Quantitative metaproteomics using Galaxy platform measuring functional microbiome via data-dependent and data-Independent acquisition mass spectrometry
Year: 2021
Type: other
Abstract: Mass spectrometry (MS) based metaproteomics is being increasingly used to determine the metabolic functions of microbial communities. Advances in quantitative MS and bioinformatics methods have enabled researchers to quantitate peptides, proteins, metabolic functions and taxonomy. In particular, Data-dependent acquisition (DDA) method uses precursor-intensity (MS1); while Data-independent acquisition (DIA) MS generates fragmentation-intensity (MS2) data for quantification, thus offering increased depth of coverage and quantitative uniformity amongst samples. Using Galaxy workflows, we set out to compare a coral sample along with associated microbiome acquired using both MS acquisition modes. The coral dataset includes biological replicates of egg-sperm bundles from control corals and corals exposed to a thermal stress (bleached). Inputs for quantitative analysis were generated using msconvert, SearchGUI/PeptideShaker and FlashLFQ (for DDA dataset); and, msconvert, SearchToLib and EncyclopeDIA tools (for DIA dataset). The peptide quantitation outputs from both the acquisitions were merged with functional and taxonomy information using the Unipept software tool. metaQuantome software suite was used for quantitative analysis, statistical evaluation, visualization and biological interpretation of the data. DDA-MS and DIA-MS detected approximately 16,000 peptides; 95% of taxonomy-specific peptides matched with the coral host (Montipora capitata), and the rest matched with symbiont (Symbiodinium). Differential abundance analysis, principal components analysis, clustered heatmap analysis generated via metaQuantome will be used to highlight the benefits of using DDA and/or DIA-MS methods for quantitative metaproteomics.
Institution: University of Minnesota
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4416518890
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