Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions

Stolze Y, Zakrzewski M, Maus I, Eikmeyer FG, Jaenicke S, Rottmann N, Siebner C, Pühler A, Schlüter A (2015)
Biotechnology for Biofuels 8(1): 14.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Background Decomposition of biomass for biogas production can be practiced under wet and dry fermentation conditions. In contrast to the dry fermentation technology, wet fermentation is characterized by a high liquid content and a relatively low total solid content. In this study, the composition and functional potential of a biogas-producing microbial community in an agricultural biogas reactor operating under wet fermentation conditions was analyzed by a metagenomic approach applying 454-pyrosequencing. The obtained metagenomic dataset and corresponding 16S rRNA gene amplicon sequences were compared to the previously sequenced comparable metagenome from a dry fermentation process, meeting explicitly identical boundary conditions regarding sample and community DNA preparation, sequencing technology, processing of sequence reads and data analyses by bioinformatics tools. Results High-throughput metagenome sequencing of community DNA from the wet fermentation process applying the pyrosequencing approach resulted in 1,532,780 reads, with an average read length of 397 bp, accounting for approximately 594 million bases of sequence information in total. Taxonomic comparison of the communities from wet and dry fermentation revealed similar microbial profiles with Bacteria being the predominant superkingdom, while the superkingdom Archaea was less abundant. In both biogas plants, the bacterial phyla Firmicutes, Bacteroidetes, Spirochaetes and Proteobacteria were identified with descending frequencies. Within the archaeal superkingdom, the phylum Euryarchaeota was most abundant with the dominant class Methanomicrobia. Functional profiles of the communities revealed that environmental gene tags representing methanogenesis enzymes were present in both biogas plants in comparable frequencies. 16S rRNA gene amplicon high-throughput sequencing disclosed differences in the sub-communities comprising methanogenic Archaea between both processes. Fragment recruitments of metagenomic reads to the reference genome of the archaeon Methanoculleus bourgensis MS2T revealed that dominant methanogens within the dry fermentation process were highly related to the reference. Conclusions Although process parameters, substrates and technology differ between the wet and dry biogas fermentations analyzed in this study, community profiles are very similar at least at higher taxonomic ranks, illustrating that core community taxa perform key functions in biomass decomposition and methane synthesis. Regarding methanogenesis, Archaea highly related to the type strain M. bourgensis MS2T dominate the dry fermentation process, suggesting the adaptation of members belonging to this species to specific fermentation process parameters.
Stichworte
Biogas; Dry fermentation; Wet fermentation; Microbial communities; Methanogenesis; Metagenomics; 16S rRNA; Methanoculleus bourgensis; Fragment recruitment
Erscheinungsjahr
2015
Zeitschriftentitel
Biotechnology for Biofuels
Band
8
Ausgabe
1
Art.-Nr.
14
ISSN
1754-6834
eISSN
1754-6834
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2722382

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Stolze Y, Zakrzewski M, Maus I, et al. Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions. Biotechnology for Biofuels. 2015;8(1): 14.
Stolze, Y., Zakrzewski, M., Maus, I., Eikmeyer, F. G., Jaenicke, S., Rottmann, N., Siebner, C., et al. (2015). Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions. Biotechnology for Biofuels, 8(1), 14. doi:10.1186/s13068-014-0193-8
Stolze, Yvonne, Zakrzewski, Martha, Maus, Irena, Eikmeyer, Felix Gregor, Jaenicke, Sebastian, Rottmann, Nils, Siebner, Clemens, Pühler, Alfred, and Schlüter, Andreas. 2015. “Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions”. Biotechnology for Biofuels 8 (1): 14.
Stolze, Y., Zakrzewski, M., Maus, I., Eikmeyer, F. G., Jaenicke, S., Rottmann, N., Siebner, C., Pühler, A., and Schlüter, A. (2015). Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions. Biotechnology for Biofuels 8:14.
Stolze, Y., et al., 2015. Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions. Biotechnology for Biofuels, 8(1): 14.
Y. Stolze, et al., “Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions”, Biotechnology for Biofuels, vol. 8, 2015, : 14.
Stolze, Y., Zakrzewski, M., Maus, I., Eikmeyer, F.G., Jaenicke, S., Rottmann, N., Siebner, C., Pühler, A., Schlüter, A.: Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions. Biotechnology for Biofuels. 8, : 14 (2015).
Stolze, Yvonne, Zakrzewski, Martha, Maus, Irena, Eikmeyer, Felix Gregor, Jaenicke, Sebastian, Rottmann, Nils, Siebner, Clemens, Pühler, Alfred, and Schlüter, Andreas. “Comparative metagenomics of biogas-producing microbial communities from production-scale biogas plants operating under wet or dry fermentation conditions”. Biotechnology for Biofuels 8.1 (2015): 14.
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Complete genome sequence of the cellulolytic thermophile Clostridium thermocellum DSM1313.
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Complete genome sequence of Methanoculleus marisnigri Romesser et al. 1981 type strain JR1.
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Desulfotomaculum carboxydivorans sp. nov., a novel sulfate-reducing bacterium capable of growth at 100% CO.
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Complete genome sequence of Thermanaerovibrio acidaminovorans type strain (Su883).
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The genome of Syntrophomonas wolfei: new insights into syntrophic metabolism and biohydrogen production.
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The genome of Clostridium kluyveri, a strict anaerobe with unique metabolic features.
Seedorf H, Fricke WF, Veith B, Bruggemann H, Liesegang H, Strittmatter A, Miethke M, Buckel W, Hinderberger J, Li F, Hagemeier C, Thauer RK, Gottschalk G., Proc. Natl. Acad. Sci. U.S.A. 105(6), 2008
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Complete genome and comparative analysis of Streptococcus gallolyticus subsp. gallolyticus, an emerging pathogen of infective endocarditis.
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Complete genome sequence of the dairy isolate Streptococcus macedonicus ACA-DC 198.
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MicrobesFlux: a web platform for drafting metabolic models from the KEGG database.
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Taxonomic distinction of saccharolytic thermophilic anaerobes: description of Thermoanaerobacterium xylanolyticum gen. nov., sp. nov., and Thermoanaerobacterium saccharolyticum gen. nov., sp. nov.; reclassification of Thermoanaerobium brockii, Clostridium thermosulfurogenes, and Clostridium thermohydrosulfuricum E100-69 as Thermoanaerobacter brockii comb. nov., Thermoanaerobacterium thermosulfurigenes comb. nov., and Thermoanaerobacter thermohydrosulfuricus comb. nov., respectively; and transfer of Clostridium thermohydrosulfuricum 39E to Thermoanaerobacter ethanolicus
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Reconstruction and regulation of the central catabolic pathway in the thermophilic propionate-oxidizing syntroph Pelotomaculum thermopropionicum.
Kosaka T, Uchiyama T, Ishii S, Enoki M, Imachi H, Kamagata Y, Ohashi A, Harada H, Ikenaga H, Watanabe K., J. Bacteriol. 188(1), 2006
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The genome of Pelotomaculum thermopropionicum reveals niche-associated evolution in anaerobic microbiota.
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Comparison of the mesophilic cellulosome-producing Clostridium cellulovorans genome with other cellulosome-related clostridial genomes.
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Petrotoga mobilis sp. nov., from a North Sea oil-production well.
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Complete genome sequence of Syntrophothermus lipocalidus type strain (TGB-C1).
Djao OD, Zhang X, Lucas S, Lapidus A, Del Rio TG, Nolan M, Tice H, Cheng JF, Han C, Tapia R, Goodwin L, Pitluck S, Liolios K, Ivanova N, Mavromatis K, Mikhailova N, Ovchinnikova G, Pati A, Brambilla E, Chen A, Palaniappan K, Land M, Hauser L, Chang YJ, Jeffries CD, Rohde M, Sikorski J, Spring S, Goker M, Detter JC, Woyke T, Bristow J, Eisen JA, Markowitz V, Hugenholtz P, Kyrpides NC, Klenk HP., Stand Genomic Sci 3(3), 2010
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Effect of biowaste sludge maturation on the diversity of thermophilic bacteria and archaea in an anaerobic reactor.
Goberna M, Insam H, Franke-Whittle IH., Appl. Environ. Microbiol. 75(8), 2009
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Fungal volatiles as indicators of food and feeds spoilage.
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Identification of genes and genomic islands correlated with high pathogenicity in Streptococcus suis using whole genome tiling microarrays.
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Complete genome sequencing of Agrobacterium sp. H13-3, the former Rhizobium lupini H13-3, reveals a tripartite genome consisting of a circular and a linear chromosome and an accessory plasmid but lacking a tumor-inducing Ti-plasmid.
Wibberg D, Blom J, Jaenicke S, Kollin F, Rupp O, Scharf B, Schneiker-Bekel S, Sczcepanowski R, Goesmann A, Setubal JC, Schmitt R, Puhler A, Schluter A., J. Biotechnol. 155(1), 2011
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QIIME allows analysis of high-throughput community sequencing data.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R., Nat. Methods 7(5), 2010
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Search and clustering orders of magnitude faster than BLAST.
Edgar RC., Bioinformatics 26(19), 2010
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UCHIME improves sensitivity and speed of chimera detection.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R., Bioinformatics 27(16), 2011
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Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.
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Infernal 1.0: inference of RNA alignments.
Nawrocki EP, Kolbe DL, Eddy SR., Bioinformatics 25(10), 2009
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FastTree: computing large minimum evolution trees with profiles instead of a distance matrix.
Price MN, Dehal PS, Arkin AP., Mol. Biol. Evol. 26(7), 2009
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