Project description:Untargeted LC/MS (re-run on Q-exactive Mass Spectrometer) metabolomics of blood samples for Asthma cohort collected as part of the Microbiome Core.
Project description:Peptides of HCC cell lines and tumour tissues were fractionated by Hp-RP to eight fractions and repeated analysed three times by the LC-MS/MS detection system consisted of the EASY-nLC 1000 coupled to the Q-Exactive HF mass spectrometer using data dependent acquisition mode.
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.
Project description:In this study, we compared the proteomes of mouse CD4+Foxp3+ regulatory T cells (Treg) and CD4+Foxp3- conventional T cells (Tconv) in order to build a data set of proteins differentially regulated in these two cell populations. The data set contains mass spectrometry results from the analysis of 7 biological replicates of Treg/Tconv cell samples purified by flow cytometry, each experiment performed from a pool of 4-5 mice. Global proteomic analysis of each sample was performed by single-run nanoLC-MS/MS, using chromatographic separation of peptides on 50cm C18 reverse-phase columns, with either a 480min gradient on LTQ-Velos orbitrap mass spectrometer (replicates 1 and 2) or a 300min gradient on Q-Exactive orbitrap mass spectrometer (replicates 3-7). Several MS injection replicates were performed for some experiments, leading to 27 raw files composing the data set. The detailed description of each analysis (file name, sample type, biological replicate number, MS technical replicate number, MS instrument used, sample name in MaxQuant ouput) is given in the table “Files list.txt”.
Project description: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 ?sseL 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 (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm.
Project description:Proteomics and phosphoproteomics analysis of MCF7 cells sensitive and resistant to the PI3K inhibitor GDC-0941. The analysis was done for parental and 3 resistant cell lines maintained in the presence or the absence of the drug. One of the resistant cell lines (G2) was also treated with vehicle or the kinase inhibitors GDC0941, MK-2206 and Ku-0063794 for 2h. The proteomics analysis of sensitive and resistant MCF7 cells in basal conditions and phosphoproteomics analysis of the G2 cells in the presence of inhibitors of the PI3K pathway were run in a nano flow ultrahigh pressure liquid chromatography (UPLC, nano Acquity, Waters) coupled to an LTQ-Orbitrap XL mass spectrometer (Thermo Fisher Scientific). The phosphoproteomics study of sensitive and resistant MCF7 cells in basal conditions was run in a Dionex UltiMate 3000 RSLCnano coupled to an Orbitrap Q Exactive Plus mass spectrometer (Thermo Fisher Scientific).
Project description:In this study, 3 biological replicates with 3 technical replicates for the conditioned media (CM) and the whole cell lysates (WCL) of C8-D1A cell line were analyzed using 108 LC-MS/MS runs. Each peptide sample was separated into 6 fractions using stageTip based High-pH fractionation. The peptide samples were analyzed using LC-MS/MS instrumentation consisting of a Nanoflow Easy-nLC 1000 that was connected to a Q Exactive mass spectrometer through a nanoelectrospray ion source. All raw files were processed in MaxQuant, version 1.3.0.5 and the Andromeda search engine against the IPI mouse database (version 3.87, 59 534 entries), containing both forward and reverse proteins sequences, and common contaminants. MS/MS searches for the secretome and the whole-cell proteome were performed with the following parameters: carbamidomethylation as a fixed modification; oxidation of methionine and protein N-terminal acetylation as variable modifications; a 20-ppm first-search tolerance; a 6-ppm main-search tolerance. Minimum peptide length was set to six residues. The false discovery rate for all peptides, PTM sites, and protein identifications was set to 0.01.