were performed per cohort and meta-analyzed. A false discovery rate < 0.05 was considered as significant threshold.RESULTS: Several metabolites were significantly associated with SLD and liver enzymes, of which 21 metabolites were associated with both traits. The most significant associations were observed with phenylalanine, triglycerides in (high-density lipoprotein, intermediate-density lipoprotein, and small low-density lipoprotein), fatty acid (FA) ratios of (18:2 linoleic acid-to-total FA, omega 6 FA-to-total FA, and polyunsaturated FA-to-total FA) from the Nightingale and glutamate and sphingomyelin from the Metabolon platform. Other associated metabolites were mainly involved in lipid, amino acid, carbohydrates, and peptide metabolism.CONCLUSION: Our study indicates a landscape of circulating metabolites associated with SLD. The identified metabolites may contribute to a better understanding of the metabolic pathways underlying SLD and hold promising for potential biomarkers in early diagnosis and monitoring of the disease.PMID:39877862 | PMC:PMC11772964 | DOI:10.1016/j.gastha.2024.09.006

JOR Spine. 2025 Jan 27;8(1):e70042. doi: 10.1002/jsp2.70042. eCollection 2025 Mar.ABSTRACTBACKGROUND: Chronic low back pain (LBP) is a significant global health concern, often linked to vertebral bone marrow lesions (BML), particularly fatty replacement (FR). This study aims to explore the relationship between the gut microbiome, serum metabolome, and FR in chronic LBP patients.METHODS: Serum metabolomic profiling and gut microbiome analysis were conducted in chronic LBP patients with and without FR (LBP + FR, n = 40; LBP, n = 40) and Healthy Controls (HC, n = 31). The study investigates alterations in branched-chain amino acids (BCAAs) levels and identifies key microbial species associated with BCAA metabolism. In vitro experiments elucidate the role of BCAAs in adipogenesis of bone marrow mesenchymal stem cells (BM-MSCs) via the SIRT4 pathway.RESULTS: Chronic LBP patients with FR exhibit depleted BCAA levels in their serum metabolome, along with alterations in the gut microbiome. Specific microbial species, including Ruminococcus gnavus, Roseburia hominis, and Lachnospiraceae bacterium 8 1 57FAA, are identified as influential in BCAA metabolism and BM-MSCs metabolism. In vitro experiments demonstrate the ability of BCAAs to induce BM-MSCs adipogenesis through SIRT4 pathway activation.CONCLUSION: This study sheds light on the intricate relationship between the disturbed gut ecosystem, serum metabolites, and FR in chronic LBP. Dysbiosis in the gut microbiome may contribute to altered BCAA degradation, subsequently promoting BM-MSCs adipogenesis and FR. Understanding these interactions provides insights for targeted therapeutic strategies to mitigate chronic LBP associated with FR by restoring gut microbial balance and modulating serum metabolite profiles.PMID:39877797 | PMC:PMC11772216 | DOI:10.1002/jsp2.70042

Front Plant Sci. 2025 Jan 14;15:1488281. doi: 10.3389/fpls.2024.1488281. eCollection 2024.ABSTRACTNitrogen deficiency is a key constraint on crop yield. Cassava, the world's sixth-largest food crop and a crucial source of feed and industrial materials, can thrive in marginal soils, yet its yield is still significantly affected by limited nitrogen availability. Investigating cassava's response mechanisms to nitrogen scarcity is therefore essential for advancing molecular breeding and identifying nitrogen-efficient varieties. This research undertook a comprehensive analysis of cassava seedlings' physiological, gene expression, and metabolite responses under low nitrogen stress. Findings revealed that nitrogen deficiency drastically suppressed seedling growth, significantly reduced nitrate and ammonium transport to aerial parts, and led to a marked increase in carbohydrate, reactive oxygen species, and ammonium ion levels in the leaves. Transcriptomic and metabolomic analyses further demonstrated notable alterations in genes and metabolites linked to carbon and nitrogen metabolism, flavonoid biosynthesis, and the purine metabolic pathway. Additionally, several transcription factors associated with cassava flavonoid biosynthesis under nitrogen-deficient conditions were identified. Overall, this study offers fresh insights and valuable genetic resources for unraveling cassava's adaptive mechanisms to nitrogen deprivation.PMID:39877744 | PMC:PMC11772423 | DOI:10.3389/fpls.2024.1488281

Front Plant Sci. 2025 Jan 14;15:1484688. doi: 10.3389/fpls.2024.1484688. eCollection 2024.ABSTRACTDrought stress inhibits Salvia miltiorrhiza Bunge (S. miltiorrhiza) seedling growth and yield. Here, we studied the effects of drought stress on the different parts of S. miltiorrhiza seedlings through physiological, transcriptomic, and metabolomics analyses, and identified key genes and metabolites related to drought tolerance. Physiological analysis showed that drought stress increased the accumulation of hydrogen peroxide (H2O2), enhanced the activity of peroxidase (POD), decreased the activity of catalase (CAT) and the contents of chlorophyll b and total chlorophyll, reduced the degree of photosynthesis, enhanced oxidative damage in S. miltiorrhiza seedlings, and inhibited the growth of S. miltiorrhiza plants. Transcriptome analyses revealed 383 genes encoding transcription factors and 80 genes encoding plant hormones as hypothetical regulators of drought resistance in S. miltiorrhiza plants. Moreover, differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs) are involved in a variety of biological processes, such as proline and glycine betaine metabolism, and biosynthesis of tanshinones and phenolic acids. Additionally, it has barely been reported that the AHL gene family may be involved in regulating the neocryptotanshinone biosynthesis. In conclusion, our results suggest that drought stress inhibits S. miltiorrhiza seedling growth by enhancing membrane lipid peroxidation, attenuating the antioxidant system, photosynthesis, and regulating proline and glycine betaine metabolism, transcription factors and plant hormones, and tanshinones and phenolic acid metabolism pathways. This study provides new insights into the complex mechanisms by which S. miltiorrhiza responds to drought stress.PMID:39877738 | PMC:PMC11772496 | DOI:10.3389/fpls.2024.1484688

Front Plant Sci. 2025 Jan 14;15:1549259. doi: 10.3389/fpls.2024.1549259. eCollection 2024.NO ABSTRACTPMID:39877735 | PMC:PMC11772417 | DOI:10.3389/fpls.2024.1549259