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PubMed2025

Polycystic ovary syndrome and idiopathic central precocious puberty: two sides of the same coin?

PURPOSE: Several studies have reported an association between central precocious puberty (CPP) and a higher prevalence of polycystic ovary syndrome (PCOS), raising the hypothesis that CPP may act as an early-life indicator of increased PCOS risk. This review investigates the shared genetic, epigenetic, and environmental factors potentially underlying CPP and PCOS, with the aim of clarifying their connection and supporting advances in early detection and management. METHODS: A comprehensive literature review was conducted using the PubMed/MEDLINE database, prioritizing research from the past two decades, supplemented by key studies from earlier years. This research included studies on genetic and epigenetic factors, endocrine-disrupting chemicals (EDCs), and metabolic influences related to CPP and PCOS. Both original research and review articles were selected, focusing on the identification of pathophysiological mechanisms and risk factors linking these disorders. RESULTS: Genome-wide association studies (GWAS) have linked genetic variants in the kisspeptin and neurokinin B signaling pathways to increased gonadotropin-releasing hormone (GnRH) secretion, triggering early puberty. Moreover, increased GnRH pulsatility contributes to elevated luteinizing hormone (LH) levels, altered LH/follicle-stimulating hormone (FSH) ratios, and ovarian hyperandrogenism—key pathophysiological features of PCOS. Additionally, epigenetic modifications, particularly changes in DNA methylation at CpG sites, have been observed in both CPP and PCOS. A hyperandrogenic intrauterine environment and inadequate fetal growth contribute to prenatal epigenetic alterations, while postnatal factors such as obesity, insulin resistance, and exposure to EDCs further influence epigenetic modifications. Although insulin resistance and hyperandrogenism are central features linking CPP to PCOS, the precise mechanisms underlying these associations remain complex and not yet fully elucidated. CONCLUSIONS: Identifying shared genetic and environmental factors influencing early puberty onset and PCOS highlights the importance of close clinical monitoring in early life, though preventive interventions remain unproven. Further research is needed to clarify these mechanisms, which will support the development of more precise prevention and treatment strategies in clinical practice.

Humans↗
PubMed2026

Active components and potential mechanisms of Wuzhuyu decoction in the treatment of ethanol-induced acute gastric mucosal injury: a network pharmacology and experimental verification.

OBJECTIVE: To investigate the underlying mechanisms and active components of Wuzhuyu decoction (, WD) in alleviating ethanol-induced acute gastric mucosal injury (GMI) using an integrated approach of network pharmacology and experimental verification. METHODS: Sprague-Dawley rats were randomly divided into six groups: control (Con), model (Mod), bismuth potassium citrate (BPC), WD at low (WD-L), medium (WD-M), and high (WD-H) doses. Following seven days of continuous intragastric administration of the respective treatments, an ethanol-induced gastric mucosal injury model was established in all groups except the control group by oral gavage of anhydrous ethanol. The gastric mucosal injury index was evaluated, and pathological changes were assessed viahematoxylin and eosin (HE) staining. Levels of tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GSH-Px) were measured by enzyme-linked immunosorbent assay (ELISA). The chemical composition was identified by ultra-performance liquid chromatography-tandem mass spectrometry. Active compounds were screened using the Swiss-absorption, distribution, metabolism, and excretion database, and their potential targets were predicted using the Swiss Target Prediction database and bioinformatics annotation database for molecular mechanism. Simultaneously, disease targets related to GMI were retrieved from the online mendelian inheritance in man and GeneCards databases. A protein-protein interaction (PPI) network was constructed, and functional enrichment analyses of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed using the Metascape database. Key predictions from the network pharmacology analysis were subsequently verified through animal experiments. Protein expression levels of B-cell lymphoma-2 (Bcl-2), Bcl-2-associated X protein (Bax), Cleaved Caspase-3, and Cleaved Caspase-9 were analyzed by Western blot. Finally, molecular docking was performed using AutoDock Vina to investigate the interactions between the active components and core targets. RESULTS: WD treatment significantly reduced the gastric mucosal injury index and the levels of TNF-α, IL-1β, MDA, while it increased the activities of SOD and GSH-Px. Histopathological examination revealed marked improvement in gastric tissue morphology. A total of 145 compounds were identified in WD. Network pharmacology analysis identified 440 overlapping targets between WD and GMI. GO and KEGG enrichment analyses highlighted the apoptosis signaling pathway as a key mechanism for WD's protective effect against ethanol-induced GMI. Experimental validation demonstrated that WD treatment reduced the apoptosis of gastric mucosal epithelial cells, promoted the expression of Bcl-2, and inhibited the expression of Bax, Cleaved Caspase-3 and Cleaved Caspase-9. Molecular docking results indicated that dehydroevodiamine, rutaecarpine, evodiamine, hexahydrocurcumin, and isorhamnetin are potential active components in WD that contribute to the inhibition of apoptosis. CONCLUSIONS: WD alleviates ethanol-induced acute GMI, at least in part, by inhibiting the apoptosis. The primary active components responsible for this effect are dehydroevodiamine, rutaecarpine, evodiamine, hexahydrocurcumin, and isorhamnetin.

Drugs, Chinese Herbal↗
PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗
PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗
PubMed2026

Comprehensive identification of carboxylic acids by using bromine isotope-based chemical isotope labelling and structure-guided molecular network.

Carboxylic acids (CAs) are important contributors to the flavor quality of sauce-flavor Chinese Baijiu, yet their comprehensive analysis remains challenging due to poor ionization efficiency, weak chromatographic retention, and limited annotation capability. Herein, we developed a workflow for the high-coverage discovery and annotation of CAs in Baijiu by coupling chemical isotope labeling-liquid chromatography-mass spectrometry with a structure-guided molecular network strategy (SGMNS). A bromine-containing derivatization reagent, 1-(3-aminopropyl)-3-bromoquinolin-1-ium bromide (APBQ), was designed and synthesized to exploit the natural isotope distribution of bromine and characteristic MS/MS fragmentation behavior. Following APBQ derivatization, the target CAs showed superior chromatographic retention and favorable analytical performance. Based on isotopic peak pairing in MS1 and diagnostic fragment validation in MS2, 372 potential CA derivatives were discovered from pooled Baijiu samples and 355 of them were validated by diagnostic fragments in MS2 spectra. To address the scarcity of derivatized spectral libraries, SGMNS was employed for annotation using a background network constructed from APBQ-labeled candidates derived from the Expanded Chinese Baijiu Compound Database. The developed method was further applied to profile Baijiu samples, revealing pronounced differences in CA composition across the seven fermentation rounds. Notably, rounds 3 to 5 exhibited the largest numbers of differential CAs. This study provided an effective analytical strategy for large-scale CA profiling, offering new insight into the chemical basis of flavor formation during multi-round fermentation of sauce-flavor Baijiu.

Isotope Labeling↗
PubMed2026

PhyloNaP: a user-friendly database of phylogeny for natural product-producing enzymes.

SUMMARY: Phylogenetic analysis is widely used to predict enzyme function, yet building annotated and reusable trees is labor-intensive and requires extensive knowledge about the specific enzymes. Existing resources rarely cover biosynthetic enzymes and lack the context needed for meaningful analysis. We present PhyloNaP, the first large-scale resource dedicated to phylogenies of biosynthetic enzymes. PhyloNaP provides ∼51 000 annotated and interactive trees enriched with chemical, functional, and taxonomic information. Users can classify their own sequences via phylogenetic placement, enabling functional inference in an evolutionary context. A contribution portal allows the community to submit curated trees. By combining scale, breadth of annotation, and interactive functionality, PhyloNaP fills a major gap in bioinformatics resources for enzyme discovery and annotation, with immediate applications to secondary metabolism and beyond. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at https://phylonap.cs.uni-tuebingen.de.

Phylogeny↗
PubMed2025

Mining thermophile photosynthesis genes: a synthetic operon expressing Chloroflexota species reaction center genes in Rhodobacter sphaeroides.

Photosynthesis is the foundation of the vast majority of life systems, and therefore the most important bioenergetic process on earth, and the greatest diversity in photosynthetic systems are found in microorganisms. However, understanding of the biophysical and biochemical processes that transduce light to chemical energy has derived from the relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of microbial DNA sequences encoding proteins that catalyze the initial photochemical reactions that has been deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42-90° C) springs available through the JGI IMG database, to generate a resource of diverse sequences that potentially are adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗
PubMed1979

Online literature retrieval in poison control.

Modern bibliographic databases include an increasing number of substantial abstracts, as well as other features which contribute to their becoming useful additional tools in poison control. This paper is a contribution to the assessment of their respective value in providing rapid and up-to-date information about toxic effects of drugs and chemicals, as well as about the therapy of poisoning. The main systems compared are Excerpta Medica, Scisearch, and Toxline.

Europe↗
PubMed2026

Systematic review of microorganism disinfection performance by chemical and ultraviolet light water treatment methods.

Safe drinking water is critical for public health, yet microbial contamination remains a significant global challenge. We conducted a systematic review to update World Health Organization guidance on water disinfection technologies by synthesizing peer-reviewed literature from 1997 to 2021 on the performance of free chlorine, chlorine dioxide, ozone, and ultraviolet (UV) light against bacteria, viruses, and protozoa. Following PRISMA guidelines, we analyzed log10 reduction values (LRVs) and contact times (Ct) or fluence (for UV) from laboratory and field studies. We included studies from multiple databases and expert-recommended studies. Results show mean Cts for 2 LRV of non-opportunistic bacteria as 6.0 (free chlorine), 0.4 (chlorine dioxide), and 1.2 (ozone) mg/L*min, and a mean UV fluence of 8.2 mJ/cm² (all bacteria). Viruses required lower Cts, except for UV-resistant adenoviruses, while protozoa required higher Cts or fluences. Opportunistic bacteria required significantly higher Cts than non-opportunistic bacteria for free chlorine and chlorine dioxide. Temperature and pH effects were inconsistent, highlighting data variability and gaps in field studies. These findings support global guidance on water treatment and may be used alongside other context-specific data to understand the roles these technologies play in reducing waterborne exposures. We recommend standardized reporting from performance studies to enable straightforward synthesis of evidence.

Disinfection↗
PubMed2026

Foundation model enables interpretable open and error-tolerant searching for mass spectrometry-based proteomics.

MOTIVATION: Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and contain complex patterns, making them inherently challenging to analyze with algorithmic approaches. In terms of the protein sequence landscape, most recent bottom-up MS-based proteomics studies consider either a diverse pool of post-translational modifications, employ large databases-as in metaproteomics or proteogenomics, study multiple isoforms of proteins, include unspecific cleavage sites or even combinations thereof. All this makes peptide and protein identifications challenging. RESULTS: Here, we present a foundation model, called yHydra, that jointly embeds spectra and peptides. This allows us to implement various downstream tasks and search modes in Euclidean space. We implement an open search which allows querying multiple ten-thousands of spectra against millions of peptides. Furthermore, we implement an error-tolerant search for identifying additional proteoforms that are not included in off-the-shelf reference proteomes. Our foundation model provides meaningful embeddings, as we interpret learned peptide embeddings in comparison to the peptide's physico-chemical properties. Hydra's open search, assigns delta masses to each identification which allows to unrestrictedly characterize post-translational modifications. The error-tolerant mode of yHydra can be used as post-processing to existing search engines or as a standalone. yHydra is evaluated on several real life data sets for the identification of modified peptide sequences and shows up to 25% increase in peptide identification at constant false discovery rate compared to the current state-of-the-art. AVAILABILITY AND IMPLEMENTATION: Code is available on Gitlab: https://gitlab.com/dacs-hpi/yHydra, and https://gitlab.com/dacs-hpi/yHydra_train.

Proteomics↗
PubMed2024

A trait-based ecological perspective on the soil microbial antibiotic-related genetic machinery.

Antibiotic resistance crisis dictates the need for resistance monitoring and the search for new antibiotics. The development of monitoring protocols is hindered by the great diversity of resistance factors, while the "streetlight effect" denies the possibility of discovering novel drugs based on existing databases. In this study, we address these challenges using high-throughput environmental screening viewed from a trait-based ecological perspective. Through an in-depth analysis of the metagenomes of 658 topsoil samples spanning Europe, we explored the distribution of 241 prokaryotic and fungal genes responsible for producing metabolites with antibiotic properties and 485 antibiotic resistance genes. We analyzed the diversity of these gene collections at different levels and modeled the distribution of each gene across environmental gradients. Our analyses revealed several nonparallel distribution patterns of the genes encoding sequential steps of enzymatic pathways synthesizing large antibiotic groups, pointing to gaps in existing databases and suggesting potential for discovering new analogues of known antibiotics. We show that agricultural activity caused a continental-scale homogenization of microbial antibiotic-related machinery, emphasizing the importance of maintaining indigenous ecosystems within the landscape mosaic. Based on the relationships between the proportion of the genes in the metagenomes with the main predictors (soil pH, land cover type, climate temperature and humidity), we illustrate how the properties of chemical structures dictate the distribution of the genes responsible for their synthesis across environments. With this understanding, we propose general principles to facilitate the discovery of antibiotics, including principally new ones, establish abundance baselines for antibiotic resistance genes, and predict their dissemination.

Soil Microbiology↗
PubMed2026

LC-IMS-MS profiling of avocado acetogenins reveals tissue-dependent distribution and cultivar-specific metabolic signatures.

This study presents a comprehensive characterisation of acetogenin-related metabolites in avocado using an LC-IMS-MS workflow. A total of 26 metabolites were semi-quantified across peel, pulp and seed tissues from three cultivars (Hass, Bacon and Fuerte). The integration of ion mobility spectrometry enabled the generation of the first experimental database of collision cross section (CCS) values for avocado acetogenins, improving confidence in metabolite annotation. Results revealed a pronounced tissue-dependent distribution, with seeds and pulp as the primary reservoir of several acetogenins, whereas the peel consistently exhibited lower concentrations. In contrast, acetogenin levels remained largely stable throughout ripening. Clear cultivar-dependent differences were observed, with Hass displaying a distinct metabolic profile compared to Bacon and Fuerte. Multivariate analysis confirmed these findings, showing tissue-dependent cultivar differentiation. This study provides new insights into avocado chemical diversity and highlights the potential of avocado by-products as consistent and promising sources of bioactive acetogenins.

Persea↗
PubMed2025

CaXML: Chemistry-informed machine learning explains mutual changes between protein conformations and calcium ions in calcium-binding proteins using structural and topological features.

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

Machine Learning↗
PubMed2025

Logical Exploration of Cinnamoyl-Containing Nonribosomal Peptides via Metabologenomic Targeting and Regulator Overexpression.

A targeted method for discovering cinnamoyl-containing nonribosomal peptides (CCNPs), a unique class of bioactive compounds, was devised by using cinnamoyl isomerase, a key enzyme in the biosynthesis of the cinnamoyl moiety, as a genome mining probe. A total of 39 hit strains were obtained, including 35 from polymerase chain reaction-based screening of the in-house bacterial library (2.5% of 1400 strains) targeting the cinnamoyl isomerase-encoding gene and 4 from the genome mining of online databases. Sequence similarity networking and phylogenetic analyses of the isomerase amplicons (∼530 bp) classified the CCNPs into three major substructure-based groups (Z-, E-, and M-type CCNPs) and revealed distinct clade-structure relationships (13 clades). To overcome the challenge of silent biosynthetic gene clusters, we activated these clusters by overexpressing conserved cluster-situated LuxR regulators combined with extensive culture optimization. CCNP production was metabolomically detected in the bacterial extracts by using the characteristic UV absorption and MS/MS fragments of cinnamoyl moieties. CCNP production was observed in 20 of the 39 hit strains, resulting in the isolation of 6 new CCNPs, including oxy-skyllamycin B (2), gwanacinnamycin (3), and luxocinnamycins A-D (4-7), with high structural novelty. Their structures were elucidated using comprehensive spectroscopic analyses and multiple-step chemical derivatizations, and the putative biosynthetic pathways were bioinformatically proposed. Gwanacinnamycin (3) exhibited significant antimycobacterial activity, whereas luxocinnamycin A (4) displayed moderate antiproliferative activity against stomach cancer cells. Our findings highlight a targeted metabologenomic approach combined with transcriptional regulator overexpression as a logical and efficient platform for the discovery of bioactive compounds from nature.

Peptides↗
PubMed2024

Microbial diversity and metabolic pathways linked to benzene degradation in petrochemical-polluted groundwater.

The rapid advance in shotgun metagenome sequencing has enabled us to identify uncultivated functional microorganisms in polluted environments. While aerobic petrochemical-degrading pathways have been extensively studied, the anaerobic mechanisms remain less explored. Here, we conducted a study at a petrochemical-polluted groundwater site in Henan Province, Central China. A total of twelve groundwater monitoring wells were installed to collect groundwater samples. Benzene appeared to be the predominant pollutant, detected in 10 out of 12 samples, with concentrations ranging from 1.4 μg/L to 5,280 μg/L. Due to the low aquifer permeability, pollutant migration occurred slowly, resulting in relatively low benzene concentrations downstream within the heavily polluted area. Deep metagenome sequencing revealed Proteobacteria as the dominant phylum, accounting for over 63 % of total abundances. Microbial α-diversity was low in heavily polluted samples, with community compositions substantially differing from those in lightly polluted samples. dmpK encoding the phenol/toluene 2-monooxygenase was detected across all samples, while the dioxygenase bedC1 was not detected, suggesting that aerobic benzene degradation might occur through monooxygenation. Sequence assembly and binning yielded 350 high-quality metagenome-assembled genomes (MAGs), with 30 MAGs harboring functional genes associated with aerobic or anaerobic benzene degradation. About 80 % of MAGs harboring functional genes associated with anaerobic benzene degradation remained taxonomically unclassified at the genus level, suggesting that our current database coverage of anaerobic benzene-degrading microorganisms is very limited. Furthermore, two genes integral to anaerobic benzene metabolism, i.e, benzoyl-CoA reductase (bamB) and glutaryl-CoA dehydrogenase (acd), were not annotated by metagenome functional analyses but were identified within the MAGs, signifying the importance of integrating both contig-based and MAG-based approaches. Together, our efforts of functional annotation and metagenome binning generate a robust blueprint of microbial functional potentials in petrochemical-polluted groundwater, which is crucial for designing proficient bioremediation strategies.

Groundwater↗
PubMed2026

13C Stable Isotope Tracing-Based MFA Reveals the Contribution of Glucose to Glycolytic and TCA Fluxes and Its Application in Depression Research.

Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U-13C6]-glucose and [U-13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.

Animals↗
PubMed2025

Biosynthetic potential of the culturable foliar fungi associated with field-grown lettuce.

Fungal endophytes and epiphytes associated with plant leaves can play important ecological roles through the production of specialized metabolites encoded by biosynthetic gene clusters (BGCs). However, their functional capacity, especially in crops like lettuce (Lactuca sativa L.), remains poorly understood. We sequenced the genomes of nine fungal isolates, representing Fusarium sp., Fulvia sp., Alternaria alternata, and Alternaria postmessia, from leaves of lettuce grown under field conditions in Arizona, USA. We used antibiotics and secondary metabolite analysis shell (antiSMASH) and the database for automated carbohydrate-active enzyme annotation (dbCAN3), to predict BGCs and carbohydrate-active enzymes (CAZymes) for each strain, and then compared them to conspecific strains from other environments and substrates. Foliar lettuce-associated fungi featured 39-95 BGCs per genome, with substantial overlap between isolates occurring in association with lettuce leaves vs. from other substrates. Species identity was a significant determinant of BGC count, while host type, isolation source, and lifestyle were not. Several BGCs, including those for alternariol and 1,3,6,8-Tetrahydroxynaphthalene (T4HN), showed 100% similarity to characterized minimum information about a biosynthetic gene cluster (MIBiG) clusters based on antiSMASH predictions. Although analysis by biosynthetic gene similarity clustering and prospecting engine (BiG-SCAPE) identified gene cluster families (GCFs) across the dataset, these reference-matching clusters were not always grouped, reflecting methodological differences in how the tools assess similarity. Comparative CAZyme analysis in a focal species (Fulvia sp.) revealed higher gene counts in a foliar lettuce-derived isolate than in tomato (Solanum lycopersicum)-associated strains, challenging assumptions about host chemical complexity. These results highlight the importance of phylogenetic context in shaping fungal functional potential and suggest that selection on microbial traits in edible leafy crops may be more subtle and species-specific than previously assumed. KEY POINTS: • Lettuce-associated fungi feature diverse biosynthetic potential • Phylogeny predicts fungal BGC content more strongly than ecological lifestyle • Findings support genome-informed microbiome strategies for leafy crops.

Lactuca↗
PubMed2026

Comparative safety of lipid-lowering drugs alone or in combination: insights from a systematic review and network meta-analysis.

BACKGROUND AND AIMS: Although the safety profile of lipid-lowering therapies (LLTs) is known, there are no comprehensive comparative assessments. We aimed to compare the risk of muscle-related events, diabetes, liver dysfunction, and cognitive disorders among LLTs through a network meta-analysis. METHODS AND RESULTS: Databases were searched from inception to May 2025. Eligible studies included adult patients, using statins, ezetimibe, PCSK9 monoclonal antibodies (PCSK9mAbs), inclisiran, bempedoic acid, or their combinations as intervention, reporting the information about any of the selected adverse events, a total sample size of ≥200 subjects, and had ≥1 month of intervention. Pooled estimates were assessed by fixed effects model within a frequentist setting. Pooled relative risks (RR) and their 95% confidence interval were estimated. A total of 303,397 subjects from 153 RCTs were included. Bempedoic acid ranked the lowest risk of myalgia (vs PCSK9mAbs, RR 0.80 [0.69, 0.93]). PCSK9mAbs were associated with lower incidence of creatine kinase (CK) elevation, diabetes, and liver dysfunction comparing to statins (statins vs PCSK9mAbs, RR 1.44 [1.14, 1.81], RR 1.13 [1.05, 1.22], and RR 1.38 [1.17, 1.62], respectively). In terms of muscle-related events and cognitive disorders, no significant risk differences were found among treatments and their combinations. CONCLUSIONS: PCSK9mAbs appear to have a more favourable safety profile regarding the risk of CK elevation, diabetes, and liver dysfunction. Bempedoic acid seem to be a better choice for subjects with high risk of myalgia. This information can be valuable when selecting therapy for specific patient subgroups at higher risk of certain adverse events.

Humans↗