ABSTRACT: the samples was directly injected into an Agilent 6890 GC interfaced to a mass spectrometer Hewlett Packard MSD 5973 for electron ionization GC-MS.
Project description:For gaining additional insights into the composition of the testicular proteome of the domestic pig (Sus scrofa domestica), we conducted 2DE-MS. Two-dimensional SDS PAGE was run on testicular lysates of three boars, with three gels per boar. Upon matching across gels, we arbitrarily selected protein spots for mass spectrometry analysis. Excised slices were vacuum dried and soaked with digestion buffer containing trypsin (0.01 μg/μl), followed by overnight incubation at 37°C in the same buffer without trypsin. Subsequently, peptides were extracted in solvents of increasing acetonitrile content, by sonication. Upon vacuum-centrifugation, peptides were reconstituted in 0.1% formic acid (FA). Following this, peptides were fractionated by reversed phase liquid chromatography (C18; buffer A: 0.1% FA dissolved in HPLC-H2O; buffer B: 0.1% FA, dissolved in CAN; flow-rate: 0.4 µL/min; gradient: 2-30% in 30 minutes). Eluted peptides were injected via an electrospray ionization interface into a Q-TOF mass spectrometer (one boar, Q TOF Ultima, Micromass/Waters, Manchester, UK) and an ion-trap mass spectrometer (two other boars, XCT ion-trap, Agilent Technologies, Waldbronn, Germany). We used ProteomeDiscoverer 2.4 (Thermo Fisher Scientific, San Jose, USA) for peptide and protein identification. Using Sequest HT, we searched peak lists (*.mgf) against the Sus scrofa reference proteome database (UniProt Proteome ID: UP000008227, 49,793 proteins).
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
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:The concentrations of twenty kinds of hormones in the follicular fluid were detected by high-performance liquid chromatography–mass spectrometry (HPLC-MS/MS). An Agilent 1200 series high-performance liquid chromatography (HPLC) instrument (Agilent, USA) was utilized. A PAL autosampler (CTC, Swiss) and a Gemini-NX-C18 column (2.0 mm×50 mm, 3 μm, Waters, USA) were used. The ion source was an API-4000 quadrupole electrostatic field orbit trap high-resolution mass spectrometer (Applied Biosystems, USA). The scanning mode was multiple reaction monitoring (MRM) (Agilent-1200 LC system coupled to an API400 mass spectrometer).
Project description:Gas chromatography coupled to mass spectrometry (GC-MS) has been a long- standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex datasets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information of metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC-MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-TOF MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-QqQ MS, LC-QqQ and NMR.
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:Mouse livers were analyzed by lipidomics and urine by metabolomics.An Agilent Ultra-Performance Liquid Chromatography/Electrospray Ion Quadrupole Time-of-Flight-Mass Spectrometer (UPLC-ESI-QTOFMS) (Agilent, Santa Clara, CA) was utilized for lipid and other metabolites proofing in this work.
Project description:Specific rotations were recorded on Anton Paar MCP 5500. Highresolution mass spectra (HRMS) were recorded on a Waters Premier GC-TOF, Waters G2-XS/APGC and
Agilent-6520 Q-TOF mass spectrometer
Project description:Honeybee semen was collected by gently squeezing the male abdomens. Samples were pooled and then centrifuged. Pelleted sperm was collected and analysed with both 2D PAGE and gel-free methods. For the 2D PAGE, protein spots were digested using trypsin. Extracted peptides, resolved on a C18 column, were analysed by an Agilent LC/MSD Trap XCT Ultra 6330 mass spectrometer. Spectra were searched against the honeybee protein sequences (RefSeq release 48) using Mascot algorithm (with 1 missed cleavage, Cys-carbamidomethylation as fixed modification, and Met-oxidation and N/Q-deamidation as variable modifications). For the MudPit analysis the sperm sample was digested by trypsin. Peptides were resolved using strong cation exchange chromatography followed by reverse phase (C18) HPLC and finally analysed by an Agilent QTOF mass spectrometer. Resulting spectrum files were converted to mzXML and merged using ProteoWizard msconvert. These were then searched against honeybee protein sequences using Mascot, Omssa and X!tandem (all with 1 missed cleavage, and Met-oxidation and N/Q-deamidation as variable modifications). Results of the three search engines were pooled using TPP.
Project description:Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is the most commonly used technique for the identification and characterization of proteins. The ionization and transmission efficiency of the electrospray process is a critical factor in LC-MS/MS, ultimately limiting the sensitivity of the approach. Despite the benefits associated with very low flow rates for the ionization efficiency, most nanoLC-MS/MS platforms are operated at relatively high flow rates, aiming for a compromise between sensitivity and practicality. The purpose of this work was to target the latter issue and to develop a robust and user-friendly nanoLC system operable at a flow rate of 20 nL/min, applicable for routine analysis in proteomics laboratories. Peptide separation was performed with an analytical column packed with 2 um porous chromatographic beads, a length of 25 cm and an inner diameter (i.d.) of 25 um. Samples were concentrated and desalted using a trapping column in a vented configuration. The nanoLC system was interfaced to a Q Exactive mass spectrometer using commercially available nanoelectrospray emitters. Practical usability, reproducibility and overall performance of the system were evaluated with a tryptic peptide mixture generated from HeLa cells. Using 100 ng of sample, we identified on average 3,721 protein groups based on 25,699 unique peptides when using linear gradients of 14 hrs. We demonstrate that the number of unique peptides identified with this system increases with decreasing flow rates and in a linear fashion with the chromatographic peak capacity of the separation. Probing the sensitivity of the complete set-up we analyzed only 10 ng of the sample, identifying an average number of 2,042 protein groups based on 11,424 peptides in an 8 hrs. gradient.