6 research outputs found

    Exo-Metabolome of <i>Pseudovibrio</i> sp. FO-BEG1 Analyzed by Ultra-High Resolution Mass Spectrometry and the Effect of Phosphate Limitation

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    <div><p>Oceanic dissolved organic matter (DOM) is an assemblage of reduced carbon compounds, which results from biotic and abiotic processes. The biotic processes consist in either release or uptake of specific molecules by marine organisms. Heterotrophic bacteria have been mostly considered to influence the DOM composition by preferential uptake of certain compounds. However, they also secrete a variety of molecules depending on physiological state, environmental and growth conditions, but so far the full set of compounds secreted by these bacteria has never been investigated. In this study, we analyzed the exo-metabolome, metabolites secreted into the environment, of the heterotrophic marine bacterium <i>Pseudovibrio</i> sp. FO-BEG1 via ultra-high resolution mass spectrometry, comparing phosphate limited with phosphate surplus growth conditions. Bacteria belonging to the <i>Pseudovibrio</i> genus have been isolated worldwide, mainly from marine invertebrates and were described as metabolically versatile <i>Alphaproteobacteria</i>. We show that the exo-metabolome is unexpectedly large and diverse, consisting of hundreds of compounds that differ by their molecular formulae. It is characterized by a dynamic recycling of molecules, and it is drastically affected by the physiological state of the strain. Moreover, we show that phosphate limitation greatly influences both the amount and the composition of the secreted molecules. By assigning the detected masses to general chemical categories, we observed that under phosphate surplus conditions the secreted molecules were mainly peptides and highly unsaturated compounds. In contrast, under phosphate limitation the composition of the exo-metabolome changed during bacterial growth, showing an increase in highly unsaturated, phenolic, and polyphenolic compounds. Finally, we annotated the detected masses using multiple metabolite databases. These analyses suggested the presence of several masses analogue to masses of known bioactive compounds. However, the annotation was successful only for a minor part of the detected molecules, underlining the current gap in knowledge concerning the biosynthetic ability of marine heterotrophic bacteria.</p></div

    Bacterial growth (A) and concentrations of solid phase extractable dissolved organic carbon (SPE-DOC) (B).

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    <p>The bars (<b>B</b>) represent the average concentrations of SPE-DOC measured in the solid phase extracts of the biological triplicates collected during growth under both +P<sub>i</sub> (black) and −P<sub>i</sub> conditions (white). The inner panel (<b>A</b>) shows the cell growth, measured as cell density over time, for the two tested conditions. Filled circles represent the cultures growing under +P<sub>i</sub> conditions and empty circles represent the cultures growing under −P<sub>i</sub> conditions. Error bars indicate the standard deviation of biological triplicates</p

    Overview of the data obtained from the ESI-negative FT-ICR-MS analysis.

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    <p>The number of masses detected in all biological triplicates of each time point is shown. The data refer to the dataset obtained after applying the filtration criteria described in the Materials and Methods section. Values in brackets represent the percentages of masses to which a unique molecular formula could be assigned and the percentages of unique molecular formula containing heteroatoms. Isotopologues of assigned molecular formulae are not counted as assigned. Overall, a unique molecular formula could be assigned to 4,122 masses, corresponding to 49% of the obtained <i>m</i>/<i>z</i>.</p

    msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics

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    Tandem mass spectrometry (MS/MS or MS<sup>2</sup>) is a widely used approach for structural annotation and identification of metabolites in complex biological samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS<sup>2</sup> experiments has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date, there has been little attention to this data-processing technique in metabolomics. Here, we present msPurity, a vendor-independent R package for liquid chromatography (LC) and direct infusion (DI) MS<sup>2</sup> that calculates a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calculated as “intensity of a selected precursor divided by the summed intensity of the isolation window”. The metric is interpolated at the recorded point of MS<sup>2</sup> acquisition using bordering full-scan spectra. Isotopic peaks of the selected precursor can be removed, and low abundance peaks that are believed to have limited contribution to the resulting MS<sup>2</sup> spectra are removed. Additionally, the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS<sup>2</sup> metabolomics data sets derived from three metabolomics data repositories. For the 10 LC-MS<sup>2</sup> DDA data sets with > ±1 Da isolation windows, the median precursor purity score ranged from 0.67 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theoretical isolation windows from LC-MS data sets of differing sample types. The theoretical isolation windows being the same width used for an anticipated DDA experiment (±0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS determined features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of <0.5 can produce unreliable spectra matching results. With this assumption, we show that for complex samples there will be a large number of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries