PubMed

NMR Biomed. 2023 Mar 22:e4935. doi: 10.1002/nbm.4935. Online ahead of print.ABSTRACTWe describe the use of NMR metabolomics to analyse blood serum samples from healthy individuals (n = 26) and those with metastatic colorectal cancer (CRC, n = 57). The assessment, employing both linear and non-linear multivariate data analysis techniques, revealed specific metabolite changes associated with metastatic CRC, including increased levels of lactate, glutamate, and pyruvate, and decreased levels of certain amino acids and total fatty acids. Biomarker ratios such as glutamate-to-glutamine and pyruvate-to-alanine were also found to be related to CRC. The study also found that glutamate was linked to progression-free survival and that both glutamate and 3-hydroxybutyrate were risk factors for metastatic CRC. Additionally, GC-FID was utilized to analyse the fatty acid profile and pathway analysis was performed on the profiled metabolites to understand the metabolic processes involved in CRC. A correlation was also found between the presence of certain metabolites in the blood of CRC patients and certain clinical features.PMID:36945883 | DOI:10.1002/nbm.4935

bioRxiv. 2023 Mar 6:2023.03.06.531302. doi: 10.1101/2023.03.06.531302. Preprint.ABSTRACTAlmost 20% of patients with COVID-19 experience long-term effects, known as post-COVID condition or long COVID. Among many lingering neurologic symptoms, chronic headache is the most common. Despite this health concern, the etiology of long COVID headache is still not well characterized. Here, we present a longitudinal multi-omics analysis of blood leukocyte transcriptomics, plasma proteomics and metabolomics of long COVID patients with chronic headache. Long COVID patients experienced a state of hyper-inflammation prior to chronic headache onset and maintained persistent inflammatory activation throughout the progression of chronic headache. Metabolomic analysis also revealed augmented arginine and lipid metabolisms, skewing towards a nitric oxide-based pro-inflammation. Furthermore, metabolisms of neurotransmitters including serotonin, dopamine, glutamate, and GABA were markedly dysregulated during the progression of long COVID headache. Overall, these findings illustrate the immuno-metabolomics landscape of long COVID patients with chronic headache, which may provide insights to potential therapeutic interventions.PMID:36945569 | PMC:PMC10028820 | DOI:10.1101/2023.03.06.531302

Res Sq. 2023 Mar 10:rs.3.rs-2662020. doi: 10.21203/rs.3.rs-2662020/v1. Preprint.ABSTRACTPurpose To determine whether recurrent GBMs are metabolically distinct from primary GBM, and whether patient plasma can be used as a liquid biopsy to reflect this difference. Methods In a single center cohort study, tissue and blood samples from 15 patients with glioblastoma (9 glioblastoma tissues at diagnosis, 3 pairs of tissue, and 6 pairs of plasma specimens at diagnosis and at recurrence) were analyzed. Results Several metabolites had significant alternations in both tumor and plasma specimens. In the tissue, the following representative metabolites had a significant increase in peak intensity at recurrence compared to diagnosis: N-alpha-methylhistamine (p = 0.037), glycerol-3-phosphate (p = 0.029), phosphocholine (p = 0.045), and succinic acid (p = 0.025). In patient plasma, metabolites that significantly increased at recurrence included: 2,4-difluorotoluene (p = 0.031), diatrizoic acid (p = 0.032), indole-3-acetate with (p = 0.029), urea (P = 0.025), pseudouridine (p = 0.042), and maltose (p = 0.035). Metabolites that significantly decreased in plasma at recurrence were: eicosenoic acid (p = 0.017), glucose-1-phosphate (p = 0.017), FA 18:2 (linoleic acid) (p = 0.017), arginine (p = 0.036), fatty acids 20:3 (homo-gamma-linolenic acid (p = 0.036), galactosamine (p = 0.007), and FA 18:3 (linolenic acid) (P = 0.012). Principal component analysis showed that the metabolomic profiles differ between tumor tissue and patient plasma. Conclusions Our data suggest that metabolomic profiles of human GBM tissue and patient plasma differ at diagnosis and at recurrence. Many metabolites involved in tumorigenesis and metabolomic flexibility were identified. A larger study using targeted metabolomic assay is warranted to measure the levels of these metabolites, which will help identify the metabolomic signatures in both GBM tissue and patient plasma for risk stratification, clinical outcome prediction, and development of new adjuvant metabolomic-targeting therapy.PMID:36945517 | PMC:PMC10029122 | DOI:10.21203/rs.3.rs-2662020/v1

bioRxiv. 2023 Mar 12:2023.03.08.531729. doi: 10.1101/2023.03.08.531729. Preprint.ABSTRACTBACKGROUND: Enzymatic reaction networks are crucial to explore the mechanistic function of metabolites and proteins in biological systems and understanding the etiology of diseases and potential target for drug discovery. The increasing number of metabolic reactions allows the development of deep learning-based methods to discover new enzymatic reactions, which will expand the landscape of existing enzymatic reaction networks to investigate the disrupted metabolisms in diseases.RESULTS: In this study, we propose the MPI-VGAE framework to predict metabolite-protein interactions (MPI) in a genome-scale heterogeneous enzymatic reaction network across ten organisms with thousands of enzymatic reactions. We improved the Variational Graph Autoencoders (VGAE) model to incorporate both molecular features of metabolites and proteins as well as neighboring features to achieve the best predictive performance of MPI. The MPI-VGAE framework showed robust performance in the reconstruction of hundreds of metabolic pathways and five functional enzymatic reaction networks. The MPI-VGAE framework was also applied to a homogenous metabolic reaction network and achieved as high performance as other state-of-art methods. Furthermore, the MPI-VGAE framework could be implemented to reconstruct the disease-specific MPI network based on hundreds of disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of new potential enzymatic reactions were predicted and validated by molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and drug targets in real-world applications.DATA AVAILABILITY AND IMPLEMENTATION: The MPI-VGAE framework and datasets are publicly accessible on GitHub https://github.com/mmetalab/mpi-vgae .AUTHOR BIOGRAPHIES: Cheng Wang received his Ph.D. in Chemistry from The Ohio State Univesity, USA. He is currently a Assistant Professor in School of Public Health at Shandong University, China. His research interests include bioinformatics, machine learning-based approach with applications to biomedical networks. Chuang Yuan is a research assistant at Shandong University. He obtained the MS degree in Biology at the University of Science and Technology of China. His research interests include biochemistry & molecular biology, cell biology, biomedicine, bioinformatics, and computational biology. Yahui Wang is a PhD student in Department of Chemistry at Washington University in St. Louis. Her research interests include biochemistry, mass spectrometry-based metabolomics, and cancer metabolism. Ranran Chen is a master graduate student in School of Public Health at University of Shandong, China. Yuying Shi is a master graduate student in School of Public Health at University of Shandong, China. Gary J. Patti is the Michael and Tana Powell Professor at Washington University in St. Louis, where he holds appointments in the Department of Chemisrty and the Department of Medicine. He is also the Senior Director of the Center for Metabolomics and Isotope Tracing at Washington University. His research interests include metabolomics, bioinformatics, high-throughput mass spectrometry, environmental health, cancer, and aging. Leyi Wei received his Ph.D. in Computer Science from Xiamen University, China. He is currently a Professor in School of Software at Shandong University, China. His research interests include machine learning and its applications to bioinformatics. Qingzhen Hou received his Ph.D. in the Centre for Integrative Bioinformatics VU (IBIVU) from Vrije Universiteit Amsterdam, the Netherlands. Since 2020, He has serveved as the head of Bioinformatics Center in National Institute of Health Data Science of China and Assistant Professor in School of Public Health, Shandong University, China. His areas of research are bioinformatics and computational biophysics.KEY POINTS: Genome-scale heterogeneous networks of metabolite-protein interaction (MPI) based on thousands of enzymatic reactions across ten organisms were constructed semi-automatically.An enzymatic reaction prediction method called Metabolite-Protein Interaction Variational Graph Autoencoders (MPI-VGAE) was developed and optimized to achieve higher performance compared with existing machine learning methods by using both molecular features of metabolites and proteins.MPI-VGAE is broadly useful for applications involving the reconstruction of metabolic pathways, functional enzymatic reaction networks, and homogenous networks (e.g., metabolic reaction networks).By implementing MPI-VGAE to Alzheimer's disease and colorectal cancer, we obtained several novel disease-related protein-metabolite reactions with biological meanings. Moreover, we further investigated the reasonable binding details of protein-metabolite interactions using molecular docking approaches which provided useful information for disease mechanism and drug design.PMID:36945484 | PMC:PMC10028866 | DOI:10.1101/2023.03.08.531729

bioRxiv. 2023 Mar 7:2023.03.03.530734. doi: 10.1101/2023.03.03.530734. Preprint.ABSTRACTCentral metabolic pathways controls virulence and antibiotic resistance, and constitute potential targets for antibacterial drugs. In Staphylococcus aureus the role of the pentose phosphate pathway (PPP) remains largely unexplored. Mutation of the 6-phosphogluconolactonase gene pgl, which encodes the only non-essential enzyme in the oxidative phase of the PPP, significantly increased MRSA resistance to β-lactam antibiotics, particularly in chemically defined media with glucose, and reduced oxacillin (OX)-induced lysis. Expression of the methicillin-resistance penicillin binding protein 2a and peptidoglycan architecture were unaffected. Carbon tracing and metabolomics revealed extensive metabolic reprogramming in the pgl mutant including increased flux to glycolysis, the TCA cycle, and several cell envelope precursors, which was consistent with increased β-lactam resistance. Morphologically, pgl mutant cells were smaller than wild-type with a thicker cell wall and ruffled surface when grown in OX. Further evidence of the pleiotropic effect of the pgl mutation was reduced resistance to Congo Red, sulfamethoxazole and oxidative stress, and increased resistance to targocil, fosfomycin and vancomycin. Reduced binding of wheat germ agglutinin (WGA) to pgl was indicative of lower wall teichoic acid/lipoteichoic acid levels or altered teichoic acid structures. Mutations in the vraFG or graRS loci reversed the increased OX resistance phenotype and restored WGA binding to wild-type levels. VraFG/GraRS was previously implicated in susceptibility to cationic antimicrobial peptides and vancomycin, and these data reveal a broader role for this multienzyme membrane complex in the export of cell envelope precursors or modifying subunits required for resistance to diverse antimicrobial agents. Altogether our study highlights important roles for the PPP and VraFG/GraRS in β-lactam resistance, which will support efforts to identify new drug targets and reintroduce β-lactams in combination with adjuvants or other antibiotics for infections caused by MRSA and other β-lactam resistant pathogens.AUTHOR SUMMARY: High-level resistance to penicillin-type (β-lactam) antibiotics significantly limits the therapeutic options for patients with MRSA infections necessitating the use of newer agents, for which reduced susceptibility has already been described. Here we report for the first time that the central metabolism pentose phosphate pathway controls MRSA resistance to penicillin-type antibiotics. We comprehensively demonstrated that mutation of the PPP gene pgl perturbed metabolism in MRSA leading to increased flux to cell envelope precursors to drive increased antibiotic resistance. Moreover, increased resistance was dependent on the VraRG/GraRS multienzyme membrane complex previously implicated in resistance to antimicrobial peptides and vancomycin. Our data thus provide new insights on MRSA mechanisms of β-lactam resistance, which will support efforts to expand the treatment options for infections caused by this and other antimicrobial resistant pathogens.PMID:36945400 | PMC:PMC10028837 | DOI:10.1101/2023.03.03.530734

J Exp Clin Cancer Res. 2023 Mar 22;42(1):69. doi: 10.1186/s13046-023-02641-0.ABSTRACTBACKGROUND: Metastases are the major cause of cancer-related morbidity and mortality. By the time cancer cells detach from their primary site to eventually spread to distant sites, they need to acquire the ability to survive in non-adherent conditions and to proliferate within a new microenvironment in spite of stressing conditions that may severely constrain the metastatic process. In this study, we gained insight into the molecular mechanisms allowing cancer cells to survive and proliferate in an anchorage-independent manner, regardless of both tumor-intrinsic variables and nutrient culture conditions.METHODS: 3D spheroids derived from lung adenocarcinoma (LUAD) and breast cancer cells were cultured in either nutrient-rich or -restricted culture conditions. A multi-omics approach, including transcriptomics, proteomics, and metabolomics, was used to explore the molecular changes underlying the transition from 2 to 3D cultures. Small interfering RNA-mediated loss of function assays were used to validate the role of the identified differentially expressed genes and proteins in H460 and HCC827 LUAD as well as in MCF7 and T47D breast cancer cell lines.RESULTS: We found that the transition from 2 to 3D cultures of H460 and MCF7 cells is associated with significant changes in the expression of genes and proteins involved in metabolic reprogramming. In particular, we observed that 3D tumor spheroid growth implies the overexpression of ALDOC and ENO2 glycolytic enzymes concomitant with the enhanced consumption of glucose and fructose and the enhanced production of lactate. Transfection with siRNA against both ALDOC and ENO2 determined a significant reduction in lactate production, viability and size of 3D tumor spheroids produced by H460, HCC827, MCF7, and T47D cell lines.CONCLUSIONS: Our results show that anchorage-independent survival and growth of cancer cells are supported by changes in genes and proteins that drive glucose metabolism towards an enhanced lactate production. Notably, this finding is valid for all lung and breast cancer cell lines we have analyzed in different nutrient environmental conditions. broader Validation of this mechanism in other cancer cells of different origin will be necessary to broaden the role of ALDOC and ENO2 to other tumor types. Future in vivo studies will be necessary to assess the role of ALDOC and ENO2 in cancer metastasis.PMID:36945054 | DOI:10.1186/s13046-023-02641-0

BMC Geriatr. 2023 Mar 21;23(1):156. doi: 10.1186/s12877-023-03897-z.ABSTRACTBACKGROUND: The sexual dimorphism represents one of the triggers of the metabolic disparities while the identification of sex-specific metabolites in the elderly has not been achieved.METHODS: A group of aged healthy population from Southwest China were recruited and clinical characteristics were collected. Fasting plasma samples were obtained and untargeted liquid chromatography-mass spectrometry-based metabolomic analyses were performed. Differentially expressed metabolites between males and females were identified from the metabolomic analysis and metabolite sets enrichment analysis was employed.RESULTS: Sixteen males and fifteen females were finally enrolled. According to clinical characteristics, no significant differences can be found except for smoking history. There were thirty-six differentially expressed metabolites between different sexes, most of which were lipids and lipid-like molecules. Twenty-three metabolites of males were increased while thirteen were decreased compared with females. The top four classes of metabolites were fatty acids and conjugates (30.6%), glycerophosphocholines (22.2%), sphingomyelins (11.1%), and flavonoids (8.3%). Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins were significantly enriched in metabolite sets enrichment analysis.CONCLUSIONS: Significant lipid metabolic differences were found between males and females among the elderly. Fatty acids and conjugates, glycerophosphocholines, and sphingomyelins may partly account for sex differences and can be potential treatment targets for sex-specific diseases.PMID:36944918 | DOI:10.1186/s12877-023-03897-z

Methods Mol Biol. 2023;2642:295-318. doi: 10.1007/978-1-0716-3044-0_16.ABSTRACTIn order to answer new biological questions, high-throughput data generated by new biotechnologies can be very meaningful but require specific and adapted statistical treatments. Thus, in the context of abiotic stress signaling studies, understanding the integration of cascading mechanisms from stress perception to biochemical and physiological adjustments necessarily entails efficient and valid analysis of multilevel and heterogeneous data. In this chapter, we propose examples to manage, analyze, and integrate multi-omics heterogeneous data. This workflow suggests and follows different general biological questions or issues answered with detailed code, data analysis, multiple visualizations, and always followed by brief interpretations. We illustrated this using the mixOmics package for the R software, as it specifically provides tools to address vertical and horizontal data integration issues. In order to illustrate this workflow, we used the usual omics datasets biologists can generate (phenomics, metabolomics, proteomics, and transcriptomics). These data were collected from two organs (leaf rosettes, floral stems) of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. They are available in the R package WallOmicsData. The workflow presented here is not limited to Arabidopsis thaliana and can be applied to any plant species. It can even be largely deployed to whatever the organisms of interest and the biological questions may be.PMID:36944885 | DOI:10.1007/978-1-0716-3044-0_16

Methods Mol Biol. 2023;2642:241-255. doi: 10.1007/978-1-0716-3044-0_14.ABSTRACTGiven that anthropogenic activities are evoking a profound effect on the climate resulting in more extreme events such as severe drought and heat waves while global demand for food is ever-increasing, understanding plant responses to stresses is critical. As metabolites are fundamental for plant growth regulation and plant lifespan and an important component of yield, illustrating how the metabolite landscape of plant changes following stress will supply important clues as to how to improve the plant resistance to stress. Recently, billions of single-nucleotide polymorphisms (SNPs) have been obtained and used to identify the associations between genetic variants of genomes and relevant crop agronomic traits through different genetic methods such as genome-wide association studies (GWAS). Therefore, in this chapter, we provide comprehensive guidelines concerning the experimental design, metabolite profiling, and metabolite-based genome-wide association studies (mGWAS) of large-scale metabolome analysis to accelerate the future identification of the valuable stress-resistant genes and metabolites.PMID:36944883 | DOI:10.1007/978-1-0716-3044-0_14

Methods Mol Biol. 2023;2642:163-177. doi: 10.1007/978-1-0716-3044-0_9.ABSTRACTBiostimulants show potentials as sustainable strategies for improved crop development and stress resilience. However, the cellular and molecular mechanisms, in particular the signaling and regulatory events, governing the agronomically observed positive effects of biostimulants on plants remain enigmatic, thus hampering novel formulation and exploration of biostimulants. Metabolomics offers opportunities to elucidate metabolic and regulatory processes that define biostimulant-induced changes in the plant's biochemistry and physiology, thus contributing to decode the modes of action of biostimulants. Here, we describe an application of metabolomics to elucidate biostimulant effects on crop plants. Using the case study of a humic substance (HS)-based biostimulant applied on maize plants, under normal and nutrient-starved stress conditions, this chapter proposes key methodological guidance and considerations of computational metabolomics approach to investigate metabolic and regulatory reconfiguration and networks underlying biostimulant-induced physiological changes in plants. Computational metabolome mining tools, in the Global Natural Products Social Molecular Networking (GNPS) ecosystem, are highlighted as well as metabolic pathway and network analysis for biological interpretation of the data.PMID:36944878 | DOI:10.1007/978-1-0716-3044-0_9

Methods Mol Biol. 2023;2642:151-161. doi: 10.1007/978-1-0716-3044-0_8.ABSTRACTLabeling plant material such as detached leaves with 15NH4+ is a very instrumental method for the characterization of metabolic pathways of mineral nitrogen assimilation and incorporation into amino acids. A procedure of labeling, followed by amino acid extraction, purification, and derivatization for gas chromatography coupled to mass spectrometry (GC/MS) analysis, is presented. The rationale of heavy isotope abundance calculations and amino acid quantification is detailed. This method is adaptable to various plant species and various kinds of investigations, such as elucidating physiological changes occurring as a result of gene mutations (overexpression or inhibition) in natural variants or genetically modified crops, or characterization of metabolic fluxes in genotypes exhibiting contrasted physiological or developmental adaptive responses to biotic and/or abiotic environmental stresses. Furthermore, the benefit of working on detached organs or pieces of organs is to investigate finely the metabolism of species that are not amenable to laboratory work, such as plants growing in natural environments or under agricultural conditions in the field.PMID:36944877 | DOI:10.1007/978-1-0716-3044-0_8

Nat Commun. 2023 Mar 21;14(1):1292. doi: 10.1038/s41467-023-36904-3.ABSTRACTThe pristine sample from the near-Earth carbonaceous asteroid (162173) Ryugu collected by the Hayabusa2 spacecraft enabled us to analyze the pristine extraterrestrial material without uncontrolled exposure to the Earth's atmosphere and biosphere. The initial analysis team for the soluble organic matter reported the detection of wide variety of organic molecules including racemic amino acids in the Ryugu samples. Here we report the detection of uracil, one of the four nucleobases in ribonucleic acid, in aqueous extracts from Ryugu samples. In addition, nicotinic acid (niacin, a B3 vitamer), its derivatives, and imidazoles were detected in search for nitrogen heterocyclic molecules. The observed difference in the concentration of uracil between A0106 and C0107 may be related to the possible differences in the degree of alteration induced by energetic particles such as ultraviolet photons and cosmic rays. The present study strongly suggests that such molecules of prebiotic interest commonly formed in carbonaceous asteroids including Ryugu and were delivered to the early Earth.PMID:36944653 | DOI:10.1038/s41467-023-36904-3

Food Chem Toxicol. 2023 Mar 19:113737. doi: 10.1016/j.fct.2023.113737. Online ahead of print.ABSTRACTObesity caused by endocrine disruptors (EDCs) has become a hot topic threatening human health. Recently, Nanoselenium Siraitia grosvenorii (NSG) has been shown to have potential health-modulating uses. Based on the results of 16S rRNA sequencing and metabolomics analysis, NSG has the unique function of improving gut microbiota and inhibiting obesity. Specifically, NSG can enhance gut microbiota diversity and change their composition. A significant positive correlation exists between the liver change in lysine and the high-importance dominant species ([Ruminococcus]_gnavus, Alistipes_finegoldii, etc.). NSG metabolites analysis showed that the lysine level increased by 44.45% and showed a significantly negatively correlated with (TG, TC, Leptin, etc.). Significantly, NSG reduces the degradation of lysine metabolism in the liver and inhibits fatty acid β-oxidation. In addition, NSG decreased Acetyl-CoA levels by 24% and regulated the downregulation of TCA genes (CS, Ogdh, Fh1, and Mdh2) and the upregulation of ketone body production genes (BDH1). NSG may have a positive effect on obesity by reducing the participation of Acetyl-CoA in the TCA cycle pathway and enhancing the ketogenic conversion of Acetyl-CoA. In conclusion, the results of this study may provide a new dietary intervention strategy for preventing endocrine disruptor-induced obesity.PMID:36944396 | DOI:10.1016/j.fct.2023.113737

Bioinformatics. 2023 Mar 21:btad143. doi: 10.1093/bioinformatics/btad143. Online ahead of print.ABSTRACTSUMMARY: Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high content imaging, proteomics, metabolomics and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.AVAILABILITY AND IMPLEMENTATION: Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.PMID:36944259 | DOI:10.1093/bioinformatics/btad143

Anal Chem. 2023 Mar 21. doi: 10.1021/acs.analchem.2c04402. Online ahead of print.ABSTRACTMulticlass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.PMID:36944135 | DOI:10.1021/acs.analchem.2c04402

PLoS One. 2023 Mar 21;18(3):e0283147. doi: 10.1371/journal.pone.0283147. eCollection 2023.ABSTRACTThe fresh leaves of Mitragyna speciosa (Korth.) Havil. have been traditionally consumed for centuries in Southeast Asia for its healing properties. Although the alkaloids of M. speciosa have been studied since the 1920s, comparative and systematic studies of metabolite composition based on different leaf maturity levels are still lacking. This study assessed the secondary metabolite composition in two different leaf stages (young and mature) of M. speciosa, using an untargeted liquid chromatography-electrospray ionisation-time-of-flight-mass spectrometry (LC-ESI-TOF-MS) metabolite profiling. The results revealed 86 putatively annotated metabolite features (RT:m/z value) comprising 63 alkaloids, 10 flavonoids, 6 terpenoids, 3 phenylpropanoids, and 1 of each carboxylic acid, glucoside, phenol, and phenolic aldehyde. The alkaloid features were further categorised into 14 subclasses, i.e., the most abundant class of secondary metabolites identified. As per previous reports, indole alkaloids are the most abundant alkaloid subclass in M. speciosa. The result of multivariate analysis (MVA) using principal component analysis (PCA) showed a clear separation of 92.8% between the young and mature leaf samples, indicating a high variance in metabolite levels between them. Akuammidine, alstonine, tryptamine, and yohimbine were tentatively identified among the many new alkaloids reported in this study, depicting the diverse biological activities of M. speciosa. Besides delving into the knowledge of metabolite distribution in different leaf stages, these findings have extended the current alkaloid repository of M. speciosa for a better understanding of its pharmaceutical potential.PMID:36943850 | DOI:10.1371/journal.pone.0283147

Food Funct. 2023 Mar 21. doi: 10.1039/d2fo04085a. Online ahead of print.ABSTRACTTo investigate the prebiotic potential of two Laminaria japonica polysaccharide (LJP) fractions with different molecular weights and structures, we conducted in vitro simulated digestion and fermentation with hyperlipidemia-associated human gut microbiota. The results indicated that the LJP fraction with higher molecular weight (HLJP) appeared to have a more complex monosaccharide composition and microstructure than did the LJP fraction with lower molecular weight (LLJP), and both fractions could not be digested by in vitro simulated digestion. After in vitro fermentation, HLJP generated more short-chain fatty acids (SCFAs) and showed stronger ability to regulate core metabolites. Intriguingly, LLJP is better at promoting the proliferation of Akkermansiaceae, while HLJP is more effective in reducing the Firmicutes/Bacteroidetes ratio and increasing the content of Bacteroidaceae and Tannerellaceae. The present study indicates that LLJP and HLJP may have probiotic effects through different approaches and these differences may be related to the molecular weight and structure of the polysaccharides.PMID:36943742 | DOI:10.1039/d2fo04085a

Microbiol Spectr. 2023 Mar 21:e0469122. doi: 10.1128/spectrum.04691-22. Online ahead of print.ABSTRACTOuter membrane vesicles (OMVs) are a primary means of communication for Gram-negative bacteria. The specific role of vesicle components in cellular communication and how components are packaged are still under investigation, but a correlation exists between OMV biogenesis and content. The two primary mechanisms of OMV biogenesis are membrane blebbing and explosive cell lysis, and vesicle content is based on the biogenesis mechanism. Hypervesiculation, which can be induced by stress conditions, also influences OMV content. Norovirus interaction with Enterobacter cloacae induces stress responses leading to increased OMV production and changes in DNA content, protein content, and vesicle size. The presence of genomic DNA and cytoplasmic proteins in these OMVs suggests some of the vesicles are formed by explosive cell lysis, so reduction or loss of these components indicates a shift away from this mechanism of biogenesis. Based on this, further investigation into bacterial stability and OMV content was conducted. Results showed that norovirus induced a dramatic shift in OMV lipid content. Specifically, the increased accumulation of phospholipids is associated with increased blebbing, thereby supporting previous observations that noroviruses shift the mechanism of OMV biogenesis. Slight differences in OMV metabolite content were also observed. While norovirus induced changes in OMV content, it did not change the lipid content of the bacterial outer membrane or the metabolite content of the bacterial cell. Overall, these results indicate that norovirus induces significant changes to OMV lipid architecture and cargo, which may be linked to a change in the mechanism of vesicle biogenesis. IMPORTANCE Extracellular vesicles from commensal bacteria are recognized for their importance in modulating host immune responses, and vesicle content is related to their impact on the host. Therefore, understanding how vesicles are formed and how their content shifts in response to stress conditions is necessary for elucidating their downstream functions. Our recent work has demonstrated that interactions between noroviruses and Enterobacter cloacae induce bacterial stress responses leading to hypervesiculation. In this article, we characterize and compare the lipid and metabolomic cargo of E. cloacae vesicles generated in the presence and absence of norovirus and show that viral interactions induce significant changes in vesicle content. Furthermore, we probe how these changes and changes to the bacterial cell may be indicative of a shift in the mechanism of vesicle biogenesis. Importantly, we find that noroviruses induce significant changes in vesicle lipid architecture and cargo that may be responsible for the immunogenic activity of these vesicles.PMID:36943087 | DOI:10.1128/spectrum.04691-22

Microbiol Spectr. 2023 Mar 21:e0333022. doi: 10.1128/spectrum.03330-22. Online ahead of print.ABSTRACTThe Integrative Human Microbiome Project and other cohort studies have indicated that inflammatory bowel disease is accompanied by dysbiosis of gut microbiota, decreased production of secondary bile acids, and increased levels of primary bile acids. Secondary bile acids, such as ursodeoxycholic acid (UDCA) and lithocholic acid (LCA), have been reported to be anti-inflammatory, yet it remains to be studied whether introducing selected bacteria strains to restore bile acid metabolism of the gut microbiome can alleviate intestinal inflammation. In this study, we screened human gut bacterial strains for bile acid metabolism and designed a consortium of three species, including Clostridium AP sp000509125, Bacteroides ovatus, and Eubacterium limosum, and named it BAC (bile acid consortium). We showed that the three-strain gut bacterial consortium BAC is capable of converting conjugated primary bile acids taurochenodeoxycholic acid and glycochenodeoxycholic acid to secondary bile acids UDCA and LCA in vitro. Oral gavage treatment with BAC in mice resulted in protective effects against dextran sulfate sodium (DSS)-induced colitis, including reduced weight loss and increased colon length. Furthermore, BAC treatment increased the fecal level of bile acids, including UDCA and LCA. BAC treatment enhanced intestinal barrier function, which may be attributed to the increased activation of the bile acid receptor TGR5 by secondary bile acids. Finally, we examined the remodeling of gut microbiota by BAC treatment. Taken together, the three-strain gut bacterial consortium BAC restored the dysregulated bile acid metabolism and alleviated DSS-induced colitis. Our study provides a proof-of-concept demonstration that a rationally designed bacterial consortium can reshape the metabolism of the gut microbiome to treat diseases. IMPORTANCE Secondary bile acids have been reported to be anti-inflammatory, yet it remains to be studied whether introducing selected bacteria strains to restore bile acid metabolism of the gut microbiome can alleviate intestinal inflammation. To address this gap, we designed a consortium of human gut bacterial strains based on their metabolic capacity to produce secondary bile acids UDCA and LCA, and we evaluated the efficacy of single bacterial strains and the bacterial consortium in treating the murine colitis model. We found that oral gavage of the bacterial consortium to mice restored secondary bile acid metabolism to increase levels of UDCA and LCA, which induced the activation of TGR5 to improve gut-barrier integrity and reduced the inflammation in murine colitis. Overall, our study demonstrates that rationally designed bacterial consortia can reshape the metabolism of the gut microbiome and provides novel insights into the application of live biotherapeutics for treating IBD.PMID:36943054 | DOI:10.1128/spectrum.03330-22

Biomed Chromatogr. 2023 Mar 21:e5620. doi: 10.1002/bmc.5620. Online ahead of print.ABSTRACTCitronella is used as a spice and traditional herbal medicine. Dried citronella is easy to store and transport, and it is unclear whether dried citronella has increased or decreased medicinal components compared to fresh citronella. In the present study, various metabolites in fresh and dry citronella were detected using a widely targeted metabolomics strategy. we identified 712 metabolites, and classified as 31 categories, and discovered 132 flavonoids. After the drying of citronella, the contents of most kinds of flavonoids increased, but the contents of amino acids, organic acids and vitamins decreased, and the content of quercetin increased significantly. Therefore, the medicinal value of citronella after drying treatment may be increased, but the nutritional value of amino acids and vitamins may be decreased. The results of this study may serve as a new theoretical reference for deal with citronella and promote the nutrition and medicinal chemical composition of citronella.PMID:36942894 | DOI:10.1002/bmc.5620