Project description:Sarcopenia analysis of the vastus lateralis from rhesus monkeys including metabolomics, proteomics, and contractile protein proteoforms.
Project description:To understand the impact of alternative translation initiation on a proteome, we performed the first study on protein turnover using positional proteomics and ribosome profiling to distinguish between N-terminal proteoforms of individual genes. Overall, we monitored the stability of 1,941 human N-terminal proteoforms, including 147 N-terminal proteoform pairs that originate from alternative translation initiation, alternative splicing or incomplete processing of the initiator methionine. Ribosome profiling of lactimidomycin and cycloheximide treated human Jurkat T-lymphocytes
Project description:To understand the impact of alternative translation initiation on a proteome, we performed the first study on protein turnover using positional proteomics and ribosome profiling to distinguish between N-terminal proteoforms of individual genes. Overall, we monitored the stability of 1,941 human N-terminal proteoforms, including 147 N-terminal proteoform pairs that originate from alternative translation initiation, alternative splicing or incomplete processing of the initiator methionine.
Project description:Anaplasma phagocytophilum is an emerging zoonotic pathogen that causes human granulocytic anaplasmosis. These intracellular bacteria establish infection by affecting cell function in both the vertebrate host and the tick vector, Ixodes scapularis. Previous studies have characterized the tick transcriptome and proteome in response to A. phagocytophilum infection. However, in the post-genomic era, the integration of omics datasets through a systems biology approach allows network-based analyses to describe the complexity and functionality of biological systems such as host-pathogen interactions and the discovery of new targets for prevention and control of infectious diseases. This study reports for the first time a systems biology integration of metabolomics, transcriptomics and proteomics data to characterize essential metabolic pathways involved in the response of tick cells to A. phagocytophilum infection. The results showed that infection affected protein processing in endoplasmic reticulum and glucose metabolic pathways in tick cells. These results supported tick-Anaplasma co-evolution by providing new evidence of how tick cells limit pathogen infection, while the pathogen benefits from the tick cell response to establish infection. The results suggested that A. phagocytophilum induces protein misfolding to limit the tick cell response and facilitate infection, but requires protein degradation to prevent ER stress and cell apoptosis to survive in infected cells. Additionally, A. phagocytophilum may benefit from the tick cellâs ability to limit bacterial infection through PEPCK inhibition leading to decreased glucose metabolism, which also results in the inhibition of cell apoptosis that increases infection of tick cells. These results support the use of this experimental approach to systematically identify tick cell pathways and molecular mechanisms involved in tick-pathogen interactions Two samples with two replicates each were analyzed. Samples included Ixodes scapularis ISE6 cells uninfected (control) and infected with Anaplasma phagocytophilum human NY18 isolate.
Project description:Anaplasma phagocytophilum is an emerging zoonotic pathogen that causes human granulocytic anaplasmosis. These intracellular bacteria establish infection by affecting cell function in both the vertebrate host and the tick vector, Ixodes scapularis. Previous studies have characterized the tick transcriptome and proteome in response to A. phagocytophilum infection. However, in the post-genomic era, the integration of omics datasets through a systems biology approach allows network-based analyses to describe the complexity and functionality of biological systems such as host-pathogen interactions and the discovery of new targets for prevention and control of infectious diseases. This study reports for the first time a systems biology integration of metabolomics, transcriptomics and proteomics data to characterize essential metabolic pathways involved in the response of tick cells to A. phagocytophilum infection. The results showed that infection affected protein processing in endoplasmic reticulum and glucose metabolic pathways in tick cells. These results supported tick-Anaplasma co-evolution by providing new evidence of how tick cells limit pathogen infection, while the pathogen benefits from the tick cell response to establish infection. The results suggested that A. phagocytophilum induces protein misfolding to limit the tick cell response and facilitate infection, but requires protein degradation to prevent ER stress and cell apoptosis to survive in infected cells. Additionally, A. phagocytophilum may benefit from the tick cell’s ability to limit bacterial infection through PEPCK inhibition leading to decreased glucose metabolism, which also results in the inhibition of cell apoptosis that increases infection of tick cells. These results support the use of this experimental approach to systematically identify tick cell pathways and molecular mechanisms involved in tick-pathogen interactions
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: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:To gain comprehensive and accurate system biological information, we developed a multiomics analytical method that integrated metabolomics and proteomics, named Windows Scanning Multiomics (WSM).
Project description:An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. We present the generation of a protein synthesis-based database from deep sequencing of ribosome-protected mRNA fragments. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse respectively and enables proteome-wide detection of 5’-extended proteoforms, uORF translation and near-cognate translation start sites.