title: Proteotyping of biogas plant microbiomes separates biogas plants according to process temperature and reactor type.
abstract: BACKGROUND
  Methane yield and biogas productivity of biogas plants (BGPs) depend on microbial community structure and function, substrate supply, and general biogas process parameters. So far, however, relatively little is known about correlations between microbial community function and process parameters. To close this knowledge gap, microbial communities of 40 samples from 35 different industrial biogas plants were evaluated by a metaproteomics approach in this study.
  
  
  RESULTS
  Liquid chromatography coupled to tandem mass spectrometry (Orbitrap Elite™ Hybrid Ion Trap-Orbitrap Mass Spectrometer) of all 40 samples as triplicate enabled the identification of 3138 different metaproteins belonging to 162 biological processes and 75 different taxonomic orders. The respective database searches were performed against UniProtKB/Swiss-Prot and seven metagenome databases. Subsequent clustering and principal component analysis of these data allowed for the identification of four main clusters associated with mesophile and thermophile process conditions, the use of upflow anaerobic sludge blanket reactors and BGP feeding with sewage sludge. Observations confirm a previous phylogenetic study of the same BGP samples that was based on 16S rRNA gene sequencing by De Vrieze et al. (Water Res 75:312-323, 2015). In particular, we identified similar microbial key players of biogas processes, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. For the elucidation of the main biomass degradation pathways, the most abundant 1 % of metaproteins was assigned to the KEGG map 1200 representing the central carbon metabolism. Additionally, the effect of the process parameters (i) temperature, (ii) organic loading rate (OLR), (iii) total ammonia nitrogen (TAN), and (iv) sludge retention time (SRT) on these pathways was investigated. For example, high TAN correlated with hydrogenotrophic methanogens and bacterial one-carbon metabolism, indicating syntrophic acetate oxidation.
  
  
  CONCLUSIONS
  This is the first large-scale metaproteome study of BGPs. Proteotyping of BGPs reveals general correlations between the microbial community structure and its function with process parameters. The monitoring of changes on the level of microbial key functions or even of the microbial community represents a well-directed tool for the identification of process problems and disturbances.Graphical abstractCorrelation between the different orders and process parameter, as well as principle component analysis of all investigated biogas plants based on the identified metaproteins.
authors.affiliation: Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany ; Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
authors.forename: R
authors.initials: R
authors.lastname: Heyer
authors.affiliation: Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
authors.forename: D
authors.initials: D
authors.lastname: Benndorf
authors.affiliation: Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
authors.forename: F
authors.initials: F
authors.lastname: Kohrs
authors.affiliation: Laboratory of Microbial Technology and Ecology (LabMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
authors.forename: J
authors.initials: J
authors.lastname: De Vrieze
authors.affiliation: Laboratory of Microbial Technology and Ecology (LabMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
authors.forename: N
authors.initials: N
authors.lastname: Boon
authors.affiliation: Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Hoffmann
authors.affiliation: Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany.
authors.forename: E
authors.initials: E
authors.lastname: Rapp
authors.affiliation: Center for Biotechnology, Genome Research of Industrial Microorganisms, Universität Bielefeld, Universitätsstraße 25, 33615 Bielefeld, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schlüter
authors.affiliation: Center for Biotechnology, Computational Metagenomics, Universität Bielefeld, Universitätsstr. 25, 33615 Bielefeld, Germany.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sczyrba
authors.affiliation: Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstraße 1, 39106 Magdeburg, Germany ; Bioprocess Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.
authors.forename: U
authors.initials: U
authors.lastname: Reichl
keywords: Anaerobic digestion; Biogas; Biogas plant; Clustering; Community function; MetaProteomeAnalyzer; Metaproteomics; Microbial resource management; Network analysis machine learning; Principal component analysis