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>
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