Haptenation of Macrophage Migration Inhibitory Factor-Dataset 2
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Mus Musculus (mouse)
SUBMITTER: 
Lorena Ndreu
LAB HEAD: Isabella Karlsson
PROVIDER: PXD031793 | Pride | 2022-03-18
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| MIF_CA_DDA.pdResult | Other | |||
| MIF_CA_DDA.raw | Raw | |||
| MIF_CA_PRM.raw | Raw | |||
| MIF_CMIT_DDA.pdResult | Other | |||
| MIF_CMIT_DDA.raw | Raw |
Items per page: 5 1 - 5 of 19 |
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Project description:During the colonization of hosts, bacterial pathogens are presented with many challenges that must be overcome for colonization to successfully occur. This requires bacterial sensing of the surroundings and adaptation to the conditions encountered. One of the major impediments to pathogen colonization of the mammalian gastrointestinal tract is the antibacterial action of bile. Salmonella enterica serovar Typhimurium has specific mechanisms involved in resistance to bile. Besides being resistant to it, Salmonella can also successfully multiply in bile, using it as a source of nutrients. This accomplishment is highly relevant to pathogenesis, as Salmonella colonizes the gallbladder of hosts, where it can be carried asymptomatically and promote further host spread and transmission. In order to gain insights into the mechanisms used by Salmonella to grow in bile, we studied the changes elicited by Salmonella in the chemical composition of bile during growth in vitro and in vivo through a metabolomics approach. Our data suggest that phospholipids are an important source of carbon and energy for Salmonella during growth in the laboratory as well as during gallbladder infections of mice. Further studies in this area will generate a better understanding of how Salmonella exploits this generally hostile environment for its own benefit. For in vitro studies, bile was extracted from C57BL/6 mice and used immediately. An overnight culture of Salmonella enterica serovar Typhimurium SL1344 was used to inoculate 10 ?L of bile at an approximate density of 5x10^6 cells/mL. The experiment was performed in duplicate, and a total of 2 uninfected and 2 infected bile samples were studied. Samples were incubated for 24 hours at 37 oC with shaking. After incubation, samples were centrifuged to remove bacteria and the supernatant was saved for metabolomic analyses. For in vivo studies, C57BL/6 mice were infected with approximately 10^8 bacterial cells by oral gavage. Four groups of three to four mice each were either infected with Salmonella or kept uninfected (total of 11 mice per treatment group). Five days after infection, all mice were sacrificed and bile was collected. Samples were centrifuged and supernatants saved for metabolomic analyses. Samples were prepared by mixing equal volumes of bile from three to four mice, generating three samples per treatment (uninfected and infected), which were used in the subsequent steps. Seven microliters of each sample was evaporated and the residue resuspended in 50% acetonitrine. 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 to the masses selected as described above, the monoisotopic neutral masses of interest were queried against the Human Metabolome Database (HMDB, http://www.hmdb.ca), with a tolerance of 0.001 Da.
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2011-08-22 | E-GEOD-27604 | biostudies-arrayexpress
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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