Proteotyping of laboratory-scale biogas plants reveals multiple steady-states in community composition
Graphical abstract
Introduction
Conversion of agricultural waste into biogas is a sustainable source of renewable energy. The so called anaerobic digestion (AD) is performed in large parallel or serial digester systems of different sizes and designs, commonly referred to as biogas plants (BGP). Additional classifications are made depending on the process temperature [1], the type (e.g. silage and/or manure and dung) and consistency (e.g. moisture content) of the used substrate [2], [3], and the ammonium or ammonia concentrations [4]. Independent from these conditions, AD process is subdivided into the four steps hydrolysis, fermentation, acetogenesis and methanogenesis [3], which are executed by different groups of microorganisms forming complex microbial communities — the microbiome [5].
Laboratory-scale digester systems (ranging from a few hundred milliliters to several liters working volume) are commonly used as a scale-down model to investigate AD [6], [7], [8]. These systems benefit from a better control over the cultivation parameters, and allow well-directed disturbances without risking costly malfunction of full-scale BGP. In a highly controlled process, the substrate is fully defined and continuous stirring enables homogeneous mixing and representative sampling. This is in contrast to full-scale BGP with occasional dead zones or floating layers [3], [9], and varying and non-sterile substrates [10]. However, microbial communities evolving in laboratory bioreactors operating under well-defined process conditions loose part of their complexity [11]. While this facilitates analytics, the question arises to what extent results can be transferred to the optimization of full-scale BGP.
Many different analytical methods are routinely applied to study microbial communities in BGP. Genomic approaches, like cloning and sequencing of microbial DNA [12] or fingerprinting of 16S rRNA genes (e.g. T-RFLP — terminal restriction fragment length polymorphisms [13]), allow to explore the diversity of Archaea and Bacteria of microbial communities. As a complementary approach, metaproteomics turned out to be well suited to capture the physiological state and functions of a microbial population [14]. State of the art methods rely on gel-free approaches [15] pushed by the rapid development of high resolving mass spectrometers (MS) for protein identification, and powerful tools for data analysis [16]. Their application revealed great potential for the characterization of mixed microbial communities, which was referred recently as proteotyping [17]. So far, this term was only used for the identification of single microorganisms by characteristic protein mass spectra derived from Matrix-Assisted Laser Desorption/Ionization-Time-Of-Flight MS analysis (MALDI-TOF-MS) [18]. In a recent review, however, the term proteotyping was extended to cover classification, characterization and identification of microorganisms as well as microbial communities by tandem MS and MS/MS-based shotgun proteomics [19]. The first comprehensive proteotyping of microbial communities in technical biocoenoses aimed at the correlation of biological processes of the microbiome in BGP with respective process parameters [17]. Applications of biostatistics and data mining tools (e.g. principal component analysis or clustering) allowed identifying correlations of taxonomies, functions and metaproteins with process parameters (e.g. temperature, substrate, reactor design or nitrogen content) from extensive lists of identified proteins — without laborious hit-by-hit evaluation.
In this study six parallel digesters were inoculated with sludge from a full-scale BGP to enrich microbial communities under defined conditions. After three month of cultivation, steady-state operation was achieved for all digesters. Subsequently, the temperature and the ammonia concentration were increased for two reactors each. Based on metaproteomics the following questions were addressed: how similar are stable microbial communities operating under exactly the same environmental conditions? Can marker species or functions be determined representing the different process regimes using proteotyping?
Section snippets
Reactor setup
For enrichment, a Sixfors multi bioreactor system (INFORS AG, Bottmingen, Switzerland) with six parallel 500 mL glass vessels was used (R1-R6; 400 mL working volume). Each reactor was equipped with an integrated Pt100 temperature probe, a pH electrode (Type 405-DPAS-SC-K8S, Mettler-Toledo GmbH, Gieβen, Germany), and gastight tubing (Santoprene® LEZ-SAN, ID 1.6 mm, thickness 1.6 mm, Medorex, Nörten-Hardenberg, Germany) connected to a Luer/Lock sampling valve (Eppendorf AG, Hamburg, Germany).
Abiotic process data
After a short delay during the first two weeks of enrichment (start-up phase) the daily biogas production increased to 0.481 ± 0.09 NL Lreactor volume−1 d−1 for R1, 0.500 ± 0.09 NL Lreactor volume−1 d−1 for R2, 0.538 ± 0.105 NL Lreactor volume−1 d−1 for R3, 0.548 ± 0.096 NL Lreactor volume−1 d−1 for R4, 0.502 ± 0.102 NL Lreactor volume−1 d−1 for R5, and 0.500 ± 0.098 NL Lreactor volume−1 d−1 for R6. The high-N regimes R5 and R6 showed decreasing gas productions to less than 125 NL Lreactor
Reactor performance
Methane contents for all cultivation regimes monitored were stable with means of 53.2–54.5% and maximum fluctuations below ±8% (Fig. 1, Fig. 2, Fig. 3). Except for the later cultivation phases of R5 and R6, the corresponding biogas productions were also at steady-state (Fig. 3). To evaluate the production of biogas, the theoretical methane content was estimated using the empirical formula of Boyle et al. [40] for the media supplied (Table 1) as 48.7% (R1-R4; R5 and R6 until day 93) and 43.3%
Conclusions
Proteotyping is a sensitive tool for characterization of microbial communities in laboratory-scale reactors and full-scale BGP. In addition, proteotyping provides valuable taxonomic and functional data. However, even for parallel cultivations in well controlled cultivations using the same inoculum, the high compositional and functional variances of microbiomes after enrichment did not enable the identification of specific markers for very different process conditions. Interestingly, besides all
Funding
R. Heyer was supported by a grant of the Federal Ministry of Food, Agriculture and Consumer Protection (BMELV) communicated by the Agency for Renewable Resources (FNR), grant no. 22404115 (Biogas-Messprogramm III). R. Kottler and E. Rapp acknowledge support by the European Union (EC) under the project “HighGlycan” (grant no. 278535).
Acknowledgment
The authors acknowledge excellent support of C. Siewert, S. Fischer (both Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany) and C. Best (Otto von Guericke University, Magdeburg, Germany) in the laboratory. Furthermore the support of S. Theuerl (Leibniz Institute for Agricultural Engineering Potsdam-Bornim, Potsdam, Germany) was very valuable for setting up the T-RFLP methods. Finally, the authors want to thank M. Leifheit and J. Greiser (Gesellschaft zur
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