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Deeply sequenced metagenome and metatranscriptome of a biogas-producing microbial community from an agricultural production-scale biogas plant
- Andreas Bremges1, 2Email author,
- Irena Maus1,
- Peter Belmann1, 2,
- Felix Eikmeyer1,
- Anika Winkler1,
- Andreas Albersmeier1,
- Alfred Pühler1,
- Andreas Schlüter†1 and
- Alexander Sczyrba†1, 2
© Bremges et al. 2015
Received: 5 May 2015
Accepted: 12 July 2015
Published: 30 July 2015
The production of biogas takes place under anaerobic conditions and involves microbial decomposition of organic matter. Most of the participating microbes are still unknown and non-cultivable. Accordingly, shotgun metagenome sequencing currently is the method of choice to obtain insights into community composition and the genetic repertoire.
Here, we report on the deeply sequenced metagenome and metatranscriptome of a complex biogas-producing microbial community from an agricultural production-scale biogas plant. We assembled the metagenome and, as an example application, show that we reconstructed most genes involved in the methane metabolism, a key pathway involving methanogenesis performed by methanogenic Archaea. This result indicates that there is sufficient sequencing coverage for most downstream analyses.
Sequenced at least one order of magnitude deeper than previous studies, our metagenome data will enable new insights into community composition and the genetic potential of important community members. Moreover, mapping of transcripts to reconstructed genome sequences will enable the identification of active metabolic pathways in target organisms.
KeywordsBiogas Anaerobic digestion Wet fermentation Methanogenesis Metagenomics Metatranscriptomics Sequencing Assembly
Production of biogas by anaerobic digestion of biomass is becoming increasingly important, as biogas is regarded a clean, renewable and environmentally compatible energy source . Moreover, generation of energy from biogas relies on a balanced carbon dioxide cycle.
Biogas production takes place under anaerobic conditions and involves microbial decomposition of organic matter, yielding methane as the main final product of the fermentation process. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. The majority of the participating microbes are still unknown, as is their influence on reactor performance. Because most of the organisms in biogas communities are non-cultivable by today’s conventional microbiological techniques, sequencing of metagenomic total community DNA currently is the best way to obtain unbiased insights into community composition and the metabolic potential of key community members.
Here, we describe the deeply sequenced metagenome and metatranscriptome of an agricultural production-scale biogas plant on the Illumina platform . We sequenced the metagenome 27X and 19X deeper, respectively, than previous studies applying 454 or SOLiD sequencing [3, 4], which focused primarily on community composition.
Metatranscriptomic sequencing of total community RNA, 230X deeper than previously reported , complements the metagenome. Combined, these data will enable a deeper exploration of the biogas-producing microbial community, with the objective of developing rational strategies for process optimization.
Digester management and process characterization
The biogas plant, located in North Rhine Westphalia, Germany, features a mesophilic continuous wet fermentation technology characterized recently . It was designed for a capacity of 537 k W e combined heat and power (CHP) generation. The process comprises three digesters: a primary and secondary digester, where the main proportion of biogas is produced, and a storage tank, where the digestate is fermented thereafter.
Characteristics of the studied biogas plant’s primary digester at the sampling date 15 November 2010
2,041 m 3
6.4 m high, diameter of 21 m
537 k W e
22.10 m S/c m
Volative organic acids (VOA)
5,327 m g/l
Total inorganic carbon (TIC)
14,397 m g/l
863 m g/l
76 m g/l
72 % maize silage, 28 % pig manure
4.0 k g o D M m −3 d −1
55 d a y s
810.5 l/k g o D M
417.8 l/k g o D M
Sampling and library construction
Samples from the primary digester of the biogas plant were taken in November 2010. Before the sampling process, approximately 15 l of the fermenter substrate were discarded before aliquots of 1 l were transferred into clean, gastight sampling vessels and transported directly to the laboratory.
For the metagenome, aliquots of 20 g of the fermentation sample were used for total community DNA preparation as described previously .
For the metatranscriptome, a random-primed cDNA library was prepared by an external vendor (Vertis Biotechnologie AG). Briefly, total RNA was first treated with 5′-P dependent Terminator exonuclease (Epicentre) to enrich for full-length mRNA carrying 5′ cap or triphosphate structures. Then, first-strand cDNA was synthesized using a N6 random primer and M-MLV-RNase H reverse transcriptase, and second-strand cDNA synthesis was performed according to the Gubler-Hoffman protocol .
Metagenomic and metatranscriptomic sequencing
Overview of the different sequencing libraries
Insert size ∗
GAIIx, Lane 6
GAIIx, Lane 7
GAIIx, Lane 8
MiSeq, Run A1 †
MiSeq, Run A2 †
DNA, Nextera ‡
MiSeq, Run B1 †
MiSeq, Run B2 †
DNA, Nextera ‡
Metagenomic and metatranscriptomic sequencing and quality control (QC)
We assembled the metagenome with Ray Meta  version 2.3.1, trying a range of k-mer sizes from 21 to 61 in steps of 10. To estimate the inclusivity of the set of assemblies, we aligned the post-quality-control sequencing reads to the assembled contigs with bowtie2  version 2.2.4. We then used samtools  version 1.1 to convert SAM to BAM, sort the alignment file and calculate the mapping statistics. Given the total assembly size and contiguity and the percentage of mapped back metagenomic reads, we selected the assembly produced with a k-mer size of 31. Here, we assembled approximately 228 M b p in 54,489 contigs greater than 1,000 b p, with an N50 value of 9,796 b p. 77 % (79 %) of metagenomic (metatranscriptomic) reads mapped back to this assembly.
Gene prediction and annotation
Metagenome assembly statistics, minimum contig size of 1,000 b p
228,382,457 b p
Number of contigs
9,796 b p
333,979 b p
Mapped DNA reads
105,461,596 (77 %)
Mapped RNA reads
57,436,058 (79 %)
Of these, full-length
172,372 (69 %)
Match in KEGG Genes
Of these, assigned KO
Of these, in KEGG pathways
Relating the metagenome and the metatranscriptome
We report extensive metagenomic and metatranscriptomic profiling of the microbial community from a production-scale biogas plant. Given the unprecedented sequencing depth and established bioinformatics, our data will be of great interest to the biogas research community in general and microbiologists working on biogas-producing microbial communities in particular. In a first applied study, our metagenome assembly was used to improve the characterization of a metaproteome generated from biogas plant fermentation samples and to investigate the metabolic activity of the microbial community .
By sharing our data, we want to actively encourage its reuse. This will hopefully result in novel biological and biotechnological insights, eventually enabling a more efficient biogas production.
Availability of supporting data
Raw sequencing data are available in the European Nucleotide Archive (ENA) under study accession PRJEB8813 (http://www.ebi.ac.uk/ena/data/view/PRJEB8813). The datasets supporting the results of this article are available in GigaScience’s GigaDB .
The complete workflow is organized in a single GNU Makefile and available on GitHub . All data and results can be reproduced by a simple invocation of make. To further support reproducibility, we bundled all tools and dependencies into one Docker container available on DockerHub . docker run executes the aforementioned Makefile inside the container. Reproduction requires roughly 89 G i B memory and 83 G i B storage, and takes less than 24 hours on 32 CPU cores.
Excluding the KEGG analysis, which relies on a commercial license of the KEGG database, all steps are performed using free and open-source software.
AB, IM and FE are supported by a fellowship from the CLIB Graduate Cluster Industrial Biotechnology. AScz is supported by an AWS in Education Research Grant award. We gratefully acknowledge funding by the German Federal Ministry of Food and Agriculture (BMEL), grant number 22006712 (joint research project Biogas-Core) and the German Federal Ministry of Education and Research (BMBF), grant number 03SF0440C (joint research project Biogas-Marker). We acknowledge support of the publication fee by Deutsche Forschungsgemeinschaft and the Open Access Publication Funds of Bielefeld University.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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