The Crosstalk between Microbiota and Metabolites in AP Mice Based on Metagenomics and Untargeted Metabolomics
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB59034 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Background and purposeMicrobiome dysfunction is known to aggravate acute pancreatitis (AP); however, the relationship between this dysfunction and metabolite alterations is not fully understood. This study explored the crosstalk between the microbiome and metabolites in AP mice.MethodsExperimental AP models were established by injecting C57/BL mice with seven doses of cerulein and one dose of lipopolysaccharide (LPS). Metagenomics and untargeted metabolomics were used to identify systemic disturbances in the microbiome and metabolites, respectively, during the progression of AP.ResultsThe gut microbiome of AP mice primarily included Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria, and "core microbiota" characterized by an increase in Proteobacteria and a decrease in Actinobacteria. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that significantly different microbes were involved in several signaling networks. Untargeted metabolomics identified 872 metabolites, of which lipids and lipid-like molecules were the most impacted. An integrated analysis of metagenomics and metabolomics indicated that acetate kinase (ackA) gene expression was associated with various gut microbiota, including Alistipes, Butyricimonas, and Lactobacillus, and was strongly correlated with the metabolite daphnoretin. The functional gene, O-acetyl-L-serine sulfhydrylase (cysK), was associated with Alistipes, Jeotgalicoccus, and Lactobacillus, and linked to bufalin and phlorobenzophenone metabolite production.ConclusionThis study identified the relationship between the gut microbiome and metabolite levels during AP, especially the Lactobacillus-, Alistipes-, and Butyricimonas-associated functional genes, ackA and cysK. Expression of these genes was significantly correlated to the production of the anti-inflammatory and antitumor metabolites daphnoretin and bufalin.
| S-EPMC10446838 | biostudies-literature
Project description:The Crosstalk between Microbiota and Metabolites in AP Mice Based on Metagenomics and Untargeted Metabolomics
| PRJEB58915 | ENA
Project description:<p><strong>BACKGROUND AND PURPOSE:</strong> The microbiota dysfunction aggravated the severity of acute pancreatitis (AP), but the relationship between microbiota dysfunction and metabolites alteration was not fully explained. This study aimed to explore the crosstalk between microbiota and metabolites in AP mice.</p><p><strong>METHODS:</strong> Experimental AP models were established by caerulein for 7 times plus lipopolysaccharide (LPS) for once in C57/BL mice. To reveal systemic disturbance of AP, metagenomics and untargeted metabolomics were applied to reveal systemic disturbance of microbiota and metabolites in AP progress, respectively.</p><p><strong>RESULTS:</strong> Gut microbiota from AP mice was mainly composed of <em>Firmicutes</em>, <em>Bacteroidetes</em>, <em>Actinobacteria</em> and <em>Proteobacteria</em>, and presented a 'core microbiota' characterized by an expansion in <em>Proteobacteria</em> as well as a decrease in <em>Actinobacteria</em>. Kyoto encyclopedia of genes and genomes (KEGG) analysis found that the significantly differential microbiota was involved in several signaling networks. Moreover, 872 metabolites were identified from untargeted metabolomics. Among them, lipids and lipid-like molecules were majorly affected. The integrated analysis of metagenomics and metabolomics showed that acetate kinase (Acka) was associated with various gut microbiota, including <em>Alistipes</em>, <em>Butyricimonas</em> and <em>Lactobacillus</em>. Particularly, Acka was tightly correlated with the metabolite daphnoretin. Besides, the functional gene of O-acetyl-L-serine sulfhydrylase (Cysk) was correlated with <em>Alistipes</em>, <em>Jeotgalicoccus</em> and <em>Lactobacillus</em>, and which was associated with metabolites of bufalin and Phlorobenzophenone.</p><p><strong>CONCLUSION:</strong> Our study uncovered the relationship between gut microbiota and metabolites during AP, especially the <em>Lactobacillus</em>, <em>Alistipes</em> and <em>Butyricimonas</em> associated functional genes (Acka and Cysk) was tightly correlated to anti-inflammation and anti-tumor metabolites (daphnoretin and bufalin).</p>
2023-08-10 | MTBLS6831 | MetaboLights
Project description:CoMet, a fully automated Computational Metabolomics method to predict changes in metabolite levels in cancer cells compared to normal references has been developed and applied to Jurkat T leukemia cells with the goal of testing the following hypothesis: up or down regulation in cancer cells of the expression of genes encoding for metabolic enzymes leads to changes in intracellular metabolite concentrations that contribute to disease progression. Nine metabolites predicted to be lowered in Jurkat cells with respect to normal lymphoblasts were examined: riboflavin, tryptamine, 3-sulfino-L-alanine, menaquinone, dehydroepiandrosterone, α-hydroxystearic acid, hydroxyacetone, seleno-L-methionine and 5,6-dimethylbenzimidazole. All, alone or in combination, exhibited antiproliferative activity. Of eleven metabolites predicted to be increased or unchanged in Jurkat cells, only two (bilirubin and androsterone) exhibited significant antiproliferative activity. These results suggest that cancer cell metabolism may be regulated to reduce the intracellular concentration of certain antiproliferative metabolites, resulting in uninhibited cellular growth and have the implication that many other endogenous metabolites with important roles in carcinogenesis are awaiting discovery. Keywords: cell type
2008-08-04 | GSE9648 | GEO
Project description:Canine pyometra frequently occurs in middle-aged to older intact bitches, which seriously affects the life of dogs and brings an economic loss to their owners. Hence, finding a key metabolite is very important for the diagnosis and development of a new safe and effective therapy for the disease. In this study, dogs with pyometra were identified by blood examinations, laboratory analyses and diagnostic imaging, and fifteen endometrium tissues of sick dogs with pyometra and fifteen controls were collected and their metabolites were identified utilizing a UHPLC-qTOF-MS-based untargeted metabolomics approach. The results indicated that the elevated inflammatory cells were observed in dogs with pyometra, suggesting that sick dogs suffered systemic inflammation. In the untargeted metabolic profile, 705 ion features in the positive polarity mode and 414 ion features in the negative polarity mode were obtained in endometrium tissues of sick dogs with pyometra, with a total of 275 differential metabolites (173 in positive and 102 in negative polarity modes). Moreover, the multivariate statistical analyses such as PCA and PLS-DA also showed that the metabolites were significantly different between the two groups. Then, these differential metabolites were subjected to pathway analysis using Metaboanalyst 4.0, and Galactose metabolism, cAMP signaling pathway and Glycerophospholipid metabolism were enriched, proving some insights into the metabolic changes during pyometra. Moreover, the receiver operating characteristic curves further confirmed kynurenic acid was expected to be a candidate biomarker of canine pyometra. In conclusion, this study provided a new idea for exploring early diagnosis methods and a safe and effective therapy for canine pyometra.
| S-EPMC9697130 | biostudies-literature
Project description:AimWe conducted a joint metabolomic-epigenomic study to identify patterns of epigenetic associations with smoking-related metabolites.Patients & methodsWe performed an untargeted metabolome-wide association study of smoking and epigenome-wide association studies of smoking-related metabolites among 180 male twins. We examined the patterns of epigenetic association linked to smoking-related metabolites using hierarchical clustering.ResultsAmong 12 annotated smoking-related metabolites identified from a metabolome-wide association study, we observed significant hypomethylation associated with increased level of N-acetylpyrrolidine, cotinine, 5-hydroxycotinine and nicotine and hypermethylation associated with increased level of 8-oxoguanine. Hierarchical clustering revealed common and unique epigenetic-metabolic associations related to smoking.ConclusionOur study suggested that a joint metabolome-epigenome approach can reveal additional details in molecular responses to the environmental exposure to understand disease risk.
| S-EPMC5925442 | biostudies-literature
Project description:Ramie is an important fiber feed dual-purpose crop in China and plays an important role in the national economy. However, ramie yield and quality can be reduced after many years of continuous cultivation. Currently, relatively little research has been conducted on rhizosphere metabolites and their pathways in continuous ramie cropping. Therefore, a healthy group (CK) and obstacle groups (XZQG, JZ, DJY, and GXD) with 8 years of continuous cultivation were selected for the study. LC-MS and GC-MS untargeted metabolomics were used to explore and analyze ramie rhizosphere metabolites and pathways. The results revealed that significant differences in the agronomic traits of ramie occurred after 8 years of continuous cultivation, with dwarfed plants and decreased yields in the obstacle groups. Metabolomic analysis identified 49 and 19 rhizosphere metabolites, including lipids, organic acids, phenols, and amino acids. In addition, four differential metabolic pathways (phenylpropanoid biosynthesis, fatty acid metabolism, amino acid metabolism, and ascorbate and aldarate metabolism) were elucidated. It was also clarified that sinapic acid, jasmonic acid, glutamine, and inositol might be the main metabolites affecting ramie continuous-cropping obstacle groups, and they were significantly correlated with ramie agronomic traits and physiological indicators. This provided important insights into the mechanisms affecting continuous ramie cropping. Accordingly, it is expected that the increase or decrease of sinapic acid, jasmonic acid, glutamine, and inositol in the soil will alleviate obstacles to continuous ramie cropping and promote the healthy development of the ramie industry in the future.
| S-EPMC10477603 | biostudies-literature
Project description:Metabolomics is a useful tool for comparing metabolite changes in plants. Because of its high sensitivity, metabolomics combined with high-resolution mass spectrometry (HR-MS) is the most widely accepted metabolomics tools. In this study, we compared the metabolites of pathogen-infected rice (Oryza sativa) with control rice using an untargeted metabolomics approach. We profiled the mass features of two rice groups using a liquid chromatography quadrupole time-of-flight mass spectrometry (QTOF-MS) system. Twelve of the most differentially induced metabolites in infected rice were selected through multivariate data analysis and identified through a mass spectral database search. The role of these compounds in metabolic pathways was finally investigated using pathway analysis. Our study showed that the most frequently induced secondary metabolites are prostanoids, a subclass of eicosanoids, which are associated with plant defense metabolism against pathogen infection. Herein, we propose a new untargeted metabolomics approach for understanding plant defense system at the metabolic level.
| S-EPMC7910874 | biostudies-literature
Project description:Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms (n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method.
| S-EPMC6161174 | biostudies-literature
Project description:Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.
| S-EPMC7281133 | biostudies-literature