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Fractionation of biogas plant sludge material improves metaproteomic characterization to investigate metabolic activity of microbial communities
Author(s) -
Kohrs Fabian,
Wolter Sophie,
Benndorf Dirk,
Heyer Robert,
Hoffmann Marcus,
Rapp Erdmann,
Bremges Andreas,
Sczyrba Alexander,
Schlüter Andreas,
Reichl Udo
Publication year - 2015
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201400557
Subject(s) - metaproteomics , biogas , microbial population biology , fractionation , bioenergy , characterization (materials science) , chemistry , biology , microbiology and biotechnology , bacteria , biofuel , ecology , chromatography , biochemistry , metagenomics , materials science , nanotechnology , genetics , gene
With the development of high resolving mass spectrometers, metaproteomics evolved as a powerful tool to elucidate metabolic activity of microbial communities derived from full-scale biogas plants. Due to the vast complexity of these microbiomes, application of suitable fractionation methods are indispensable, but often turn out to be time and cost intense, depending on the method used for protein separation. In this study, centrifugal fractionation has been applied for fractionation of two biogas sludge samples to analyze proteins extracted from (i) crude fibers, (ii) suspended microorganisms, and (iii) secreted proteins in the supernatant using a gel-based approach followed by LC-MS/MS identification. This fast and easy method turned out to be beneficial to both the quality of SDS-PAGE and the identification of peptides and proteins compared to untreated samples. Additionally, a high functional metabolic pathway coverage was achieved by combining protein hits found exclusively in distinct fractions. Sample preparation using centrifugal fractionation influenced significantly the number and the types of proteins identified in the microbial metaproteomes. Thereby, comparing results from different proteomic or genomic studies, the impact of sample preparation should be considered. All MS data have been deposited in the ProteomeXchange with identifier PXD001508 (http://proteomecentral.proteomexchange.org/dataset/PXD001508).