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|Public on Feb 08, 2011
|Feces, Infected, 4
infection: Salmonella enterica serovar Typhimurium SL1344
molecule type: metabolites
|Oral infection with 7.5x10^7 Salmonella enterica serovar Typhimurium SL1344.
|To extract metabolites from livers and feces, acetonitrile was added to samples (approximately 1 μL of acetonitrile per 1 μg of tissue), which were then homogenized. The samples were then cleared by centrifugation and the supernatant was collected into a new tube and dried at room temperature using a centrifuge equipped with a vacuum pump. All extracts were kept at -80oC until further use.
Each sample was measured twice.
|Raw mass spectrometry data were processed using a custom-developed software package, as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). First, raw mass spectra acquired from each sample group were batch-processed using the instrument vendor’s data analysis software, DataAnalysis®, but with a home-written VBA script to do automatic internal mass calibration with the reference masses of the spiked calibration standards and a known contaminant, N-butylbenzensulfonamide. Monoisotopic peaks corresponding to the isotopic pattern distributions were then automatically determined and those with a signal/noise ratio above 3 were picked. Their m/z values were converted to neutral masses by subtracting 1.007276 for positive ion mode or adding 1.007276 for negative ion mode. Next, the resulting mass lists from all the mass spectra within each set of uninfected/infected groups detected in positive or negative ion modes were further processed with another customized software program developed with LabVIEW® (National Instruments, Austin, USA). The first step of this software is to remove the adduct ions (M+Na)+ and (M+K)+ in positive ion mode and (M+Cl)- in negative ion mode from the mass lists based on the expected mass differences for these ions within 2 ppm to yield a list of unique biochemical component masses together with their peak intensities. The peak intensities of all the monoisotopic neutral masses are subsequently normalized to the intra-sample total ion intensity. Masses observed in at least three of four samples (feces) or two of three samples (livers) of one of the conditions (infected or uninfected) were aligned and combined into unique metabolite features from the masses that matched within 2 ppm across all the data. Finally, a two-dimensional data matrix (mass vs. relative intensity) was generated for each sample group and saved in a format amenable for further data analysis. To identify differences in metabolite composition between samples from uninfected and infected mice, we first filtered our list of masses for metabolites which were present in one set of samples (uninfected or infected) but not the other. Additionally, we averaged the mass intensities of metabolites in each group and calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities to the masses present in only one of the sample groups or showing at least a 2-fold change in intensities between the sample groups, the monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org), a software designed to incorporate mass queries into metabolic pathways. Masses were searched against the Mus musculus database within a mass error of 3 ppm.
|May 21, 2010
|Last update date
|Feb 09, 2011
|L. Caetano M. Antunes
|The University of British Columbia
|Michael Smith Laboratories
|#367 - 2185 East Mall
|A metabolomics analysis of Salmonella infection