PubMed

Phenomics. 2022 Jun 15;3(1):1-21. doi: 10.1007/s43657-022-00061-2. eCollection 2023 Feb.ABSTRACTSoil salinity is among the abiotic stressors that threaten agriculture the most, and purslane (Portulaca oleracea L.) is a dicot species adapted to inland salt desert and saline habitats that hyper accumulates salt and has high phytoremediation potential. Many researchers consider purslane a suitable model species to study the mechanisms of plant tolerance to drought and salt stresses. Here, a robust salinity stress protocol was developed and used to characterize the morphophysiological responses of young purslane plants to salinity stress; then, leaf tissue underwent characterization by distinct omics platforms to gain further insights into its response to very high salinity stress. The salinity stress protocol did generate different levels of stress by gradients of electrical conductivity at field capacity and water potential in the saturation extract of the substrate, and the morphological parameters indicated three distinct stress levels. As expected from a halophyte species, these plants remained alive under very high levels of salinity stress, showing salt crystal-like structures constituted mainly by Na+, Cl-, and K+ on and around closed stomata. A comprehensive and large-scale metabolome and transcriptome single and integrated analyses were then employed using leaf samples. The multi-omics integration (MOI) system analysis led to a data-set of 51 metabolic pathways with at least one enzyme and one metabolite differentially expressed due to salinity stress. These data sets (of genes and metabolites) are valuable for future studies aimed to deepen our knowledge on the mechanisms behind the high tolerance of this species to salinity stress. In conclusion, besides showing that this species applies salt exclusion already in young plants to support very high levels of salinity stress, the initial analysis of metabolites and transcripts data sets already give some insights into other salt tolerance mechanisms used by this species to support high levels of salinity stress.SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-022-00061-2.PMID:36947413 | PMC:PMC9883379 | DOI:10.1007/s43657-022-00061-2

Plant Foods Hum Nutr. 2023 Mar 22. doi: 10.1007/s11130-023-01055-9. Online ahead of print.ABSTRACTAntioxidative and antiaging abilities of probiotic fermented ginseng (PG) were evaluated in Caenorhabditis elegans (C. elegans). Lifespan and effect on heat stress and acute oxidative stress in C. elegans were significantly enhanced by PG. Antioxidative enzymes such as T-SOD, GSH-PX, CAT were significantly up-regulated, and MDA, ROS and apoptosis levels were significantly down-regulated. At the same time, PG exerted antioxidant and anti-aging activities by reducing the expression of DAF-2 mRNA and increasing the expression of SKN-1 and SOD-3 mRNA in C. elegans. In addition, the mechanism of antioxidative and antiaging activities of PG was explored through gut microbiota sequencing and untargeted metabolomics. The results of gut microbiota indicated that PG could significantly improve the composition and structure of microbes in the gut of C. elegans, and the relative abundance of beneficial bacteria was up-regulated. Untargeted metabolomic results elucidated that PG modulated antioxidant and antiaging activities through neuroactive ligand-receptor interaction, Citrate cycle (TCA cycle), pyruvate metabolism, ascorbate and aldarate metabolism and D-Arginine and D-ornithine metabolism of C. elegans. These results indicated that PG had excellent antioxidant and anti-aging activities, providing research value for the development of functional foods and improvement of aging-related diseases.PMID:36947370 | DOI:10.1007/s11130-023-01055-9

Microb Ecol. 2023 Mar 22. doi: 10.1007/s00248-023-02200-2. Online ahead of print.ABSTRACTFungal pigments are characterized by a diverse set of chemical backbones, some of which present photosensitizer-like structures. From the genus Cortinarius, for example, several biologically active photosensitizers have been identified leading to the hypothesis that photoactivity might be a more general phenomenon in the kingdom Fungi. This paper aims at testing the hypothesis. Forty-eight fruiting body-forming species producing pigments from all four major biosynthetic pathways (i.e., shikimate-chorismate, acetate-malonate, mevalonate, and nitrogen heterocycles) were selected and submitted to a workflow combining in vitro chemical and biological experiments with state-of-the-art metabolomics. Fungal extracts were profiled by high-resolution mass spectrometry and subsequently explored by spectral organization through feature-based molecular networking (FBMN), including advanced metabolite dereplication techniques. Additionally, the photochemical properties (i.e., light-dependent production of singlet oxygen), the phenolic content, and the (photo)cytotoxic activity of the extracts were studied. Different levels of photoactivity were found in species from all four metabolic groups, indicating that light-dependent effects are common among fungal pigments. In particular, extracts containing pigments from the acetate-malonate pathway, e.g., extracts from Bulgaria inquinans, Daldinia concentrica, and Cortinarius spp., were not only efficient producers of singlet oxygen but also exhibited photocytotoxicity against three different cancer cell lines. This study explores the distribution of photobiological traits in fruiting body forming fungi and highlights new sources for phototherapeutics.PMID:36947169 | DOI:10.1007/s00248-023-02200-2

J Agric Food Chem. 2023 Mar 22. doi: 10.1021/acs.jafc.3c00050. Online ahead of print.ABSTRACTHerbaspirillum sp. ZXN111 and its mutants (Δacc, Δtyrb, and Δacc-tyrb), which show PGP activity on Zijuan, were tested for tea plants' colonization characteristics and the strain-dependent response of tea metabolites. The results showed that strain ZXN111 could widely colonize in different tea cultivars of Zijuan, Yunkang-10, Longjin 43, and Shuchazao, but with significant colonization preference to Zijuan, which might be ascribed to anthocyanins' chemotaxis. After 9 weeks of co-cultivation, l-theanine and theobromine in Zijuan leaves that were inoculated with wild-type ZXN111 were decreased, while theobromine, caffeine, and l-theanine that were inoculated with mutant Δacc were increased; especially l-theanine increased much significantly. Metabolomics analysis showed that tea metabolite profiling of inoculant groups was clearly separated from the control; therein, the flavanols were downregulated in ZXN111 and Δacc groups, but the l-theanine of the Δacc group was significantly upregulated compared to control and ZXN111 groups. These results indicated that strain ZXN111, especially of mutant Δacc, improved Zijuan tea flavor.PMID:36946772 | DOI:10.1021/acs.jafc.3c00050

Cancer Res. 2023 Mar 22:OF1-OF19. doi: 10.1158/0008-5472.CAN-22-1826. Online ahead of print.ABSTRACTClinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers.SIGNIFICANCE: Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors.PMID:36946761 | DOI:10.1158/0008-5472.CAN-22-1826

Mol Nutr Food Res. 2023 Mar 22:e2200633. doi: 10.1002/mnfr.202200633. Online ahead of print.ABSTRACTSCOPE: Holothuria leucospilota polysaccharides (HLP) are bioactive polysaccharides with immunomodulatory effects. This study aimed to investigate the impact of HLP on dextran sodium sulfate (DSS)-induced colitis in rats and further investigate the complex interactions between changes in intestinal microbiota, co-metabolites, and intestinal inflammation under HLP intervention.METHODS AND RESULTS: The ulcerative colitis (UC) model of Sprague Dawley (SD) rats was established by a normal diet with 3%DSS. The effects of HLP on UC were studied by gavage of different doses of HLP for two weeks. The results showed that HLP alleviated the inflammation of UC and reduced histological damage and secretion of TNF-α, IL-6, IL-1β, and IL-10. After HLP treatment, the intestinal flora of UC rats was regulated, and the flora diversity was restored. Fecal metabolomics analysis revealed the modulatory effects of HLP on amino acid metabolism, antimicrobial peptide anabolism and energy metabolism in rats with colitis. Correlation analysis of microbial and intestinal metabolites revealed the potential mechanism of HLP affecting colitis.CONCLUSION: HLP repaired the intestinal compartment's metabolic disorder by regulating intestinal flora's structure and alleviating colonic mucosal injury and inflammation in colitis rats. This article is protected by copyright. All rights reserved.PMID:36946468 | DOI:10.1002/mnfr.202200633

Food Funct. 2023 Mar 22. doi: 10.1039/d2fo03030f. Online ahead of print.ABSTRACTThe herb Astragali Radix is a food-medicine herb. A major component of Astragali Radix, astragaloside IV (AS-IV), has neuroprotective effects in IS, but its mechanisms are not well understood. Our research used a transient middle cerebral artery occlusion (MCAO) rat model for longitudinal multi-omics analyses of the side of the brain affected by ischemia. Based on transcriptomic and proteomic analysis, we found that 396 differential expression targets were up-regulated and 114 differential expression targets were down-regulated. A total of 117 differential metabolites were identified based on metabonomics. Finally, we found 8 hub genes corresponding to the compound-reaction-enzyme-gene network using the Metscape plug-in for Cytoscape 3.7.1. We found that the related key metabolites were 3,4-dihydroxy-L-phenylalanine, 2-aminomuconate semialdehyde, (R)-3-hydroxybutanoate, etc., and the affected pathways were tyrosine metabolism, tryptophan metabolism, butanoate metabolism, purine metabolism, etc. We further validated these targets using 4D-PRM proteomics and found that seven targets were significantly different, including Aprt, Atic, Gaa, Galk1, Glb1, Me2, and Hexa. We aimed to uncover the mechanism of AS-IV in the treatment of ischemic brain injury through a comprehensive strategy combining transcriptomics, proteomics, and metabolomics.PMID:36946308 | DOI:10.1039/d2fo03030f

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