Project description:Two-stage two-phase biogas reactor systems consisting each of one batch downflow hydrolysis reactor (HR, vol. 10 L), one process fluid storage tank (vol. 10 L), and one downstream upflow anaerobic filter reactor (AF, vol. 10 L), were operated at mesophilic (M, 37 °C) and thermophilic (T, 55 °C) temperatures and over a period of > 750 d (Figure 1, Additional file 1). For each reactor system and for each process temperature, two replicates were conducted in parallel, denominated further as biological replicates. Further process details were as previously published. Start-up of all fermenters were performed using liquid fermenter material from a biogas plant converting cattle manure in co-digestion with grass and maize silage and other biomass at varying concentrations and at mesophilic temperatures. Silage of perennial ryegrass (Lolium perenne L.) was digested as sole substrate in batches of varying amounts with retention times of 28 d (storage of bale silage at -20 °C, cutting length 3 cm, volatile substances (VS) 32 % of fresh mass (FM), total Kjeldahl nitrogen 7.6 g kgFM-1, NH4+-N 0.7 g kgFM-1, acetic acid 2.6 g kgFM-1, propionic acid < 0.04 g kgFM-1, lactic acid 2.6 g kgFM-1, ethanol 2.2 g kgFM-1, C/N ratio 19.3, chemical oxygen demand (COD) 357.7 g kgFM-1, analysis of chemical properties according to [6]. No spoilage was observed in the silage. Biogas yields were calculated as liters normalized to 0 °C and 1013 hPa (LN) per kilogram volatile substances (kgVS). For chemical analysis, samples were taken from the effluents of HR and AF. For sequencing of 16S rRNA gene amplicon libraries, microbial metagenomes, and microbial metatranscriptomes, samples were taken from the silage digestate in the HR digested for 2 d. At this time point, high AD rates were detected as indicated by the fast increase of volatile fatty acids (VFA), e.g., acetic acid. Sampling was performed at two different organic loading rates (OLR), i.e., batch-fermentation of 500 g (denominated as “low OLR”, samples MOLR500 and TOLR500) and 1,500 g silage (denominated as “increased OLR”, samples MOLR1500 and TOLR1500).

Project description:Background: Methane yield and biogas productivity of biogas plants depend on microbial community structure and functionality, substrate supply, and general process parameters. Little is known, however, about the correlations between microbial community function and the process parameters. To close this knowledge gap the microbial community of 40 industrial biogas plants was evaluated by a metaproteomics approach in this study. Results: Liquid chromatography coupled to tandem mass spectrometry (Elite Hybrid Ion Trap Orbitrap) enabled the identification of 3138 metaproteins belonging to 162 biological processes and 75 different taxonomic orders. Therefore, database searches were performed against UniProtKB/Swiss-Prot and several metagenome databases. Subsequent clustering and principal component analysis of these data allowed to identify four main clusters associated to mesophilic and thermophilic process conditions, upflow anaerobic sludge blanket reactors and sewage sludge as substrate. Observations confirm a previous phylogenetic study of the same biogas plant samples that was based on 16S-rRNA gene by De Vrieze et al. (2015) (De Vrieze, Saunders et al. 2015). Both studies described similar microbial key players of the biogas process, namely Bacillales, Enterobacteriales, Bacteriodales, Clostridiales, Rhizobiales and Thermoanaerobacteriales as well as Methanobacteriales, Methanosarcinales and Methanococcales. In addition, a correlation study and a Gephi graph network based on the correlations between the taxonomic orders and process parameters suggested the presence of various trophic interactions, e.g. syntrophic hydrogen transfer between Thermoanaerobacteriales and Methanomicrobiales. For the elucidation of the main biomass degradation pathways the most abundant 1% of metaproteins were 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. Conclusion: This study shows the benefit of large-scale proteotyping of biogas plants, enabling the identification of general correlations between the process parameters and the microbial community structure and function. Changes in the level of microbial key functions or even in the microbial community type represent a valuable hint for process problems and disturbances.

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