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:<p>Individuals vary widely in their drug responses, which can be dangerous and expensive due to treatment delays and adverse effects. Growing evidence implicates the gut microbiome in this variability, however the molecular mechanisms remain largely unknown. We measured the ability of 76 diverse human gut bacteria to metabolize 271 oral drugs and found that many of these drugs are chemically modified by microbes. We combined high-throughput genetics with mass spectrometry to systematically identify drug-metabolizing microbial gene products. These microbiome-encoded enzymes can directly and significantly impact intestinal and systemic drug metabolism in mice, and can explain drug-metabolizing activities of human gut bacteria and communities based on their genomic contents. These causal links between microbiota gene content and metabolic activities connect interpersonal microbiome variability to interpersonal differences in drug metabolism, which has implications for medical therapy and drug development across multiple disease indications.</p><p><br></p><p>Additional data related to this study can also be found by the following links;</p><p>- Raw sequencing data; <a href='https://www.ebi.ac.uk/ena/data/view/PRJEB31790' rel='noopener noreferrer' target='_blank'>ENA (accession no. PRJEB31790)</a></p><p>- Data for Figures; <a href='https://doi.org/10.6084/m9.figshare.8119058' rel='noopener noreferrer' target='_blank'>FigShare</a> </p><p>- Analysis pipeline schemes, scripts and input files for analzing data and generating figures; <a href='https://github.com/mszimmermann/drug-bacteria-gene_mapping' rel='noopener noreferrer' target='_blank'>GitHub</a> and archived <a href='https://doi.org/10.5281/zenodo.2827640' rel='noopener noreferrer' target='_blank'>Zenodo</a></p>
Project description:Raw data of TMT-labeled proteome of Chinese breast cancers. Uploaded by Zhi-Ming Shao Lab, Fudan University Shanghai Cancer Center (FUSCC)
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.
Project description:A consortium of ten labs from the DC and Baltimore area was formed to compare three leading platforms. Each lab was given identical RNA samples (A1 and B1) which were processed according to what each lab considered best practice. Five of the labs used Affymetrix GeneChips, three used two-color spotted cDNA arrays, and two used two-color long oligo arrays. Keywords: repeat sample