PubMed

Philos Trans R Soc Lond B Biol Sci. 2024 May 6;379(1901):20230075. doi: 10.1098/rstb.2023.0075. Epub 2024 Mar 18.ABSTRACTThe gut microbiota is crucial for intestinal health, including gastrointestinal (GI) motility. How commensal bacterial species influence GI motility has not been fully elucidated. A major factor of GI motility is the gut contraction promoting the propulsive movement of orally ingested materials. Here, we developed a method to monitor and quantify gut contractions in living Drosophila melanogaster larvae. We found that the culture medium of an isolated strain Lactiplantibacillus plantarum Lsi promoted gut contraction in vivo, which was not observed in Leuconostoc sp. Leui nor Acetobacter persici Ai culture medium. To identify bacteria-derived metabolites, we performed metabolome analysis of the culture media by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Of the 66 metabolites detected, we found that some metabolites changed in a species-specific manner. Among them, acetylcholine was specifically produced by L. plantarum. Feeding exogenous acetylcholine increased the frequency of gut contractions, which was blocked by D-tubocurarine, an inhibitor of nicotinic acetylcholine receptors. In this study, we propose a mechanism by which the gut microbiota influences Drosophila gut motility. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.PMID:38497270 | DOI:10.1098/rstb.2023.0075

ACS Omega. 2024 Feb 29;9(10):11200-11216. doi: 10.1021/acsomega.3c05591. eCollection 2024 Mar 12.ABSTRACTMedicinally valuable components derived from natural resources are highly desirable as prospective alternatives to synthetic drugs to treat fatal diseases, such as cancer and diabetes mellitus. Suaeda maritima (L.) Dumort (Amaranthaceae) (S. maritima) is a halophyte plant that can thrive in saline environments and possesses excellent medicinal properties. Hence, for the present investigation, S. maritima has been chosen, and its phytochemical constituents have been extracted utilizing various solvents, including hexane, acetone, and methanol, and identified by GC-MS, LC-MS, and HPLC analyses. The antioxidant activity of the compounds using DPPH, ABTS, and reducing power assays demonstrated that all three extracts of S. maritima possessed significant radical scavenging activity comparable to standard ascorbic acid with lower IC50 values (69.20-95.58 μg/mL). In addition, the evaluation of antidiabetic activity by α-amylase inhibition and α-glucosidase inhibition methods revealed that the acetone extract of S. maritima (SMAE) displayed equipotent activity of standard acarbose with an IC50 of 32.6 μg/mL. Advantageously, SMAE also exhibited better inhibition activity against the growth of lung cancer cells with an IC50 of 78.19. μg/mL and less toxicity on the noncancerous HUVEC cells with a high IC50 of 300 μg/mL. In addition, the cancer cell death mechanism via the apoptotic pathway induced by SMAE was confirmed by DAPI staining and ROS analysis. The analysis of ADME properties, including absorption, distribution, metabolism, and excretion, witnessed that the physicochemical and druglikeness factors were best catered by stigmasterol, γ-sitosterol, and vitamin E. Further, the key phytochemicals identified from SMAE were docked with CtBP1 and SOX2 bound to importin-α target proteins associated with carcinogenic pathways using Schrodinger software. The results showed that the phytochemicals, scilicet, stigmasterol, γ-sitosterol, octadecadienoic acid, and vitamin E, showed a good binding affinity with Glide scores in the range -2.845-4.018 kcal/mol. Overall, the findings support that the least investigated traditional edible medicinal mangrove-related S. maritima is high in pharmacologically active constituents and might be one of the finest sources of naturally derived molecules for drug development and delivery systems.PMID:38496978 | PMC:PMC10938337 | DOI:10.1021/acsomega.3c05591

ACS Omega. 2024 Feb 26;9(10):11987-11997. doi: 10.1021/acsomega.3c09742. eCollection 2024 Mar 12.ABSTRACTThe influence of enrichment of culturable microorganisms in in situ coal seams on biomethane production potential of other coal seams has been rarely studied. In this study, we enriched culturable microorganisms from three in situ coal seams with three coal ranks and conducted indoor anaerobic biomethane production experiments. Microbial community composition, gene functions, and metabolites in different culture units by 16S rRNA high-throughput sequencing combined with liquid chromatography-mass spectrometry-time-of-flight (LC-MS-TOF). The results showed that biomethane production in the bituminous coal group (BC)cc resulted in the highest methane yield of 243.3 μmol/g, which was 12.3 times higher than that in the control group (CK). Meanwhile, Methanosarcina was the dominant archaeal genus in the three experimental groups (37.42 ± 11.16-52.62 ± 2.10%), while its share in the CK was only 2.91 ± 0.48%. Based on the functional annotation, the relative abundance of functional genes in the three experimental groups was mainly related to the metabolism of nitrogen-containing heterocyclic compounds such as purines and pyrimidines. Metabolite analysis showed that enriched microorganisms promoted the degradation of a total of 778 organic substances in bituminous coal, including 55 significantly different metabolites (e.g., purines and pyrimidines). Based on genomic and metabolomic analyses, this paper reconstructed the heterocyclic compounds degradation coupled methane metabolism pathway and thereby preliminarily elucidated that enriched culturable bacteria from different coal-rank seams could promote the degradation of bituminous coal and intensify biogenic methane yields.PMID:38496961 | PMC:PMC10938392 | DOI:10.1021/acsomega.3c09742

Heliyon. 2024 Mar 8;10(6):e27472. doi: 10.1016/j.heliyon.2024.e27472. eCollection 2024 Mar 30.ABSTRACTAngiotensin-converting enzyme 2 (ACE2) polymorphisms are associated with increased risk of type 2 diabetes mellitus (T2DM), obesity and dyslipidemia, which have been determined in various populations. Consistently, ACE2 knockout (ACE2 KO) mice display damaged energy metabolism in multiple tissues, especially the key metabolic tissues such as liver, skeletal muscle and epididymal white adipose tissue (eWAT) and show even more severe phenotype under high-fat diet (HFD) induced metabolic stress. However, the effects of ACE2 on global metabolomics profiling and the tissue sensitivity remain unclear. To understand how tissues independently and collectively respond to ACE2, we performed untargeted metabolomics in serum in ACE2 KO and control wild type (WT) mice both on normal diet (ND) and HFD, and in three key metabolic tissues (liver, skeletal muscle and eWAT) after HFD treatment. The results showed significant alterations in metabolic profiling in ACE2 KO mice. We identified 275 and 168 serum metabolites differing significantly between WT and ACE2 KO mice fed on ND and HFD, respectively. And the altered metabolites in the ACE2 KO group varied from 90 to 196 in liver, muscle and eWAT. The alterations in ND and HFD serum were most similar. Compared with WT mice, ACE2 KO mice showed an increase in N-phenylacetylglutamine (PAGln), methyl indole-3-acetate, 5-hydroxytryptophol, cholic acid, deoxycholic acid and 12(S)-HETE, while LPC (19:0) and LPE (16:1) decreased. Moreover, LPC (20:0), LPC (20:1) and PC (14:0e/6:0) were reduced in both ND and HFD serum, paralleling the decreases identified in HFD skeletal muscle. Interestingly, DL-tryptophan, indole and Gly-Phe decreased in both ND and HFD serum but were elevated in HFD liver of ACE2 KO mice. A low level of l-ergothioneine was observed among liver, muscle, and epididymal fat tissue of ACE2 KO mice. Pathway analysis demonstrated that different tissues exhibited different dysregulated metabolic pathways. In conclusion, these results revealed that ACE2 deficiency leads to an overall state of metabolic distress, which may provide a new insight into the underlying pathogenesis in metabolic disorders in both ACE2 KO mice and in patients with certain genetic variant of ACE2 gene.PMID:38496880 | PMC:PMC10944221 | DOI:10.1016/j.heliyon.2024.e27472

bioRxiv [Preprint]. 2024 Mar 9:2024.03.08.584156. doi: 10.1101/2024.03.08.584156.ABSTRACTSpecies of the Bacteroidales order are among the most abundant and stable bacterial members of the human gut microbiome with diverse impacts on human health. While Bacteroidales strains and species are genomically and functionally diverse, order-wide comparative analyses are lacking. We cultured and sequenced the genomes of 408 Bacteroidales isolates from healthy human donors representing nine genera and 35 species and performed comparative genomic, gene-specific, mobile gene, and metabolomic analyses. Families, genera, and species could be grouped based on many distinctive features. However, we also show extensive DNA transfer between diverse families, allowing for shared traits and strain evolution. Inter- and intra-specific diversity is also apparent in the metabolomic profiling studies. This highly characterized and diverse Bacteroidales culture collection with strain-resolved genomic and metabolomic analyses can serve as a resource to facilitate informed selection of strains for microbiome reconstitution.PMID:38496653 | PMC:PMC10942478 | DOI:10.1101/2024.03.08.584156

Res Sq [Preprint]. 2024 Mar 5:rs.3.rs-3921099. doi: 10.21203/rs.3.rs-3921099/v1.ABSTRACTMultiomics analyses have identified multiple potential biomarkers of the incidence and prevalence of complex diseases. However, it is not known which type of biomarker is optimal for clinical purposes. Here, we make a systematic comparison of 90 million genetic variants, 1,453 proteins, and 325 metabolites from 500,000 individuals with complex diseases from the UK Biobank. A machine learning pipeline consisting of data cleaning, data imputation, feature selection, and model training using cross-validation and comparison of the results on holdout test sets showed that proteins were most predictive, followed by metabolites, and genetic variants. Only five proteins per disease resulted in median (min-max) areas under the receiver operating characteristic curves for incidence of 0.79 (0.65-0.86) and 0.84 (0.70-0.91) for prevalence. In summary, our work suggests the potential of predicting complex diseases based on a limited number of proteins. We provide an interactive atlas (macd.shinyapps.io/ShinyApp/) to find genomic, proteomic, or metabolomic biomarkers for different complex diseases.PMID:38496611 | PMC:PMC10942575 | DOI:10.21203/rs.3.rs-3921099/v1

medRxiv [Preprint]. 2024 Mar 8:2024.03.07.24303907. doi: 10.1101/2024.03.07.24303907.ABSTRACTDespite the high prevalence of neurodevelopmental disorders, there are a lack of clinical studies examining the impact of pregnancy diet on child neurodevelopment. This observational clinical study examined the associations between pregnancy dietary patterns and neurodevelopmental diagnoses, as well as their symptoms, in a prospective cohort of 10-year-old children (n=508). Data-driven dietary patterns were derived from self-reported food frequency questionnaires. An Unhealthy dietary pattern in pregnancy (per SD change) was significantly associated with attention deficit hyperactivity disorder (ADHD) OR 1.66 [1.21 - 2.27], p=0.002 and autism diagnosis OR 2.22 [1.33 - 3.74], p=0.002 and associated symptoms p<0.001. Findings for ADHD were validated in two large (n=656, n=348), independent mother-child cohorts via blood metabolome modelling. Objective metabolite scores, assessed at five timepoints in mothers and children in two independent mother-child cohorts, indicated that the strongest association with ADHD was during early-to mid-pregnancy. These results provide evidence for targeted prenatal dietary interventions to prevent neurodevelopmental disorders in children.PMID:38496582 | PMC:PMC10942528 | DOI:10.1101/2024.03.07.24303907

bioRxiv [Preprint]. 2024 Feb 22:2024.02.21.581486. doi: 10.1101/2024.02.21.581486.ABSTRACTOne of the most important properties of human embryonic stem cells (hESCs) is related to their pluripotent states. In our recent study, we identified a previously unrecognized pluripotent state induced by RSeT medium. This state makes primed hESCs resistant to conversion to naïve pluripotent state. In this study, we have further characterized the metabolic features in these RSeT hESCs, including metabolic gene expression, metabolomic analysis, and various functional assays. The commonly reported metabolic modes include glycolysis or both glycolysis and oxidative phosphorylation (i.e., metabolic bivalency) in pluripotent stem cells. However, besides the presence of metabolic bivalency, RSeT hESCs exhibited a unique metabolome with additional fatty acid oxidation and imbalanced nucleotide metabolism. This metabolic quadrivalency is linked to hESC growth independent of oxygen tension and restricted capacity for naïve reprogramming in these cells. Thus, this study provides new insights into pluripotent state transitions and metabolic stress-associated hPSC growth in vitro .PMID:38496581 | PMC:PMC10942463 | DOI:10.1101/2024.02.21.581486

bioRxiv [Preprint]. 2024 Mar 4:2024.02.29.582775. doi: 10.1101/2024.02.29.582775.ABSTRACTWe previously reported that in the absence of Prostaglandin D2 synthase (L-PGDS) peripheral nerves are hypomyelinated in development and that with aging they present aberrant myelin sheaths. We now demonstrate that L-PGDS expressed in Schwann cells is part of a coordinated program aiming at preserving myelin integrity. In vivo and in vitro lipidomic, metabolomic and transcriptomic analyses confirmed that myelin lipids composition, Schwann cells energetic metabolism and key enzymes controlling these processes are altered in the absence of L-PGDS. Moreover, Schwann cells undergo a metabolic rewiring and turn to acetate as the main energetic source. Further, they produce ketone bodies to ensure glial cell and neuronal survival. Importantly, we demonstrate that all these changes correlate with morphological myelin alterations and describe the first physiological pathway implicated in preserving PNS myelin. Collectively, we posit that myelin lipids serve as a reservoir to provide ketone bodies, which together with acetate represent the adaptive substrates Schwann cells can rely on to sustain the axo-glial unit and preserve the integrity of the PNS.PMID:38496560 | PMC:PMC10942270 | DOI:10.1101/2024.02.29.582775

bioRxiv [Preprint]. 2024 Mar 10:2024.03.08.583994. doi: 10.1101/2024.03.08.583994.ABSTRACTFungal pathogens exhibit extensive strain heterogeneity, including variation in virulence. Whether closely related non-pathogenic species also exhibit strain heterogeneity remains unknown. Here, we comprehensively characterized the pathogenic potentials (i.e., the ability to cause morbidity and mortality) of 16 diverse strains of Aspergillus fischeri , a non-pathogenic close relative of the major pathogen Aspergillus fumigatus . In vitro immune response assays and in vivo virulence assays using a mouse model of pulmonary aspergillosis showed that A. fischeri strains varied widely in their pathogenic potential. Furthermore, pangenome analyses suggest that A. fischeri genomic and phenotypic diversity is even greater. Genomic, transcriptomic, and metabolomic profiling identified several pathways and secondary metabolites associated with variation in virulence. Notably, strain virulence was associated with the simultaneous presence of the secondary metabolites hexadehydroastechrome and gliotoxin. We submit that examining the pathogenic potentials of non-pathogenic close relatives is key for understanding the origins of fungal pathogenicity.PMID:38496489 | PMC:PMC10942418 | DOI:10.1101/2024.03.08.583994

bioRxiv [Preprint]. 2024 Mar 7:2024.03.05.583545. doi: 10.1101/2024.03.05.583545.ABSTRACTTuberculosis (TB), caused by Mycobacterium tuberculosis ( Mtb ), continues to be a major public health problem worldwide. The human immunodeficiency virus (HIV) is another equally important life-threatening pathogen. Further, co-infections with HIV and Mtb have severe effects in the host, with people infected with HIV being fifteen to twenty-one times more likely to develop active TB. The use of an appropriate animal model for HIV/ Mtb co-infection that can recapitulate the diversity of the immune response in humans would be a useful tool for conducting basic and translational research in HIV/ Mtb infections. The present study was focused on developing a humanized mouse model for investigations on HIV- Mtb co-infection. Using NSG-SGM3 mice that can engraft human stem cells, our studies showed that they were able to engraft human CD34+ stem cells which then differentiate into a full-lineage of human immune cell subsets. After co-infection with HIV and Mtb , these mice showed decrease in CD4+ T cell counts overtime and elevated HIV load in the sera, similar to the infection pattern of humans. Additionally, Mtb caused infections in both lungs and spleen, and induced the development of granulomatous lesions in the lungs, detected by CT scan and histopathology. Distinct metabolomic profiles were also observed in the tissues from different mouse groups after co-infections. Our results suggest that the humanized NSG-SGM3 mice are able to recapitulate the effects of HIV and Mtb infections and co-infection in the human host at pathological, immunological and metabolism levels, providing a dependable small animal model for studying HIV/ Mtb co-infection.PMID:38496484 | PMC:PMC10942347 | DOI:10.1101/2024.03.05.583545

Res Sq [Preprint]. 2024 Mar 8:rs.3.rs-3952944. doi: 10.21203/rs.3.rs-3952944/v1.ABSTRACTBackground Patients with COVID-19 under invasive mechanical ventilation are at higher risk of developing ventilator-associated pneumonia (VAP), associated with increased healthcare costs, and unfavorable prognosis. The underlying mechanisms of this phenomenon have not been thoroughly dissected. Therefore, this study attempted to bridge this gap by performing a lung microbiota analysis and evaluating the host immune responses that could drive the development of VAP. Materials and methods In this prospective cohort study, mechanically ventilated patients with confirmed SARS-CoV-2 infection were enrolled. Nasal swabs (NS), endotracheal aspirates (ETA), and blood samples were collected initially within 12 hours of intubation and again at 72 hours post-intubation. Plasma samples underwent cytokine and metabolomic analyses, while NS and ETA samples were sequenced for lung microbiome examination. The cohort was categorized based on the development of VAP. Data analysis was conducted using RStudio version 4.3.1. Results In a study of 36 COVID-19 patients on mechanical ventilation, significant differences were found in the nasal and pulmonary microbiome, notably in Staphylococcus and Enterobacteriaceae , linked to VAP. Patients with VAP showed a higher SARS-CoV-2 viral load, elevated neutralizing antibodies, and reduced inflammatory cytokines, including IFN-δ, IL-1β, IL-12p70, IL-18, IL-6, TNF-α, and CCL4. Metabolomic analysis revealed changes in 22 metabolites in non-VAP patients and 27 in VAP patients, highlighting D-Maltose-Lactose, Histidinyl-Glycine, and various phosphatidylcholines, indicating a metabolic predisposition to VAP. Conclusions This study reveals a critical link between respiratory microbiome alterations and ventilator-associated pneumonia in COVID-19 patients, with elevated SARS-CoV-2 levels and metabolic changes, providing novel insights into the underlying mechanisms of VAP with potential management and prevention implications.PMID:38496464 | PMC:PMC10942552 | DOI:10.21203/rs.3.rs-3952944/v1

Mol Ther Nucleic Acids. 2024 Feb 16;35(2):102155. doi: 10.1016/j.omtn.2024.102155. eCollection 2024 Jun 11.ABSTRACTEndometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.PMID:38495844 | PMC:PMC10943971 | DOI:10.1016/j.omtn.2024.102155

Biomed Opt Express. 2024 Feb 22;15(3):1798-1812. doi: 10.1364/BOE.504954. eCollection 2024 Mar 1.ABSTRACTWith applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the pixel-rate of the image sensors. Complementarily, to improve throughput further, here we propose to acquire images in a compressed form so that more information can be transferred beyond the existing hardware bottleneck of the image sensor. To this end, we present a numerical simulation of a learnable optical compression-decompression framework that learns content-specific features. The proposed differentiable quantitative phase microscopy (∂-QPM) first uses learnable optical processors as image compressors. The intensity representations produced by these optical processors are then captured by the imaging sensor. Finally, a reconstruction network running on a computer decompresses the QPM images post aquisition. In numerical experiments, the proposed system achieves compression of × 64 while maintaining the SSIM of ∼0.90 and PSNR of ∼30 dB on cells. The results demonstrated by our experiments open up a new pathway to QPM systems that may provide unprecedented throughput improvements.PMID:38495703 | PMC:PMC10942716 | DOI:10.1364/BOE.504954

Kidney Med. 2024 Feb 16;6(4):100793. doi: 10.1016/j.xkme.2024.100793. eCollection 2024 Apr.ABSTRACTRATIONALE & OBJECTIVE: While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD).STUDY DESIGN: Prospective cohort study.SETTING & PARTICIPANTS: A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline.EXPOSURES: Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites.OUTCOMES: Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease).ANALYTICAL APPROACH: Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD.RESULTS: Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6).LIMITATIONS: Residual confounding and sample-storage duration.CONCLUSIONS: We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.PMID:38495599 | PMC:PMC10940775 | DOI:10.1016/j.xkme.2024.100793

Comput Struct Biotechnol J. 2024 Mar 2;23:1106-1116. doi: 10.1016/j.csbj.2024.02.028. eCollection 2024 Dec.ABSTRACTFlavonoid glycosides are widespread in plants, and are of great interest owing to their diverse biological activities and effectiveness in preventing chronic diseases. Periploca forrestii, a renowned medicinal plant of the Apocynaceae family, contains diverse flavonoid glycosides and is clinically used to treat rheumatoid arthritis and traumatic injuries. However, the mechanisms underlying the biosynthesis of these flavonoid glycosides have not yet been elucidated. In this study, we used widely targeted metabolomics and full-length transcriptome sequencing to identify flavonoid diversity and biosynthetic genes in P. forrestii. A total of 120 flavonoid glycosides, including 21 C-, 96 O-, and 3 C/O-glycosides, were identified and annotated. Based on 24,123 full-length coding sequences, 99 uridine diphosphate sugar-utilizing glycosyltransferases (UGTs) were identified and classified into 14 groups. Biochemical assays revealed that four UGTs exhibited O-glycosyltransferase activity toward apigenin and luteolin. Among them, PfUGT74B4 and PfUGT92A8 were highly promiscuous and exhibited multisite O-glycosylation or consecutive glycosylation activities toward various flavonoid aglycones. These four glycosyltransferases may significantly contribute to the diversity of flavonoid glycosides in P. forrestii. Our findings provide a valuable genetic resource for further studies on P. forrestii and insights into the metabolic engineering of bioactive flavonoid glycosides.PMID:38495554 | PMC:PMC10940802 | DOI:10.1016/j.csbj.2024.02.028

EClinicalMedicine. 2024 Mar 8;70:102498. doi: 10.1016/j.eclinm.2024.102498. eCollection 2024 Apr.ABSTRACTBACKGROUND: Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA).METHODS: Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed.FINDINGS: Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively.INTERPRETATION: We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation.FUNDING: The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.PMID:38495518 | PMC:PMC10940947 | DOI:10.1016/j.eclinm.2024.102498

Food Chem X. 2024 Mar 5;22:101257. doi: 10.1016/j.fochx.2024.101257. eCollection 2024 Jun 30.ABSTRACTIn this study, high-throughput sequencing and metabolomics analysis were conducted to analyze the microbial and metabolites of dry-cured Sanchuan ham, Laowo ham, Nuodeng ham, and Heqing ham that have fermented for two years produced from western Yunnan China. Results showed that at the genus level, the dominant bacteria in the four types of ham were Halomonas and Staphylococcus, while the dominant fungi were Aspergillus and Yamadazyma. A total 422 different metabolites were identified in four types of ham, mainly amino acids, peptides, fatty acids, and their structural analogs, which were involved in pantothenate and coenzyme A biosynthesis, caffeine, and tyrosine metabolism. The dominant microorganisms of the four types of ham were mainly related to the metabolism of fatty acids and amino acids. This research enhances the identification degree of these four types of dry-cured ham and provides a theoretical basis for developing innovative and distinctive ham products.PMID:38495458 | PMC:PMC10943036 | DOI:10.1016/j.fochx.2024.101257

Front Plant Sci. 2024 Mar 1;15:1346853. doi: 10.3389/fpls.2024.1346853. eCollection 2024.ABSTRACTThe impact of water-deficit (WD) stress on plant metabolism has been predominantly studied at the whole tissue level. However, plant tissues are made of several distinct cell types with unique and differentiated functions, which limits whole tissue 'omics'-based studies to determine only an averaged molecular signature arising from multiple cell types. Advancements in spatial omics technologies provide an opportunity to understand the molecular mechanisms underlying plant responses to WD stress at distinct cell-type levels. Here, we studied the spatiotemporal metabolic responses of two poplar (Populus tremula× P. alba) leaf cell types -palisade and vascular cells- to WD stress using matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). We identified unique WD stress-mediated metabolic shifts in each leaf cell type when exposed to early and prolonged WD stresses and recovery from stress. During water-limited conditions, flavonoids and phenolic metabolites were exclusively accumulated in leaf palisade cells. However, vascular cells mainly accumulated sugars and fatty acids during stress and recovery conditions, respectively, highlighting the functional divergence of leaf cell types in response to WD stress. By comparing our MALDI-MSI metabolic data with whole leaf tissue gas chromatography-mass spectrometry (GC-MS)-based metabolic profile, we identified only a few metabolites including monosaccharides, hexose phosphates, and palmitic acid that showed a similar accumulation trend at both cell-type and whole leaf tissue levels. Overall, this work highlights the potential of the MSI approach to complement the whole tissue-based metabolomics techniques and provides a novel spatiotemporal understanding of plant metabolic responses to WD stress. This will help engineer specific metabolic pathways at a cellular level in strategic perennial trees like poplars to help withstand future aberrations in environmental conditions and to increase bioenergy sustainability.PMID:38495374 | PMC:PMC10940329 | DOI:10.3389/fpls.2024.1346853

World J Hepatol. 2024 Feb 27;16(2):112-114. doi: 10.4254/wjh.v16.i2.112.ABSTRACTIn the present issue of the World Journal of Hepatology, Ferrassi et al examine the problem of liver fibrosis staging in chronic hepatitis C. They identify novel biomarkers in an effort to predict accurate fibrosis staging with the aid of the metabolome of Hepatitis C patients. Overall I think Ferrassi et al took a different approach in identifying fibrosis biomarkers, by looking at the patients' metabolome. Their biomarkers clearly separate patients from controls. They can also separate out, patients with minimal fibrosis (F0-F1 stage) and patients with cirrhosis (F4 stage). Obviously, if these biomarkers were to be widely used, tests for all the important metabolites would need to be readily available for use in hospitals or outpatient setting and that may prove difficult and above all, costly. Nevertheless, this step could eventually lead to a metabolomic approach for novel biomarkers of Fibrosis. Obviously, it would need to be validated, but could represent a step towards the Holy Grail of Hepatology.PMID:38495275 | PMC:PMC10941745 | DOI:10.4254/wjh.v16.i2.112