Gene Expression in AJ and AcB60 mice following Salmonella infection
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE65893 | GEO | 2015/02/12
SECONDARY ACCESSION(S): PRJNA275319
REPOSITORIES: GEO
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Project description:Analysis of differential genome-wide gene expression within the splenic homogenate of A/J and AcB60 mice, both naïve mice and those infected with Salmonella Typhimrium Total RNA was islolated from the splenic homogenate of naïve A/J and AcB60 mice, as well as mice infected with 1000 CFUs of Salmonella Typhimuirum and sacrified 3, 5, and 7 days post-infection
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2007-10-18 | GSE7496 | GEO
Project description:Salmonella enteritidis is suggested to translocate in the small intestine. Previously we identified that prebiotics, fermented in the colon, increased Salmonella translocation in rats, suggesting involvement of the colon in translocation. Effects of Salmonella on colonic gene expression in vivo are largely unknown. The aim of this study was to characterize time dependent Salmonella induced changes of colonic mucosal gene expression in rats using whole genome microarrays. Rats were orally infected with Salmonella enteritidis to mimic a foodbore infection and colonic gene expression was determined at day 1, 3 and 6 post-infection (n=8 per timepoint). Agilent rat whole genome microarray (G4131A Agilent Technologies) were used. Results indicate that colon is clearly a target tissue for Salmonella considering the abundant changes in mucosal gene expression observed. Experiment Overall Design: In the present study, large-scale gene expression analysis was performed to reveal whether Salmonella induced changes of colonic mucosal gene expression in rats. Wistar rats were infected with Salmonella enteritidis. Non-infected control rats were sham-treated. Rats were sacrificed on day 1, 3 or 6 post infection or sham-treatment (n=8 rats per treatment and per time point). RNA was isolated from colonic mucosal scrapings. mRNA samples of 8 rats per group were pooled. Each pooled group-sample was hybridised in duplicate on Agilent rat whole genome microarrays containing 44290 60-mer spots. From the 12 arrays one duplicate array (Colon mucosa non-infected day6) did not pass quality control and was left out from further analysis.
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Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. 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 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected 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 uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.
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