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      Sponges With Microbial Symbionts Transform Dissolved Organic Matter and Take Up Organohalides

      , , ,
      Frontiers in Marine Science
      Frontiers Media SA

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          Abstract

          Seawater dissolved organic matter (DOM) is a large reservoir of carbon composed of a complex and poorly characterized mixture of molecules. Sponges have long been known to consume dissolved organic carbon (DOC) from this mixture, but the role of microbial sponge symbionts in this process is complex, and the molecules involved remain largely unknown. In order to better understand how sponge processing changes seawater DOM, we used untargeted metabolomics to characterize DOM in samples of incurrent and excurrent seawater taken from sponges on the fore-reef off Carrie Bow Cay, Belize, over 2 years. We collected samples from three sponge species each with either high or low microbial abundance (HMA, LMA) to explore the relationship between symbiont abundance and DOM alterations. Analyses revealed that sponges took up metabolites and changed the composition of seawater DOM, but only for the three HMA species, and none of the LMA species, implicating microbial symbionts in this uptake. Using a new mass spectra classification tool, we found that putative compositions of features depleted in the excurrent samples of HMA sponges were similar in both years and were dominated by organic acids and derivatives (74%) and organic nitrogen compounds (19%). Interestingly, HMA sponges also took up halogenated compounds (containing chlorine or bromine), providing evidence of a previously unknown mechanism of halide cycling. The metabolites taken up by HMA sponges may be used as a food source or as building blocks of chemical defenses, selective advantages that may have guided the evolution of microbial symbioses in sponges.

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          A Cross-platform Toolkit for Mass Spectrometry and Proteomics

          Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples 1 , identify pathways affected by endogenous and exogenous perturbations 2 , and characterize protein complexes 3 . Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access 4,5 . In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.
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            XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

            Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
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              CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

              Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis. © 2011 American Chemical Society
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                Author and article information

                Journal
                Frontiers in Marine Science
                Front. Mar. Sci.
                Frontiers Media SA
                2296-7745
                May 20 2021
                May 20 2021
                : 8
                Article
                10.3389/fmars.2021.665789
                0fa32109-1ff6-408a-89ec-05bf67bb61a6
                © 2021

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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