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Characterization of Bathyarchaeota genomes assembled from metagenomes of biofilms residing in mesophilic and thermophilic biogas reactors
Background: Previous studies on the Miscellaneous Crenarchaeota Group, recently assigned to the novel archaeal phylum Bathyarchaeota, reported on the dominance of these Archaea within the anaerobic carbohydrate cycle performed by the deep marine biosphere. For the first time, members of this phylum were identified also in mesophilic and thermophilic biogas-forming biofilms and characterized in detail. Results: Metagenome shotgun libraries of biofilm microbiomes were sequenced using the Illumina MiSeq system. Taxonomic classification revealed that between 0.1 and 2% of all classified sequences were assigned to Bathyarchaeota. Individual metagenome assemblies followed by genome binning resulted in the reconstruction of five metagenome-assembled genomes (MAGs) of Bathyarchaeota. MAGs were estimated to be 65-92% complete, ranging in their genome sizes from 1.1 to 2.0 Mb. Phylogenetic classification based on core gene sets confirmed their placement within the phylum Bathyarchaeota clustering as a separate group diverging from most of the recently known Bathyarchaeota clusters. The genetic repertoire of these MAGs indicated an energy metabolism based on carbohydrate and amino acid fermentation featuring the potential for extracellular hydrolysis of cellulose, cellobiose as well as proteins. In addition, corresponding transporter systems were identified. Furthermore, genes encoding enzymes for the utilization of carbon monoxide and/or carbon dioxide via the Wood-Ljungdahl pathway were detected. Conclusions: For the members of Bathyarchaeota detected in the biofilm microbiomes, a hydrolytic lifestyle is proposed. This is the first study indicating that Bathyarchaeota members contribute presumably to hydrolysis and subsequent fermentation of organic substrates within biotechnological biogas production processes.
Process disturbances in agricultural biogas production—causes, mechanisms and effects on the biogas microbiome: A review
Disturbances of the anaerobic digestion process reduce the economic and environmental performance of biogas systems. A better understanding of the highly complex process is of crucial importance in order to avoid disturbances. This review defines process disturbances as significant changes in the functionality within the microbial community leading to unacceptable and severe decreases in biogas production and requiring an active counteraction to be overcome. The main types of process disturbances in agricultural biogas production are classified as unfavorable process temperatures, fluctuations in the availability of macro- and micronutrients (feedstock variability), overload of the microbial degradation potential, process-related accumulation of inhibiting metabolites such as hydrogen (H 2 ), ammonium/ammonia (NH 4 + /NH 3 ) or hydrogen sulphide (H 2 S) and inhibition by other organic and inorganic toxicants. Causes, mechanisms and effects on the biogas microbiome are discussed. The need for a knowledge-based microbiome management to ensure a stable and efficient production of biogas with low susceptibility to disturbances is derived and an outlook on potential future process monitoring and control by means of microbial indicators is provided.
Pre- And post-adoption beliefs about the diffusion and continuation of biogas-based cooking fuel technology in Pakistan
A high level of acceptance and adoption is necessary to facilitate the widespread utilization of renewable energy technologies for cooking, as such utilization is essential for displacing the population's massive dependence on fossil fuels and solid biomass. Economic and demographic aspects have been the focus of recent literature in exploring the adoption phenomenon of biogas technology. However, literature to date has given little attention to the behavioral factors and the perceptions of the end-users. Our study does not only include behavioral factors, but it employs a hybrid model to explore the continued attentions of users based on their post-adoption beliefs and performance expectations. Using a survey conducted in Pakistan in 2017, the study conducts a multivariate analysis through structural equation modeling to measure the effect of pre- and post-adoption beliefs and expectation on adoption and the continuing intention of households towards biogas technology. Results show that the acceptance of the households towards biogas technology is highly influenced by their perceptions on the benefits, as well as their trust in the technology. The perceived cost and risk attached to the technology are found to be negatively correlated with the acceptance. Households' intentions to continue the use of biogas technology is highly influenced by the satisfaction level of the users of biogas technology. With the integrated model of adoption and continuation, the study illustrates the dynamic process in obtaining a deeper understanding of a user's behavior to better formulate the policies for increasing the rate of technology adoption.
The future agricultural biogas plant in Germany: A vision
After nearly two decades of subsidized and energy crop-oriented development, agricultural biogas production in Germany is standing at a crossroads. Fundamental challenges need to be met. In this article we sketch a vision of a future agricultural biogas plant that is an integral part of the circular bioeconomy and works mainly on the base of residues. It is flexible with regard to feedstocks, digester operation, microbial communities and biogas output. It is modular in design and its operation is knowledge-based, information-driven and largely automated. It will be competitive with fossil energies and other renewable energies, profitable for farmers and plant operators and favorable for the national economy. In this paper we discuss the required contribution of research to achieve these aims.
Impact of process temperature and organic loading rate on cellulolytic/hydrolytic biofilm microbiomes during biomethanation of ryegrass silage revealed by genome-centered metagenomics and metatranscriptomics
Background: Anaerobic digestion (AD) of protein-rich grass silage was performed in experimental two-stage two-phase biogas reactor systems at low vs. increased organic loading rates (OLRs) under mesophilic (37 °C) and thermophilic (55 °C) temperatures. To follow the adaptive response of the biomass-attached cellulolytic/hydrolytic biofilms at increasing ammonium/ammonia contents, genome-centered metagenomics and transcriptional profiling based on metagenome assembled genomes (MAGs) were conducted. Results: In total, 78 bacterial and archaeal MAGs representing the most abundant members of the communities, and featuring defined quality criteria were selected and characterized in detail. Determination of MAG abundances under the tested conditions by mapping of the obtained metagenome sequence reads to the MAGs revealed that MAG abundance profiles were mainly shaped by the temperature but also by the OLR. However, the OLR effect was more pronounced for the mesophilic systems as compared to the thermophilic ones. In contrast, metatranscriptome mapping to MAGs subsequently normalized to MAG abundances showed that under thermophilic conditions, MAGs respond to increased OLRs by shifting their transcriptional activities mainly without adjusting their proliferation rates. This is a clear difference compared to the behavior of the microbiome under mesophilic conditions. Here, the response to increased OLRs involved adjusting of proliferation rates and corresponding transcriptional activities. The analysis led to the identification of MAGs positively responding to increased OLRs. The most outstanding MAGs in this regard, obviously well adapted to higher OLRs and/or associated conditions, were assigned to the order Clostridiales (Acetivibrio sp.) for the mesophilic biofilm and the orders Bacteroidales (Prevotella sp. and an unknown species), Lachnospirales (Herbinix sp. and Kineothrix sp.) and Clostridiales (Clostridium sp.) for the thermophilic biofilm. Genome-based metabolic reconstruction and transcriptional profiling revealed that positively responding MAGs mainly are involved in hydrolysis of grass silage, acidogenesis and/or acetogenesis. Conclusions: An integrated-omics approach enabled the identification of new AD biofilm keystone species featuring outstanding performance under stress conditions such as increased OLRs. Genome-based knowledge on the metabolic potential and transcriptional activity of responsive microbiome members will contribute to the development of improved microbiological AD management strategies for biomethanation of renewable biomass. © 2020 The Author(s).
Biomethane Potential Test: Influence of Inoculum and the Digestion System
High precision of measurement of methane potential is important for the economic operation of biogas plants in the future. The biochemical methane potential (BMP) test based on the VDI 4630 protocol is the state-of-the-art method to determine the methane potential in Germany. The coefficient of variation (CV) of methane yield was >10% in several previous inter-laboratory tests. The aim of this work was to investigate the effects of inoculum and the digestion system on the measurement variability. Methane yield and methane percentage of five substrates were investigated in a Hohenheim biogas yield test (D-HBT) by using five inocula, which were used several times in inter- laboratory tests. The same substrates and inocula were also tested in other digestion systems. To control the quality of the inocula, the effect of adding trace elements (TE) and the microbial community was investigated. Adding TE had no influence for the selected, well- supplied inocula and the community composition depended on the source of the inocula. The CV of the specific methane yield was <4.8% by using different inocula in one D-HBT (D-HBT1) and <12.8% by using different digestion systems compared to D-HBT1. Incubation time between 7 and 14 days resulted in a deviation in CV of <4.8%.
Prediction of the biogas production using GA and ACO input features selection method for ANN model
This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9.