Dataset Information


A metabolomics analysis of gallbladder lipid droplet formation due to Salmonella infection

ABSTRACT: To cause disease, Salmonella enterica serovar Typhimurium requires two type-III secretion systems, encoded on Salmonella Pathogenicity Islands 1 and 2 (SPI-1 and -2). These secretion systems serve to deliver virulence proteins, termed effectors, into the host cell cytosol. While the importance of these effector proteins to promote colonization and replication within the host has been established, the specific roles of individual secreted effectors in the disease process are not well understood. In this study, we used an in vivo gallbladder epithelial cell infection model to study the function of the SPI-2-encoded effector, SseL. Deletion of the sseL gene resulted in bacterial filamentation and elongation and unusual localization of Salmonella within infected epithelial cells. Infection with the ΔsseL strain also caused dramatic changes in lipid metabolism and led to massive accumulation of lipid droplets in infected cells. Some of these changes were investigated through metabolomics of gallbladder tissue. This phenotype was directly attributed to the deubiquitinase activity of SseL, as a Salmonella strain carrying a single point mutation in the catalytic cysteine resulted in the same phenotype as the deletion mutant. Excessive buildup of lipids due to the absence of a functional sseL gene was also observed in S. Typhimurium-infected livers. These results demonstrate that SseL alters host lipid metabolism in infected epithelial cells by modifying ubiquitination patterns of cellular targets. Female C57BL/6 mice were infected with the indicated strain of Salmonella enterica serovar Typhimurium by oral gavage. Four gallbladders were collected and pooled per sample group and metabolites extracted using a mixture of methanol and chloroform. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140). To identify differences in metabolite composition between different groups of samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from each group of mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix ( Masses were searched against the Mus musculus database within a mass error of 3 ppm.

ORGANISM(S): Mus musculus  

SUBMITTER: Christoph H Borchers   Matthew A Croxen  Julian A Guttman  Sigrid D Auweter  B B Finlay  L. Caetano M. Antunes  L C Antunes  Jun Han  Alfredo Menendez  Wayne Vogl  Ellen T Arena 

PROVIDER: E-GEOD-27604 | ArrayExpress | 2011-08-22



Similar Datasets

2011-02-08 | E-GEOD-21720 | ArrayExpress
2011-02-08 | E-GEOD-25687 | ArrayExpress
2011-08-23 | GSE27604 | GEO
2011-02-08 | GSE21720 | GEO
2011-07-06 | E-GEOD-30404 | ArrayExpress
2002-12-22 | E-GEOD-92 | ArrayExpress
2011-03-10 | E-GEOD-27703 | ArrayExpress
2018-10-24 | PXD002594 | Pride
2019-05-14 | E-GEOD-53280 | ExpressionAtlas
2011-02-08 | GSE25687 | GEO