Taxonomic composition and gene content of a methane-producing microbial community isolated from a biogas reactor
Introduction
Formation of biogas (methane) through fermentation of organic material in anaerobic reactors has the potential of tackling important, contemporary challenges: the degradation of biomass and production of energy from renewable primary products and wastes. Biogas is a clean renewable energy that promises to be a good substitution for traditional fossil fuels. Methane bioproduction is a complex, multi-step process, involving many different microbial species. Fermenting bacteria hydrolyze complex organic compounds, including polysaccharides, cellulose and xylan into oligomers and monomers (Schink, 1997). The produced intermediates are further transformed into acetate, carbon dioxide, and hydrogen by secondary fermenters. The final methanogenesis is conducted by methanogenic archea, which are highly specialized and can only use acetate, H2, CO2, formate or some C1 compounds as energy substrates (Thauer, 1998).
To improve methane yield from biomass fermentation, a better understanding of the microbial community composition and metabolic processes carried out by microbes residing in biogas reactors is required. A total community DNA sample from an agricultural biogas reactor continuously fed with maize silage, green rye, and small proportions of chicken manure has been sequenced (Schlüter et al., 2008, this issue) by means of the massively parallel pyrosequencing (Margulies et al., 2005). Initial analysis by Schlüter et al. focused on the characterization of contigs assembled from the sample. Herein, to obtain a quantitative picture of the taxonomic composition, the entire sample was characterized without a prior assembly step. Additionally, several protein families were studied in more detail in order to obtain a better understanding of the bioconversion process occurring in the studied bioreactor. In particular, to scrutinize which microorganisms digest polysaccharides and oligosaccharides, the taxonomic origin of glycosyl hydrolase protein families were predicted using a phylogenetic analysis.
To quantitatively characterize the taxonomic composition of the biogas reactor sample, the source organisms or taxonomic origins of reads were inferred using two independent approaches: Fragments of 16S rDNA genes were identified and subsequently classified into a higher order taxonomy using the RDP rRNA Classifier (Wang et al., 2007). This approach has the advantage of yielding a high accuracy (between 99% for phylum and 83% for genus for 200 bp fragments), but only a limited number of reads can be taxonomically characterized. In order to obtain a more detailed picture of the composition, the second approach inferred the taxonomic origins of all Pfam protein family members identified in a sample using our recently published CARMA software (Krause et al., 2008).
Following the taxonomic analysis, the genetic potential of the microbes constituting the sample was characterized. For this task, protein encoding sequences (coding sequences, CDSs) were identified based on a search for protein family members using Pfam profile hidden Markov models (pHMM) (Finn et al., 2008). Pfam is a comprehensive collection of protein families, mainly representing protein domains. The major strengths of the Pfam-based analysis is the high accuracy of the pHMMs for the detection of short functional sequences when compared to pair-wise sequence comparison methods, such as Blast (Altschul et al., 1990). The high accuracy makes Pfam profile HMMs particularly adequate for the analysis of short DNA fragments obtained by pyrosequencing. On the other hand, one limitation of this approach is that usually only between 10% and 15% of reads from a sample match a Pfam family (Krause et al., 2008). Therefore, gene fragments were additionally identified based on a Blast comparison of reads with public protein database, including the database of Clusters of Orthologous Groups of proteins (COG) (Tatusov et al., 1997).
In this study, fragments of genes identified in community sequence reads are defined as environmental gene tags (EGTs). After assigning a putative gene function to each EGT, the resulting profiles could be used for a quantitative gene content analysis in order to reveal habitat-specific genetic fingerprints.
Using the described approach, detailed insights into the taxonomic composition and gene content of a methane-producing microbial community from an agricultural biogas plant were obtained.
Section snippets
Origin and sequencing of the methane producing bacterial community of a biogas reactor
The biogas-producing microbial sample studied herein was taken from the first fermenter of the agricultural biogas plant in Bielefeld-Jöllenbeck (Germany) in August, 2007. The reactor had approximately 41 °C with a pH of 7.7 and was continuously fed with maize silage (63%), green rye (35%), and small proportions of chicken manure (2%). The sample was sequenced in a whole-genome-shotgun approach using the Genome Sequencer FLX system (Margulies et al., 2005). The sequencing run yielded 616,072
Composition of the biogas-producing microbial community isolated from the first fermenter of an agricultural biogas plant
The composition of the biogas-producing microbial community was assessed using two complementary approaches: First, the taxonomic origins of reads were predicted by using 16S rDNAs as phylogenetic anchors. To get a more detailed picture of the taxonomic composition of the biogas reactor sample, in a second approach the CARMA software (Krause et al., 2008) was applied The main idea of CARMA is to identify gene fragments (EGTs) in community reads using Pfam profile hidden Markov models and to
Conclusions
A methane-producing microbial community of an agricultural biogas reactor was quantitatively characterized by means of a metagenomic approach, yielding insights into the taxonomic composition and gene content of the endogenous microbial consortium. In Schlüter et al., this issue, a rough overview of abundant taxonomic groups was obtained, by assigning assembled contigs to species based on a best Blast hit. Conversely, this study provides a detailed quantitative picture of the composition of the
Acknowledgements
LK was supported by the Federal Ministry of Education and Research (BMBF) project 0313805A. AG acknowledges the BMBF for financial support. NND was supported by the German Academic Exchange Service (DAAD). KR was financially supported by the BMBF through the GenoMik-Plus network (grant 0313805A). We would also like to acknowledge Björn Fischer, Achim Neumann, Ralf Nolte, Volker Tölle, and Torsten Kasch for their support on running our software at the Center for Biotechnology. Jan Mußgnug (Algae
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