Metabolites

Background & AimsMetabolic disorders such as dyslipidemia, metabolic dysfunction-associated steatotic liver disease (MASLD), and diabetes are promoted by chronic pro-inflammatory and pro-oxidative states. Paraoxonase 1 (PON1), a liver-derived HDL-associated enzyme, plays an important antioxidant role by hydrolyzing oxidized lipids and protecting against oxidative stress- induced damage. Genetic variation in PON1, particularly in promoter and coding regions, modulates enzyme expression and activity, thereby influencing susceptibility to metabolic and cardiovascular diseases. This study investigated the genetic determinants of serum paraoxonase (PONase) activity and their relationship with dysmetabolic phenotypes. MethodsA genome-wide association study was conducted in 922 Portuguese individuals from the PREVADIAB2 cohort. Genetic variants and haplotypes related to PONase activity were analyzed, and associations with dysglycemia and liver fibrosis were evaluated in individuals aged over 55 years. ResultsWe identified two key PON1 variants as determinants of PONase activity: rs2057681 (in strong linkage disequilibrium with the non-synonymous Q192R variant) and rs854572 (located in the promoter region). Analysis of rs854572-rs2057681 haplotypes revealed that specific combinations differentially modulate PONase activity and confer risk or protection for dysglycemia and liver fibrosis, depending on the rs2057681 genotype context. Notably, although PONase activity was strongly associated with PON1 variants, it did not directly correlate with dysmetabolic phenotypes, suggesting that genetic context and haplotype structure, rather than enzyme activity alone, shape disease susceptibility. ConclusionsThese findings highlight the complex genetic architecture of PON1 and its role in metabolic disease risk, supporting the use of PON1 genetic information to uncover predisposition to dysmetabolic conditions. Our results provide insights into the interplay between PON1 genetics, enzyme function, and dysmetabolism, with implications for risk stratification in metabolic liver disease. Lay SummaryPON1 is a liver-derived gene that encodes an enzyme involved in protection against oxidative stress, a key contributor to metabolic liver disease and diabetes. In this study, we found that specific combinations of PON1 genetic variants are associated with abnormalities in blood glucose regulation and with markers of liver fibrosis. These associations were dependent on genetic configuration rather than enzyme activity alone, suggesting that PON1 genetic information may help identify individuals at higher risk of metabolic liver disease.

This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships re-flected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, esti-mates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.

Cancer-related casualties are the most common cause of death worldwide. The discovery of biomarkers is of utmost importance for diagnosis and disease monitoring. Herein, we performed a comprehensive metabolomics biomarker discovery effort in plasma from 615 lung, ovarian and colorectal cancer patients at diagnosis and 95 non-cancerous control subjects. This pan-cancer investigation identified specific panels of metabolites in the entire sample cohort with a high discriminating power and demonstrated by combined ROC AUC values of up to 0.95. The identified metabolites are mainly associated with lipid and amino acid metabolism as well as xenobiotic transformation. These metabolite panels of high predictive power provide new metabolic insights in these cancers and demonstrate the potential of metabolomics for improved diagnosis and monitoring disease progression.

Background. Breast cancer (BC) remains the most diagnosed malignancy and leading cancer-related cause of mortality in women worldwide. Although blood-based untargeted metabolomics has emerged as a promising modality for detecting early-stage BC, the clinical translation of this approach has been bottlenecked by two unresolved issues: (i) the field has almost exclusively relied on serum or plasma, which require venipuncture and cold-chain logistics, and (ii) machine-learning models reported on such data are frequently validated with protocols that are blind to analytical batch structure, producing optimistically biased performance estimates. Methods. We present a breast cancer detection study based on dried blood spots (DBS), an analytical matrix that enables self-collection and ambient-temperature shipping. A cohort of 2,734 participants (114 biopsy-confirmed BC cases; 2,620 non-cancer controls) was profiled by untargeted LC-MS/MS on a Thermo Scientific Orbitrap IQ-X coupled to a Vanquish UHPLC. A 39-metabolite panel meeting MSI Level 1 identification criteria was pre-specified a priori from the published breast-cancer metabolomics literature, frozen prior to LC-MS acquisition, and applied to the present cohort without any feature selection on the data. Six standard supervised-learning architectures (LASSO, Elastic Net, Linear SVM, PLS-DA, OPLS-DA, XGBoost) were evaluated on this pre-specified panel; OPLS-DA is reported only in the sex-matched subgroup analysis where a single-seed 5-fold stratified protocol permits a directly comparable fit. Per-batch control-median normalization is applied upstream; kNN imputation, log transform, and robust scaling are fit within each training fold. The evaluation battery comprises batch-aware StratifiedGroupKFold CV at single-seed (seed=42) with inter-seed SD quantified across 10 independent seeds, batch-aware nested CV, a 100-seed held-out 20%-batch validation with disjoint-batch isotonic probability calibration (30% calibration partition), PPV/NPV reporting at multiple operating points and three deployment prevalences, subgroup analyses by TNM stage and tumor grade, pathway-ablation sensitivity analysis, and a 1,000-iteration permutation test. Results. Under batch-aware evaluation (StratifiedGroupKFold, single-seed=42), AUC ranged from 0.914 to 0.949 across classifiers, with LASSO achieving 0.928 and XGBoost 0.949; inter-seed SD across 10 seeds was 0.002-0.006. At 95% specificity, LASSO reached 75.4% sensitivity and XGBoost 81.6%. Held-out batch validation (100 seeds) yielded mean AUC 0.912 for Elastic Net and 0.935 for XGBoost, confirming robust generalization. All 39 panel features showed high coefficient stability, and permutation testing on representative classifiers (LASSO, Linear SVM, PLS-DA) yielded p <= 0.001. Subgroup analyses showed weaker detection of stage IIA tumors (AUC 0.87, n=40) compared with stage IIB/IIIA (AUC 0.95), consistent with stronger metabolic signatures in more advanced disease. Bootstrap coefficient consistency of the Elastic Net classifier confirmed that all 39 panel features received a non-zero multivariate weight in >=80% of 100 stratified bootstraps. Conclusions. On this cohort of diagnosed, pre-treatment breast-cancer cases, DBS LC-MS metabolomic profiling delivers classification performance (AUC 0.928 for LASSO and 0.949 for XGBoost under batch-aware GroupKFold CV at single-seed=42; held-out AUC 0.912-0.935) that is robust across classifier families and biological pathways. The DBS matrix is non-radiating, self-collectable by finger-prick, and mailable at ambient temperature. Performance is weaker on stage IIA than on more advanced disease, and prospective validation in an independent asymptomatic screening cohort is required before clinical positioning as a decentralized triage modality.

SO_SCPLOWUMMARYC_SCPLOWChemotherapy in breast cancer (BC) can substantially affect mental wellness. Advances in metabolomics enable comprehensive profiling of metabolic changes over time during and after treatment, offering insights into biological mechanisms linking chemotherapy to mental health outcomes. To study the association between metabolite profiles and mental wellness, correlation-based analyses are particularly useful. Spearmans rho is a widely used correlation measure and popular alternative to Pearsons correlation, since it also applies to non-linear association between variables. However, existing methods are not designed for longitudinal data and do not allow for covariate adjustments. In this paper, we propose a novel regression-based framework grounded in a class of semiparametric models, the functional response models, to extend this popular correlation measure to longitudinal settings with missing data under the missing at random assumption. This framework facilitates inferences about temporal changes in correlations over time and association of explanatory variables for such changes. We use simulation studies to evaluate performance of the approach with moderate sample sizes. We apply the approach to a one-year longitudinal substudy of the EPIGEN study to examine the longitudinal association between metabolite profiles and mental wellness in BC patients undergoing chemotherapy. The identified metabolites may serve as candidates for future in-depth bioinformatics analyses and translational investigations.

BackgroundConstructing digital twins in healthcare requires biological data sources that are simultaneously informative, dynamic, and practical for routine collection. Dried blood spot (DBS) sampling combined with untargeted metabolomics is well suited to meet these requirements: DBS can be self-collected at home and mailed at ambient temperature, while untargeted LC-MS/MS captures thousands of metabolites reflecting individual physiology, lifestyle, and exposures. We previously demonstrated proof-of-concept individual identification from DBS-derived metabolomic profiles in 277 volunteers (80-92% accuracy). Here, we report a large-scale validation on a substantially expanded cohort. MethodsWe collected 18,288 DBS samples from 1,257 individuals across 134 analytical batches over 15 months. Samples were self-collected at home, mailed via standard postal service, and analyzed by untargeted LC-MS/MS on a high-resolution Orbitrap platform in positive ESI mode. Our classification pipeline comprises batch-aware normalization, supervised feature selection, biological signal filtering, dimensionality reduction, and user-level majority voting across all available samples. This voting reflects the real-world use case: participants contribute multiple self-collected DBS cards over time, taken at different times of day and under varying conditions. We employed GroupKFold cross-validation with group=batch to ensure zero batch leakage between training and testing sets. ResultsIn 10-fold GroupKFold cross-validation (group=batch, zero batch leakage), our pipeline achieved 94.1% user-level identification accuracy (85.5% sample-level). In a fully held-out validation on 17 future batches -- with all feature selection, normalization, and model fitting performed exclusively on training data -- performance was even stronger: 96.1% user-level and 92.6% sample-level across 1,134 classes (chance level: 0.088%). Feature selection stability was confirmed via bootstrap analysis. We identified batch leakage as a critical methodological pitfall for the field: naive random splitting inflated accuracy by sharing 92.8% of test samples (user, batch) pairs with the training set. The top discriminative metabolites span biologically relevant pathways including amino acid metabolism, fatty acid transport, and sphingolipid biosynthesis. ConclusionsUntargeted metabolomics from dried blood spots supports batch-aware, closed-set individual identification in a single-laboratory setting, with potential relevance for longitudinal sample-to-person linkage in future digital twin workflows.

Lipid metabolism is increasingly recognized as a hallmark of cancer, yet translating lipidomic discoveries into clinically actionable biomarkers remains constrained by analytical variability and limited standardized validation frameworks. This challenge is further compounded by a chicken-or-egg problem, where expensive standards and labelled internal standards are required to identify and quantitate target lipids, but the diagnostic importance of these targets is uncertain until they can be reliably measured. Previous work had indicated the potential of 48 lipid biomarker species for the prediction of breast cancer from plasma samples using high resolution liquid chromatography mass spectrometry. This study aimed to identify each of these 48 species and develop a quantitative method to determine the absolute concentrations of these lipids in plasma to provide the basis for the development of a clinical assay for use in breast cancer detection. In doing so, we present a pragmatic workflow that bridges lipid discovery with lipid identification and robust quantitative analysis. A curated library of 48 lipid species was established using authentic standards to verify plasma lipids through retention-time matching and high-resolution spectral comparison. In plasma, 41 lipids were confidently identified based on co-elution with standards and diagnostic fragment ions. Method qualification, including assessment of accuracy, precision, recovery, and linearity, was performed across all 48 lipids in parallel with identification, and 46 lipids ultimately met all predefined qualification criteria. Notably, practical constraints, including time, cost, and availability of authentic standards, necessitated performing identification and targeted method development in parallel, highlighting challenges inherent to translating lipidomics into commercial or clinical assays. This workflow provides a reproducible framework for harmonizing lipid identification and quantification, enabling the reliable integration of lipidomic data into biomarker discovery and clinical applications.

Background & Aims: Per- and polyfluoroalkyl substances (PFAS) are persistent endocrine-disrupting chemicals associated with metabolic dysfunction, including metabolic dysfunction-associated steatotic liver disease (MASLD). While PFAS perturb lipid and bile acid (BA) metabolism in a sex-specific manner, the underlying mechanisms remain unclear. We tested whether steroid hormones mediate PFAS-associated metabolic alterations. Methods: In 104 patients with biopsy-characterized MASLD, we performed sex-stratified analyses applied liquid chromatography coupled to mass spectrometry (LC-MS) for chemical analysis, integrating circulating steroids, PFAS exposure, hepatic lipidomics and BA profiles. Results: Steroid hormones were associated with MASLD severity in a sexually-dimorphic manner. Dihydrotestosterone showed consistent inverse associations with steatosis, fibrosis, necroinflammation and insulin resistance, particularly in females. PFAS exposure was associated with altered steroid profiles, predominantly indicating suppressed steroidogenesis in females. These PFAS-associated hormonal changes were linked to downstream alterations in hepatic lipids and BAs. Mediation analysis supported indirect effects of PFAS on metabolic pathways via steroids, including testosterone/epi-testosterone-mediated effects on ether phospholipids and estradiol-mediated effects on lithocholic acid. Females exhibited stronger PFAS-steroid-BA associations, whereas males showed weaker, lipid-centric effects. Conclusions: PFAS exposure is associated with sex-specific disruption of steroid hormone pathways that may link environmental exposure to lipid and BA dysregulation in MASLD. These findings identify steroid hormones as potential key mediators of PFAS-associated metabolic dysfunction and highlight sex as a critical determinant in environmental liver disease.

Aims The oral glucose tolerance test (OGTT) is effective for detecting post-load dysglycemia, but it is burdensome and therefore not routinely used. Continuous glucose monitoring (CGM) offers a convenient way to capture real-world glucose patterns, yet it remains unclear whether CGM-derived metrics reflect OGTT-defined dysglycemia. We therefore aimed to evaluate CGM-derived and clinical metrics for predicting OGTT 2-hour glucose, classifying OGTT-defined dysglycemia, and assessing day-to-day repeatability. Methods We analyzed a cohort with paired free-living CGM and OGTT. Multiple CGM-derived metrics and clinical measures were compared for prediction of OGTT 2-hour glucose, classification of OGTT-defined dysglycemia, and day-to-day stability. Predictive performance was assessed primarily by leave-one-out (LOO) R^2, and day-to-day repeatability by intraclass correlation coefficients (ICC). Results The glycemic persistence index (GPI), a metric integrating the magnitude and duration of glycemic elevation, was the strongest single predictor of OGTT 2-hour glucose (LOO R^2 = 0.439). GPI also showed strong day-to-day repeatability (ICC = 0.665) and ranked first on a combined prediction-stability score. For classification of OGTT-defined dysglycemia, HbA1c had a slightly higher AUC than GPI, but GPI plus HbA1c performed best overall, indicating complementary information. Conclusions GPI was a strong predictor of OGTT 2-hour glucose and showed a favorable balance between predictive performance and day-to-day stability, supporting its potential utility as a CGM-derived marker of dysglycemia.

Chronic obstructive pulmonary disease (COPD), a chronic lung disease, involves complex metabolic disturbances that remain poorly characterized using non-invasive matrices. The metabolic alterations associated with cigarette smoke (CS), one of the major drivers of disease progression in COPD patients, have not been explored in detail. This study primarily aimed to investigate the metabolic signatures in COPD patients categorized into smoker (n=15), ex-smoker (n=11), and non-smoker (n=3) subgroups. Utilizing saliva as a noninvasive sample, we identified 26 metabolites with differential expression in smokers and 31 in ex-smokers. However, no such significant alteration was observed in the non-smokers subgroup. The multivariate analysis distinctly separated the COPD subgroups from healthy controls. Additionally, pathway enrichment analysis revealed perturbations in key metabolic pathways, including unsaturated fatty acid biosynthesis, arginine biosynthesis, the tricarboxylic acid (TCA) cycle, and pyruvate metabolism. Moreover, univariate Random forest analysis identified four metabolites (cyclopentanone, tetradecane 4-methyl, acetophenone, and scyllo-inositol) as potential biomarkers distinguishing COPD subgroups from healthy controls. This study offers novel molecular insights into the association of smoking with disease progression and provides a mechanistic understanding of COPD in different subgroups for better management of the disease. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/717654v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@11db4org.highwire.dtl.DTLVardef@1451fb5org.highwire.dtl.DTLVardef@124b62aorg.highwire.dtl.DTLVardef@133872a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Endophytic fungi residing within medicinal plants are emerging as prolific sources of structurally diverse bioactive secondary metabolites with applications in drug discovery. Azadirachta indica (Neem) and Melia azedarach (Melia), members of the Meliaceae family, are renowned for their rich phytochemical composition; however, the contribution of their endophytic fungi communities to this chemical diversity remains largely unexplored. Herein, endophytic fungi were isolated from leaves and bark of Neem and Melia collected in Kenya and cultured under distinct physical conditions, solid (plates) and liquid (broth) media to assess how culture environment influences compound production. Compounds were extracted and analyzed using gas chromatography-mass spectrometry (GCMS) to profile the chemical diversity associated with each endophytic fungi, physical culturing state and host plant. GCMS analysis revealed that while the host plant identity influences the presence of specific compounds, the dominant determinant of chemical diversity was intrinsic biosynthetic capacity of the endophytic fungi themselves. Several compounds were unique to endophytic fungi cultures, highlighting their role as independent sources of bioactive compounds. Culture conditions moderately influence metabolite profiles, demonstrating the importance of optimizing growth environments in experimental design and natural product bioprospecting. From the Neem samples, we found 53 compounds uniquely present in the broth samples (consisting of Neem powder and endophytic fungi), 22 found exclusively with the endophytic fungi from the Neem, and 31 compounds shared between the broth and the endophytic fungi samples. In Melia samples, 109 compounds were uniquely present in broth samples from Melia plant (consisting of Melia powder and endophytic fungi), 22 compounds were found exclusively with the endophytic fungi from the Melia, and 55 were shared between the broth and the endophytic fungi samples. Our comparative analysis assessed the Neem and Melia endophytic fungi exclusive samples and reported 12 shared compounds. 10 compounds were unique to Neem and 10 unique to Melia; however, their identities varied between the two categories. While GCMS enabled the identification of volatile and semi-volatile metabolites, future studies employing complementary metabolomic approaches, such as liquid chromatography-mass spectrometry (LCMS), ultra-high-performance liquid chromatography MS/MS (UHPLC MS/MS), or nuclear magnetic resonance (NMR) spectroscopy, would expand coverage to non-volatile, polar, and high molecular weight compounds, providing a more comprehensive understanding of endophyte-derived chemical diversity. These findings provide insights into the interplay between medicinal plants and their endophytes and establish a foundation for leveraging endophytic fungi from Neem and Melia as scalable sources of structurally complex natural products for pharmaceutical and biotechnological applications while minimizing ecological impact.

Background: Older in-patients have a higher prevalence of diabetes and cognitive impairment. Cognitive impairment can make blood glucose management more challenging, since patients might not remember to measure blood glucose or report symptoms. Investigating the accuracy of continuous glucose monitoring (CGM) compared to usual care will inform clinical interpretations in this vulnerable population. Aim: To compare CGM derived glucose metrics and point-of-care tests (POCT) in older in-patients with T2DM and cognitive impairment and to investigate CGM accuracy compared to POCT in the hospital settings with the same population. Methods: Thirty-two older people with comorbid T2DM and cognitive impairment were recruited within a tertiary care hospital in the UK. All participants were naive to CGM and were asked to wear blinded Dexcom G7 sensors for up to 10 days. All participants received usual care in their hospital stay including the use of POCT. Key accuracy metrics comprised the mean absolute relative difference (MARD), median absolute relative difference (median ARD), and Clarke Error Grid (CEG), correlation (R2) analysis. In addition, the percentage of CGM readings falling within +/-20% of reference glucose values when the reference was >5.6 mmol/L, or within +/-1.1 mmol/L when the reference was <=5.6 mmol/L (+/-20%/1.1 mmol/L) was calculated to assess analytical and clinical accuracy. Results: Thirty participants completed the study. CGM derived mean glucose for time in range (TIR= 4-10 mmol/mol) was 36.23% (min= 0%, max= 90%), time above range (TAR >= 10 mmol/mol) was 62.87% and time below range (TBR <= 3.9 mmol/mol) was 1.03%. Mean TIR based on available POCT readings was 40.84%, TAR was 57.24% and TBR 1.81%, showing similar readings as CGM derived glucose metrics. Comparison of the two resulted in a MARD of 17.4%, and median ARD of 12.2% and the outcome of +/-20%/1.1 mmol/L analysis was 72.3%. CEG analysis revealed that 99.3% of the data points fell within the clinically acceptable zones (Zone A and Zone B), and there was a strong correlation (R2=0.82) between CGM and POCT. CGM captured more hypoglycaemic readings in our participants. Conclusion: Our study suggests that CGM and POCT derived glucose metrics are largely similar for in-patients with diabetes and cognitive impairment. CGM remains as a safe and clinically acceptable tool, and able to capture more nocturnal hypoglycaemia compared to POCT in a subgroup of patients. These initial findings show that CGM might be a viable alternative for people with comorbid T2DM and cognitive impairment.

O_LIPlants produce diverse bouquets of specialized metabolites (SMs), yet only a fraction of the vast phytochemical space has been explored to date. Comparative analysis of SM profiles can reveal hotspots of biochemical novelty, while systematic profiling across taxonomic levels does presently not cover large plant families. C_LIO_LITo study core and accessory SM profiles in the Brassicaceae plant family, we fingerprinted 14 species by Liquid-Chromatography Mass-Spectrometry (LCMS/MS). We develop standardized experimental and computational workflows integrating in silico annotation tools to study consensus compound class and substructure distributions of SMs. Furthermore, we investigate the congruence of chemotaxonomy and species phylogeny across an extended panel of 17 species. C_LIO_LIUnique metabolite profiles were outstanding in Camelina sativa, Capsella rubella, and B. vulgaris, with the largest unique terpenoid profile annotated in C. sativa, accounting for 33.5% and 55.6% in positive and negative ionization mode, respectively. Substructure motifs were found to overlap with compound class predictions, highlighted for triterpenoids in Camelinodae. Furthermore, dual-tissue chemotaxonomic clustering resembled relationships of Brassica subgenomes across tissues. C_LIO_LIWe anticipate that our systematic approach can serve as a blueprint for investigating biochemical diversity in other plant lineages and can boost the characterization of plant natural product pathways. C_LI

The ability to understand and affect the course of complex, multi-system diseases like diabetes has been limited by a lack of well-designed, high-quality and large multimodal datasets. The NIH Bridge2AI AI-READI project (aireadi.org) aims to address this shortfall by generating an AI-ready dataset to support AI discoveries in type 2 diabetes mellitus (T2DM). This manual of procedures provides a detailed description of the AI-READI protocol.

Background and AimsFenofibrate is widely prescribed for hyperlipidaemia and has been associated with rare but severe cases of drug-induced liver injury (DILI), yet its effects on liver sinusoidal endothelial cells (LSECs) remain to be investigated. LSECs maintain a highly permeable specialized sinusoidal barrier characterized by transcellular pores (fenestrations), regulating the bidirectional transfer of circulating compounds to and from the hepatocytes. As drug-induced alterations in fenestration architecture could influence xenobiotic access to hepatocytes, these changes may modulate pathways associated with DILI. Understanding the effects of fenofibrate on LSEC ultrastructure may therefore provide insights into previously underexplored endothelial contributions to hepatic drug responses. MethodsBoth fenofibrate and its active metabolite, fenofibric acid, were evaluated for their effects on LSEC ultrastructure, mechanical properties, and functional markers. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) and were used to quantify fenestration architecture. AFM was additionally used to measure cellular mechanical properties, which were interpreted in the context of fluorescence-based quantification of cytoskeletal organization. Gene expression, viability, and cytotoxicity were assessed using PCR-based and biochemical assays. ResultsFenofibrate reduced fenestration number and porosity at both tested concentration (10, and 25 {micro}M). It also decreased the apparent Youngs modulus of LSECs, accompanied by changes in tubulin and actin architecture, without detectable cytotoxicity. In contrast, treatment with fenofibric acid did not result in significant structural or mechanical effects on LSECs, even at higher concentrations. ConclusionsTogether, these data identify LSECs as a drug-responsive hepatic cell type for fenofibrate, suggesting that LSECs could represent an underrecognized contributor to the complex, multifactorial processes underlying DILI. This work provides a framework for evaluating endothelial contributions to fenofibrate-associated liver effects in more complex models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/718907v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1d3f60corg.highwire.dtl.DTLVardef@bea13aorg.highwire.dtl.DTLVardef@14b27d8org.highwire.dtl.DTLVardef@124e0d3_HPS_FORMAT_FIGEXP M_FIG Fenofibrate reduces LSEC fenestrations and metabolic activity at higher concentrations, while its metabolite, fenofibric acid, does not affect LSEC, regardless of its concentration. C_FIG

BackgroundNicotinamide mononucleotide (NMN) and nicotinamide riboside (NR) are NAD+ precursor supplements widely marketed for metabolic health benefits. Despite billions of dollars in annual sales, no head-to-head randomized controlled trial (RCT) has compared their effects on metabolic endpoints, and no systematic characterization of why reliable comparison is currently impossible has been published. ObjectiveTo characterize the structural heterogeneity of the NMN and NR trial evidence bases across population, dose, duration, and biomarker dimensions; to formally assess transitivity; and to estimate indirect NMN versus NR effects where methodologically feasible using the Bucher indirect comparison method. MethodsFive databases (PubMed, Embase, Scopus, Web of Science, Cochrane CENTRAL) were searched from January 2018 to May 2025. Eligible studies were RCTs of oral NMN or NR versus placebo in adults reporting metabolic outcomes. A formal transitivity assessment was conducted comparing effect modifier distributions across NMN and NR trial arms. Random-effects pairwise meta-analyses were conducted for each precursor versus placebo, and Bucher indirect comparisons estimated NMN versus NR effects through the common placebo node. Risk of bias was assessed using RoB 2 and certainty of evidence using the GRADE/CINeMA framework. ResultsFifteen studies (5 NMN, 10 NR; 740 participants) were included. The NMN and NR trial evidence bases were systematically asymmetric across every major effect modifier: NR was dosed 1.9 to 9.2 times higher than NMN on a molar basis; NMN trials were conducted predominantly in East Asian populations while NR trials were predominantly Western; and available NAD+ pharmacodynamic measures used incompatible assay matrices precluding indirect comparison. Across 14 metabolically comparable outcomes, no indirect comparison reached statistical significance and all were rated Very Low certainty by GRADE/CINeMA, consistent with the structural limitations of the evidence base. Leave-one-out sensitivity analyses showed zero pairwise significance changes and one indirect significance change (triglycerides upon exclusion of Conze 2019). ConclusionCurrent evidence is structurally insufficient to support reliable indirect comparison of NMN and NR for metabolic outcomes. The barriers are quantifiable and modifiable: future head-to-head trials should use equimolar dosing (approximately 1,150 mg NMN is molar-equivalent to 1,000 mg NR), harmonized whole-blood NAD+ assays reported in mol/L, minimum 24 weeks duration, and enrollment of metabolically at-risk populations to generate interpretable comparative evidence. RegistrationPROSPERO 2026 CRD420261330487; registered prior to data screening.

The liver is widely considered to be one of the most conserved organs amongst vertebrates, with it being involved in blood detoxification, bile production and the metabolism of xenobiotic compounds. Liver organoids have previously been derived from several species and used as models of drug metabolism, toxicity, and fundamental tissue biology. To date, however, these models have not been developed from ruminant species, specifically cattle and sheep. Here we present the first report of the development and comprehensive characterisation of bovine and ovine liver organoids derived from primary liver tissue. When initially established, organoids from both species were comprised of KRT19- and KRT18-positive cholangiocytes. The capacity for organoids to differentiate into hepatocyte-enriched cultures was evaluated and it was noted that there was an increase in hepatocyte markers in bovine cultures. A comparative analysis of the liver tissue and organoids of both species revealed species-specific differences in gene expression, which were conserved within organoid cultures. Most notably, bovine liver tissue and organoids had enriched expression of genes associated with fatty acid uptake and storage whereas ovine samples had higher expression of genes associated with fatty acid conversion, highlighting fundamental differences between these two ruminant species. Differences in expression of cytochrome P450 family genes were identified alongside those associated with an inflammatory response specifically in bovine samples, whereas ovine samples had higher expression of genes associated with a protective immune response. Despite this, transcriptomic analysis of organoids from both species, cultured in both growth and differentiation media, revealed preserved expression of genes associated with key liver functions, including gluconeogenesis and xenobiotic metabolism. Transcripts associated with the flavin-containing monooxygenases (FMO) family were expressed in both organoid growth media and organoid development media (OGM and ODM respectively), and both species could metabolise triclabendazole into its primary metabolite triclabendazole sulfoxide, therefore validating the potential of the organoids to be applied as in vitro models of metabolism and/or toxicity. Overall, this study provides novel insights into differences in liver composition and function between ruminant species, as well as providing novel experimental models of the liver for both cattle and sheep.

Carnitines are a structurally diverse class of metabolites formed by conjugation of L-carnitine with fatty acids, amino acids, xenobiotics, and microbial metabolites. They play roles in transport, mitochondrial and peroxisomal metabolism, detoxification, and systemic signaling, yet their chemical diversity remains incompletely defined. We applied a pan-repository data mining strategy of LC-MS/MS data across GNPS/MassIVE, MetaboLights, and Metabolomics Workbench using MassQL diagnostic fragment ion filtering to systematically extract acylcarnitine spectra. This yielded a library of 34,222 unique MS/MS spectra representing 2,857 atomic compositions, corresponding to 3,872,050 detections. These datasets provide an MS/MS library for annotation, discovery, and contextualization of acylcarnitines, enabling identification of previously unknown carnitines, such as dihydroferulic acid conjugated carnitines and supporting future exploration of this metabolite class across host metabolism, diet, microbial activity, pharmacological exposures, and metabolic dysregulation.

PurposeAlanine transaminases (ALT), encoded by the GPT gene, catalyzes the reversible conversion of pyruvate and glutamate to alanine and alpha-ketoglutarate, thereby correlating carbohydrate and amino acid metabolism. However, its role in the human neural retina remains unclear. This study aimed to explore the expression, localization, and metabolic function of ALT in the human neural retina and its potential involvement in retinal diseases. MethodsALT1 and ALT2 expression and localization were examined in the retinas of healthy and diabetic retinopathy (DR) donors via immunoblotting and immunofluorescence. ALT function was assessed in ex vivo human retinal explants using pharmacological inhibition with beta-chloro-L-alanine (BCLA), followed by the analyses of enzyme activity, tissue injury, and transcriptomic responses. Stable-isotope tracing with 13C-and 15N-labelled substrates combined with GC-MS was used to define ALT-dependent carbon and nitrogen fluxes in macular and peripheral retinas. Redox level (NADPH/NADP+) was also evaluated under tert-butyl hydroperoxide-induced oxidative stress. ResultsALT1 and ALT2 were both expressed in the human neural retina, with prominent localization in Muller glia and photoreceptor inner segments. ALT1 displayed a diffuse cytoplasmic distribution, whereas ALT2 demonstrated a punctate pattern consistent with mitochondrial localization. In DR retinas, ALT1 expression was spatially disorganized and heterogeneous, while ALT2 remained comparatively preserved. Inhibition of ALT with BCLA markedly reduced ALT activity without causing overt cytotoxicity or major transcriptional changes. Isotope tracing demonstrated that retinal ALT predominantly channels pyruvate-derived carbon into alanine, whereas alanine was minimally contributed to pyruvate production under basal conditions. ALT inhibition suppressed alanine synthesis and release, redirected nitrogen flux towards glutamate, glutamine, and aspartate, and uncovered distinct metabolic adaptations in macular but not peripheral retinas. Under oxidative stress, ALT inhibition induced the decrease of NADP+/NADPH ratio and LDH release, indicating improved redox balance and reduced tissue injury. ConclusionsALT is previously unrecognized as a regulator of carbon and nitrogen partitioner in the human neural retina, contributing to redox homeostasis under stress. The altered distribution of ALT1 in DR retina and the protective metabolic effects of ALT inhibition suggest ALT as a potential contributor to retinal metabolic vulnerability and a candidate therapeutic target in retinal diseases.

Accurate, simultaneous, and efficient quantification of chemically diverse phytohormone species is a critical task towards understanding the complex system of phytohormone signaling pathways. Quantification of phytohormones with the commonly used technique liquid chromatography coupled to tandem mass spectrometry is susceptible to the influence of non-phytohormone components present in the sample, a phenomenon referred to as matrix effect. To reduce matrix effect, some phytohormone quantification methods include additional steps of cleanup of crude extracts. However, to what extent additional purification steps provide increased accuracy compared to simpler, less laborious methods is seldomly evaluated. We evaluated three previously described phytohormone extraction methods, two of which include solid-phase extraction and one that does not, in their ability to minimize matrix effect and generate accurate estimates of phytohormone species spanning six classifications, from fruit and leaf tissue of Solanum lycopersicum cv. Micro-Tom (tomato). Our results show that, while the methods that included solid phase extraction occasionally outperformed each other regarding matrix effect and/or recovery efficiency for broad range of phytohormones, they rarely outperformed the simpler single-phase extraction method. Short AbstractAccurate, simultaneous quantification of chemically diverse phytohormones by LC-MS/MS is frequently confounded by matrix effects, leading to the incorporation of additional purification steps. We systematically compared three published extraction protocols with or without solid-phase extraction in tomato tissues across six hormone classes. Solid-phase methods occasionally improved matrix suppression or recovery, but did not consistently outperform the single-phase approach, questioning the added value of extra cleanup steps, particularly when high-throughput is desired, as in the case of systems biology interrogations.